Monitoring Information for a Binary Outcome

library(impart)
library(deming) # Required for deming::theilsen()

This vignette demonstrates how to use impart to monitor the amount of information contained in the accrued data so that analyses are not under- or over-powered. To see all available vignettes in impart, use the vignettes command:

print(vignette(package = "impart"))

Monitoring Ongoing Studies

In a fixed sample size design, the timing of the final analysis is based on the last participant’s last visit. Group sequential analyses are based when pre-specified fractions of the maximum sample size have their final outcome observed. Timing analyses in such studies only depends on counting the number of final outcomes observed during the study.

In information-monitored designs, the times at which analyses are performed and recruitment is stopped depend on when the information reaches pre-specified thresholds. The amount of information contained in the data depends on the number of observations, the completeness of the data, the analytic methods used, and the interrelationships among the observed data. Depending on the analysis methods used, an individual could contribute baseline covariates and treatment assignment information, post-randomization outcomes, and even post-randomization auxiliary variables.

Challenges in Information Monitoring

A new challenge in analyzing data in an ongoing trial is how to appropriately treat missing data. Suppose the following are observed:

• A participant is newly enrolled, has their baseline covariates measured, is randomized, is still on study, and has not yet entered any follow-up windows
• A participant is randomized, but did not attend any follow-up visits

In both of these cases, the covariates, and treatment assignment are observed, but the outcomes are missing. In the first case, outcomes are not yet observed: while they are missing, they could still be observed when the participant enters the study window. In the latter case, they are known to be missing: the study window is closed without the outcome being observed.

A convention can be used to differentiate outcomes that are not yet observed from those known to be missing:

• Completed assessments have both an observed outcome and an observed assessment time.
• Missed assessments have a missing outcome, but an observed assessment time:
  • If an assessment occurred, but no valid outcome was obtained, the time of the assessment is used.
  • If an assessment was missed, the end of the study window is used, indicating the last time an outcome could have been observed per protocol.
• Not-yet-observed assessments are missing both an outcome and an assessment time.