Multistate modeling of clinical hold in randomized clinical trials

Abstract

A clinical hold order by the Food and Drug Administration (FDA) to the sponsor of a clinical trial is a measure to delay a proposed or to suspend an ongoing clinical investigation. The phase III clinical trial START serves as motivating data example to explore implications and potential statistical approaches for a trial continuing after a clinical hold is lifted. In spite of a modified intention-to-treat (ITT) analysis introduced to account for the clinical hold by excluding patients potentially affected most by the clinical hold, results of the trial did not show a significant improvement of overall survival duration, and the question remains whether the negative result was an effect of the clinical hold. In this paper, we propose a multistate model incorporating the clinical hold as well as disease progression as intermediate events to investigate the impact of the clinical hold on the treatment effect. Moreover, we consider a simple counterfactual censoring approach as alternative strategy to the modified ITT analysis to deal with a clinical hold. Using a realistic simulation study informed by the START data and with a design based on our multistate model, we show that the modified ITT analysis used in the START trial was reasonable. However, the censoring approach will be shown to have some benefits in terms of power and flexibility.

A connection between survival multistate models and causal inference for external treatment interruptions

“…The considerations within this paper also considerably extend earlier investigations of Nießl et al, 2 who proposed a multistate model incorporating the CH, analyzed the impact of the CH, and suggested several methods to account for its impact, including a causal censoring approach that censors all patients at the start of the CH regardless of treatment group and progression status. However, Nießl et al 2 did not provide a formal and general argumentation.…”

“…The considerations within this paper also considerably extend earlier investigations of Nießl et al, 2 who proposed a multistate model incorporating the CH, analyzed the impact of the CH, and suggested several methods to account for its impact, including a causal censoring approach that censors all patients at the start of the CH regardless of treatment group and progression status. However, Nießl et al 2 did not provide a formal and general argumentation. One advantage of the censoring and filtering approaches considered in the present article is that the number of observed events included in the analysis is much higher confirming that the understanding of the causal relations might help to improve the analysis strategy.…”

“…3 Multistate modeling of clinical hold Section 3.1 describes the motivating data example, the START trial sponsored by Merck KGaA. Section 3.2 recapitulates how the CH is incorporated in a multistate model as proposed in Nießl et al 2 Section 3.3 suggests two methods to account for CH impact extending the censoring approach proposed by Nießl et al 2…”

“…The START study served as a motivating example for Nießl et al 2 to evaluate the potential implications of the CH on the treatment effect on overall survival (OS) and to suggest analysis methods to account for treatment interruptions induced by the CH. They showed the multistate model framework as a suitable and flexible tool to investigate the impact of the CH on the treatment effect supporting discussions around appropriate analysis methods.…”

Challenges in Drug Development Posed by the COVID‐19 Pandemic: An Opportunity for Clinical Pharmacology

“…While case-by-case assessments of risk, impact, and mitigation are needed, we identify opportunities from a quantitative clinical pharmacology perspective to maximize value of data, complementing statistical methodologic innovations that have been described to address interruptions in clinical trials. 9 While in-home visits will result in reduced schedules of pharmacokinetic and pharmacodynamic data collections, the timing of these visits relative to drug administration times can be distributed across visit days and across the trial population to maximize information in the resulting data using principles of optimal sampling, for subsequent population pharmacokinetic-pharmacodynamic analyses. Thus, a model-based assessment of pharmacodynamic data may still be feasible to allow "learning" about dose-response and other key objectives of early-phase drug development while the data may not support the planned inferential statistical analyses.…”