Package 'maicplus' reference manual

Title:	Matching Adjusted Indirect Comparison
Description:	Facilitates performing matching adjusted indirect comparison (MAIC) analysis where the endpoint of interest is either time-to-event (e.g. overall survival) or binary (e.g. objective tumor response). The method is described by Signorovitch et al (2012) <doi:10.1016/j.jval.2012.05.004>.
Authors:	Gregory Chen [aut], Michael Seo [aut], Isaac Gravestock [aut, cre], Miranta Antoniou [ctb], Chrysostomos Kalyvas [ctb], MSD, Inc. [cph, fnd], F. Hoffmann-La Roche AG [cph, fnd]
Maintainer:	Isaac Gravestock <[email protected]>
License:	Apache License 2.0
Version:	0.1.1
Built:	2024-11-25 15:25:10 UTC
Source:	CRAN

Binary outcome data from single arm trial

Description

Binary outcome data from single arm trial

Usage

adrs_sat
adrs_sat

Format

A data frame with 500 rows and 5 columns:

USUBJID: Unique subject identifiers for patients.
ARM: Assigned treatment arm.
AVAL: Analysis value, in this dataset an indicator of response.
PARAM: Parameter type of AVAL.
RESPONSE: Indicator of response.

Binary outcome data from two arm trial

Description

Binary outcome data from two arm trial

Usage

adrs_twt
adrs_twt

Format

A data frame with 1000 rows and 5 columns:

USUBJID: Unique subject identifiers for patients.
ARM: Assigned treatment arm, "A", "C".
AVAL: Analysis value, in this dataset an indicator of response.
PARAM: Parameter type of AVAL.
RESPONSE: Indicator of response.

Patient data from single arm study

Description

Patient data from single arm study

Usage

adsl_sat
adsl_sat

Format

a data frame with 500 rows and 8 columns:

USUBJID: Unique subject identifiers for patients.
ARM: Assigned treatment arm.
AGE: Age in years at baseline.
SEX: Sex of patient recorded as character "Male"/"Female".
SMOKE: Smoking status at baseline as integer 1/0.
ECOG0: Indicator of ECOG score = 0 at baseline as integer 1/0.
N_PR_THER: Number of prior therapies received as integer ⁠1, 2, 3, 4⁠.
SEX_MALE: Indicator of SEX == "Male" as numeric 1/0.

Patient data from two arm trial

Description

Patient data from two arm trial

Usage

adsl_twt
adsl_twt

Format

A data frame with 1000 rows and 8 columns:

USUBJID: Unique subject identifiers for patients.
ARM: Assigned treatment arm.
AGE: Age in years at baseline.
SEX: Sex of patient recorded as character "Male"/"Female"
SMOKE: Smoking status at baseline as integer 1/0.
ECOG0: Indicator of ECOG score = 0 at baseline as integer 1/0.
N_PR_THER: Number of prior therapies received as integer ⁠1, 2, 3, 4⁠.
SEX_MALE: Indicator of SEX == "Male" as numeric 1/0

Survival data from single arm trial

Description

Survival data from single arm trial

Usage

adtte_sat
adtte_sat

Format

A data frame with 500 rows and 10 columns:

USUBJID: Unique subject identifiers for patients.
ARM: Assigned treatment arm, "A".
AVAL: Analysis value which in this dataset overall survival time in days.
AVALU: Unit of AVAL.
PARAMCD: Paramater code of AVAL, "OS".
PARAM: Parameter name of AVAL, ⁠"Overall Survival⁠.
CNSR: Censoring indicator 0/1.
TIME: Survival time in days.
EVENT: Event indicator 0/1.

Survival data from two arm trial

Description

Survival data from two arm trial

Usage

adtte_twt
adtte_twt

Format

A data frame with 1000 rows and 10 columns:

USUBJID: Unique subject identifiers for patients.
ARM: Assigned treatment arm, "A", "C".
AVAL: Analysis value which in this dataset overall survival time in days.
AVALU: Unit of AVAL.
PARAMCD: Parameter code of AVAL, "OS".
PARAM: Parameter name of AVAL, ⁠"Overall Survival⁠.
CNSR: Censoring indicator 0/1.
TIME: Survival time in days.
EVENT: Event indicator 0/1.

Aggregate effect modifier data from published study

Description

This data is formatted to be used in center_ipd().

Usage

agd
agd

Format

A data frame with 3 rows and 9 columns:

STUDY: The study name, Study_XXXX
ARM: Study arm name or total
N: Number of observations in study arm
AGE_MEAN: Mean age in study arm
AGE_MEDIAN: Median age in study arm
AGE_SD: Standard deviation of age in study arm
SEX_MALE_COUNT: Number of male patients
ECOG0_COUNT: Number of patients with ECOG score = 0
SMOKE_COUNT: Number of smokers
N_PR_THER_MEDIAN: Median number of prior therapies

Basic Kaplan Meier (KM) plot function

Description

This function can generate a basic KM plot with or without risk set table appended at the bottom. In a single plot, it can include up to 4 KM curves. This depends on number of levels in 'treatment' column in the input data.frame kmdat

Usage

basic_kmplot(
  kmdat,
  endpoint_name = "Time to Event Endpoint",
  time_scale = NULL,
  time_grid = NULL,
  show_risk_set = TRUE,
  main_title = "Kaplan-Meier Curves",
  subplot_heights = NULL,
  suppress_plot_layout = FALSE,
  use_colors = NULL,
  use_line_types = NULL,
  use_pch_cex = 0.65,
  use_pch_alpha = 100
)
basic_kmplot(
  kmdat,
  endpoint_name = "Time to Event Endpoint",
  time_scale = NULL,
  time_grid = NULL,
  show_risk_set = TRUE,
  main_title = "Kaplan-Meier Curves",
  subplot_heights = NULL,
  suppress_plot_layout = FALSE,
  use_colors = NULL,
  use_line_types = NULL,
  use_pch_cex = 0.65,
  use_pch_alpha = 100
)

Arguments

`kmdat`	a `data.frame`, must consist `treatment`, `time` (unit in days), `n.risk`, `censor`, `surv`, similar to an output from `maicplus:::survfit_makeup`
`endpoint_name`	a string, name of time to event endpoint, to be show in the last line of title
`time_scale`	a string, time unit of median survival time, taking a value of 'years', 'months', 'weeks' or 'days'
`time_grid`	a numeric vector in the unit of `time_scale`, risk set table and x axis of the km plot will be defined based on this time grid
`show_risk_set`	logical, show risk set table or not, TRUE by default
`main_title`	a string, main title of the KM plot
`subplot_heights`	a numeric vector, heights argument to `graphic::layout()`,NULL by default which means user will use the default setting
`suppress_plot_layout`	logical, suppress the layout setting in this function so that user can specify layout outside of the function, FALSE by default
`use_colors`	a character vector of length up to 4, colors to the KM curves, it will be passed to `col` of `lines()`
`use_line_types`	a numeric vector of length up to 4, line type to the KM curves, it will be passed to `lty` of `lines()`
`use_pch_cex`	a scalar between 0 and 1, point size to indicate censored individuals on the KM curves, it will be passed to `cex` of `points()`
`use_pch_alpha`	a scalar between 0 and 255, degree of color transparency of points to indicate censored individuals on the KM curves, it will be passed to `cex` of `points()`

Value

a KM plot with or without risk set table appended at the bottom, with up to 4 KM curves

Examples

library(survival)
data(adtte_sat)
data(pseudo_ipd_sat)

combined_data <- rbind(adtte_sat[, c("TIME", "EVENT", "ARM")], pseudo_ipd_sat)
kmobj <- survfit(Surv(TIME, EVENT) ~ ARM, combined_data, conf.type = "log-log")
kmdat <- do.call(rbind, survfit_makeup(kmobj))
kmdat$treatment <- factor(kmdat$treatment)

# without risk set table
basic_kmplot(kmdat,
  time_scale = "month",
  time_grid = seq(0, 20, by = 2),
  show_risk_set = FALSE,
  main_title = "Kaplan-Meier Curves",
  subplot_heights = NULL,
  suppress_plot_layout = FALSE,
  use_colors = NULL,
  use_line_types = NULL
)

# with risk set table
basic_kmplot(kmdat,
  time_scale = "month",
  time_grid = seq(0, 20, by = 2),
  show_risk_set = TRUE,
  main_title = "Kaplan-Meier Curves",
  subplot_heights = NULL,
  suppress_plot_layout = FALSE,
  use_colors = NULL,
  use_line_types = NULL
)
library(survival)
data(adtte_sat)
data(pseudo_ipd_sat)

combined_data <- rbind(adtte_sat[, c("TIME", "EVENT", "ARM")], pseudo_ipd_sat)
kmobj <- survfit(Surv(TIME, EVENT) ~ ARM, combined_data, conf.type = "log-log")
kmdat <- do.call(rbind, survfit_makeup(kmobj))
kmdat$treatment <- factor(kmdat$treatment)

# without risk set table
basic_kmplot(kmdat,
  time_scale = "month",
  time_grid = seq(0, 20, by = 2),
  show_risk_set = FALSE,
  main_title = "Kaplan-Meier Curves",
  subplot_heights = NULL,
  suppress_plot_layout = FALSE,
  use_colors = NULL,
  use_line_types = NULL
)

# with risk set table
basic_kmplot(kmdat,
  time_scale = "month",
  time_grid = seq(0, 20, by = 2),
  show_risk_set = TRUE,
  main_title = "Kaplan-Meier Curves",
  subplot_heights = NULL,
  suppress_plot_layout = FALSE,
  use_colors = NULL,
  use_line_types = NULL
)

Basic Kaplan Meier (KM) plot function using ggplot

Description

This function generates a basic KM plot using ggplot.

