| Type: | Package |
| Title: | Marginal Structural Models with Latent Class Growth Analysis of Treatment Trajectories |
| Version: | 0.1.4 |
| Maintainer: | Awa Diop <awa.diop.2@ulaval.ca> |
| 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>. |
| License: | GPL (≥ 3) |
| Encoding: | UTF-8 |
| Imports: | stats, e1071, flexmix, ggplot2, survival, sandwich, utils |
| RoxygenNote: | 7.3.1 |
| URL: | https://github.com/awamaeva/R-package-trajmsm |
| BugReports: | https://github.com/awamaeva/R-package-trajmsm/issues |
| NeedsCompilation: | no |
| Packaged: | 2025-06-16 20:29:45 UTC; YaHawa |
| Author: | Awa Diop [aut, cre], Denis Talbot [aut] |
| Repository: | CRAN |
| Date/Publication: | 2025-06-16 20:50:02 UTC |
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),
...
)
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")
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
)
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)
Counterfactual means via G-Formula
Description
Calculates counterfactual means using the g-formula approach.
Usage
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 )
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, ...)
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)
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
)
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)
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
)
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
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
)
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
)
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
## Not run:
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")
## End(Not run)
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
)
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
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
)
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
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
)
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
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
)
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
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
)
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 |
treshold |
For weight truncation. |
Value
Provides a matrix of estimates for LCGA-MSM, obtained using IPW.
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
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
)
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