Package 'trajmsm' reference manual

Title:	Marginal Structural Models with Latent Class Growth Analysis of Treatment Trajectories
Description:	Implements marginal structural models combined with a latent class growth analysis framework for assessing the causal effect of treatment trajectories. Based on the approach described in "Marginal Structural Models with Latent Class Growth Analysis of Treatment Trajectories" Diop, A., Sirois, C., Guertin, J.R., Schnitzer, M.E., Candas, B., Cossette, B., Poirier, P., Brophy, J., Mésidor, M., Blais, C. and Hamel, D., (2023) <doi:10.1177/09622802231202384>.
Authors:	Awa Diop [aut, cre], Denis Talbot [aut]
Maintainer:	Awa Diop <[email protected]>
License:	GPL (>= 3)
Version:	0.1.3
Built:	2024-12-05 07:09:21 UTC
Source:	CRAN

Wrapper for flexmix

Description

Call the package flexmix to build trajectory groups

Usage

build_traj(
  obsdata,
  formula,
  number_traj,
  identifier,
  family = "binomial",
  seed = 945,
  control = list(iter.max = 1000, minprior = 0),
  ...
)
build_traj(
  obsdata,
  formula,
  number_traj,
  identifier,
  family = "binomial",
  seed = 945,
  control = list(iter.max = 1000, minprior = 0),
  ...
)

Arguments

`obsdata`	Data to build trajectory groups in long format.
`formula`	Designate the formula to model the longitudinal variable of interest.
`number_traj`	An integer to fix the number of trajectory groups.
`identifier`	A string to designate the column name for the unique identifier.
`family`	Designate the type of distribution ("gaussian", "binomial", "poisson", "gamma").
`seed`	Set a seed for replicability.
`control`	Object of class FLXcontrol.
`...`	Additional arguments passed to the flexmix function.

Value

A list containing the posterior probability matrix and the fitted trajectory model.

Examples

obsdata_long = gendata(n = 1000,format = "long", total_followup = 6, seed = 945)
formula = as.formula(cbind(statins, 1 - statins) ~ time)
restraj = build_traj(obsdata = obsdata_long, number_traj = 3, formula = formula, identifier = "id")
obsdata_long = gendata(n = 1000,format = "long", total_followup = 6, seed = 945)
formula = as.formula(cbind(statins, 1 - statins) ~ time)
restraj = build_traj(obsdata = obsdata_long, number_traj = 3, formula = formula, identifier = "id")

Generate data trajectories for MSM

Description

Provides datasets for running examples for LCGA-MSM and LCGA-HRMSM.

Usage

gendata(
  n,
  include_censor = FALSE,
  format = c("long", "wide"),
  start_year = 2011,
  total_followup,
  timedep_outcome = FALSE,
  seed
)
gendata(
  n,
  include_censor = FALSE,
  format = c("long", "wide"),
  start_year = 2011,
  total_followup,
  timedep_outcome = FALSE,
  seed
)

Arguments

`n`	Number of observations to generate.
`include_censor`	Logical, if TRUE, includes censoring.
`format`	Character, either "long" or "wide" for the format of the output data frame.
`start_year`	Baseline year.
`total_followup`	Number of measuring times.
`timedep_outcome`	Logical, if TRUE, includes a time-dependent outcome.
`seed`	Use a specific seed value to ensure the simulated data is replicable.

Value

A data frame with generated data trajectories.

Examples

gendata(n = 100, include_censor = FALSE, format = "wide",total_followup = 3, seed = 945)
gendata(n = 100, include_censor = FALSE, format = "wide",total_followup = 3, seed = 945)

Counterfactual means via G-Formula

Description

Calculates counterfactual means using the g-formula approach.

Usage

gformula(
  formula,
  baseline,
  covariates,
  treatment,
  outcome,
  ntimes_interval,
  obsdata
)
gformula(
  formula,
  baseline,
  covariates,
  treatment,
  outcome,
  ntimes_interval,
  obsdata
)

Arguments

`formula`	Specification of the model for the outcome to be fitted.
`baseline`	Names of the baseline covariates.
`covariates`	Names of the time-varying covariates (should be a list).
`treatment`	Names of the time-varying treatment.
`outcome`	Name of the outcome variable.
`ntimes_interval`	Length of a time-interval (s).
`obsdata`	Observed data in wide format.

Value

list_gform_countermeans

List of counterfactual means obtained with g-formula.

Author(s)

Awa Diop, Denis Talbot

Examples


obsdata = gendata(n = 1000, format = "wide", total_followup = 6, seed = 945)
years <- 2011:2016
baseline_var <- c("age","sex")
variables <- c("hyper", "bmi")
var_cov <- c("statins","hyper", "bmi")
covariates <- lapply(years, function(year) {
paste0(variables, year)})
treatment_var <- paste0("statins", 2011:2016)
formula = paste0("y ~", paste0(treatment_var,collapse = "+"), "+",
                paste0(unlist(covariates), collapse = "+"),"+",
                paste0(baseline_var, collapse = "+"))
res_gform <- gformula(formula = formula, baseline = baseline_var, covariates = covariates,
treatment = treatment_var, outcome = "y", ntimes_interval = 6, obsdata =   obsdata )

obsdata = gendata(n = 1000, format = "wide", total_followup = 6, seed = 945)
years <- 2011:2016
baseline_var <- c("age","sex")
variables <- c("hyper", "bmi")
var_cov <- c("statins","hyper", "bmi")
covariates <- lapply(years, function(year) {
paste0(variables, year)})
treatment_var <- paste0("statins", 2011:2016)
formula = paste0("y ~", paste0(treatment_var,collapse = "+"), "+",
                paste0(unlist(covariates), collapse = "+"),"+",
                paste0(baseline_var, collapse = "+"))
res_gform <- gformula(formula = formula, baseline = baseline_var, covariates = covariates,
treatment = treatment_var, outcome = "y", ntimes_interval = 6, obsdata =   obsdata )

ggplot Trajectory

Description

Use "ggplot2" to plot trajectory groups produced by the function "build_traj" using the observed treatment.

