Identifying treatment effects using trimmed means when data are missing not at random

Single-world intervention graphs for defining, identifying, and communicating estimands in clinical trials

Confusion often arises when attempting to articulate target estimand(s) of a clinical trial in plain language. We aim to rectify this confusion by using a type of causal graph called the Single-World Intervention Graph (SWIG) to provide a visual representation of the estimand that can be effectively communicated to interdisciplinary stakeholders. These graphs not only display estimands, but also illustrate the assumptions under which a causal estimand is identifiable by presenting the graphical relationships between the treatment, intercurrent events, and clinical outcomes. To demonstrate its usefulness in pharmaceutical research, we present examples of SWIGs for various intercurrent event strategies specified in the ICH E9(R1) addendum, as well as an example from a real-world clinical trial for chronic pain. code to generate all the SWIGs shown is this paper is made available. We advocate clinical trialists adopt the use of SWIGs in their estimand discussions during the planning stages of their studies.

Defining estimands for efficacy assessment in single arm phase 1b or phase 2 clinical trials in oncology early development

The addendum of the ICH E9 guideline on the statistical principles for clinical trials introduced the estimand framework. The framework is designed to strengthen the dialog between different stakeholders, to introduce greater clarity in the clinical trial objectives and to provide alignment between the estimand and statistical analysis. Estimand framework related publications thus far have mainly focused on randomized clinical trials. The intention of the Early Development Estimand Nexus (EDEN), a task force of the cross-industry Oncology Estimand Working Group (www.oncoestimand.org), is to apply it to single arms Phase 1b or Phase 2 trials designed to detect a treatment-related efficacy signal, typically measured by objective response rate. Key recommendations regarding the estimand attributes include that in a single arm early clinical trial, the treatment attribute should start when the first dose is received by the participant. Focusing on the estimation of an absolute effect, the population-level summary measure should reflect only the property used for the estimation. Another major component introduced in the ICH E9 addendum is the definition of intercurrent events and the associated possible ways to handle them. Different strategies reflect different clinical questions of interest that can be answered based on the journeys an individual subject can take during a trial. We provide detailed strategy recommendations for intercurrent events typically seen in early-stage oncology. We highlight where implicit assumptions should be made transparent as whenever follow-up is suspended, a while-on-treatment strategy is implied.

Sensitivity to missing not at random dropout in clinical trials: Use and interpretation of the trimmed means estimator

Outcome values in randomized controlled trials (RCTs) may be missing not at random (MNAR), if patients with extreme outcome values are more likely to drop out (eg, due to perceived ineffectiveness of treatment, or adverse effects). In such scenarios, estimates from complete case analysis (CCA) and multiple imputation (MI) will be biased. We investigate the use of the trimmed means (TM) estimator for the case of univariable missingness in one continuous outcome. The TM estimator operates by setting missing values to the most extreme value, and then “trimming” away equal fractions of both groups, estimating the treatment effect using the remaining data. The TM estimator relies on two assumptions, which we term the “strong MNAR” and “location shift” assumptions. We derive formulae for the TM estimator bias resulting from the violation of these assumptions for normally distributed outcomes. We propose an adjusted TM estimator, which relaxes the location shift assumption and detail how our bias formulae can be used to establish the direction of bias of CCA and TM estimates, to inform sensitivity analyses. The TM approach is illustrated in a sensitivity analysis of the CoBalT RCT of cognitive behavioral therapy (CBT) in 469 individuals with 46 months follow‐up. Results were consistent with a beneficial CBT treatment effect, with MI estimates closer to the null and TM estimates further from the null than the CCA estimate. We propose using the TM estimator as a sensitivity analysis for data where extreme outcome value dropout is plausible.