Usage

basic_kmplot2(
  kmlist,
  kmlist_name,
  endpoint_name = "Time to Event Endpoint",
  show_risk_set = TRUE,
  main_title = "Kaplan-Meier Curves",
  break_x_by = NULL,
  censor = TRUE,
  xlab = "Time",
  xlim = NULL,
  use_colors = NULL,
  use_line_types = NULL
)
basic_kmplot2(
  kmlist,
  kmlist_name,
  endpoint_name = "Time to Event Endpoint",
  show_risk_set = TRUE,
  main_title = "Kaplan-Meier Curves",
  break_x_by = NULL,
  censor = TRUE,
  xlab = "Time",
  xlim = NULL,
  use_colors = NULL,
  use_line_types = NULL
)

Arguments

`kmlist`	a list of `survfit` object
`kmlist_name`	a vector indicating the treatment names of each `survfit` object
`endpoint_name`	a string, name of time to event endpoint, to be show in the last line of title
`show_risk_set`	logical, show risk set table or not, TRUE by default
`main_title`	a string, main title of the KM plot
`break_x_by`	bin parameter for `survminer`
`censor`	indicator to include censor information
`xlab`	label name for x-axis of the plot
`xlim`	x limit for the x-axis of the plot
`use_colors`	a character vector of length up to 4, colors to the KM curves, it will be passed to 'col' of `lines()`
`use_line_types`	a numeric vector of length up to 4, line type to the KM curves, it will be passed to `lty` of `lines()`

Value

A Kaplan-Meier plot object created with survminer::ggsurvplot().

Examples

library(survival)
data(adtte_sat)
data(pseudo_ipd_sat)

kmobj_A <- survfit(Surv(TIME, EVENT) ~ ARM,
  data = adtte_sat,
  conf.type = "log-log"
)

kmobj_B <- survfit(Surv(TIME, EVENT) ~ ARM,
  data = pseudo_ipd_sat,
  conf.type = "log-log"
)

kmlist <- list(kmobj_A = kmobj_A, kmobj_B = kmobj_B)
kmlist_name <- c("A", "B")

basic_kmplot2(kmlist, kmlist_name)
library(survival)
data(adtte_sat)
data(pseudo_ipd_sat)

kmobj_A <- survfit(Surv(TIME, EVENT) ~ ARM,
  data = adtte_sat,
  conf.type = "log-log"
)

kmobj_B <- survfit(Surv(TIME, EVENT) ~ ARM,
  data = pseudo_ipd_sat,
  conf.type = "log-log"
)

kmlist <- list(kmobj_A = kmobj_A, kmobj_B = kmobj_B)
kmlist_name <- c("A", "B")

basic_kmplot2(kmlist, kmlist_name)

Bucher method for combining treatment effects

Description

Given two treatment effects of A vs. C and B vs. C derive the treatment effects of A vs. B using the Bucher method. Two-sided confidence interval and Z-test p-value are also calculated. Treatment effects and standard errors should be in log scale for hazard ratio, odds ratio, and risk ratio. Treatment effects and standard errors should be in natural scale for risk difference and mean difference.

Usage

bucher(trt, com, conf_lv = 0.95)

## S3 method for class 'maicplus_bucher'
print(x, ci_digits = 2, pval_digits = 3, exponentiate = FALSE, ...)
bucher(trt, com, conf_lv = 0.95)

## S3 method for class 'maicplus_bucher'
print(x, ci_digits = 2, pval_digits = 3, exponentiate = FALSE, ...)

Arguments

`trt`	a list of two scalars for the study with the experimental arm. `'est'` is the point estimate and `'se'` is the standard error of the treatment effect. For time-to-event data, `'est'` and `'se'` should be point estimate and standard error of the log hazard ratio. For binary data, `'est'` and `'se'` should be point estimate and standard error of the log odds ratio, log risk ratio, or risk difference. For continuous data, `'est'` and `'se'` should be point estimate and standard error of the mean difference.
`com`	same as `trt`, but for the study with the control arm
`conf_lv`	a numerical scalar, prescribe confidence level to derive two-sided confidence interval for the treatment effect
`x`	`maicplus_bucher` object
`ci_digits`	an integer, number of decimal places for point estimate and derived confidence limits
`pval_digits`	an integer, number of decimal places to display Z-test p-value
`exponentiate`	whether the treatment effect and confidence interval should be exponentiated. This applies to relative treatment effects. Default is set to false.
`...`	not used

Value

a list with 5 elements,

est: a scalar, point estimate of the treatment effect
se: a scalar, standard error of the treatment effect
ci_l: a scalar, lower confidence limit of a two-sided CI with prescribed nominal level by conf_lv
ci_u: a scalar, upper confidence limit of a two-sided CI with prescribed nominal level by conf_lv
pval: p-value of Z-test, with null hypothesis that est is zero

Methods (by generic)

print(maicplus_bucher): Print method for maicplus_bucher objects

Examples

trt <- list(est = log(1.1), se = 0.2)
com <- list(est = log(1.3), se = 0.18)
result <- bucher(trt, com, conf_lv = 0.9)
print(result, ci_digits = 3, pval_digits = 3)
trt <- list(est = log(1.1), se = 0.2)
com <- list(est = log(1.3), se = 0.18)
result <- bucher(trt, com, conf_lv = 0.9)
print(result, ci_digits = 3, pval_digits = 3)

Center individual patient data (IPD) variables using aggregate data averages

Description

This function subtracts IPD variables (prognostic variables and/or effect modifiers) by the aggregate data averages. This centering is needed in order to calculate weights. IPD and aggregate data variable names should match.

Usage

center_ipd(ipd, agd)
center_ipd(ipd, agd)

Arguments

`ipd`	IPD variable names should match the aggregate data names without the suffix. This would involve either changing the aggregate data name or the ipd name. For instance, if we binarize SEX variable with MALE as a reference using dummize_ipd, function names the new variable as SEX_MALE. In this case, SEX_MALE should also be available in the aggregate data.
`agd`	pre-processed aggregate data which contain STUDY, ARM, and N. Variable names should be followed by legal suffixes (i.e. MEAN, MEDIAN, SD, or PROP). Note that COUNT suffix is no longer accepted.

Value

centered ipd using aggregate level data averages

Examples

data(adsl_sat)
data(agd)
agd <- process_agd(agd)
ipd_centered <- center_ipd(ipd = adsl_sat, agd = agd)
data(adsl_sat)
data(agd)
agd <- process_agd(agd)
ipd_centered <- center_ipd(ipd = adsl_sat, agd = agd)

Centered patient data from single arm trial

Description

Centered patient data from single arm trial

Usage

centered_ipd_sat
centered_ipd_sat

Format

A data frame with 500 rows and 14 columns:

USUBJID: Unique subject identifiers for patients.
ARM: Assigned treatment arm.
AGE: Age in years at baseline.
SEX: Sex of patient recorded as character "Male"/"Female".
SMOKE: Smoking status at baseline as integer 1/0.
ECOG0: Indicator of ECOG score = 0 at baseline as integer 1/0.
N_PR_THER: Number of prior therapies received as integer ⁠1, 2, 3, 4⁠.
SEX_MALE: Indicator of SEX == "Male" as numeric 1/0.
AGE_CENTERED: Age in years at baseline relative to average in aggregate data agd.
AGE_MEDIAN_CENTERED: AGE greater/less than MEDIAN_AGE in agd coded as 1/0 and then centered at 0.5.
AGE_SQUARED_CENTERED: AGE squared and centered with respect to the AGE in agd. The squared age in the aggregate data is derived from the $E(X^2)$ term in the variance formula.
SEX_MALE_CENTERED: SEX_MALE centered by the proportion of male patients in agd
ECOG0_CENTERED: ECOG0 centered by the proportion of ECOG0 in agd
SMOKE_CENTERED: SMOKE centered by the proportion of SMOKE in agd
N_PR_THER_MEDIAN_CENTERED: N_PR_THER centered by the median in agd.

Centered patient data from two arm trial

Description

Centered patient data from two arm trial

Usage

centered_ipd_twt
centered_ipd_twt

Format

A data frame with 1000 rows and 14 columns:

USUBJID: Unique subject identifiers for patients.
ARM: Assigned treatment arm.
AGE: Age in years at baseline.
SEX: Sex of patient recorded as character "Male"/"Female".
SMOKE: Smoking status at baseline as integer 1/0.
ECOG0: Indicator of ECOG score = 0 at baseline as integer 1/0.
N_PR_THER: Number of prior therapies received as integer ⁠1, 2, 3, 4⁠.
SEX_MALE: Indicator of SEX == "Male" as numeric 1/0.
AGE_CENTERED: Age in years at baseline relative to average in aggregate data agd.
AGE_MEDIAN_CENTERED: AGE greater/less than MEDIAN_AGE in agd coded as 1/0 and then centered at 0.5.
AGE_SQUARED_CENTERED: AGE squared and centered with respect to the AGE in agd. The squared age in the aggregate data is derived from the $E(X^2)$ term in the variance formula.
SEX_MALE_CENTERED: SEX_MALE centered by the proportion of male patients in agd
ECOG0_CENTERED: ECOG0 centered by the proportion of ECOG0 in agd
SMOKE_CENTERED: SMOKE centered by the proportion of SMOKE in agd
N_PR_THER_MEDIAN_CENTERED: N_PR_THER centered by the median in agd.

Check to see if weights are optimized correctly

Description

This function checks to see if the optimization is done properly by checking the covariate averages before and after adjustment.

Usage

check_weights(weighted_data, processed_agd)

## S3 method for class 'maicplus_check_weights'
print(
  x,
  mean_digits = 2,
  prop_digits = 2,
  sd_digits = 3,
  digits = getOption("digits"),
  ...
)
check_weights(weighted_data, processed_agd)

## S3 method for class 'maicplus_check_weights'
print(
  x,
  mean_digits = 2,
  prop_digits = 2,
  sd_digits = 3,
  digits = getOption("digits"),
  ...
)

Arguments

`weighted_data`	object returned after calculating weights using `estimate_weights`
`processed_agd`	a data frame, object returned after using `process_agd` or aggregated data following the same naming convention
`x`	object from check_weights
`mean_digits`	number of digits for rounding mean columns in the output
`prop_digits`	number of digits for rounding proportion columns in the output
`sd_digits`	number of digits for rounding mean columns in the output
`digits`	minimal number of significant digits, see print.default.
`...`	further arguments to print.data.frame

Value

data.frame of weighted and unweighted covariate averages of the IPD, average of aggregate data, and sum of inner products of covariate $x_i$ and the weights ( $exp(x_i\beta)$ )

Methods (by generic)

print(maicplus_check_weights): Print method for check_weights objects

Examples

data(weighted_sat)
data(agd)
check_weights(weighted_sat, process_agd(agd))

data(weighted_sat)
data(agd)
check_weights(weighted_sat, process_agd(agd))

Create dummy variables from categorical variables in an individual patient data (ipd)

Description

This is a convenient function to convert categorical variables into dummy binary variables. This would be especially useful if the variable has more than two factors. Note that the original variable is kept after a variable is dummized.

