te_stats_glm_logit-class: Fit Models using logistic stats::glm

In CAM-Roche/RandomisedTrialsEmulation: Causal Analysis of Observational Time-to-Event Data

Fit Models using logistic stats::glm

Description

The classes and (internal) methods defined for using stats::glm to fit the logistic regression models.

Usage

## S4 method for signature 'te_stats_glm_logit'
fit_weights_model(object, data, formula, label)

## S4 method for signature 'te_stats_glm_logit'
fit_outcome_model(object, data, formula, weights = NULL)

## S4 method for signature 'te_stats_glm_logit_outcome_fitted'
predict(
  object,
  newdata,
  predict_times,
  conf_int = TRUE,
  samples = 100,
  type = c("cum_inc", "survival")
)

Arguments

object	Object to dispatch method on
data	data.frame containing outcomes and covariates as defined in formula.
formula	formula describing the model.
label	A short string describing the model.
weights	numeric vector of weights.
newdata	Baseline trial data that characterise the target trial population that marginal cumulative incidences or survival probabilities are predicted for. newdata must have the same columns and formats of variables as in the fitted marginal structural model specified in trial_msm() or initiators(). If newdata contains rows with followup_time > 0 these will be removed.
predict_times	Specify the follow-up visits/times where the marginal cumulative incidences or survival probabilities are predicted.
conf_int	Construct the point-wise 95-percent confidence intervals of cumulative incidences for the target trial population under treatment and non-treatment and their differences by simulating the parameters in the marginal structural model from a multivariate normal distribution with the mean equal to the marginal structural model parameter estimates and the variance equal to the estimated robust covariance matrix.
samples	Number of samples used to construct the simulation-based confidence intervals.
type	Specify cumulative incidences or survival probabilities to be predicted. Either cumulative incidence ("cum_inc") or survival probability ("survival").

Functions

• fit_weights_model(te_stats_glm_logit): Fit the weight models object via calculate_weights on trial_sequence

• fit_outcome_model(te_stats_glm_logit): Fit the outcome model object via fit_msm on trial_sequence

• predict(te_stats_glm_logit_outcome_fitted): Predict from the fitted model object via predict on trial_sequence

See Also

Other model_fitter_classes: te_parsnip_model-class

CAM-Roche/RandomisedTrialsEmulation documentation built on April 14, 2025, 7:44 a.m.