Usage

ggtraj(traj_data, treatment, time, identifier, class, FUN = mean, ...)
ggtraj(traj_data, treatment, time, identifier, class, FUN = mean, ...)

Arguments

`traj_data`	Merged datasets containing observed data in long format and trajectory groups.
`treatment`	Name of the time-varying treatment.
`time`	Name of the time variable.
`identifier`	Name of the identifier variable.
`class`	Name of the trajectory groups.
`FUN`	Specify which statistics to display, by default calculate the mean.
`...`	Additional arguments to be passed to ggplot functions.

Value

A ggplot object representing the trajectory groups using the observed treatment.

Examples


obsdata_long = gendata(n = 1000, format = "long", total_followup = 12, seed = 945)
restraj = build_traj(obsdata = obsdata_long, number_traj = 3,
formula = as.formula(cbind(statins, 1 - statins) ~ time), identifier = "id")
datapost = restraj$data_post
head(datapost)
traj_data_long <- merge(obsdata_long, datapost, by = "id")
    AggFormula <- as.formula(paste("statins", "~", "time", "+", "class"))
    Aggtraj_data <- aggregate(AggFormula, data = traj_data_long, FUN = mean)
    Aggtraj_data
#Aggtraj_data with labels
traj_data_long[ , "traj_group"] <- factor(ifelse(traj_data_long[ , "class"] == "3" ,"Group1" ,
ifelse (traj_data_long[ , "class"]== "1" , "Group2" ,"Group3")))
AggFormula <- as.formula(paste("statins", "~", "time", "+", "traj_group"))
Aggtraj_data <- aggregate(AggFormula, data = traj_data_long, FUN = mean)
ggtraj(traj_data =  Aggtraj_data,
treatment = "statins",time= "time",identifier="id",class = "traj_group", FUN = mean)

obsdata_long = gendata(n = 1000, format = "long", total_followup = 12, seed = 945)
restraj = build_traj(obsdata = obsdata_long, number_traj = 3,
formula = as.formula(cbind(statins, 1 - statins) ~ time), identifier = "id")
datapost = restraj$data_post
head(datapost)
traj_data_long <- merge(obsdata_long, datapost, by = "id")
    AggFormula <- as.formula(paste("statins", "~", "time", "+", "class"))
    Aggtraj_data <- aggregate(AggFormula, data = traj_data_long, FUN = mean)
    Aggtraj_data
#Aggtraj_data with labels
traj_data_long[ , "traj_group"] <- factor(ifelse(traj_data_long[ , "class"] == "3" ,"Group1" ,
ifelse (traj_data_long[ , "class"]== "1" , "Group2" ,"Group3")))
AggFormula <- as.formula(paste("statins", "~", "time", "+", "traj_group"))
Aggtraj_data <- aggregate(AggFormula, data = traj_data_long, FUN = mean)
ggtraj(traj_data =  Aggtraj_data,
treatment = "statins",time= "time",identifier="id",class = "traj_group", FUN = mean)

Inverse Probability Weighting

Description

Compute stabilized and unstabilized weights, with or without censoring.

Usage

inverse_probability_weighting(
  numerator = c("stabilized", "unstabilized"),
  identifier,
  baseline,
  covariates,
  treatment,
  include_censor = FALSE,
  censor,
  obsdata
)
inverse_probability_weighting(
  numerator = c("stabilized", "unstabilized"),
  identifier,
  baseline,
  covariates,
  treatment,
  include_censor = FALSE,
  censor,
  obsdata
)

Arguments

`numerator`	To choose between stabilized and unstabilized weights.
`identifier`	Name of the column of the unique identifier.
`baseline`	Name of the baseline covariates.
`covariates`	Name of the time-varying covariates.
`treatment`	Name of the time-varying treatment.
`include_censor`	Logical value TRUE/FALSE to include or not a censoring variable.
`censor`	Name of the censoring variable.
`obsdata`	Observed data in wide format.

Value

Inverse Probability Weights (Stabilized and Unstabilized) with and without censoring.

Author(s)

Awa Diop, Denis Talbot

Examples

obsdata = gendata(n = 1000, format = "wide",total_followup = 3, seed = 945)
baseline_var <- c("age","sex")
covariates <- list(c("hyper2011", "bmi2011"),
c("hyper2012", "bmi2012"),c("hyper2013", "bmi2013"))
treatment_var <- c("statins2011","statins2012","statins2013")
stabilized_weights = inverse_probability_weighting(numerator = "stabilized",
identifier = "id", covariates = covariates, treatment = treatment_var,
baseline = baseline_var, obsdata = obsdata)
obsdata = gendata(n = 1000, format = "wide",total_followup = 3, seed = 945)
baseline_var <- c("age","sex")
covariates <- list(c("hyper2011", "bmi2011"),
c("hyper2012", "bmi2012"),c("hyper2013", "bmi2013"))
treatment_var <- c("statins2011","statins2012","statins2013")
stabilized_weights = inverse_probability_weighting(numerator = "stabilized",
identifier = "id", covariates = covariates, treatment = treatment_var,
baseline = baseline_var, obsdata = obsdata)

Counterfactual means for a Pooled LTMLE

Description

Function to estimate counterfactual means for a pooled LTMLE.