Usage

dummize_ipd(raw_ipd, dummize_cols, dummize_ref_level)
dummize_ipd(raw_ipd, dummize_cols, dummize_ref_level)

Arguments

`raw_ipd`	ipd data that contains variable to dummize
`dummize_cols`	vector of column names to binarize
`dummize_ref_level`	vector of reference level of the variables to binarize

Value

ipd with dummized columns

Examples

data(adsl_twt)
dummize_ipd(adsl_twt, dummize_cols = c("SEX"), dummize_ref_level = c("Male"))

data(adsl_twt)
dummize_ipd(adsl_twt, dummize_cols = c("SEX"), dummize_ref_level = c("Male"))

Derive individual weights in the matching step of MAIC

Description

Assuming data is properly processed, this function takes individual patient data (IPD) with centered covariates (effect modifiers and/or prognostic variables) as input, and generates weights for each individual in IPD trial to match the covariates in aggregate data.

The plot function displays individuals weights with key summary in top right legend that includes median weight, effective sample size (ESS), and reduction percentage (what percent ESS is reduced from the original sample size). There are two options of plotting: base R plot and ggplot. The default for base R plot is to plot unscaled and scaled separately. The default for ggplot is to plot unscaled and scaled weights on a same plot.

Usage

estimate_weights(
  data,
  centered_colnames = NULL,
  start_val = 0,
  method = "BFGS",
  n_boot_iteration = NULL,
  set_seed_boot = 1234,
  boot_strata = "ARM",
  ...
)

## S3 method for class 'maicplus_estimate_weights'
plot(
  x,
  ggplot = FALSE,
  bin_col = "#6ECEB2",
  vline_col = "#688CE8",
  main_title = NULL,
  scaled_weights = TRUE,
  bins = 50,
  ...
)
estimate_weights(
  data,
  centered_colnames = NULL,
  start_val = 0,
  method = "BFGS",
  n_boot_iteration = NULL,
  set_seed_boot = 1234,
  boot_strata = "ARM",
  ...
)

## S3 method for class 'maicplus_estimate_weights'
plot(
  x,
  ggplot = FALSE,
  bin_col = "#6ECEB2",
  vline_col = "#688CE8",
  main_title = NULL,
  scaled_weights = TRUE,
  bins = 50,
  ...
)

Arguments

`data`	a numeric matrix, centered covariates of IPD, no missing value in any cell is allowed
`centered_colnames`	a character or numeric vector (column indicators) of centered covariates
`start_val`	a scalar, the starting value for all coefficients of the propensity score regression
`method`	a string, name of the optimization algorithm (see 'method' argument of `base::optim()`) The default is `"BFGS"`, other options are `"Nelder-Mead"`, `"CG"`, `"L-BFGS-B"`, `"SANN"`, and `"Brent"`
`n_boot_iteration`	an integer, number of bootstrap iterations. By default is NULL which means bootstrapping procedure will not be triggered, and hence the element `"boot"` of output list object will be NULL.
`set_seed_boot`	a scalar, the random seed for conducting the bootstrapping, only relevant if `n_boot_iteration` is not NULL. By default, use seed 1234
`boot_strata`	a character vector of column names in `data` that defines the strata for bootstrapping. This ensures that samples are drawn proportionally from each defined stratum. If `NULL`, no stratification during bootstrapping process. By default, it is "ARM"
`...`	Additional `control` parameters passed to stats::optim.
`x`	object from estimate_weights
`ggplot`	indicator to print base weights plot or `ggplot` weights plot
`bin_col`	a string, color for the bins of histogram
`vline_col`	a string, color for the vertical line in the histogram
`main_title`	title of the plot. For ggplot, name of scaled weights plot and unscaled weights plot, respectively.
`scaled_weights`	(base plot only) an indicator for using scaled weights instead of regular weights
`bins`	(`ggplot` only) number of bin parameter to use

Value

a list with the following 4 elements,

data: a data.frame, includes the input data with appended column 'weights' and 'scaled_weights'. Scaled weights has a summation to be the number of rows in data that has no missing value in any of the effect modifiers
centered_colnames: column names of centered effect modifiers in data
nr_missing: number of rows in data that has at least 1 missing value in specified centered effect modifiers
ess: effective sample size, square of sum divided by sum of squares
opt: R object returned by base::optim(), for assess convergence and other details
boot_strata: 'strata' from a boot::boot object
boot_seed: column names in data of the stratification factors
boot: a n by 2 by k array or NA, where n equals to number of rows in data, and k equals n_boot_iteration. The 2 columns in the second dimension include a column of numeric indexes of the rows in data that are selected at a bootstrapping iteration and a column of weights. boot is NA when argument n_boot_iteration is set as NULL

Methods (by generic)

plot(maicplus_estimate_weights): Plot method for estimate_weights objects

Examples

data(centered_ipd_sat)
centered_colnames <- grep("_CENTERED", colnames(centered_ipd_sat), value = TRUE)
weighted_data <- estimate_weights(data = centered_ipd_sat, centered_colnames = centered_colnames)

# To later estimate bootstrap confidence intervals, we calculate the weights
# for the bootstrap samples:
weighted_data_boot <- estimate_weights(
  data = centered_ipd_sat, centered_colnames = centered_colnames, n_boot_iteration = 100
)

plot(weighted_sat)

if (requireNamespace("ggplot2")) {
  plot(weighted_sat, ggplot = TRUE)
}
data(centered_ipd_sat)
centered_colnames <- grep("_CENTERED", colnames(centered_ipd_sat), value = TRUE)
weighted_data <- estimate_weights(data = centered_ipd_sat, centered_colnames = centered_colnames)

# To later estimate bootstrap confidence intervals, we calculate the weights
# for the bootstrap samples:
weighted_data_boot <- estimate_weights(
  data = centered_ipd_sat, centered_colnames = centered_colnames, n_boot_iteration = 100
)

plot(weighted_sat)

if (requireNamespace("ggplot2")) {
  plot(weighted_sat, ggplot = TRUE)
}

Calculate standard error from the reported confidence interval.

Description

Comparator studies often only report confidence interval of the treatment effects. This function calculates standard error of the treatment effect given the reported confidence interval. For relative treatment effect (i.e. hazard ratio, odds ratio, and risk ratio), the function would log the confidence interval. For risk difference and mean difference, we do not log the confidence interval. The option to log the confidence interval is controlled by 'log' parameter.

Usage

find_SE_from_CI(CI_lower = NULL, CI_upper = NULL, CI_perc = 0.95, log = TRUE)
find_SE_from_CI(CI_lower = NULL, CI_upper = NULL, CI_perc = 0.95, log = TRUE)

Arguments

`CI_lower`	Reported lower percentile value of the treatment effect
`CI_upper`	Reported upper percentile value of the treatment effect
`CI_perc`	Percentage of confidence interval reported
`log`	Whether the confidence interval should be logged. For relative treatment effect, log should be applied because estimated log treatment effect is approximately normally distributed.

Value

Standard error of log relative treatment effect if 'log' is true and standard error of the treatment effect if 'log' is false

Examples

find_SE_from_CI(CI_lower = 0.55, CI_upper = 0.90, CI_perc = 0.95)
find_SE_from_CI(CI_lower = 0.55, CI_upper = 0.90, CI_perc = 0.95)

Create pseudo IPD given aggregated binary data

Description

Create pseudo IPD given aggregated binary data

Usage

get_pseudo_ipd_binary(binary_agd, format = c("stacked", "unstacked"))
get_pseudo_ipd_binary(binary_agd, format = c("stacked", "unstacked"))

Arguments

`binary_agd`	a data.frame that take different formats depending on `format`
`format`	a string, "stacked" or "unstacked"

Value

a data.frame of pseudo binary IPD, with columns USUBJID, ARM, RESPONSE

Examples

# example of unstacked
testdat <- data.frame(Yes = 280, No = 120)
rownames(testdat) <- "B"
get_pseudo_ipd_binary(
  binary_agd = testdat,
  format = "unstacked"
)

# example of stacked
get_pseudo_ipd_binary(
  binary_agd = data.frame(
    ARM = rep("B", 2),
    RESPONSE = c("YES", "NO"),
    COUNT = c(280, 120)
  ),
  format = "stacked"
)
# example of unstacked
testdat <- data.frame(Yes = 280, No = 120)
rownames(testdat) <- "B"
get_pseudo_ipd_binary(
  binary_agd = testdat,
  format = "unstacked"
)

# example of stacked
get_pseudo_ipd_binary(
  binary_agd = data.frame(
    ARM = rep("B", 2),
    RESPONSE = c("YES", "NO"),
    COUNT = c(280, 120)
  ),
  format = "stacked"
)

Convert Time Values Using Scaling Factors

Description

Convert Time Values Using Scaling Factors

Usage

get_time_as(times, as = NULL)
get_time_as(times, as = NULL)

Arguments

`times`	Numeric time values
`as`	A time scale to convert to. One of "days", "weeks", "months", "years"

Value

Returns a numeric vector calculated from times / get_time_conversion(factor = as)

Examples

get_time_as(50, as = "months")
get_time_as(50, as = "months")

Helper function to summarize outputs from glm fit

Description

Helper function to summarize outputs from glm fit

Usage

glm_makeup(binobj, legend = "before matching", weighted = FALSE)
glm_makeup(binobj, legend = "before matching", weighted = FALSE)

Arguments

`binobj`	returned object from `stats::glm`
`legend`	label to indicate the binary fit
`weighted`	logical flag indicating whether weights have been applied in the glm fit

Value

A data.frame containing a summary of the number of events and subjects in a logistic regression model.

