MMRM vs joint modeling of longitudinal responses and time to study drug discontinuation in clinical trials using a "de jure" estimand

Functional loss and mortality in randomized clinical trials for amyotrophic lateral sclerosis: to combine, or not to combine – that is the estimand

Amyotrophic lateral sclerosis is a rapidly progressive disease leading to death in, on average, 3–5 years after first symptom onset. Consequently, there are frequently a non‐negligible number of patients who die during the course of a clinical trial. This introduces bias in end points such as daily functioning, muscle strength, and quality of life. In this paper, we outline how the choice of strategy to handle death affects the interpretation of the trial results. We provide a general overview of the considerations, positioned in the estimand framework, and discuss the possibility that not every strategy provides a clinically relevant answer in each setting. The relevance of a strategy changes as a function of the intended trial duration, hypothesized treatment effect, and population included. It is important to consider this trade‐off at the design stage of a clinical trial, as this will clarify the exact research question that is being answered, and better guide the planning, design, and analysis of the study.

Reflection on modern methods: shared-parameter models for longitudinal studies with missing data

A primary goal of longitudinal studies is to examine trends over time. Reported results from these studies often depend on strong, unverifiable assumptions about the missing data. Whereas the risk of substantial bias from missing data is widely known, analyses exploring missing-data influences are commonly done either ad hoc or not at all. This article outlines one of the three primary recognized approaches for examining missing-data effects that could be more widely used, i.e. the shared-parameter model (SPM), and explains its purpose, use, limitations and extensions. We additionally provide synthetic data and reproducible research code for running SPMs in SAS, Stata and R programming languages to facilitate their use in practice and for teaching purposes in epidemiology, biostatistics, data science and related fields. Our goals are to increase understanding and use of these methods by providing introductions to the concepts and access to helpful tools.