Usage

pltmle(
  formula,
  outcome,
  treatment,
  covariates,
  baseline,
  ntimes_interval,
  number_traj,
  time,
  time_values,
  identifier,
  obsdata,
  traj,
  total_followup,
  treshold = treshold,
  class_var,
  class_pred
)
pltmle(
  formula,
  outcome,
  treatment,
  covariates,
  baseline,
  ntimes_interval,
  number_traj,
  time,
  time_values,
  identifier,
  obsdata,
  traj,
  total_followup,
  treshold = treshold,
  class_var,
  class_pred
)

Arguments

`formula`	Specification of the model for the outcome to be fitted.
`outcome`	Name of the outcome variable.
`treatment`	Time-varying treatment.
`covariates`	Covariates.
`baseline`	Name of baseline covariates.
`ntimes_interval`	Length of a time-interval (s).
`number_traj`	An integer to choose the number of trajectory groups.
`time`	Name of the time variable.
`time_values`	Measuring times.
`identifier`	Name of the column of the unique identifier.
`obsdata`	Observed data in wide format.
`traj`	Matrix of indicators for the trajectory groups.
`total_followup`	Number of measuring times per interval.
`treshold`	For weight truncation.
`class_var`	Name of the trajectory group variable.
`class_pred`	Vector of predicted trajectory groups.

Value

`list_pltmle_countermeans`	Counterfactual means and influence functions with the pooled ltmle.
`D`	Influence functions

Author(s)

Awa Diop, Denis Talbot

Examples


obsdata_long = gendata(n = 2000, format = "long",total_followup = 3, seed = 945)
baseline_var <- c("age","sex")
covariates <- list(c("hyper2011", "bmi2011"),
c("hyper2012", "bmi2012"),c("hyper2013", "bmi2013"))
treatment_var <- c("statins2011","statins2012","statins2013")
time_values <- c(2011,2012,2013)
formulaA = as.formula(cbind(statins, 1 - statins) ~ time)
restraj = build_traj(obsdata = obsdata_long, number_traj = 3,
formula = formulaA, identifier = "id")
datapost = restraj$data_post
trajmsm_long <- merge(obsdata_long, datapost, by = "id")
    AggFormula <- as.formula(paste("statins", "~", "time", "+", "class"))
    AggTrajData <- aggregate(AggFormula, data = trajmsm_long, FUN = mean)
    AggTrajData
trajmsm_long[ , "traj_group"] <- trajmsm_long[ , "class"]
obsdata= reshape(trajmsm_long, direction = "wide", idvar = "id",
v.names = c("statins","bmi","hyper"), timevar = "time", sep ="")
formula =  as.formula(" y ~ statins2011 + statins2012 + statins2013 +
hyper2011 + bmi2011 + hyper2012 + bmi2012 +
 hyper2013 + bmi2013 + age + sex ")
class = factor(predict_traj(identifier = "id", total_followup = 3,
        treatment = "statins", time = "time", time_values = time_values,
        trajmodel = restraj$traj_model)$post_class);
traj=t(sapply(1:8,function(x)sapply(1:3,function(i)ifelse(class[x]==i,1,0))))
traj[,1]=1
res_pltmle = pltmle(formula = formula, outcome = "y",treatment = treatment_var,
covariates = covariates, baseline = baseline_var, ntimes_interval = 3, number_traj = 3,
 time =  "time",time_values = time_values,identifier = "id",obsdata = obsdata,
traj=traj, treshold = 0.99, class_pred= class, class_var = "class")
res_pltmle$counter_means

obsdata_long = gendata(n = 2000, format = "long",total_followup = 3, seed = 945)
baseline_var <- c("age","sex")
covariates <- list(c("hyper2011", "bmi2011"),
c("hyper2012", "bmi2012"),c("hyper2013", "bmi2013"))
treatment_var <- c("statins2011","statins2012","statins2013")
time_values <- c(2011,2012,2013)
formulaA = as.formula(cbind(statins, 1 - statins) ~ time)
restraj = build_traj(obsdata = obsdata_long, number_traj = 3,
formula = formulaA, identifier = "id")
datapost = restraj$data_post
trajmsm_long <- merge(obsdata_long, datapost, by = "id")
    AggFormula <- as.formula(paste("statins", "~", "time", "+", "class"))
    AggTrajData <- aggregate(AggFormula, data = trajmsm_long, FUN = mean)
    AggTrajData
trajmsm_long[ , "traj_group"] <- trajmsm_long[ , "class"]
obsdata= reshape(trajmsm_long, direction = "wide", idvar = "id",
v.names = c("statins","bmi","hyper"), timevar = "time", sep ="")
formula =  as.formula(" y ~ statins2011 + statins2012 + statins2013 +
hyper2011 + bmi2011 + hyper2012 + bmi2012 +
 hyper2013 + bmi2013 + age + sex ")
class = factor(predict_traj(identifier = "id", total_followup = 3,
        treatment = "statins", time = "time", time_values = time_values,
        trajmodel = restraj$traj_model)$post_class);
traj=t(sapply(1:8,function(x)sapply(1:3,function(i)ifelse(class[x]==i,1,0))))
traj[,1]=1
res_pltmle = pltmle(formula = formula, outcome = "y",treatment = treatment_var,
covariates = covariates, baseline = baseline_var, ntimes_interval = 3, number_traj = 3,
 time =  "time",time_values = time_values,identifier = "id",obsdata = obsdata,
traj=traj, treshold = 0.99, class_pred= class, class_var = "class")
res_pltmle$counter_means

Predict trajectory groups for deterministic treatment regimes

Description

Function to predict trajectory groups for deterministic treatment regimes used with gformula and pooled LTMLE.

