Mortality is often the primary outcome in randomized controlled trials (RCTs) of severly ill patients. Functional outcomes measuring physical function, mental health, and quality of life are often included as secondary outcomes and are increasingly being considered as primary outcomes in RCTs. When mortality is common and occurs at different rates across treatment groups, comparisons of functional outcomes across treatment groups among survivors can be misleading. In addition, the functional outcomes are often not observed for all survivors due to drop out or missed visits.

Several statistical approaches have been proposed that incorporate mortality and the functional outcoems to make treatment comparisons. Accounting for missing data among survivors can be accomplished by utilizing multiple imputation with sensitivity analyses for modelling assumptions required in the mutliple imputation.

To streamline the user experience and make the application as intuitive as possible, we assume the following structure for any uploaded data:

• The data is generated from a RCT of an intervention vs. usual care/placebo/control (only 2 distinct groups may be compared)
• Patient mortality, including time of death, is recorded
• Functional outcomes for each patient are to be measured at pre-specified time points (e.g. at baseline, 3, 6, and 12-months post randomization)
• The goal is to compare the functional outcome (e.g. 12-month quality of life) or a function of the functional outcome (e.g. change in physical function comparing 12-months to baseline)
• If there are missing values for the functional outcome among survivors, there is a set of baseline variables that are predictive of the functional outcome. These baseline variables and the treatment assignment are used to 'fill-in' the missing values for the functional outcome among survivors using a multiple imputation approach.

Our tool allows researchers to conduct the following analyses:

1. 'Survivors' analysis: Estimate the difference in the mean of the functional outcome across treatment groups among survivors
2. Survivor Averaged Causal Effect (SACE): Estiamte the difference in the mean of the functional outcome across treatment groups among patients who would have survived regardless of which treatment they received
3. Composite Endpoint: Create a composite endpoint requiring the researcher to rank patient mortality relative to the functional outcome. The distribution of the composite endpoint is compared across the treatment groups.
4. A sensitvity analysis to the assumptions required in the multiple imputation approach for missing data among survivors.

Details of the methods employed can be found in the paper (under revision) Inference in Randomized Controlled Trials with Death and Missingness.

This research was also partially supported by contracts from FDA and PCORI and NIH grant R24HL111895.