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

Translating questions to estimands in randomized clinical trials with intercurrent events

Intercurrent (post-treatment) events occur frequently in randomized trials, and investigators often express interest in treatment effects that suitably take account of these events. Contrasts that naively condition on intercurrent events do not have a straight-forward causal interpretation, and the practical relevance of other commonly used approaches is debated. In this work, we discuss how to formulate and choose an estimand, beyond the marginal intention-to-treat effect, from the point of view of a decision maker and drug developer. In particular, we argue that careful articulation of a practically useful research question should either reflect decision making at this point in time or future drug development. Indeed, a substantially interesting estimand is simply a formalization of the (plain English) description of a research question. A common feature of estimands that are practically useful is that they correspond to possibly hypothetical but well-defined interventions in identifiable (sub)populations. To illustrate our points, we consider five examples that were recently used to motivate consideration of principal stratum estimands in clinical trials. In all of these examples, we propose alternative causal estimands, such as conditional effects, sequential regime effects, and separable effects, that correspond to explicit research questions of substantial interest.

Study design flaws and statistical challenges in evaluating fertility treatments

Health interventions should be tested before being introduced into clinical practice, to find out whether they work and whether they are harmful. However, research studies will only provide reliable answers to these questions if they are appropriately designed and analysed. But these are not trivial tasks. We review some methodological challenges that arise when evaluating fertility interventions and explain the implications for a non-statistical audience. These include flexibility in outcomes and analyses; use of surrogate outcomes instead of live birth; use of inappropriate denominators; evaluating cumulative outcomes and time to live birth; allowing each patient or couple to contribute to a research study more than once. We highlight recurring errors and present solutions. We conclude by highlighting the importance of collaboration between clinical and methodological experts, as well as people with experience of subfertility, for realising high-quality research.

Lay summary

We do research to find out whether fertility treatments are beneficial and to make sure they don't cause harm. However, research will only provide reliable answers if it is done properly. It is not unusual for researchers to make mistakes when they are designing research studies and analysing the data that we get from them. In this review, we describe some of the mistakes people make when they do research about fertility treatments and explain how to avoid them. These include challenges which arise due to the large number of things that can be measured and reported when looking to see if fertility treatments work; failure to check whether the treatment increases the number of live births; failing to include all study participants in calculations;challenges in studies where participants may have more than one treatment attempt. We conclude by highlighting the importance of collaboration between clinical and methodological experts, as well as people with experience of fertility problems.