Usage

predict_traj(
  identifier,
  total_followup,
  treatment,
  time,
  time_values,
  trajmodel
)
predict_traj(
  identifier,
  total_followup,
  treatment,
  time,
  time_values,
  trajmodel
)

Arguments

`identifier`	Name of the column of the unique identifier.
`total_followup`	Number of measuring times.
`treatment`	Name of the time-varying treatment.
`time`	Name of the variable time.
`time_values`	Values of the time variable.
`trajmodel`	Trajectory model built with the observed treatment.

Value

A data.frame with the posterior probabilities.

Author(s)

Awa Diop, Denis Talbot

Split observed data into multiple subsets

Description

Function to split the data into multiple subsets of size s each one subset corresponding to one time-interval.

Usage

split_data(
  obsdata,
  total_followup,
  ntimes_interval,
  time,
  time_values,
  identifier
)
split_data(
  obsdata,
  total_followup,
  ntimes_interval,
  time,
  time_values,
  identifier
)

Arguments

`obsdata`	Observed data in wide format.
`total_followup`	Total length of follow-up.
`ntimes_interval`	Number of measuring times per interval.
`time`	Name of the time variable.
`time_values`	Measuring times.
`identifier`	Identifier of individuals.

Value

all_df

All subsets, list of time intervals.

Author(s)

Awa Diop Denis Talbot

Examples

obsdata = gendata(n = 1000, format = "long", total_followup = 8, seed = 945)
years <- 2011:2018
res = split_data(obsdata = obsdata, total_followup = 8,
ntimes_interval = 6,time = "time", time_values = years,identifier = "id")
obsdata = gendata(n = 1000, format = "long", total_followup = 8, seed = 945)
years <- 2011:2018
res = split_data(obsdata = obsdata, total_followup = 8,
ntimes_interval = 6,time = "time", time_values = years,identifier = "id")

History Restricted MSM and Latent Class of Growth Analysis estimated with G-formula.

Description

Estimate parameters of LCGA-HRMSM using g-formula. and bootstrap to get standard errors.

Usage

trajhrmsm_gform(
  degree_traj = c("linear", "quadratic", "cubic"),
  rep = 50,
  treatment,
  covariates,
  baseline,
  outcome,
  ntimes_interval,
  total_followup,
  time,
  time_values,
  identifier,
  var_cov,
  number_traj = 3,
  family = "poisson",
  obsdata
)
trajhrmsm_gform(
  degree_traj = c("linear", "quadratic", "cubic"),
  rep = 50,
  treatment,
  covariates,
  baseline,
  outcome,
  ntimes_interval,
  total_followup,
  time,
  time_values,
  identifier,
  var_cov,
  number_traj = 3,
  family = "poisson",
  obsdata
)

Arguments

`degree_traj`	To specify the polynomial degree for modelling the time-varying treatment.
`rep`	Number of repetition for the bootstrap.
`treatment`	Name of the time-varying treatment.
`covariates`	Names of the time-varying covariates (should be a list).
`baseline`	Name of baseline covariates.
`outcome`	Name of the outcome variable.
`ntimes_interval`	Length of a time-interval (s).
`total_followup`	Total length of follow-up.
`time`	Name of the time variable.
`time_values`	Measuring times.
`identifier`	Name of the column of the unique identifier.
`var_cov`	Names of the time-varying variables.
`number_traj`	Number of trajectory groups.
`family`	Specification of the error distribution and link function to be used in the model.
`obsdata`	Data in a long format.

Value

A list containing the following components:

results_hrmsm_gform: Matrix of estimates for LCGA-MSM, obtained using the g-formula method.
result_coef_boot: Matrix of estimates obtained with bootstrap.
restraj: Fitted trajectory model.
mean_adh: Matrix of mean adherence per trajectory group.

Author(s)

Awa Diop Denis Talbot

Examples


obsdata_long = gendata(n = 5000, format = "long", total_followup = 8,
timedep_outcome = TRUE,  seed = 845)
baseline_var <- c("age","sex")
years <- 2011:2018
variables <- c("hyper", "bmi")
covariates <- lapply(years, function(year) {
paste0(variables, year)})
treatment_var <- paste0("statins", 2011:2018)
var_cov <- c("statins","hyper", "bmi")
reshrmsm_gform = trajhrmsm_gform(degree_traj = "linear", rep=50 ,
treatment = treatment_var,covariates = covariates, baseline = baseline_var,
outcome = "y",var_cov = var_cov, ntimes_interval = 6, total_followup = 8,
 time = "time",time_values = years, identifier = "id",
number_traj = 3, family = "poisson", obsdata = obsdata_long)
reshrmsm_gform$results_hrmsm_gform

obsdata_long = gendata(n = 5000, format = "long", total_followup = 8,
timedep_outcome = TRUE,  seed = 845)
baseline_var <- c("age","sex")
years <- 2011:2018
variables <- c("hyper", "bmi")
covariates <- lapply(years, function(year) {
paste0(variables, year)})
treatment_var <- paste0("statins", 2011:2018)
var_cov <- c("statins","hyper", "bmi")
reshrmsm_gform = trajhrmsm_gform(degree_traj = "linear", rep=50 ,
treatment = treatment_var,covariates = covariates, baseline = baseline_var,
outcome = "y",var_cov = var_cov, ntimes_interval = 6, total_followup = 8,
 time = "time",time_values = years, identifier = "id",
number_traj = 3, family = "poisson", obsdata = obsdata_long)
reshrmsm_gform$results_hrmsm_gform

History Restricted MSM and Latent Class of Growth Analysis estimated with IPW.

Description

Estimate parameters of LCGA-HRMSM using IPW.