Examples

data(adrs_sat)
pseudo_adrs <- get_pseudo_ipd_binary(
  binary_agd = data.frame(
    ARM = rep("B", 2),
    RESPONSE = c("YES", "NO"),
    COUNT = c(280, 120)
  ),
  format = "stacked"
)
pseudo_adrs$RESPONSE <- as.numeric(pseudo_adrs$RESPONSE)
combined_data <- rbind(adrs_sat[, c("USUBJID", "ARM", "RESPONSE")], pseudo_adrs)
combined_data$ARM <- as.factor(combined_data$ARM)
binobj_dat <- stats::glm(RESPONSE ~ ARM, combined_data, family = binomial("logit"))
glm_makeup(binobj_dat)
data(adrs_sat)
pseudo_adrs <- get_pseudo_ipd_binary(
  binary_agd = data.frame(
    ARM = rep("B", 2),
    RESPONSE = c("YES", "NO"),
    COUNT = c(280, 120)
  ),
  format = "stacked"
)
pseudo_adrs$RESPONSE <- as.numeric(pseudo_adrs$RESPONSE)
combined_data <- rbind(adrs_sat[, c("USUBJID", "ARM", "RESPONSE")], pseudo_adrs)
combined_data$ARM <- as.factor(combined_data$ARM)
binobj_dat <- stats::glm(RESPONSE ~ ARM, combined_data, family = binomial("logit"))
glm_makeup(binobj_dat)

Kaplan Meier (KM) plot function for anchored and unanchored cases

Description

It is wrapper function of basic_kmplot. The argument setting is similar to maic_anchored and maic_unanchored, and it is used in those two functions.

Usage

kmplot(
  weights_object,
  tte_ipd,
  tte_pseudo_ipd,
  trt_ipd,
  trt_agd,
  trt_common = NULL,
  normalize_weights = FALSE,
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  km_conf_type = "log-log",
  km_layout = c("all", "by_trial", "by_arm"),
  ...
)
kmplot(
  weights_object,
  tte_ipd,
  tte_pseudo_ipd,
  trt_ipd,
  trt_agd,
  trt_common = NULL,
  normalize_weights = FALSE,
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  km_conf_type = "log-log",
  km_layout = c("all", "by_trial", "by_arm"),
  ...
)

Arguments

`weights_object`	an object returned by `estimate_weight`
`tte_ipd`	a data frame of individual patient data (IPD) of internal trial, contain at least `"USUBJID"`, `"EVENT"`, `"TIME"` columns and a column indicating treatment assignment
`tte_pseudo_ipd`	a data frame of pseudo IPD by digitized KM curves of external trial (for time-to-event endpoint), contain at least `"EVENT"`, `"TIME"`
`trt_ipd`	a string, name of the interested investigation arm in internal trial `dat_igd` (real IPD)
`trt_agd`	a string, name of the interested investigation arm in external trial `dat_pseudo` (pseudo IPD)
`trt_common`	a string, name of the common comparator in internal and external trial, by default is NULL, indicating unanchored case
`normalize_weights`	logical, default is `FALSE`. If `TRUE`, `scaled_weights` (normalized weights) in `weights_object$data` will be used.
`trt_var_ipd`	a string, column name in `tte_ipd` that contains the treatment assignment
`trt_var_agd`	a string, column name in `tte_pseudo_ipd` that contains the treatment assignment
`km_conf_type`	a string, pass to `conf.type` of `survfit`
`km_layout`	a string, only applicable for unanchored case (`trt_common = NULL`), indicated the desired layout of output KM curve.
`...`	other arguments in `basic_kmplot`

Value

In unanchored case, a KM plot with risk set table. In anchored case, depending on km_layout,

if "by_trial", 2 by 1 plot, first all KM curves (incl. weighted) in IPD trial, and then KM curves in AgD trial, with risk set table.
if "by_arm", 2 by 1 plot, first KM curves of trt_agd and trt_ipd (with and without weights), and then KM curves of trt_common in AgD trial and IPD trial (with and without weights). Risk set table is appended.
if "all", 2 by 2 plot, all plots in "by_trial" and "by_arm" without risk set table appended.

Examples

# unanchored example using kmplot
data(weighted_sat)
data(adtte_sat)
data(pseudo_ipd_sat)

kmplot(
  weights_object = weighted_sat,
  tte_ipd = adtte_sat,
  tte_pseudo_ipd = pseudo_ipd_sat,
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  endpoint_name = "Overall Survival",
  trt_ipd = "A",
  trt_agd = "B",
  trt_common = NULL,
  km_conf_type = "log-log",
  time_scale = "month",
  time_grid = seq(0, 20, by = 2),
  use_colors = NULL,
  use_line_types = NULL,
  use_pch_cex = 0.65,
  use_pch_alpha = 100
)
# anchored example using kmplot
data(weighted_twt)
data(adtte_twt)
data(pseudo_ipd_twt)

# plot by trial
kmplot(
  weights_object = weighted_twt,
  tte_ipd = adtte_twt,
  tte_pseudo_ipd = pseudo_ipd_twt,
  trt_ipd = "A",
  trt_agd = "B",
  trt_common = "C",
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  endpoint_name = "Overall Survival",
  km_conf_type = "log-log",
  km_layout = "by_trial",
  time_scale = "month",
  time_grid = seq(0, 20, by = 2),
  use_colors = NULL,
  use_line_types = NULL,
  use_pch_cex = 0.65,
  use_pch_alpha = 100
)

# plot by arm
kmplot(
  weights_object = weighted_twt,
  tte_ipd = adtte_twt,
  tte_pseudo_ipd = pseudo_ipd_twt,
  trt_ipd = "A",
  trt_agd = "B",
  trt_common = "C",
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  endpoint_name = "Overall Survival",
  km_conf_type = "log-log",
  km_layout = "by_arm",
  time_scale = "month",
  time_grid = seq(0, 20, by = 2),
  use_colors = NULL,
  use_line_types = NULL,
  use_pch_cex = 0.65,
  use_pch_alpha = 100
)

# plot all
kmplot(
  weights_object = weighted_twt,
  tte_ipd = adtte_twt,
  tte_pseudo_ipd = pseudo_ipd_twt,
  trt_ipd = "A",
  trt_agd = "B",
  trt_common = "C",
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  endpoint_name = "Overall Survival",
  km_conf_type = "log-log",
  km_layout = "all",
  time_scale = "month",
  time_grid = seq(0, 20, by = 2),
  use_colors = NULL,
  use_line_types = NULL,
  use_pch_cex = 0.65,
  use_pch_alpha = 100
)
# unanchored example using kmplot
data(weighted_sat)
data(adtte_sat)
data(pseudo_ipd_sat)

kmplot(
  weights_object = weighted_sat,
  tte_ipd = adtte_sat,
  tte_pseudo_ipd = pseudo_ipd_sat,
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  endpoint_name = "Overall Survival",
  trt_ipd = "A",
  trt_agd = "B",
  trt_common = NULL,
  km_conf_type = "log-log",
  time_scale = "month",
  time_grid = seq(0, 20, by = 2),
  use_colors = NULL,
  use_line_types = NULL,
  use_pch_cex = 0.65,
  use_pch_alpha = 100
)
# anchored example using kmplot
data(weighted_twt)
data(adtte_twt)
data(pseudo_ipd_twt)

# plot by trial
kmplot(
  weights_object = weighted_twt,
  tte_ipd = adtte_twt,
  tte_pseudo_ipd = pseudo_ipd_twt,
  trt_ipd = "A",
  trt_agd = "B",
  trt_common = "C",
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  endpoint_name = "Overall Survival",
  km_conf_type = "log-log",
  km_layout = "by_trial",
  time_scale = "month",
  time_grid = seq(0, 20, by = 2),
  use_colors = NULL,
  use_line_types = NULL,
  use_pch_cex = 0.65,
  use_pch_alpha = 100
)

# plot by arm
kmplot(
  weights_object = weighted_twt,
  tte_ipd = adtte_twt,
  tte_pseudo_ipd = pseudo_ipd_twt,
  trt_ipd = "A",
  trt_agd = "B",
  trt_common = "C",
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  endpoint_name = "Overall Survival",
  km_conf_type = "log-log",
  km_layout = "by_arm",
  time_scale = "month",
  time_grid = seq(0, 20, by = 2),
  use_colors = NULL,
  use_line_types = NULL,
  use_pch_cex = 0.65,
  use_pch_alpha = 100
)

# plot all
kmplot(
  weights_object = weighted_twt,
  tte_ipd = adtte_twt,
  tte_pseudo_ipd = pseudo_ipd_twt,
  trt_ipd = "A",
  trt_agd = "B",
  trt_common = "C",
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  endpoint_name = "Overall Survival",
  km_conf_type = "log-log",
  km_layout = "all",
  time_scale = "month",
  time_grid = seq(0, 20, by = 2),
  use_colors = NULL,
  use_line_types = NULL,
  use_pch_cex = 0.65,
  use_pch_alpha = 100
)

Kaplan-Meier (KM) plot function for anchored and unanchored cases using ggplot

Description

This is wrapper function of basic_kmplot2. The argument setting is similar to maic_anchored and maic_unanchored, and it is used in those two functions.

