Package 'MAICtools' reference manual

Title:	Performing Matched-Adjusted Indirect Comparisons (MAIC)
Description:	A generalised workflow for Matching-Adjusted Indirect Comparison (MAIC) analysis, which supports both anchored and non-anchored MAIC methods. In MAIC, unbiased trial outcome comparison is achieved by weighting the subject-level outcomes of the intervention trial so that the weighted aggregate measures of prognostic or effect-modifying variables match those of the comparator trial. Measurements supported include time-to-event (e.g., overall survival) and binary (e.g., objective tumor response). The method is described in Signorovitch et al. (2010) <doi:10.2165/11538370-000000000-00000> and Signorovitch et al. (2012) <doi:10.1016/j.jval.2012.05.004>.
Authors:	Xiao Qi [aut, cre]
Maintainer:	Xiao Qi <[email protected]>
License:	MIT + file LICENSE
Version:	0.1.1
Built:	2024-12-11 19:29:41 UTC
Source:	CRAN

Description of AgD_bl dataset

Description

An example data frame containing aggregate summary data from the comparator study.

Usage

  data(AgD_bl)
data(AgD_bl)

Format

A data frame with X rows and Y variables:

STUDY: Label of the comparator study, e.g., "Study XX-1".
TRT: Grouping variable, e.g., "active" or "control".
N: Number of subjects in each group.
AGEGR: Baseline characteristics variables for matching.
SEX: Baseline characteristics variables for matching.
ECOG: Baseline characteristics variables for matching.
SMK: Baseline characteristics variables for matching.
METBRAIN: Baseline characteristics variables for matching.
METLIVER: Baseline characteristics variables for matching.
BMI.mean: Baseline characteristics variables for matching.
BMI.sd: Baseline characteristics variables for matching.
DIAG.mean: Baseline characteristics variables for matching.
DIAG.sd: Baseline characteristics variables for matching.

Examples

  data(AgD_bl)
  head(AgD_bl)
data(AgD_bl)
  head(AgD_bl)

Description of AgD_eff dataset

Description

An example data frame containing aggregate results data from the comparator study.

Usage

  data(AgD_eff)
data(AgD_eff)

Format

A data frame with X rows and Y variables:

STUDY: Label of the comparator study, e.g., "Study XX-1".
PARAM: Subsets to be analyzed, e.g., "PFSINV", "OS".
EST: Point estimate of the effect size.
CIL: The lower confidence limit of the point estimate of the effect size.
CIU: The upper confidence limit of the point estimate of the effect size.

Examples

  data(AgD_eff)
  head(AgD_eff)
data(AgD_eff)
  head(AgD_eff)

Conduct Anchored Matching-Adjusted Indirect Comparison (MAIC).

Description

The endpoint of interest is either time-to-event (e.g., overall survival) or binary (e.g., objective tumor response). The methods described in this documentation are based on those originally outlined by Signorovitch et al., 2012, and further detailed in the National Institute for Health and Care Excellence (NICE) Decision Support Unit (DSU) Technical Support Document (TSD) 18.