Usage

trajhrmsm_ipw(
  degree_traj = c("linear", "quadratic", "cubic"),
  numerator = c("stabilized", "unstabilized"),
  identifier,
  baseline,
  covariates,
  treatment,
  outcome,
  var_cov,
  include_censor = FALSE,
  ntimes_interval,
  total_followup,
  time,
  time_values,
  family = "poisson",
  censor = censor,
  number_traj,
  obsdata,
  weights = NULL,
  treshold = 0.999
)
trajhrmsm_ipw(
  degree_traj = c("linear", "quadratic", "cubic"),
  numerator = c("stabilized", "unstabilized"),
  identifier,
  baseline,
  covariates,
  treatment,
  outcome,
  var_cov,
  include_censor = FALSE,
  ntimes_interval,
  total_followup,
  time,
  time_values,
  family = "poisson",
  censor = censor,
  number_traj,
  obsdata,
  weights = NULL,
  treshold = 0.999
)

Arguments

`degree_traj`	To specify the polynomial degree for modelling the time-varying treatment.
`numerator`	To choose between stabilized and unstabilized weights.
`identifier`	Name of the column of the unique identifier.
`baseline`	Names of the baseline covariates.
`covariates`	Names of the time-varying covariates (should be a list).
`treatment`	Name of the time-varying treatment.
`outcome`	Name of the outcome variable.
`var_cov`	Names of the time-varying variables.
`include_censor`	Logical, if TRUE, includes censoring.
`ntimes_interval`	Length of a time-interval (s).
`total_followup`	Total length of follow-up.
`time`	Name of the time variable.
`time_values`	Values of the time variable.
`family`	specification of the error distribution and link function to be used in the model.
`censor`	Name of the censoring variable.
`number_traj`	Number of trajectory groups.
`obsdata`	Data in a long format.
`weights`	A vector of estimated weights. If NULL, the weights are computed by the function.
`treshold`	For weight truncation.

Value

Provides a matrix of estimates for LCGA-HRMSM, obtained using IPW.

Author(s)

Awa Diop, Denis Talbot

Examples


obsdata_long = gendata(n = 5000, format = "long", total_followup = 8,
timedep_outcome = TRUE,  seed = 845)
baseline_var <- c("age","sex")
years <- 2011:2018
variables <- c("hyper", "bmi")
covariates <- lapply(years, function(year) {
paste0(variables, year)})
treatment_var <- paste0("statins", 2011:2018)
var_cov <- c("statins","hyper", "bmi","y")
reshrmsm_ipw <- trajhrmsm_ipw(degree_traj = "linear", numerator = "stabilized",
identifier = "id", baseline = baseline_var,
covariates = covariates, treatment = treatment_var,
outcome = "y", var_cov= var_cov,include_censor = FALSE,
 ntimes_interval = 6,total_followup = 8, time = "time", time_values = 2011:2018,
family = "poisson", number_traj = 3, obsdata = obsdata_long, treshold = 1)
reshrmsm_ipw$res_trajhrmsm_ipw

obsdata_long = gendata(n = 5000, format = "long", total_followup = 8,
timedep_outcome = TRUE,  seed = 845)
baseline_var <- c("age","sex")
years <- 2011:2018
variables <- c("hyper", "bmi")
covariates <- lapply(years, function(year) {
paste0(variables, year)})
treatment_var <- paste0("statins", 2011:2018)
var_cov <- c("statins","hyper", "bmi","y")
reshrmsm_ipw <- trajhrmsm_ipw(degree_traj = "linear", numerator = "stabilized",
identifier = "id", baseline = baseline_var,
covariates = covariates, treatment = treatment_var,
outcome = "y", var_cov= var_cov,include_censor = FALSE,
 ntimes_interval = 6,total_followup = 8, time = "time", time_values = 2011:2018,
family = "poisson", number_traj = 3, obsdata = obsdata_long, treshold = 1)
reshrmsm_ipw$res_trajhrmsm_ipw

History Restricted MSM and Latent Class of Growth Analysis estimated with a Pooled LTMLE.

Description

Estimate parameters of LCGA-HRMSM using a Pooled LTMLE.

Usage

trajhrmsm_pltmle(
  degree_traj = c("linear", "quadratic", "cubic"),
  treatment,
  covariates,
  baseline,
  outcome,
  ntimes_interval,
  total_followup,
  time,
  time_values,
  identifier,
  var_cov,
  number_traj = 3,
  family = "poisson",
  obsdata,
  treshold = 0.99
)
trajhrmsm_pltmle(
  degree_traj = c("linear", "quadratic", "cubic"),
  treatment,
  covariates,
  baseline,
  outcome,
  ntimes_interval,
  total_followup,
  time,
  time_values,
  identifier,
  var_cov,
  number_traj = 3,
  family = "poisson",
  obsdata,
  treshold = 0.99
)

Arguments

`degree_traj`	To specify the polynomial degree for modelling the time-varying treatment.
`treatment`	Name of time-varying treatment.
`covariates`	Names of time-varying covariates (should be a list).
`baseline`	Names of baseline covariates.
`outcome`	Name of the outcome variable.
`ntimes_interval`	Length of a time-interval (s).
`total_followup`	Total length of follow-up.
`time`	Name of the time variable.
`time_values`	Measuring times.
`identifier`	Name of the column for unique identifiant.
`var_cov`	Names of the time-varying variables.
`number_traj`	Number of trajectory groups.
`family`	Specification of the error distribution and link function to be used in the model.
`obsdata`	Data in a long format.
`treshold`	For weight truncation.

Value

A list containing the following components:

results_hrmsm_pltmle: Matrix of estimates for LCGA-HRMSM, obtained using the pooled ltlmle method.
restraj: Fitted trajectory model.
mean_adh: Matrix of the mean adherence per trajectory group.