Usage

kmplot2(
  weights_object,
  tte_ipd,
  tte_pseudo_ipd,
  trt_ipd,
  trt_agd,
  trt_common = NULL,
  normalize_weights = FALSE,
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  km_conf_type = "log-log",
  km_layout = c("all", "by_trial", "by_arm"),
  time_scale,
  ...
)
kmplot2(
  weights_object,
  tte_ipd,
  tte_pseudo_ipd,
  trt_ipd,
  trt_agd,
  trt_common = NULL,
  normalize_weights = FALSE,
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  km_conf_type = "log-log",
  km_layout = c("all", "by_trial", "by_arm"),
  time_scale,
  ...
)

Arguments

`weights_object`	an object returned by `estimate_weight`
`tte_ipd`	a data frame of individual patient data (IPD) of internal trial, contain at least `"USUBJID"`, `"EVENT"`, `"TIME"` columns and a column indicating treatment assignment
`tte_pseudo_ipd`	a data frame of pseudo IPD by digitized KM curves of external trial (for time-to-event endpoint), contain at least `"EVENT"`, `"TIME"`
`trt_ipd`	a string, name of the interested investigation arm in internal trial `dat_igd` (real IPD)
`trt_agd`	a string, name of the interested investigation arm in external trial `dat_pseudo` (pseudo IPD)
`trt_common`	a string, name of the common comparator in internal and external trial, by default is NULL, indicating unanchored case
`normalize_weights`	logical, default is `FALSE`. If `TRUE`, `scaled_weights` (normalized weights) in `weights_object$data` will be used.
`trt_var_ipd`	a string, column name in `tte_ipd` that contains the treatment assignment
`trt_var_agd`	a string, column name in `tte_pseudo_ipd` that contains the treatment assignment
`km_conf_type`	a string, pass to `conf.type` of `survfit`
`km_layout`	a string, only applicable for unanchored case (`trt_common = NULL`), indicated the desired layout of output KM curve.
`time_scale`	a string, time unit of median survival time, taking a value of 'years', 'months', weeks' or 'days'
`...`	other arguments in `basic_kmplot2`

Value

In unanchored case, a KM plot with risk set table. In anchored case, depending on km_layout,

if "by_trial", 2 by 1 plot, first all KM curves (incl. weighted) in IPD trial, and then KM curves in AgD trial, with risk set table.
if "by_arm", 2 by 1 plot, first KM curves of trt_agd and trt_ipd (with and without weights), and then KM curves of trt_common in AgD trial and IPD trial (with and without weights). Risk set table is appended.
if "all", 2 by 2 plot, all plots in "by_trial" and "by_arm" without risk set table appended.

Examples

# unanchored example using kmplot2
data(weighted_sat)
data(adtte_sat)
data(pseudo_ipd_sat)

kmplot2(
  weights_object = weighted_sat,
  tte_ipd = adtte_sat,
  tte_pseudo_ipd = pseudo_ipd_sat,
  trt_ipd = "A",
  trt_agd = "B",
  trt_common = NULL,
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  endpoint_name = "Overall Survival",
  km_conf_type = "log-log",
  time_scale = "month",
  break_x_by = 2,
  xlim = c(0, 20)
)
# anchored example using kmplot2
data(weighted_twt)
data(adtte_twt)
data(pseudo_ipd_twt)

# plot by trial
kmplot2(
  weights_object = weighted_twt,
  tte_ipd = adtte_twt,
  tte_pseudo_ipd = pseudo_ipd_twt,
  trt_ipd = "A",
  trt_agd = "B",
  trt_common = "C",
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  endpoint_name = "Overall Survival",
  km_conf_type = "log-log",
  km_layout = "by_trial",
  time_scale = "month",
  break_x_by = 2
)

# plot by arm
kmplot2(
  weights_object = weighted_twt,
  tte_ipd = adtte_twt,
  tte_pseudo_ipd = pseudo_ipd_twt,
  trt_ipd = "A",
  trt_agd = "B",
  trt_common = "C",
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  endpoint_name = "Overall Survival",
  km_conf_type = "log-log",
  km_layout = "by_arm",
  time_scale = "month",
  break_x_by = 2
)

# plot all
kmplot2(
  weights_object = weighted_twt,
  tte_ipd = adtte_twt,
  tte_pseudo_ipd = pseudo_ipd_twt,
  trt_ipd = "A",
  trt_agd = "B",
  trt_common = "C",
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  endpoint_name = "Overall Survival",
  km_conf_type = "log-log",
  km_layout = "all",
  time_scale = "month",
  break_x_by = 2,
  xlim = c(0, 20),
  show_risk_set = FALSE
)
# unanchored example using kmplot2
data(weighted_sat)
data(adtte_sat)
data(pseudo_ipd_sat)

kmplot2(
  weights_object = weighted_sat,
  tte_ipd = adtte_sat,
  tte_pseudo_ipd = pseudo_ipd_sat,
  trt_ipd = "A",
  trt_agd = "B",
  trt_common = NULL,
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  endpoint_name = "Overall Survival",
  km_conf_type = "log-log",
  time_scale = "month",
  break_x_by = 2,
  xlim = c(0, 20)
)
# anchored example using kmplot2
data(weighted_twt)
data(adtte_twt)
data(pseudo_ipd_twt)

# plot by trial
kmplot2(
  weights_object = weighted_twt,
  tte_ipd = adtte_twt,
  tte_pseudo_ipd = pseudo_ipd_twt,
  trt_ipd = "A",
  trt_agd = "B",
  trt_common = "C",
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  endpoint_name = "Overall Survival",
  km_conf_type = "log-log",
  km_layout = "by_trial",
  time_scale = "month",
  break_x_by = 2
)

# plot by arm
kmplot2(
  weights_object = weighted_twt,
  tte_ipd = adtte_twt,
  tte_pseudo_ipd = pseudo_ipd_twt,
  trt_ipd = "A",
  trt_agd = "B",
  trt_common = "C",
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  endpoint_name = "Overall Survival",
  km_conf_type = "log-log",
  km_layout = "by_arm",
  time_scale = "month",
  break_x_by = 2
)

# plot all
kmplot2(
  weights_object = weighted_twt,
  tte_ipd = adtte_twt,
  tte_pseudo_ipd = pseudo_ipd_twt,
  trt_ipd = "A",
  trt_agd = "B",
  trt_common = "C",
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  endpoint_name = "Overall Survival",
  km_conf_type = "log-log",
  km_layout = "all",
  time_scale = "month",
  break_x_by = 2,
  xlim = c(0, 20),
  show_risk_set = FALSE
)

Anchored MAIC for binary and time-to-event endpoint

Description

This is a wrapper function to provide adjusted effect estimates and relevant statistics in anchored case (i.e. there is a common comparator arm in the internal and external trial).

Usage

maic_anchored(
  weights_object,
  ipd,
  pseudo_ipd,
  trt_ipd,
  trt_agd,
  trt_common,
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  normalize_weights = FALSE,
  endpoint_type = "tte",
  endpoint_name = "Time to Event Endpoint",
  eff_measure = c("HR", "OR", "RR", "RD"),
  boot_ci_type = c("norm", "basic", "stud", "perc", "bca"),
  time_scale = "months",
  km_conf_type = "log-log",
  binary_robust_cov_type = "HC3"
)
maic_anchored(
  weights_object,
  ipd,
  pseudo_ipd,
  trt_ipd,
  trt_agd,
  trt_common,
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  normalize_weights = FALSE,
  endpoint_type = "tte",
  endpoint_name = "Time to Event Endpoint",
  eff_measure = c("HR", "OR", "RR", "RD"),
  boot_ci_type = c("norm", "basic", "stud", "perc", "bca"),
  time_scale = "months",
  km_conf_type = "log-log",
  binary_robust_cov_type = "HC3"
)

Arguments

`weights_object`	an object returned by `estimate_weight`
`ipd`	a data frame that meet format requirements in 'Details', individual patient data (IPD) of internal trial
`pseudo_ipd`	a data frame, pseudo IPD from digitized KM curve of external trial (for time-to-event endpoint) or from contingency table (for binary endpoint)
`trt_ipd`	a string, name of the interested investigation arm in internal trial `ipd` (internal IPD)
`trt_agd`	a string, name of the interested investigation arm in external trial `pseudo_ipd` (pseudo IPD)
`trt_common`	a string, name of the common comparator in internal and external trial
`trt_var_ipd`	a string, column name in `ipd` that contains the treatment assignment
`trt_var_agd`	a string, column name in `ipd` that contains the treatment assignment
`normalize_weights`	logical, default is `FALSE`. If `TRUE`, `scaled_weights` (normalized weights) in `weights_object$data` will be used.
`endpoint_type`	a string, one out of the following "binary", "tte" (time to event)
`endpoint_name`	a string, name of time to event endpoint, to be show in the last line of title
`eff_measure`	a string, "RD" (risk difference), "OR" (odds ratio), "RR" (relative risk) for a binary endpoint; "HR" for a time-to-event endpoint. By default is `NULL`, "OR" is used for binary case, otherwise "HR" is used.
`boot_ci_type`	a string, one of `c("norm","basic", "stud", "perc", "bca")` to select the type of bootstrap confidence interval. See boot::boot.ci for more details.
`time_scale`	a string, time unit of median survival time, taking a value of 'years', 'months', 'weeks' or 'days'. NOTE: it is assumed that values in TIME column of `ipd` and `pseudo_ipd` is in the unit of days
`km_conf_type`	a string, pass to `conf.type` of `survfit`
`binary_robust_cov_type`	a string to pass to argument `type` of sandwich::vcovHC, see possible options in the documentation of that function. Default is `"HC3"`

Details

It is required that input ipd and pseudo_ipd to have the following columns. This function is not sensitive to upper or lower case of letters in column names.

USUBJID - character, unique subject ID
ARM - character or factor, treatment indicator, column name does not have to be 'ARM'. User specify in trt_var_ipd and trt_var_agd

For time-to-event analysis, the follow columns are required:

EVENT - numeric, 1 for censored/death, 0 otherwise
TIME - numeric column, observation time of the EVENT; unit in days

For binary outcomes:

RESPONSE - numeric, 1 for event occurred, 0 otherwise

Value

A list, contains 'descriptive' and 'inferential'

Examples

# Anchored example using maic_anchored for time-to-event data
data(weighted_twt)
data(adtte_twt)
data(pseudo_ipd_twt)

result_tte <- maic_anchored(
  weights_object = weighted_twt,
  ipd = adtte_twt,
  pseudo_ipd = pseudo_ipd_twt,
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  trt_ipd = "A",
  trt_agd = "B",
  trt_common = "C",
  endpoint_name = "Overall Survival",
  endpoint_type = "tte",
  eff_measure = "HR",
  time_scale = "month",
  km_conf_type = "log-log",
)
result_tte$descriptive$summary
result_tte$inferential$summary
# Anchored example using maic_anchored for binary outcome
data(weighted_twt)
data(adrs_twt)

# Reported summary data
pseudo_adrs <- get_pseudo_ipd_binary(
  binary_agd = data.frame(
    ARM = c("B", "C", "B", "C"),
    RESPONSE = c("YES", "YES", "NO", "NO"),
    COUNT = c(280, 120, 200, 200)
  ),
  format = "stacked"
)