Arguments

`ipds_wts`	A data frame containing individual patient data from the intervention study, with a column containing the estimated weights (derived using `estimate_weights`).
`intervention.arm`	The name of the grouping column in the data frame specified by `ipds_wts`, e.g., intervention.arm = TRT. The default is `TRT`.
`agds_eff`	A data frame containing aggregate efficacy results from the comparator study.
`comparator`	The name of the study column in the data frame specified by `agds_eff`, e.g., `comparator = STUDY`. The default is `STUDY`.
`comparator.study`	A character specifying the comparator study, which must be quoted and cannot be empty (e.g., `comparator.study = "Study XX-1"`). This is the value of the study column in `agds_eff` set by the `comparator` parameter.
`ipds.param.var`	The name of the column that specifies only a subset of the `ipds_wts` to be used.
`ipds.param`	A character specifying the subset of the rows to be used. This is the value of the column set by the `ipds.param.var`.
`agds.param.var`	The name of the column that specifies only a specific result of the `agds_eff` to be used.
`agds.param`	A character specifying the subset of the rows to be used. This is the value of the column set by the `agds.param.var`.
`agds.estimate`	The column name of the point estimate of the effect size.
`agds.ci.lower`	The column name for the lower confidence limit of the point estimate of the effect size.
`agds.ci.upper`	The column name for the upper confidence limit of the point estimate of the effect size.
`time`	The name of the survival or follow-up time column in the `ipds_wts`.
`status`	The status indicator, normally 0 = event, 1 = censored. Can be reset using the `event` parameter.
`event`	A numeric value that represents the survival status, 0 = event, 1 = censored.
`response`	The name of the response status column in the `ipds_wts`.
`stralist`	A string specifying the stratification factors in a stratified analysis, e.g., `stralist = "BPDL1, CNSBRAIN, AGEGR"`.
`dtype`	Two options are available: "HR" and "OR". The default is "HR".
`wt.col`	The name of the estimated weights column in the data frame specified by `ipds_wts`. The default is `wt`.
`CIw`	The numeric value specifying the width of the confidence interval, with a default of 0.95.
`digits`	Specify the number of decimal places for the output results.

Value

A data frame containing the anchored matching-adjusted indirect comparison results.

Examples


results1 <- anchored_maic(
  ipds_wts = pts, intervention.arm = TRT,
  agds_eff = AgD_eff, comparator = STUDY,
  comparator.study = "Study XX-1",
  ipds.param.var = PARAMCD, ipds.param = "OS",
  agds.param.var = PARAM, agds.param = "OS",
  agds.estimate = EST, agds.ci.lower = CIL, agds.ci.upper = CIU,
  time = AVAL, status = CNSR, event = 0,
  stralist = "BPDL1, CNSBRAIN, AGEGR", dtype = "HR",
  wt.col = wt, CIw = 0.95, digits = 2)

results1

results2 <- anchored_maic(
  ipds_wts = pts, intervention.arm = TRT,
  agds_eff = AgD_eff, comparator = STUDY,
  comparator.study = "Study XX-1",
  agds.param.var = PARAM, agds.param = "ORR",
  agds.estimate = EST, agds.ci.lower = CIL, agds.ci.upper = CIU,
  response = RESP,
  stralist = "BPDL1, CNSBRAIN, AGEGR", dtype = "OR",
  wt.col = wt, CIw = 0.95, digits = 2)

results2


results1 <- anchored_maic(
  ipds_wts = pts, intervention.arm = TRT,
  agds_eff = AgD_eff, comparator = STUDY,
  comparator.study = "Study XX-1",
  ipds.param.var = PARAMCD, ipds.param = "OS",
  agds.param.var = PARAM, agds.param = "OS",
  agds.estimate = EST, agds.ci.lower = CIL, agds.ci.upper = CIU,
  time = AVAL, status = CNSR, event = 0,
  stralist = "BPDL1, CNSBRAIN, AGEGR", dtype = "HR",
  wt.col = wt, CIw = 0.95, digits = 2)

results1

results2 <- anchored_maic(
  ipds_wts = pts, intervention.arm = TRT,
  agds_eff = AgD_eff, comparator = STUDY,
  comparator.study = "Study XX-1",
  agds.param.var = PARAM, agds.param = "ORR",
  agds.estimate = EST, agds.ci.lower = CIL, agds.ci.upper = CIU,
  response = RESP,
  stralist = "BPDL1, CNSBRAIN, AGEGR", dtype = "OR",
  wt.col = wt, CIw = 0.95, digits = 2)