Author(s)

Awa Diop Denis Talbot

Examples


obsdata_long = gendata(n = 5000, format = "long",
total_followup = 8, timedep_outcome = TRUE,  seed = 845)
baseline_var <- c("age","sex")
years <- 2011:2018
variables <- c("hyper", "bmi")
covariates <- lapply(years, function(year) {
  paste0(variables, year)})
treatment_var <- paste0("statins", 2011:2018)
var_cov <- c("statins","hyper", "bmi","y")
respltmle = trajhrmsm_pltmle(degree_traj = "linear", treatment = treatment_var,
covariates = covariates, baseline = baseline_var,
outcome = paste0("y", 2016:2018),var_cov = var_cov, ntimes_interval = 6,
total_followup = 8, time = "time",time_values = years, identifier = "id",
number_traj = 3, family = "poisson", obsdata = obsdata_long,treshold = 1)
respltmle$results_hrmsm_pltmle

obsdata_long = gendata(n = 5000, format = "long",
total_followup = 8, timedep_outcome = TRUE,  seed = 845)
baseline_var <- c("age","sex")
years <- 2011:2018
variables <- c("hyper", "bmi")
covariates <- lapply(years, function(year) {
  paste0(variables, year)})
treatment_var <- paste0("statins", 2011:2018)
var_cov <- c("statins","hyper", "bmi","y")
respltmle = trajhrmsm_pltmle(degree_traj = "linear", treatment = treatment_var,
covariates = covariates, baseline = baseline_var,
outcome = paste0("y", 2016:2018),var_cov = var_cov, ntimes_interval = 6,
total_followup = 8, time = "time",time_values = years, identifier = "id",
number_traj = 3, family = "poisson", obsdata = obsdata_long,treshold = 1)
respltmle$results_hrmsm_pltmle

Parametric g-formula

Description

Estimate parameters of LCGA-MSM using g-formula and bootstrap to get standard errors.

Usage

trajmsm_gform(
  formula = formula,
  rep = 50,
  identifier,
  baseline,
  covariates,
  treatment,
  outcome,
  total_followup,
  time = time,
  time_values,
  var_cov,
  trajmodel,
  ref,
  obsdata
)
trajmsm_gform(
  formula = formula,
  rep = 50,
  identifier,
  baseline,
  covariates,
  treatment,
  outcome,
  total_followup,
  time = time,
  time_values,
  var_cov,
  trajmodel,
  ref,
  obsdata
)

Arguments

`formula`	Specification of the model for the outcome to be fitted.
`rep`	Number of repetitions for the bootstrap.
`identifier`	Name of the column of the unique identifier.
`baseline`	Vector of names of the baseline covariates.
`covariates`	List of names of the time-varying covariates.
`treatment`	Vector of names of the time-varying treatment.
`outcome`	Name of the outcome of interest.
`total_followup`	Total length of follow-up.
`time`	Name of the time variable.
`time_values`	Measuring times.
`var_cov`	Names of the time-varying covariates.
`trajmodel`	Trajectory model built with the observed treatment.
`ref`	The reference trajectory group.
`obsdata`	Observed data in wide format.

Value

Provides a matrix of estimates for LCGA-MSM, obtained using the g-formula method.

Author(s)

Awa Diop Denis Talbot

Examples


obsdata_long = gendata(n = 1000, format = "long", total_followup = 6, seed = 845)
years <- 2011:2016
baseline_var <- c("age","sex")
variables <- c("hyper", "bmi")
var_cov <- c("statins","hyper", "bmi")
covariates <- lapply(years, function(year) {
paste0(variables, year)})
treatment_var <- paste0("statins", 2011:2016)
formula_treatment = as.formula(cbind(statins, 1 - statins) ~ time)
restraj = build_traj(obsdata = obsdata_long, number_traj = 3,
formula = formula_treatment, identifier = "id")
datapost = restraj$data_post
trajmsm_long <- merge(obsdata_long, datapost, by = "id")
    AggFormula <- as.formula(paste("statins", "~", "time", "+", "class"))
    AggTrajData <- aggregate(AggFormula, data = trajmsm_long, FUN = mean)
    AggTrajData
obsdata = reshape(data = trajmsm_long, direction = "wide", idvar = "id",
v.names = c("statins","bmi","hyper"), timevar = "time", sep ="")
formula = paste0("y ~", paste0(treatment_var,collapse = "+"), "+",
                paste0(unlist(covariates), collapse = "+"),"+",
                paste0(baseline_var, collapse = "+"))
resmsm_gform <- trajmsm_gform(formula = formula, identifier = "id",rep = 5,
baseline = baseline_var, covariates = covariates, var_cov = var_cov,
treatment = treatment_var, outcome = "y", total_followup = 6,time = "time",
time_values = years, trajmodel = restraj$traj_model,ref = "1", obsdata =   obsdata )
resmsm_gform

obsdata_long = gendata(n = 1000, format = "long", total_followup = 6, seed = 845)
years <- 2011:2016
baseline_var <- c("age","sex")
variables <- c("hyper", "bmi")
var_cov <- c("statins","hyper", "bmi")
covariates <- lapply(years, function(year) {
paste0(variables, year)})
treatment_var <- paste0("statins", 2011:2016)
formula_treatment = as.formula(cbind(statins, 1 - statins) ~ time)
restraj = build_traj(obsdata = obsdata_long, number_traj = 3,
formula = formula_treatment, identifier = "id")
datapost = restraj$data_post
trajmsm_long <- merge(obsdata_long, datapost, by = "id")
    AggFormula <- as.formula(paste("statins", "~", "time", "+", "class"))
    AggTrajData <- aggregate(AggFormula, data = trajmsm_long, FUN = mean)
    AggTrajData
obsdata = reshape(data = trajmsm_long, direction = "wide", idvar = "id",
v.names = c("statins","bmi","hyper"), timevar = "time", sep ="")
formula = paste0("y ~", paste0(treatment_var,collapse = "+"), "+",
                paste0(unlist(covariates), collapse = "+"),"+",
                paste0(baseline_var, collapse = "+"))
resmsm_gform <- trajmsm_gform(formula = formula, identifier = "id",rep = 5,
baseline = baseline_var, covariates = covariates, var_cov = var_cov,
treatment = treatment_var, outcome = "y", total_followup = 6,time = "time",
time_values = years, trajmodel = restraj$traj_model,ref = "1", obsdata =   obsdata )
resmsm_gform

Marginal Structural Model and Latent Class of Growth Analysis estimated with IPW

Description

Estimate parameters of LCGA-MSM using IPW.