# inferential result
result_binary <- maic_anchored(
  weights_object = weighted_twt,
  ipd = adrs_twt,
  pseudo_ipd = pseudo_adrs,
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  trt_ipd = "A",
  trt_agd = "B",
  trt_common = "C",
  endpoint_name = "Binary Event",
  endpoint_type = "binary",
  eff_measure = "OR"
)

result_binary$descriptive$summary
result_binary$inferential$summary
# Anchored example using maic_anchored for time-to-event data
data(weighted_twt)
data(adtte_twt)
data(pseudo_ipd_twt)

result_tte <- maic_anchored(
  weights_object = weighted_twt,
  ipd = adtte_twt,
  pseudo_ipd = pseudo_ipd_twt,
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  trt_ipd = "A",
  trt_agd = "B",
  trt_common = "C",
  endpoint_name = "Overall Survival",
  endpoint_type = "tte",
  eff_measure = "HR",
  time_scale = "month",
  km_conf_type = "log-log",
)
result_tte$descriptive$summary
result_tte$inferential$summary
# Anchored example using maic_anchored for binary outcome
data(weighted_twt)
data(adrs_twt)

# Reported summary data
pseudo_adrs <- get_pseudo_ipd_binary(
  binary_agd = data.frame(
    ARM = c("B", "C", "B", "C"),
    RESPONSE = c("YES", "YES", "NO", "NO"),
    COUNT = c(280, 120, 200, 200)
  ),
  format = "stacked"
)

# inferential result
result_binary <- maic_anchored(
  weights_object = weighted_twt,
  ipd = adrs_twt,
  pseudo_ipd = pseudo_adrs,
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  trt_ipd = "A",
  trt_agd = "B",
  trt_common = "C",
  endpoint_name = "Binary Event",
  endpoint_type = "binary",
  eff_measure = "OR"
)

result_binary$descriptive$summary
result_binary$inferential$summary

Unanchored MAIC for binary and time-to-event endpoint

Description

This is a wrapper function to provide adjusted effect estimates and relevant statistics in unanchored case (i.e. there is no common comparator arm in the internal and external trial).

Usage

maic_unanchored(
  weights_object,
  ipd,
  pseudo_ipd,
  trt_ipd,
  trt_agd,
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  normalize_weights = FALSE,
  endpoint_type = "tte",
  endpoint_name = "Time to Event Endpoint",
  eff_measure = c("HR", "OR", "RR", "RD"),
  boot_ci_type = c("norm", "basic", "stud", "perc", "bca"),
  time_scale = "months",
  km_conf_type = "log-log",
  binary_robust_cov_type = "HC3"
)
maic_unanchored(
  weights_object,
  ipd,
  pseudo_ipd,
  trt_ipd,
  trt_agd,
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  normalize_weights = FALSE,
  endpoint_type = "tte",
  endpoint_name = "Time to Event Endpoint",
  eff_measure = c("HR", "OR", "RR", "RD"),
  boot_ci_type = c("norm", "basic", "stud", "perc", "bca"),
  time_scale = "months",
  km_conf_type = "log-log",
  binary_robust_cov_type = "HC3"
)

Arguments

`weights_object`	an object returned by `estimate_weight`
`ipd`	a data frame that meet format requirements in 'Details', individual patient data (IPD) of internal trial
`pseudo_ipd`	a data frame, pseudo IPD from digitized KM curve of external trial (for time-to-event endpoint) or from contingency table (for binary endpoint)
`trt_ipd`	a string, name of the interested investigation arm in internal trial `dat_igd` (real IPD)
`trt_agd`	a string, name of the interested investigation arm in external trial `pseudo_ipd` (pseudo IPD)
`trt_var_ipd`	a string, column name in `ipd` that contains the treatment assignment
`trt_var_agd`	a string, column name in `ipd` that contains the treatment assignment
`normalize_weights`	logical, default is `FALSE`. If `TRUE`, `scaled_weights` (normalized weights) in `weights_object$data` will be used.
`endpoint_type`	a string, one out of the following "binary", "tte" (time to event)
`endpoint_name`	a string, name of time to event endpoint, to be show in the last line of title
`eff_measure`	a string, "RD" (risk difference), "OR" (odds ratio), "RR" (relative risk) for a binary endpoint; "HR" for a time-to-event endpoint. By default is `NULL`, "OR" is used for binary case, otherwise "HR" is used.
`boot_ci_type`	a string, one of `c("norm","basic", "stud", "perc", "bca")` to select the type of bootstrap confidence interval. See boot::boot.ci for more details.
`time_scale`	a string, time unit of median survival time, taking a value of 'years', 'months', 'weeks' or 'days'. NOTE: it is assumed that values in TIME column of `ipd` and `pseudo_ipd` is in the unit of days
`km_conf_type`	a string, pass to `conf.type` of `survfit`
`binary_robust_cov_type`	a string to pass to argument `type` of sandwich::vcovHC, see possible options in the documentation of that function. Default is `"HC3"`

Details

For time-to-event analysis, it is required that input ipd and pseudo_ipd to have the following columns. This function is not sensitive to upper or lower case of letters in column names.

USUBJID - character, unique subject ID
ARM - character or factor, treatment indicator, column name does not have to be 'ARM'. User specify in trt_var_ipd and trt_var_agd
EVENT - numeric, 1 for censored/death, 0 for otherwise
TIME - numeric column, observation time of the EVENT; unit in days

Value

A list, contains 'descriptive' and 'inferential'

Examples

#
# unanchored example using maic_unanchored for time-to-event data
#
data(centered_ipd_sat)
data(adtte_sat)
data(pseudo_ipd_sat)

#### derive weights
weighted_data <- estimate_weights(
  data = centered_ipd_sat,
  centered_colnames = grep("_CENTERED$", names(centered_ipd_sat)),
  start_val = 0,
  method = "BFGS"
)

weighted_data2 <- estimate_weights(
  data = centered_ipd_sat,
  centered_colnames = grep("_CENTERED$", names(centered_ipd_sat)),
  start_val = 0,
  method = "BFGS",
  n_boot_iteration = 100,
  set_seed_boot = 1234
)

# inferential result
result <- maic_unanchored(
  weights_object = weighted_data,
  ipd = adtte_sat,
  pseudo_ipd = pseudo_ipd_sat,
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  trt_ipd = "A",
  trt_agd = "B",
  endpoint_name = "Overall Survival",
  endpoint_type = "tte",
  eff_measure = "HR",
  time_scale = "month",
  km_conf_type = "log-log"
)
result$descriptive$summary
result$inferential$summary

result_boot <- maic_unanchored(
  weights_object = weighted_data2,
  ipd = adtte_sat,
  pseudo_ipd = pseudo_ipd_sat,
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  trt_ipd = "A",
  trt_agd = "B",
  endpoint_name = "Overall Survival",
  endpoint_type = "tte",
  eff_measure = "HR",
  time_scale = "month",
  km_conf_type = "log-log"
)
result$descriptive$summary
result$inferential$summary
#
# unanchored example using maic_unanchored for binary outcome
#

data(centered_ipd_sat)
data(adrs_sat)

centered_ipd_sat
centered_colnames <- grep("_CENTERED$", colnames(centered_ipd_sat), value = TRUE)
weighted_data <- estimate_weights(data = centered_ipd_sat, centered_colnames = centered_colnames)
weighted_data2 <- estimate_weights(
  data = centered_ipd_sat, centered_colnames = centered_colnames,
  n_boot_iteration = 100
)

# get dummy binary IPD
pseudo_adrs <- get_pseudo_ipd_binary(
  binary_agd = data.frame(
    ARM = rep("B", 2),
    RESPONSE = c("YES", "NO"),
    COUNT = c(280, 120)
  ),
  format = "stacked"
)

# unanchored binary MAIC, with CI based on sandwich estimator
maic_unanchored(
  weights_object = weighted_data,
  ipd = adrs_sat,
  pseudo_ipd = pseudo_adrs,
  trt_ipd = "A",
  trt_agd = "B",
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  endpoint_type = "binary",
  endpoint_name = "Binary Endpoint",
  eff_measure = "RR",
  # binary specific args
  binary_robust_cov_type = "HC3"
)

# unanchored binary MAIC, with bootstrapped CI
maic_unanchored(
  weights_object = weighted_data2,
  ipd = adrs_sat,
  pseudo_ipd = pseudo_adrs,
  trt_ipd = "A",
  trt_agd = "B",
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  endpoint_type = "binary",
  endpoint_name = "Binary Endpoint",
  eff_measure = "RR",
  # binary specific args
  binary_robust_cov_type = "HC3"
)

#---------------------------------
#
# unanchored example using maic_unanchored for time-to-event data
#
data(centered_ipd_sat)
data(adtte_sat)
data(pseudo_ipd_sat)

#### derive weights
weighted_data <- estimate_weights(
  data = centered_ipd_sat,
  centered_colnames = grep("_CENTERED$", names(centered_ipd_sat)),
  start_val = 0,
  method = "BFGS"
)

weighted_data2 <- estimate_weights(
  data = centered_ipd_sat,
  centered_colnames = grep("_CENTERED$", names(centered_ipd_sat)),
  start_val = 0,
  method = "BFGS",
  n_boot_iteration = 100,
  set_seed_boot = 1234
)

# inferential result
result <- maic_unanchored(
  weights_object = weighted_data,
  ipd = adtte_sat,
  pseudo_ipd = pseudo_ipd_sat,
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  trt_ipd = "A",
  trt_agd = "B",
  endpoint_name = "Overall Survival",
  endpoint_type = "tte",
  eff_measure = "HR",
  time_scale = "month",
  km_conf_type = "log-log"
)
result$descriptive$summary
result$inferential$summary

result_boot <- maic_unanchored(
  weights_object = weighted_data2,
  ipd = adtte_sat,
  pseudo_ipd = pseudo_ipd_sat,
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  trt_ipd = "A",
  trt_agd = "B",
  endpoint_name = "Overall Survival",
  endpoint_type = "tte",
  eff_measure = "HR",
  time_scale = "month",
  km_conf_type = "log-log"
)
result$descriptive$summary
result$inferential$summary
#
# unanchored example using maic_unanchored for binary outcome
#

data(centered_ipd_sat)
data(adrs_sat)

centered_ipd_sat
centered_colnames <- grep("_CENTERED$", colnames(centered_ipd_sat), value = TRUE)
weighted_data <- estimate_weights(data = centered_ipd_sat, centered_colnames = centered_colnames)
weighted_data2 <- estimate_weights(
  data = centered_ipd_sat, centered_colnames = centered_colnames,
  n_boot_iteration = 100
)

