The implications of outcome truncation in reproductive medicine RCTs: a simulation platform for trialists and simulation study

TL;DR

This study introduces a simulation platform to assess how outcome truncation affects the validity of statistical analyses in reproductive medicine RCTs, revealing potential biases and issues in inference.

Contribution

The paper presents a novel simulation tool to evaluate the impact of outcome truncation on statistical inference in reproductive medicine trials.

Findings

Outcome truncation can bias estimates and affect error rates.

Binary outcomes may lead to no observed events in one arm, impacting analysis.

Adverse effects are generally moderate within realistic parameters.

Abstract

Randomised controlled trials in reproductive medicine are often subject to outcome truncation, where study outcomes are only defined in a subset of participants. Examples include birthweight (measurable only in the subgroup of participants who give birth) and miscarriage (which can only occur in participants who become pregnant). These are typically analysed by making a comparison between treatment arms within the subgroup (comparing birthweights in the subgroup who gave birth, or miscarriages in the subgroup who became pregnant). However, this approach does not represent a randomised comparison when treatment influences the probability of being observed (i.e. survival). The practical implications of this for reproductive trials are unclear. We developed a simulation platform to investigate the implications of outcome truncation for reproductive medicine trials. We used this to perform a simulation study, in which we considered the bias, Type 1 error, coverage, and precision of standard statistical analyses for truncated continuous and binary outcomes. Increasing treatment effect on the intermediate variable, strength of confounding between the intermediate and outcome variables, and interactions between treatment and confounder were found to adversely affect performance. However, within parameter ranges we would consider to be more realistic, the adverse effects were generally not drastic. For binary outcomes, the study highlighted that outcome truncation may lead to none of the participants in a study arm experiencing the outcome event. This was found to have severe consequences for inferences, and this may have implications for meta-analysis.