Usage

trajmsm_ipw(
  formula1,
  formula2,
  family,
  identifier,
  treatment,
  covariates,
  baseline,
  obsdata,
  numerator = "stabilized",
  include_censor = FALSE,
  censor,
  weights = NULL,
  treshold = 0.99
)
trajmsm_ipw(
  formula1,
  formula2,
  family,
  identifier,
  treatment,
  covariates,
  baseline,
  obsdata,
  numerator = "stabilized",
  include_censor = FALSE,
  censor,
  weights = NULL,
  treshold = 0.99
)

Arguments

`formula1`	Specification of the model for the outcome to be fitted for a binomial or gaussian distribution.
`formula2`	Specification of the model for the outcome to be fitted for a survival outcome.
`family`	Specification of the error distribution and link function to be used in the model.
`identifier`	Name of the column of the unique identifier.
`treatment`	Time-varying treatment.
`covariates`	Names of the time-varying covariates (should be a list).
`baseline`	Name of the baseline covariates.
`obsdata`	Dataset to be used in the analysis.
`numerator`	Type of weighting ("stabilized" or "unstabilized").
`include_censor`	Logical, if TRUE, includes censoring.
`censor`	Name of the censoring variable.
`weights`	A vector of estimated weights. If NULL, the weights are computed by the function `IPW`.
`treshold`	For weight truncation.

Value

Provides a matrix of estimates for LCGA-MSM, obtained using IPW.

Examples


obsdata_long = gendata(n = 1000, format = "long", total_followup = 6, seed = 845)
years <- 2011:2016
baseline_var <- c("age","sex")
variables <- c("hyper", "bmi")
covariates <- lapply(years, function(year) {
paste0(variables, year)})
treatment_var <- paste0("statins", 2011:2016)
formula_treatment = as.formula(cbind(statins, 1 - statins) ~ time)
restraj = build_traj(obsdata = obsdata_long, number_traj = 3,
formula = formula_treatment, identifier = "id")
datapost = restraj$data_post
trajmsm_long <- merge(obsdata_long, datapost, by = "id")
    AggFormula <- as.formula(paste("statins", "~", "time", "+", "class"))
    AggTrajData <- aggregate(AggFormula, data = trajmsm_long, FUN = mean)
    AggTrajData
trajmsm_long$ipw_group <- relevel(trajmsm_long$class, ref = "1")
obsdata = reshape(data = trajmsm_long, direction = "wide", idvar = "id",
v.names = c("statins","bmi","hyper"), timevar = "time", sep ="")
formula = paste0("y ~", paste0(treatment_var,collapse = "+"), "+",
                paste0(unlist(covariates), collapse = "+"),"+",
                paste0(baseline_var, collapse = "+"))

resmsm_ipw = trajmsm_ipw(formula1 = as.formula("y ~ ipw_group"),
           identifier = "id", baseline = baseline_var, covariates = covariates,
           treatment = treatment_var, family = "binomial",
           obsdata = obsdata,numerator = "stabilized", include_censor = FALSE, treshold = 0.99)
resmsm_ipw

obsdata_long = gendata(n = 1000, format = "long", total_followup = 6, seed = 845)
years <- 2011:2016
baseline_var <- c("age","sex")
variables <- c("hyper", "bmi")
covariates <- lapply(years, function(year) {
paste0(variables, year)})
treatment_var <- paste0("statins", 2011:2016)
formula_treatment = as.formula(cbind(statins, 1 - statins) ~ time)
restraj = build_traj(obsdata = obsdata_long, number_traj = 3,
formula = formula_treatment, identifier = "id")
datapost = restraj$data_post
trajmsm_long <- merge(obsdata_long, datapost, by = "id")
    AggFormula <- as.formula(paste("statins", "~", "time", "+", "class"))
    AggTrajData <- aggregate(AggFormula, data = trajmsm_long, FUN = mean)
    AggTrajData
trajmsm_long$ipw_group <- relevel(trajmsm_long$class, ref = "1")
obsdata = reshape(data = trajmsm_long, direction = "wide", idvar = "id",
v.names = c("statins","bmi","hyper"), timevar = "time", sep ="")
formula = paste0("y ~", paste0(treatment_var,collapse = "+"), "+",
                paste0(unlist(covariates), collapse = "+"),"+",
                paste0(baseline_var, collapse = "+"))

resmsm_ipw = trajmsm_ipw(formula1 = as.formula("y ~ ipw_group"),
           identifier = "id", baseline = baseline_var, covariates = covariates,
           treatment = treatment_var, family = "binomial",
           obsdata = obsdata,numerator = "stabilized", include_censor = FALSE, treshold = 0.99)
resmsm_ipw

Pooled LTMLE

Description

Estimate parameters of LCGA-MSM using pooled LTMLE with influence functions to estimate standard errors.