# get dummy binary IPD
pseudo_adrs <- get_pseudo_ipd_binary(
  binary_agd = data.frame(
    ARM = rep("B", 2),
    RESPONSE = c("YES", "NO"),
    COUNT = c(280, 120)
  ),
  format = "stacked"
)

# unanchored binary MAIC, with CI based on sandwich estimator
maic_unanchored(
  weights_object = weighted_data,
  ipd = adrs_sat,
  pseudo_ipd = pseudo_adrs,
  trt_ipd = "A",
  trt_agd = "B",
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  endpoint_type = "binary",
  endpoint_name = "Binary Endpoint",
  eff_measure = "RR",
  # binary specific args
  binary_robust_cov_type = "HC3"
)

# unanchored binary MAIC, with bootstrapped CI
maic_unanchored(
  weights_object = weighted_data2,
  ipd = adrs_sat,
  pseudo_ipd = pseudo_adrs,
  trt_ipd = "A",
  trt_agd = "B",
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  endpoint_type = "binary",
  endpoint_name = "Binary Endpoint",
  eff_measure = "RR",
  # binary specific args
  binary_robust_cov_type = "HC3"
)

#---------------------------------

Helper function to retrieve median survival time from a `survival::survfit` object

Description

Extract and display median survival time with confidence interval

Usage

medSurv_makeup(km_fit, legend = "before matching", time_scale)
medSurv_makeup(km_fit, legend = "before matching", time_scale)

Arguments

`km_fit`	returned object from `survival::survfit`
`legend`	a character string, name used in 'type' column in returned data frame
`time_scale`	a character string, 'years', 'months', 'weeks' or 'days', time unit of median survival time

Value

a data frame with a index column 'type', median survival time and confidence interval

Examples

data(adtte_sat)
data(pseudo_ipd_sat)
library(survival)
combined_data <- rbind(adtte_sat[, c("TIME", "EVENT", "ARM")], pseudo_ipd_sat)
kmobj <- survfit(Surv(TIME, EVENT) ~ ARM, combined_data, conf.type = "log-log")

# Derive median survival time
medSurv <- medSurv_makeup(kmobj, legend = "before matching", time_scale = "day")
medSurv
data(adtte_sat)
data(pseudo_ipd_sat)
library(survival)
combined_data <- rbind(adtte_sat[, c("TIME", "EVENT", "ARM")], pseudo_ipd_sat)
kmobj <- survfit(Surv(TIME, EVENT) ~ ARM, combined_data, conf.type = "log-log")

# Derive median survival time
medSurv <- medSurv_makeup(kmobj, legend = "before matching", time_scale = "day")
medSurv

Diagnosis plot of proportional hazard assumption for anchored and unanchored

Description

Diagnosis plot of proportional hazard assumption for anchored and unanchored

Usage

ph_diagplot(
  weights_object,
  tte_ipd,
  tte_pseudo_ipd,
  trt_ipd,
  trt_agd,
  trt_common = NULL,
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  endpoint_name = "Time to Event Endpoint",
  time_scale,
  zph_transform = "log",
  zph_log_hazard = TRUE
)
ph_diagplot(
  weights_object,
  tte_ipd,
  tte_pseudo_ipd,
  trt_ipd,
  trt_agd,
  trt_common = NULL,
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  endpoint_name = "Time to Event Endpoint",
  time_scale,
  zph_transform = "log",
  zph_log_hazard = TRUE
)

Arguments

`weights_object`	an object returned by `estimate_weight`
`tte_ipd`	a data frame of individual patient data (IPD) of internal trial, contain at least "USUBJID", "EVENT", "TIME" columns and a column indicating treatment assignment
`tte_pseudo_ipd`	a data frame of pseudo IPD by digitized KM curves of external trial (for time-to-event endpoint), contain at least "EVENT", "TIME"
`trt_ipd`	a string, name of the interested investigation arm in internal trial `tte_ipd` (real IPD)
`trt_agd`	a string, name of the interested investigation arm in external trial `tte_pseudo_ipd` (pseudo IPD)
`trt_common`	a string, name of the common comparator in internal and external trial, by default is NULL, indicating unanchored case
`trt_var_ipd`	a string, column name in `tte_ipd` that contains the treatment assignment
`trt_var_agd`	a string, column name in `tte_pseudo_ipd` that contains the treatment assignment
`endpoint_name`	a string, name of time to event endpoint, to be show in the last line of title
`time_scale`	a string, time unit of median survival time, taking a value of 'years', 'months', 'weeks' or 'days'
`zph_transform`	a string, pass to `survival::cox.zph`, default is "log"
`zph_log_hazard`	a logical, if TRUE (default), y axis of the time dependent hazard function is log-hazard, otherwise, hazard.

Value

a 3 by 2 plot, include log-cumulative hazard plot, time dependent hazard function and unscaled Schoenfeld residual plot, before and after matching

Examples

# unanchored example using ph_diagplot
data(weighted_sat)
data(adtte_sat)
data(pseudo_ipd_sat)

ph_diagplot(
  weights_object = weighted_sat,
  tte_ipd = adtte_sat,
  tte_pseudo_ipd = pseudo_ipd_sat,
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  trt_ipd = "A",
  trt_agd = "B",
  trt_common = NULL,
  endpoint_name = "Overall Survival",
  time_scale = "week",
  zph_transform = "log",
  zph_log_hazard = TRUE
)
# anchored example using ph_diagplot
data(weighted_twt)
data(adtte_twt)
data(pseudo_ipd_twt)

ph_diagplot(
  weights_object = weighted_twt,
  tte_ipd = adtte_twt,
  tte_pseudo_ipd = pseudo_ipd_twt,
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  trt_ipd = "A",
  trt_agd = "B",
  trt_common = "C",
  endpoint_name = "Overall Survival",
  time_scale = "week",
  zph_transform = "log",
  zph_log_hazard = TRUE
)
# unanchored example using ph_diagplot
data(weighted_sat)
data(adtte_sat)
data(pseudo_ipd_sat)

ph_diagplot(
  weights_object = weighted_sat,
  tte_ipd = adtte_sat,
  tte_pseudo_ipd = pseudo_ipd_sat,
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  trt_ipd = "A",
  trt_agd = "B",
  trt_common = NULL,
  endpoint_name = "Overall Survival",
  time_scale = "week",
  zph_transform = "log",
  zph_log_hazard = TRUE
)
# anchored example using ph_diagplot
data(weighted_twt)
data(adtte_twt)
data(pseudo_ipd_twt)

ph_diagplot(
  weights_object = weighted_twt,
  tte_ipd = adtte_twt,
  tte_pseudo_ipd = pseudo_ipd_twt,
  trt_var_ipd = "ARM",
  trt_var_agd = "ARM",
  trt_ipd = "A",
  trt_agd = "B",
  trt_common = "C",
  endpoint_name = "Overall Survival",
  time_scale = "week",
  zph_transform = "log",
  zph_log_hazard = TRUE
)

PH Diagnosis Plot of Log Cumulative Hazard Rate versus time or log-time

Description

This plot is also known as log negative log survival rate.

Usage

ph_diagplot_lch(
  km_fit,
  time_scale,
  log_time = TRUE,
  endpoint_name = "",
  subtitle = "",
  exclude_censor = TRUE
)
ph_diagplot_lch(
  km_fit,
  time_scale,
  log_time = TRUE,
  endpoint_name = "",
  subtitle = "",
  exclude_censor = TRUE
)

Arguments

`km_fit`	returned object from `survival::survfit`
`time_scale`	a character string, 'years', 'months', 'weeks' or 'days', time unit of median survival time
`log_time`	logical, TRUE (default) or FALSE
`endpoint_name`	a character string, name of the endpoint
`subtitle`	a character string, subtitle of the plot
`exclude_censor`	logical, should censored data point be plotted

Details

a diagnosis plot for proportional hazard assumption, versus log-time (default) or time

Value

a plot of log cumulative hazard rate

Examples

library(survival)
data(adtte_sat)
data(pseudo_ipd_sat)
combined_data <- rbind(adtte_sat[, c("TIME", "EVENT", "ARM")], pseudo_ipd_sat)
kmobj <- survfit(Surv(TIME, EVENT) ~ ARM, combined_data, conf.type = "log-log")
ph_diagplot_lch(kmobj,
  time_scale = "month", log_time = TRUE,
  endpoint_name = "OS", subtitle = "(Before Matching)"
)
library(survival)
data(adtte_sat)
data(pseudo_ipd_sat)
combined_data <- rbind(adtte_sat[, c("TIME", "EVENT", "ARM")], pseudo_ipd_sat)
kmobj <- survfit(Surv(TIME, EVENT) ~ ARM, combined_data, conf.type = "log-log")
ph_diagplot_lch(kmobj,
  time_scale = "month", log_time = TRUE,
  endpoint_name = "OS", subtitle = "(Before Matching)"
)

PH Diagnosis Plot of Schoenfeld residuals for a Cox model fit

Description

PH Diagnosis Plot of Schoenfeld residuals for a Cox model fit

Usage

ph_diagplot_schoenfeld(
  coxobj,
  time_scale = "months",
  log_time = TRUE,
  endpoint_name = "",
  subtitle = ""
)
ph_diagplot_schoenfeld(
  coxobj,
  time_scale = "months",
  log_time = TRUE,
  endpoint_name = "",
  subtitle = ""
)

Arguments

`coxobj`	object returned from `coxph`
`time_scale`	a character string, 'years', 'months', 'weeks' or 'days', time unit of median survival time
`log_time`	logical, TRUE (default) or FALSE
`endpoint_name`	a character string, name of the endpoint
`subtitle`	a character string, subtitle of the plot

Value

a plot of Schoenfeld residuals

Examples

library(survival)
data(adtte_sat)
data(pseudo_ipd_sat)
combined_data <- rbind(adtte_sat[, c("TIME", "EVENT", "ARM")], pseudo_ipd_sat)
unweighted_cox <- coxph(Surv(TIME, EVENT == 1) ~ ARM, data = combined_data)
ph_diagplot_schoenfeld(unweighted_cox,
  time_scale = "month", log_time = TRUE,
  endpoint_name = "OS", subtitle = "(Before Matching)"
)
library(survival)
data(adtte_sat)
data(pseudo_ipd_sat)
combined_data <- rbind(adtte_sat[, c("TIME", "EVENT", "ARM")], pseudo_ipd_sat)
unweighted_cox <- coxph(Surv(TIME, EVENT == 1) ~ ARM, data = combined_data)
ph_diagplot_schoenfeld(unweighted_cox,
  time_scale = "month", log_time = TRUE,
  endpoint_name = "OS", subtitle = "(Before Matching)"
)

Plot MAIC weights in a histogram with key statistics in legend

Description

Generates a base R histogram of weights. Default is to plot either unscaled or scaled weights and not both.