results2

Check Whether the Variables are Balanced After Weighting

Description

Check Whether the Variables are Balanced After Weighting

Arguments

`ipds_wts`	A data frame containing individual patient data from the intervention study, with a column containing the estimated weights (derived using `estimate_weights`).
`agds`	A data frame containing aggregate summary data from the comparator study.
`summary.list`	A character list with two elements giving the names of variables for summarizing: the first is a vector of binary variables, and the second is a vector of continuous variables. The variable names must match the column names in ipds and do not need to be the same as those in matching.list. Use c() if a type is absent.
`matching.list`	A character list with two elements giving the names of variables for matching: the first is a vector of binary variables, and the second is a vector of continuous variables. The variable names must match the column names in ipds and agds. Use c() if a type is absent.
`intervention.arm`	The name of the grouping column in the data frame specified by ipds, e.g., `intervention.arm = TRT`. The default is `TRT`.
`comparator`	The name of the study column in the data frame specified by agds, e.g., `comparator = STUDY`. The default is `STUDY`.
`comparator.study`	A character specifying the comparator study, which must be quoted and cannot be empty (e.g., `comparator.study = "Study XX-1"`). This is the value of the study column in agds set by the comparator parameter.
`comparator.arm`	The name of the grouping column in the data frame specified by agds, e.g., `comparator.arm = TRT`. The default is `TRT`.
`comparator.n`	The name of the subjects number column in the data frame specified by agds, e.g., `comparator.n = N`. The default is `N`.
`wt.col`	The name of the estimated weights column in the data frame specified by ipds_wts. The default is `wt`.

Value

A data frame containing all specified variables summarised before and after weighting.

Examples


cov <- list(
  binary = c("ECOG", "SMK", "METBRAIN"),
  continuous = c("BMI", "DIAG")
)

cov_all <- list(
  binary = c("SEX", "ECOG", "SMK", "METBRAIN", "METLIVER"),
  continuous = c("BMI", "DIAG", "WEIGHT", "HEIGHT")
)

baseline <- check_matching(
  ipds_wts = pts, agds = AgD_bl,
  summary.list = cov_all, matching.list = cov,
  intervention.arm = TRT,
  comparator = STUDY, comparator.study = "Study XX-1",
  comparator.n = N, comparator.arm = TRT)

baseline


cov <- list(
  binary = c("ECOG", "SMK", "METBRAIN"),
  continuous = c("BMI", "DIAG")
)

cov_all <- list(
  binary = c("SEX", "ECOG", "SMK", "METBRAIN", "METLIVER"),
  continuous = c("BMI", "DIAG", "WEIGHT", "HEIGHT")
)

baseline <- check_matching(
  ipds_wts = pts, agds = AgD_bl,
  summary.list = cov_all, matching.list = cov,
  intervention.arm = TRT,
  comparator = STUDY, comparator.study = "Study XX-1",
  comparator.n = N, comparator.arm = TRT)

baseline

Convert a Longer Table Generated by check_matching() Into a Wider Table

Description

Convert a Longer Table Generated by check_matching() Into a Wider Table

Arguments

`baseline.longer`	A data frame containing the summarised results generated by check_matching().
`intervention.arm`	The name of the grouping column in the data frame specified by ipds, e.g., intervention.arm = TRT. The default is TRT.
`digits`	Specify the number of decimal places for the output results.

Value

A data frame containing the summarized results in a wider format.

Examples


cov <- list(
  binary = c("ECOG", "SMK", "METBRAIN"),
  continuous = c("BMI", "DIAG")
)

cov_all <- list(
  binary = c("SEX", "ECOG", "SMK", "METBRAIN", "METLIVER"),
  continuous = c("BMI", "DIAG", "WEIGHT", "HEIGHT")
)

baseline <- check_matching(
  ipds_wts = pts, agds = AgD_bl,
  summary.list = cov_all, matching.list = cov,
  intervention.arm = TRT,
  comparator = STUDY, comparator.study = "Study XX-1",
  comparator.n = N, comparator.arm = TRT)

baseline_summary <- check_matching2wider(
  baseline.longer = baseline,
  intervention.arm = TRT)

baseline_summary


cov <- list(
  binary = c("ECOG", "SMK", "METBRAIN"),
  continuous = c("BMI", "DIAG")
)

cov_all <- list(
  binary = c("SEX", "ECOG", "SMK", "METBRAIN", "METLIVER"),
  continuous = c("BMI", "DIAG", "WEIGHT", "HEIGHT")
)

baseline <- check_matching(
  ipds_wts = pts, agds = AgD_bl,
  summary.list = cov_all, matching.list = cov,
  intervention.arm = TRT,
  comparator = STUDY, comparator.study = "Study XX-1",
  comparator.n = N, comparator.arm = TRT)

baseline_summary <- check_matching2wider(
  baseline.longer = baseline,
  intervention.arm = TRT)

baseline_summary

Estimate Effective Sample Size (ESS)

