Using principal stratification in analysis of clinical trials

[Special issue PRO] A demonstration of estimands and sensitivity analyses for time-to-deterioration of patient reported outcomes

In oncology trials, health-related quality of life (HRQoL), specifically patient-reported symptom burden and functional status, can support the interpretation of survival endpoints, such as progression-free survival. However, applying time-to-event endpoints to patient-reported outcomes (PRO) data is challenging. For example, in time-to-deterioration analyses clinical events such as disease progression are common in many settings and are often handled through censoring the patient at the time of occurrence; however, disease progression and HRQoL are often related leading to informative censoring. Special consideration to the definition of events and intercurrent events (ICEs) is necessary. In this work, we demonstrate time-to-deterioration of PRO estimands and sensitivity analyses to answer research questions using composite, hypothetical, and treatment policy strategies applied to a single endpoint of disease-related symptoms. Multiple imputation methods under both the missing-at-random and missing-not-at-random assumptions are used as sensitivity analyses of primary estimands. Hazard ratios ranged from 0.52 to 0.66 over all the estimands and sensitivity analyses modeling a robust treatment effect favoring the treatment in time to disease symptom deterioration or death. Differences in the estimands include how people who experience disease progression or discontinue the randomized treatment due to AEs are accounted for in the analysis. We use the estimand framework to define interpretable and principled approaches for different time-to-deterioration research questions and provide practical recommendations. Reporting the proportions of patient events and patient censoring by reason helps understand the mechanisms that drive the results, allowing for optimal interpretation.

Current developments of the estimand concept

Since the introduction of the estimand in therapeutical studies, several adaptions have been developed. This short article highlights the important aspects of the estimand concept. A literature research was conducted to identify different extensions to this framework. Different modified strategies for intercurrent events are presented, as well as examples of methods to implement the estimand in clinical studies. The article reflects that the estimand is an ongoing research field with further exploration.

Principal stratification analysis of noncompliance with time-to-event outcomes

Post-randomization events, also known as intercurrent events, such as treatment noncompliance and censoring due to a terminal event, are common in clinical trials. Principal stratification is a framework for causal inference in the presence of intercurrent events. The existing literature on principal stratification lacks generally applicable and accessible methods for time-to-event outcomes. In this paper, we focus on the noncompliance setting. We specify 2 causal estimands for time-to-event outcomes in principal stratification and provide a nonparametric identification formula. For estimation, we adopt the latent mixture modeling approach and illustrate the general strategy with a mixture of Bayesian parametric Weibull-Cox proportional hazards model for the outcome. We utilize the Stan programming language to obtain automatic posterior sampling of the model parameters. We provide analytical forms of the causal estimands as functions of the model parameters and an alternative numerical method when analytical forms are not available. We apply the proposed method to the ADAPTABLE (Aspirin Dosing: A Patient-Centric Trial Assessing Benefits and Long-Term Effectiveness) trial to evaluate the causal effect of taking 81 versus 325 mg aspirin on the risk of major adverse cardiovascular events. We develop the corresponding R package PStrata.

Using modified intention-to-treat as a principal stratum estimator for failure to initiate treatment

BACKGROUND

A common intercurrent event affecting many trials is when some participants do not begin their assigned treatment. For example, in a double-blind drug trial, some participants may not receive any dose of study medication. Many trials use a 'modified intention-to-treat' approach, whereby participants who do not initiate treatment are excluded from the analysis. However, it is not clear (a) the estimand being targeted by such an approach and (b) the assumptions necessary for such an approach to be unbiased.

METHODS

Using potential outcome notation, we demonstrate that a modified intention-to-treat analysis which excludes participants who do not begin treatment is estimating a principal stratum estimand (i.e. the treatment effect in the subpopulation of participants who would begin treatment, regardless of which arm they were assigned to). The modified intention-to-treat estimator is unbiased for the principal stratum estimand under the assumption that the intercurrent event is not affected by the assigned treatment arm, that is, participants who initiate treatment in one arm would also do so in the other arm (i.e. if someone began the intervention, they would also have begun the control, and vice versa).

RESULTS

We identify two key criteria in determining whether the modified intention-to-treat estimator is likely to be unbiased: first, we must be able to measure the participants in each treatment arm who experience the intercurrent event, and second, the assumption that treatment allocation will not affect whether the participant begins treatment must be reasonable. Most double-blind trials will satisfy these criteria, as the decision to start treatment cannot be influenced by the allocation, and we provide an example of an open-label trial where these criteria are likely to be satisfied as well, implying that a modified intention-to-treat analysis which excludes participants who do not begin treatment is an unbiased estimator for the principal stratum effect in these settings. We also give two examples where these criteria will not be satisfied (one comparing an active intervention vs usual care, where we cannot identify which usual care participants would have initiated the active intervention, and another comparing two active interventions in an unblinded manner, where knowledge of the assigned treatment arm may affect the participant's choice to begin or not), implying that a modified intention-to-treat estimator will be biased in these settings.

CONCLUSION

A modified intention-to-treat analysis which excludes participants who do not begin treatment can be an unbiased estimator for the principal stratum estimand. Our framework can help identify when the assumptions for unbiasedness are likely to hold, and thus whether modified intention-to-treat is appropriate or not.

Defining Clinical Trial Estimands: A Practical Guide for Study Teams with Examples Based on a Psychiatric Disorder

While the ICH E9(R1) Addendum on "Estimands and Sensitivity Analysis in Clinical Trials" was released in late 2019, the widespread implementation of defining and reporting estimands across clinical trials is still in progress and the engagement of non-statistical functions in this process is also in progress. Case studies are sought after, especially those with documented clinical and regulatory feedback. This paper describes an interdisciplinary process for implementing the estimand framework, devised by the Estimands and Missing Data Working Group (a group with clinical, statistical, and regulatory representation) of the International Society for CNS Clinical Trials and Methodology. This process is illustrated by specific examples using various types of hypothetical trials evaluating a treatment for major depressive disorder. Each of the estimand examples follows the same template and features all steps of the proposed process, including identifying the trial stakeholder(s), the decisions they need to make about the investigated treatment in their specific role and the questions that would support their decision making. Each of the five strategies for handling intercurrent events are addressed in at least one example; the featured endpoints are also diverse, including continuous, binary and time to event. Several examples are presented that include specifications for a potential trial design, key trial implementation elements needed to address the estimand, and main and sensitivity estimator specifications. Ultimately this paper highlights the need to incorporate multi-disciplinary collaborations into implementing the ICH E9(R1) framework.