predict_marginal function

Predict marginal cumulative incidences with confidence intervals for a target trial population

This function predicts the marginal cumulative incidences when a target trial population receives either the treatment or non-treatment at baseline (for an intention-to-treat analysis) or either sustained treatment or sustained non-treatment (for a per-protocol analysis). The difference between these cumulative incidences is the estimated causal effect of treatment. Currently, the predict function only provides marginal intention-to-treat and per-protocol effects, therefore it is only valid when estimand_type = "ITT" or estimand_type = "PP".

predict(object, ...)

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

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

## S3 method for class 'TE_msm'
predict(
  object,
  newdata,
  predict_times,
  conf_int = TRUE,
  samples = 100,
  type = c("cum_inc", "survival"),
  ...
)

Arguments

• object: Object from trial_msm() or initiators() or trial_sequence .
• ...: Further arguments passed to or from other methods.
• 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").

Returns

A list of three data frames containing the cumulative incidences for each of the assigned treatment options (treatment and non-treatment) and the difference between them.

Examples

# Prediction for initiators() or trial_msm() objects -----

# If necessary set the number of `data.table` threads
data.table::setDTthreads(2)

data("te_model_ex")
predicted_ci <- predict(te_model_ex, predict_times = 0:30, samples = 10)

# Plot the cumulative incidence curves under treatment and non-treatment
plot(predicted_ci[[1]]$followup_time, predicted_ci[[1]]$cum_inc,
  type = "l",
  xlab = "Follow-up Time", ylab = "Cumulative Incidence",
  ylim = c(0, 0.7)
)
lines(predicted_ci[[1]]$followup_time, predicted_ci[[1]]$`2.5%`, lty = 2)
lines(predicted_ci[[1]]$followup_time, predicted_ci[[1]]$`97.5%`, lty = 2)

lines(predicted_ci[[2]]$followup_time, predicted_ci[[2]]$cum_inc, type = "l", col = 2)
lines(predicted_ci[[2]]$followup_time, predicted_ci[[2]]$`2.5%`, lty = 2, col = 2)
lines(predicted_ci[[2]]$followup_time, predicted_ci[[2]]$`97.5%`, lty = 2, col = 2)
legend("topleft", title = "Assigned Treatment", legend = c("0", "1"), col = 1:2, lty = 1)

# Plot the difference in cumulative incidence over follow up
plot(predicted_ci[[3]]$followup_time, predicted_ci[[3]]$cum_inc_diff,
  type = "l",
  xlab = "Follow-up Time", ylab = "Difference in Cumulative Incidence",
  ylim = c(0.0, 0.5)
)
lines(predicted_ci[[3]]$followup_time, predicted_ci[[3]]$`2.5%`, lty = 2)
lines(predicted_ci[[3]]$followup_time, predicted_ci[[3]]$`97.5%`, lty = 2)