Description

Estimate Effective Sample Size (ESS)

Arguments

`ipds_wts`	A data frame containing individual patient data from the intervention study, with a column containing the estimated weights (derived using `estimate_weights`).
`agds`	A data frame containing aggregate summary data from the comparator study.
`intervention.arm`	The name of the grouping column in the data frame specified by ipds, e.g., intervention.arm = TRT. The default is TRT.
`comparator`	The name of the study column in the data frame specified by agds, e.g., comparator = STUDY. The default is STUDY.
`comparator.study`	A character specifying the comparator study, which must be quoted and cannot be empty (e.g., comparator.study = "Study XX-1"). This is the value of the study column in agds set by the comparator parameter.
`comparator.arm`	The name of the grouping column in the data frame specified by agds, e.g., comparator.arm = TRT. The default is TRT.
`comparator.n`	A The name of the subjects number column in the data frame specified by agds, e.g., comparator.n = N. The default is N.
`wt.col`	The name of the estimated weights column in the data frame specified by ipds_wts. The default is wt.
`digits`	Specify the number of decimal places for the output results.

Value

A data frame containing effective sample size (ESS) after weighting.

Examples


ess <- estimate_ess(
  ipds_wts = pts, agds = AgD_bl,
  intervention.arm = TRT,
  comparator = STUDY, comparator.study = "Study XX-1", comparator.arm = TRT,
  comparator.n = N)
ess


ess <- estimate_ess(
  ipds_wts = pts, agds = AgD_bl,
  intervention.arm = TRT,
  comparator = STUDY, comparator.study = "Study XX-1", comparator.arm = TRT,
  comparator.n = N)
ess

Functions for the Estimation of Propensity Weights

Description

Functions for the Estimation of Propensity Weights

Arguments

`ipds`	A data frame containing individual patient data from the intervention study, with baseline characteristic variables for matching.
`agds`	A data frame containing aggregate summary data from the comparator study.
`matching.list`	A character list with two elements giving the names of variables for matching: the first is a vector of binary variables, and the second is a vector of continuous variables. The variable names must match the column names in ipds and agds. Use c() if a type is absent.
`intervention.arm`	The name of the grouping column in the data frame specified by ipds, e.g., intervention.arm = TRT. The default is TRT.
`comparator`	The name of the study column in the data frame specified by agds, e.g., comparator = STUDY. The default is STUDY.
`comparator.study`	A character specifying the comparator study, which must be quoted and cannot be empty (e.g., comparator.study = "Study XX-1"). This is the value of the study column in agds set by the comparator parameter.
`comparator.arm`	The name of the grouping column in the data frame specified by agds, e.g., comparator.arm = TRT. The default is TRT.
`opt.method`	The optim method to be used. The default is "BFGS".
`seed`	The seed for centralized variable missing value imputation (KNN method).
`...`	Refer to optim for additional parameters.

Value

A data frame containing individual patient data, calculated weights, and rescaled weights.

Examples


cov <- list(
  c("ECOG", "SMK", "METBRAIN"),
  c("BMI", "DIAG")
)

pts <- estimate_weights(
  ipds = IPD,
  agds = AgD_bl,
  matching.list = cov,
  intervention.arm = TRT,
  comparator = STUDY,
  comparator.study = "Study XX-1",
  comparator.arm = TRT
)

cov <- list(
  c("ECOG", "SMK", "METBRAIN"),
  c("BMI", "DIAG")
)

pts <- estimate_weights(
  ipds = IPD,
  agds = AgD_bl,
  matching.list = cov,
  intervention.arm = TRT,
  comparator = STUDY,
  comparator.study = "Study XX-1",
  comparator.arm = TRT
)