Usage

trajmsm_pltmle(
  formula = formula,
  identifier,
  baseline,
  covariates,
  treatment,
  outcome,
  number_traj,
  total_followup,
  time,
  time_values,
  trajmodel,
  ref,
  treshold = 0.99,
  obsdata,
  class_var
)
trajmsm_pltmle(
  formula = formula,
  identifier,
  baseline,
  covariates,
  treatment,
  outcome,
  number_traj,
  total_followup,
  time,
  time_values,
  trajmodel,
  ref,
  treshold = 0.99,
  obsdata,
  class_var
)

Arguments

`formula`	Specification of the model for the outcome to be fitted.
`identifier`	Name of the column for unique identifiant.
`baseline`	Names of the baseline covariates.
`covariates`	Names of the time-varying covariates (should be a list).
`treatment`	Name of the time-varying treatment.
`outcome`	Name of the outcome variable.
`number_traj`	An integer to choose the number of trajectory groups.
`total_followup`	Total length of follow-up.
`time`	Name of the time variable.
`time_values`	Measuring times.
`trajmodel`	Trajectory model built with the observed treatment.
`ref`	The reference group.
`treshold`	For weight truncation.
`obsdata`	Observed data in wide format.
`class_var`	Name of the trajectory group variable.

Value

Provides a matrix of estimates for LCGA-MSM, obtained using the pooled ltlmle method.

results_msm_pooledltmle

Estimates of a LCGA-MSM with pooled LTMLE.

Author(s)

Awa Diop, Denis Talbot

Examples


obsdata_long = gendata(n = 1000, format = "long", total_followup = 6, seed = 845)
years <- 2011:2016
baseline_var <- c("age","sex")
variables <- c("hyper", "bmi")
covariates <- lapply(years, function(year) {
paste0(variables, year)})
treatment_var <- paste0("statins", 2011:2016)
formula_treatment = as.formula(cbind(statins, 1 - statins) ~ time)
restraj = build_traj(obsdata = obsdata_long, number_traj = 3,
formula = formula_treatment, identifier = "id")
datapost = restraj$data_post
trajmsm_long <- merge(obsdata_long, datapost, by = "id")
    AggFormula <- as.formula(paste("statins", "~", "time", "+", "class"))
    AggTrajData <- aggregate(AggFormula, data = trajmsm_long, FUN = mean)
trajmsm_wide = reshape(data = trajmsm_long, direction = "wide", idvar = "id",
v.names = c("statins","bmi","hyper"), timevar = "time", sep ="")
formula = paste0("y ~", paste0(treatment_var,collapse = "+"), "+",
                paste0(unlist(covariates), collapse = "+"),"+",
                paste0(baseline_var, collapse = "+"))
resmsm_pltmle <- trajmsm_pltmle(formula = formula, identifier = "id",
 baseline = baseline_var,
 covariates = covariates, treatment = treatment_var,
 outcome = "y", time = "time", time_values = years,
 number_traj = 3, total_followup = 6,
 trajmodel = restraj$traj_model, ref = "1", obsdata = trajmsm_wide,
  treshold = 1,class_var = "class")
 resmsm_pltmle
 
obsdata_long = gendata(n = 1000, format = "long", total_followup = 6, seed = 845)
years <- 2011:2016
baseline_var <- c("age","sex")
variables <- c("hyper", "bmi")
covariates <- lapply(years, function(year) {
paste0(variables, year)})
treatment_var <- paste0("statins", 2011:2016)
formula_treatment = as.formula(cbind(statins, 1 - statins) ~ time)
restraj = build_traj(obsdata = obsdata_long, number_traj = 3,
formula = formula_treatment, identifier = "id")
datapost = restraj$data_post
trajmsm_long <- merge(obsdata_long, datapost, by = "id")
    AggFormula <- as.formula(paste("statins", "~", "time", "+", "class"))
    AggTrajData <- aggregate(AggFormula, data = trajmsm_long, FUN = mean)
trajmsm_wide = reshape(data = trajmsm_long, direction = "wide", idvar = "id",
v.names = c("statins","bmi","hyper"), timevar = "time", sep ="")
formula = paste0("y ~", paste0(treatment_var,collapse = "+"), "+",
                paste0(unlist(covariates), collapse = "+"),"+",
                paste0(baseline_var, collapse = "+"))
resmsm_pltmle <- trajmsm_pltmle(formula = formula, identifier = "id",
 baseline = baseline_var,
 covariates = covariates, treatment = treatment_var,
 outcome = "y", time = "time", time_values = years,
 number_traj = 3, total_followup = 6,
 trajmodel = restraj$traj_model, ref = "1", obsdata = trajmsm_wide,
  treshold = 1,class_var = "class")
 resmsm_pltmle

Package 'trajmsm'

Help Index

Wrapper for flexmix

Description

Usage

Arguments

Value

Examples

Generate data trajectories for MSM

Description

Usage

Arguments

Value

Examples

Counterfactual means via G-Formula

Description

Usage

Arguments

Value

Author(s)

Examples

ggplot Trajectory

Description

Usage

Arguments

Value

Examples

Inverse Probability Weighting

Description

Usage

Arguments

Value

Author(s)

Examples

Counterfactual means for a Pooled LTMLE

Description

Usage

Arguments

Value

Author(s)

Examples

Predict trajectory groups for deterministic treatment regimes

Description

Usage

Arguments

Value

Author(s)

Split observed data into multiple subsets

Description

Usage

Arguments

Value

Author(s)

Examples

History Restricted MSM and Latent Class of Growth Analysis estimated with G-formula.

Description

Usage

Arguments

Value

Author(s)

Examples

History Restricted MSM and Latent Class of Growth Analysis estimated with IPW.

Description

Usage

Arguments

Value

Author(s)

Examples

History Restricted MSM and Latent Class of Growth Analysis estimated with a Pooled LTMLE.

Description

Usage

Arguments

Value

Author(s)

Examples

Parametric g-formula

Description

Usage

Arguments

Value