Usage

plot_weights_base(
  weighted_data,
  bin_col,
  vline_col,
  main_title,
  scaled_weights
)
plot_weights_base(
  weighted_data,
  bin_col,
  vline_col,
  main_title,
  scaled_weights
)

Arguments

`weighted_data`	object returned after calculating weights using estimate_weights
`bin_col`	a string, color for the bins of histogram
`vline_col`	a string, color for the vertical line in the histogram
`main_title`	title of the plot
`scaled_weights`	an indicator for using scaled weights instead of regular weights

Value

a plot of unscaled or scaled weights

Examples

plot_weights_base(weighted_sat,
  bin_col = "#6ECEB2",
  vline_col = "#688CE8",
  main_title = c("Scaled Individual Weights", "Unscaled Individual Weights"),
  scaled_weights = TRUE
)
plot_weights_base(weighted_sat,
  bin_col = "#6ECEB2",
  vline_col = "#688CE8",
  main_title = c("Scaled Individual Weights", "Unscaled Individual Weights"),
  scaled_weights = TRUE
)

Plot MAIC weights in a histogram with key statistics in legend using `ggplot2`

Description

Generates a ggplot histogram of weights. Default is to plot both unscaled and scaled weights on a same graph.

Usage

plot_weights_ggplot(weighted_data, bin_col, vline_col, main_title, bins)
plot_weights_ggplot(weighted_data, bin_col, vline_col, main_title, bins)

Arguments

`weighted_data`	object returned after calculating weights using estimate_weights
`bin_col`	a string, color for the bins of histogram
`vline_col`	a string, color for the vertical line in the histogram
`main_title`	Name of scaled weights plot and unscaled weights plot, respectively.
`bins`	number of bin parameter to use

Value

a plot of unscaled and scaled weights

Examples

if (requireNamespace("ggplot2")) {
  plot_weights_ggplot(weighted_sat,
    bin_col = "#6ECEB2",
    vline_col = "#688CE8",
    main_title = c("Scaled Individual Weights", "Unscaled Individual Weights"),
    bins = 50
  )
}
if (requireNamespace("ggplot2")) {
  plot_weights_ggplot(weighted_sat,
    bin_col = "#6ECEB2",
    vline_col = "#688CE8",
    main_title = c("Scaled Individual Weights", "Unscaled Individual Weights"),
    bins = 50
  )
}

Pre-process aggregate data

Description

This function checks the format of the aggregate data. Data is required to have three columns: STUDY, ARM, and N. Column names that do not have legal suffixes (MEAN, MEDIAN, SD, COUNT, or PROP) are dropped. If a variable is a count variable, it is converted to proportions by dividing the sample size (N). Note, when the count is specified, proportion is always calculated based on the count, that is, specified proportion will be ignored if applicable. If the aggregated data comes from multiple sources (i.e. different analysis population) and sample size differs for each variable, one option is to specify proportion directly instead of count by using suffix _PROP.

Usage

process_agd(raw_agd)
process_agd(raw_agd)

Arguments

raw_agd

raw aggregate data should contain STUDY, ARM, and N. Variable names should be followed by legal suffixes (i.e. MEAN, MEDIAN, SD, COUNT, or PROP).

Value

pre-processed aggregate level data

Examples

data(agd)
agd <- process_agd(agd)

data(agd)
agd <- process_agd(agd)

Pseudo individual patient survival data from published study

Description

Pseudo individual patient survival data from published study

Usage

pseudo_ipd_sat
pseudo_ipd_sat

Format

A data frame with 300 rows and 3 columns:

TIME: Survival time in days.
EVENT: Event indicator 0/1.
ARM: Assigned treatment arm, "B".

Pseudo individual patient survival data from published two arm study

Description

Pseudo individual patient survival data from published two arm study

Usage

pseudo_ipd_twt
pseudo_ipd_twt

Format

A data frame with 800 rows and 3 columns:

TIME: Survival time in days.
EVENT: Event indicator 0/1.
ARM: Assigned treatment arm, "B", "C".

Get and Set Time Conversion Factors

Description

Get and Set Time Conversion Factors

Usage

set_time_conversion(
  default = "days",
  days = 1,
  weeks = 7,
  months = 365.25/12,
  years = 365.25
)

get_time_conversion(factor = c("days", "weeks", "months", "years"))
set_time_conversion(
  default = "days",
  days = 1,
  weeks = 7,
  months = 365.25/12,
  years = 365.25
)

get_time_conversion(factor = c("days", "weeks", "months", "years"))

Arguments

`default`	The default time scale, commonly whichever has factor = 1
`days`	Factor to divide data time units to get time in days
`weeks`	Factor to divide data time units to get time in weeks
`months`	Factor to divide data time units to get time in months
`years`	Factor to divide data time units to get time in years
`factor`	Time factor to get.

Value

No value returned. Conversion factors are stored internally and used within functions.

Examples

# The default time scale is days:
set_time_conversion(default = "days", days = 1, weeks = 7, months = 365.25 / 12, years = 365.25)

# Set the default time scale to years
set_time_conversion(
  default = "years",
  days = 1 / 365.25,
  weeks = 1 / 52.17857,
  months = 1 / 12,
  years = 1
)

# Get time scale factors:
get_time_conversion("years")
get_time_conversion("weeks")
# The default time scale is days:
set_time_conversion(default = "days", days = 1, weeks = 7, months = 365.25 / 12, years = 365.25)

# Set the default time scale to years
set_time_conversion(
  default = "years",
  days = 1 / 365.25,
  weeks = 1 / 52.17857,
  months = 1 / 12,
  years = 1
)

# Get time scale factors:
get_time_conversion("years")
get_time_conversion("weeks")

Helper function to select set of variables used for Kaplan-Meier plot

Description

Helper function to select set of variables used for Kaplan-Meier plot

Usage

survfit_makeup(km_fit, single_trt_name = "treatment")
survfit_makeup(km_fit, single_trt_name = "treatment")

Arguments

`km_fit`	returned object from `survival::survfit`
`single_trt_name`	name of treatment if no strata are specified in `km_fit`

Value

a list of data frames of variables from survival::survfit(). Data frame is divided by treatment.

Examples

library(survival)
data(adtte_sat)
data(pseudo_ipd_sat)
combined_data <- rbind(adtte_sat[, c("TIME", "EVENT", "ARM")], pseudo_ipd_sat)
kmobj <- survfit(Surv(TIME, EVENT) ~ ARM, combined_data, conf.type = "log-log")
survfit_makeup(kmobj)
library(survival)
data(adtte_sat)
data(pseudo_ipd_sat)
combined_data <- rbind(adtte_sat[, c("TIME", "EVENT", "ARM")], pseudo_ipd_sat)
kmobj <- survfit(Surv(TIME, EVENT) ~ ARM, combined_data, conf.type = "log-log")
survfit_makeup(kmobj)

Weighted object for single arm trial data

Description

Weighted object for single arm trial data

Usage

weighted_sat
weighted_sat

Format

A maicplus_estimate_weights object created by estimate_weights() containing

data: patient level data with weights
centered_colnames: Columns used in MAIC
nr_missing: Number of observations with missing data
ess: Expected sample size
opt: Information from optim from weight calculation
boot: Parameters and bootstrap sample weights, NULL in this object

Weighted object for two arm trial data

Description

The weighted patient data for a two arm trial generated from the centered patient data (centered_ipd_twt). It has weights calculated for 100 bootstrap samples.

The object is generated using the following code:

estimate_weights(
  data = centered_ipd_twt,
  centered_colnames = c(
    "AGE_CENTERED",
    "AGE_MEDIAN_CENTERED",
    "AGE_SQUARED_CENTERED",
    "SEX_MALE_CENTERED",
    "ECOG0_CENTERED",
    "SMOKE_CENTERED"
    ),
  n_boot_iteration = 100
 )

Usage

weighted_twt
weighted_twt

Format

A maicplus_estimate_weights object created by estimate_weights() containing

data: patient level data with weights
centered_colnames: Columns used in MAIC
nr_missing: Number of observations with missing data
ess: Expected sample size
opt: Information from optim from weight calculation
boot: Parameters and bootstrap sample weights for the 100 samples

Package 'maicplus'

Help Index

Binary outcome data from single arm trial

Description

Usage

Format

See Also

Binary outcome data from two arm trial

Description

Usage

Format

See Also

Patient data from single arm study

Description

Usage

Format

See Also

Patient data from two arm trial

Description

Usage

Format

See Also

Survival data from single arm trial

Description

Usage

Format

See Also

Survival data from two arm trial

Description

Usage

Format

See Also

Aggregate effect modifier data from published study

Description

Usage

Format

See Also

Basic Kaplan Meier (KM) plot function

Description

Usage

Arguments

Value

Examples

Basic Kaplan Meier (KM) plot function using ggplot

Description

Usage

Arguments

Value

Examples

Bucher method for combining treatment effects

Description

Usage

Arguments

Value

Methods (by generic)

Examples

Center individual patient data (IPD) variables using aggregate data averages

Description

Usage

Arguments

Value

Examples

Centered patient data from single arm trial

Description

Usage

Format

See Also

Centered patient data from two arm trial

Description

Usage

Format

See Also

Check to see if weights are optimized correctly

Description

Usage

Arguments

Value

Methods (by generic)

Examples

Create dummy variables from categorical variables in an individual patient data (ipd)

Helper function to retrieve median survival time from a `survival::survfit` object