Histograms of Weights and Rescaled Weights Distributions

Description

Histograms of Weights and Rescaled Weights Distributions

Arguments

`ipds_wts`	A data frame containing individual patient data from the intervention study, with a column containing the estimated weights (derived using `estimate_weights`).
`intervention.arm`	The name of the grouping column in the data frame specified by ipds, e.g., `intervention.arm = TRT`. The default is `TRT`.
`wt.col`	The name of the estimated weights column in the data frame specified by ipds_wts. The default is `wt`.
`rswt.col`	The name of the estimated rescaled weights column in the data frame specified by ipds_wts. The default is `wt_rs`.
`bin`	The number of bins or bars of the histogram.
`xstepby`	An integer guiding the breaks on the X-axis.
`ystepby`	An integer guiding the breaks on the Y-axis.
`...`	Refer to geom_histogram for additional parameters.

Value

Histograms of weights and rescaled weights distributions.

Examples


hist_weights(pts, intervention.arm = TRT, xstepby = 2, ystepby = 50)

hist_weights(pts, intervention.arm = TRT, xstepby = 2, ystepby = 50)

Description of IPD dataset

Description

An example data frame containing individual patient data from the intervention study, with baseline characteristic variables for matching.

Usage

  data(IPD)
data(IPD)

Format

A data frame with X rows and Y variables:

SUBJID: Subject Unique Identifier.
TRT: Grouping variable, e.g., "active" or "control".
BPDL1: Stratification factors for stratified analysis.
CNSBRAIN: Stratification factors for stratified analysis.
AGEGR: Stratification factors for stratified analysis.
WEIGHT: Baseline characteristic variables for matching or summarizing.
HEIGHT: Baseline characteristic variables for matching or summarizing.
BMI: Baseline characteristic variables for matching or summarizing.
DIAG: Baseline characteristic variables for matching or summarizing.
SEX: Baseline characteristic variables for matching or summarizing.
ECOG: Baseline characteristic variables for matching or summarizing.
SMK: Baseline characteristics variables for matching.
METBRAIN: Baseline characteristics variables for matching.
METLIVER: Baseline characteristics variables for matching.
PARAMCD: Subsets to be analyzed, e.g., "PFSINV", "OS".
AVAL: Survival or follow up time.
CNSR: The status indicator, 0 = event, 1 = censored.
RESP: Response status, 1 = responder, 0 = non-responder.

Examples

  data(IPD)
  head(IPD)
data(IPD)
  head(IPD)

Description of pseudo dataset

Description

An example data frame containing pseudo patient data from the comparator study

Usage

  data(pseudo)
data(pseudo)

Format

A data frame with X rows and Y variables:

SUBJID: Subject Unique Identifier.
PARAMCD: Subsets to be analyzed, e.g., "PFSINV", "OS".
ARM: Label of the comparator study, = "Comparator".
AVAL: Survival or follow up time.
CNSR: The status indicator, 0 = event, 1 = censored.
wt: Weights, = 1.

Examples

  data(pseudo)
  head(pseudo)
data(pseudo)
  head(pseudo)

Description of pts dataset

Description

An example data frame containing individual patient data and estimated weights.

Usage

  data(pts)
data(pts)

Format

A data frame with X rows and Y variables:

SUBJID: Subject Unique Identifier.
TRT: Grouping variable, e.g., "active" or "control".
BPDL1: Stratification factors for stratified analysis.
CNSBRAIN: Stratification factors for stratified analysis.
AGEGR: Stratification factors for stratified analysis.
WEIGHT: Baseline characteristic variables for matching or summarizing.
HEIGHT: Baseline characteristic variables for matching or summarizing.
BMI: Baseline characteristic variables for matching or summarizing.
DIAG: Baseline characteristic variables for matching or summarizing.
SEX: Baseline characteristic variables for matching or summarizing.
ECOG: Baseline characteristic variables for matching or summarizing.
SMK: Baseline characteristics variables for matching.
METBRAIN: Baseline characteristics variables for matching.
METLIVER: Baseline characteristics variables for matching.
PARAMCD: Subsets to be analyzed, e.g., "PFSINV", "OS".
AVAL: Survival or follow up time.
CNSR: The status indicator, 0 = event, 1 = censored.
RESP: Response status, 1 = responder, 0 = non-responder.
wt: Estimated propensity weights.
wt_rs: Estimated rescaled propensity weights.

Examples

  data(pts)
  head(pts)
data(pts)
  head(pts)

Summarize the Distribution of Weight Values

Description

Summarize the Distribution of Weight Values

Arguments

`ipds_wts`	A data frame containing individual patient data from the intervention study, with a column containing the estimated weights (derived using `estimate_weights`).
`intervention.arm`	The name of the grouping column in the data frame specified by ipds, e.g., intervention.arm = TRT. The default is TRT.
`wt.col`	The name of the estimated weights column in the data frame specified by ipds_wts. The default is wt.
`rswt.col`	The name of the estimated rescaled weights column in the data frame specified by ipds_wts. The default is wt_rs.
`digits`	Specify the number of decimal places for the output results.

Value

A data frame containing a summary table of weights and rescaled weights.

Examples


summarize_weights(ipds_wts = pts, intervention.arm = TRT)


summarize_weights(ipds_wts = pts, intervention.arm = TRT)

Generate a Kaplan-Meier Plot with Individual Efficacy Data and Pseudo Efficacy Data.

Description

Generate a Kaplan-Meier Plot with Individual Efficacy Data and Pseudo Efficacy Data.

Arguments

`unds_wts`	A combined data frame containing individual efficacy data from the intervention study and pseudo efficacy data from the comparator study.
`unds.arm`	The name of the grouping column in the combined data frame specified by unds_wts, e.g., comparator.arm = TRT. The default is TRT.
`unds.param.var`	The name of the column that specifies only a subset of the rows of the data to be used.
`unds.param`	A character specifying the subset of the rows to be used. This is the value of the column set by the unds.param.var.
`time`	The name of the survival or follow up time column in the combined data frame.
`status`	The status indicator, normally 0 = event, 1 = censored. Can be reseted using the event parameter.
`event`	A numeric value that represents the survival status, 0 = event, 1 = censored.
`wt.col`	The name of the estimated weights column in the data frame specified by unds_wts. The default is wt.
`km.xlim`	A numeric value specifying the right limit of the scale on the X-axis.
`xstepby`	An integer guiding the breaks on the X-axis.
`km.ylim`	A numeric value specifying the upper limit of the scale on the Y-axis.
`ystepby`	An integer guiding the breaks on the Y-axis.
`xlab`	A character giving label of the X-axis. The default is "Time (Months)".
`ylab`	A character giving label of the Y-axis. The default is "Survival probability".
`km.legend`	A character vector of length >=1 to appear in the legend.
`km.title`	A character used to set the main title at the top.
`...`	Refer to ggsurvplot for additional parameters..

Value

A Kaplan-Meier plot object that contains individual efficacy data from the intervention study and pseudo efficacy data from the comparator study.

Examples


unanchored_kmplot(
  unds_wts = unpts, unds.arm = ARM,
  unds.param.var = PARAMCD, unds.param = "OS",
  time = AVAL, status = CNSR, event = 0,
  wt.col = wt, km.xlim = 35, xstepby = 3,
  km.legend = c("Arm A", "ARM B"),
  km.title = "AAAA")


unanchored_kmplot(
  unds_wts = unpts, unds.arm = ARM,
  unds.param.var = PARAMCD, unds.param = "OS",
  time = AVAL, status = CNSR, event = 0,
  wt.col = wt, km.xlim = 35, xstepby = 3,
  km.legend = c("Arm A", "ARM B"),
  km.title = "AAAA")

Conduct non-Anchored Matching-Adjusted Indirect Comparison (MAIC).

Description

Conduct non-Anchored Matching-Adjusted Indirect Comparison (MAIC).

Arguments

`unds_wts`	A combined data frame containing individual efficacy data from the intervention study and pseudo efficacy data from the comparator study.
`unds.arm`	The name of the grouping column in the combined data frame specified by unds_wts, e.g., comparator.arm = TRT. The default is TRT.
`comparator.study`	A character specifying or presenting the comparator study, e.g., comparator.study = "Study XX-1".
`unds.param.var`	The name of the column that specifies only a subset of the rows of the data to be used.
`unds.param`	A character specifying the subset of the rows to be used. This is the value of the column set by the unds.param.var.
`time`	The name of the survival or follow up time column in the combined data frame.
`status`	The status indicator, normally 0 = event, 1 = censored. Can be reseted using the event parameter.
`event`	A numeric value that represents the survival status, 0 = event, 1 = censored.
`response`	The name of the response status column in the unds_wts.
`dtype`	Two options are available: "HR" and "OR". The default is "HR".
`wt.col`	The name of the estimated weights column in the data frame specified by unds_wts. The default is wt.
`CIw`	The numeric value specifying the width of the confidence interval, with a default of 0.95.
`digits`	Specify the number of decimal places for the output results.

Value

A data frame containing the non-anchored matching-adjusted indirect comparison results.

Examples


results3 <- unanchored_maic(
  unds_wts = unpts, unds.arm = ARM,
  comparator.study = "Study XX-1",
  unds.param.var = PARAMCD, unds.param = "OS",
  time = AVAL, status = CNSR, event = 0,
  dtype = "HR")

results3

results4 <- unanchored_maic(
  unds_wts = unpts, unds.arm = ARM,
  #' unds.param = "ORR",
  #' comparator.study = "Study XX-1",
  response = CNSR,
  dtype = "OR")

results4


results3 <- unanchored_maic(
  unds_wts = unpts, unds.arm = ARM,
  comparator.study = "Study XX-1",
  unds.param.var = PARAMCD, unds.param = "OS",
  time = AVAL, status = CNSR, event = 0,
  dtype = "HR")

results3

results4 <- unanchored_maic(
  unds_wts = unpts, unds.arm = ARM,
  #' unds.param = "ORR",
  #' comparator.study = "Study XX-1",
  response = CNSR,
  dtype = "OR")

results4

Conduct non-Anchored Matching-Adjusted Indirect Comparison (MAIC) and Calculate Confidence Intervals (CIs) Using Bootstrap.

Description

Two different methods for estimating a 95% confidence interval (CI) from
the bootstrap samples were explored:
* Percentile CIs
* Bias-corrected and accelerated (BCa) CIs

Arguments

`ipds`	A data frame containing individual patient data from the intervention study, with baseline characteristic variables for matching.
`psds`	A data frame containing pseudo data from the comparator study.
`agds`	A data frame containing aggregate summary data from the comparator study.
`matching.list`	A character list with two elements giving the names of variables for matching: the first is a vector of binary variables, and the second is a vector of continuous variables. The variable names must match the column names in ipds and agds. Use c() if a type is absent.
`intervention.arm`	The name of the grouping column in the data frame specified by ipds, e.g., intervention.arm = TRT. The default is TRT.
`comparator`	The name of the study column in the data frame specified by agds, e.g., comparator = STUDY. The default is STUDY.
`comparator.study`	A character specifying the comparator study, which must be quoted and cannot be empty (e.g., comparator.study = "Study XX-1"). This is the value of the study column in agds set by the comparator parameter.
`comparator.arm`	The name of the grouping column in the data frame specified by agds, e.g., comparator.arm = TRT. The default is TRT.
`ipds.param.var`	The name of the column that specifies only a subset of the ipds to be used.
`ipds.param`	A character specifying the subset of the rows to be used. This is the value of the column set by the ipds.param.var.
`psds.param.var`	The name of the column that specifies only a specifyed result of the psds to be used.
`psds.param`	A character specifying the subset of the rows to be used. This is the value of the column set by the psds.param.var.
`time`	The name of the survival or follow up time column.
`status`	The status indicator, normally 0 = event, 1 = censored. Can be reseted using the event parameter.
`event`	A numeric value that represents the survival status, 0 = event, 1 = censored.
`response`	The name of the response status column.
`dtype`	Two options are available: "HR" and "OR". The default is "HR".
`n.samples`	The number of bootstrap replicates.
`CIw`	The numeric value specifying the width of the confidence interval, with a default of 0.95.
`digits`	Specify the number of decimal places for the output results.
`...`	Refer to boot for additional parameters.

Value

A list containing 2 objects. First, a data frame containing the non-anchored matching-adjusted indirect comparison results. Second, a bootstrapping diagnostics histogram.

Examples


cov <- list(
  c("ECOG", "SMK", "METBRAIN"),
  c("BMI", "DIAG")
)

results5 <- unanchored_maic_bootstrap(
  ipds = IPD,
  agds = AgD_bl,
  psds = pseudo,
  matching.list = cov,
  intervention.arm = TRT,
  comparator = STUDY,
  comparator.study = "Study XX-1",
  comparator.arm = TRT,
  time = AVAL, status = CNSR, event = 0,
  dtype = "HR",
  ipds.param.var = PARAMCD, ipds.param = "OS",
  psds.param.var = NULL, psds.param = NULL,
  n.samples = 1000
)

results5$results
results5$plot


cov <- list(
  c("ECOG", "SMK", "METBRAIN"),
  c("BMI", "DIAG")
)

results5 <- unanchored_maic_bootstrap(
  ipds = IPD,
  agds = AgD_bl,
  psds = pseudo,
  matching.list = cov,
  intervention.arm = TRT,
  comparator = STUDY,
  comparator.study = "Study XX-1",
  comparator.arm = TRT,
  time = AVAL, status = CNSR, event = 0,
  dtype = "HR",
  ipds.param.var = PARAMCD, ipds.param = "OS",
  psds.param.var = NULL, psds.param = NULL,
  n.samples = 1000
)

results5$results
results5$plot

Description of unpts dataset

Description

A combined data frame containing individual efficacy data from the intervention study and pseudo efficacy data from the comparator study.

Usage

  data(unpts)
data(unpts)

Format

A data frame with X rows and Y variables:

SUBJID: Subject Unique Identifier.
PARAMCD: Subsets to be analyzed, e.g., "PFSINV", "OS".
ARM: Label of the study, "Intervention" for the intervention study and "Cmparator" for the comparator study.
AVAL: Survival or follow up time.
CNSR: The status indicator, 0 = event, 1 = censored.
wt: Weights to be used.

Examples

  data(unpts)
  head(unpts)
data(unpts)
  head(unpts)

Package 'MAICtools'

Help Index

Description of AgD_bl dataset

Description

Usage

Format

Examples

Description of AgD_eff dataset

Description

Usage

Format

Examples

Conduct Anchored Matching-Adjusted Indirect Comparison (MAIC).

Description

Arguments

Value

Examples

Check Whether the Variables are Balanced After Weighting

Description

Arguments

Value

Examples

Convert a Longer Table Generated by check_matching() Into a Wider Table

Description

Arguments

Value

Examples

Estimate Effective Sample Size (ESS)

Description

Arguments

Value

Examples

Functions for the Estimation of Propensity Weights

Description

Arguments

Value

Examples

Histograms of Weights and Rescaled Weights Distributions

Description

Arguments

Value

Examples

Description of IPD dataset

Description

Usage

Format

Examples

Description of pseudo dataset

Description

Usage

Format

Examples

Description of pts dataset

Description

Usage

Format

Examples

Summarize the Distribution of Weight Values

Description

Arguments

Value

Examples

Generate a Kaplan-Meier Plot with Individual Efficacy Data and Pseudo Efficacy Data.

Description

Arguments

Value

Examples

Conduct non-Anchored Matching-Adjusted Indirect Comparison (MAIC).

Description

Arguments

Value

Examples

Conduct non-Anchored Matching-Adjusted Indirect Comparison (MAIC) and Calculate Confidence Intervals (CIs) Using Bootstrap.

Description

Arguments

Value

Examples

Description of unpts dataset

Description

Usage