Estimation of treatment effects and model diagnostics with two-way time-varying treatment switching: an application to a head and neck study

Treatment switching frequently occurs in clinical trials due to ethical reasons. Intent-to-treat analysis without adjusting for switching yields biased and inefficient estimates of the treatment effects. In this paper, we propose a class of semiparametric semi-competing risks transition survival models to accommodate two-way time-varying switching. Theoretical properties of the proposed method are examined. An efficient expectation-maximization algorithm is derived to obtain maximum likelihood estimates and model diagnostic tools. Existing software is used to implement the algorithm. Simulation studies are conducted to demonstrate the validity of the model. The proposed method is further applied to data from a clinical trial with patients having recurrent or metastatic squamous-cell carcinoma of head and neck.

Semiparametric copula-based analysis for treatment effects in the presence of treatment switching

In controlled trials, "treatment switching" occurs when patients in one treatment group switch to alternative treatments during the trial, and poses challenges to treatment effect evaluation owing to crossover of the treatments groups. In this work, we assume that treatment switching can occur after some disease progression event and view the progression and death events as two semicompeting risks. The proposed model consists of a copula model for the joint distribution of time-to-progression (TTP) and overall survival (OS) up to the earlier of the two events, as well as a conditional hazard model for OS subsequent to progression. The copula model facilitates assessing the marginal distributions of TTP and OS separately from the association between the two events, and, in particular, the treatment effect on OS in the absence of treatment switching. The proposed conditional hazard model for death subsequent to progression allows us to assess the treatment switching (crossover) effect on OS given occurrence of progression and covariates. Semiparametric proportional hazards models are employed in the marginal models for TTP and OS. A nonparametric maximum likelihood procedure is developed for model inference, which is verified through asymptotic theory and simulation studies. The proposed analysis is applied to a lung cancer dataset to illustrate its real utility.

Sensitivity analysis for subsequent treatments in confirmatory oncology clinical trials: A two-stage stochastic dynamic treatment regime approach

Subsequent treatments can result in a difficulty in interpretation of the overall survival results in confirmatory oncology clinical trials. To complement the intention-to-treat (ITT) analysis affected by subsequent treatment patterns unintentional in the trial protocol, several causal methods targeting the per-protocol effect have been proposed. When two or more types of subsequent treatments are allowed in the trial protocol, however, these methods cannot answer clinical questions such as how sensitive the ITT analysis result is to higher or lower proportions of each subsequent treatment allowed in the trial protocol than observed, and to what extent ITT analysis result is generalizable to subsequent treatment patterns other than observed one. To answer these clinical questions, we propose a sensitivity analysis method for subsequent treatments using the inverse probability of treatment weighting method for stochastic dynamic treatment regimes (DTRs). We formulate oncology clinical trials with subsequent treatments as two-stage designs in which initial treatments are randomized, but subsequent treatments are observational. In this formulation, we use stochastic DTRs to simulate specific proportions of each subsequent treatment and compare an initial experimental treatment with an initial control treatment under various proportions of each subsequent treatment. We applied our proposed method to a motivating randomized noninferiority trial for metastatic breast cancer. Simulation results are also reported to show the usefulness of the proposed method.

Application of causal inference methods in the analyses of randomised controlled trials: a systematic review

BACKGROUND

Applications of causal inference methods to randomised controlled trial (RCT) data have usually focused on adjusting for compliance with the randomised intervention rather than on using RCT data to address other, non-randomised questions. In this paper we review use of causal inference methods to assess the impact of aspects of patient management other than the randomised intervention in RCTs.

METHODS

We identified papers that used causal inference methodology in RCT data from Medline, Premedline, Embase, Cochrane Library, and Web of Science from 1986 to September 2014, using a forward citation search of five seminal papers, and a keyword search. We did not include studies where inverse probability weighting was used solely to balance baseline characteristics, adjust for loss to follow-up or adjust for non-compliance to randomised treatment. Studies where the exposure could not be assigned were also excluded.

RESULTS

There were 25 papers identified. Nearly half the papers (11/25) estimated the causal effect of concomitant medication on outcome. The remainder were concerned with post-randomisation treatment regimens (sequential treatments, n =5 ), effects of treatment timing (n = 2) and treatment dosing or duration (n = 7). Examples were found in cardiovascular disease (n = 5), HIV (n = 7), cancer (n = 6), mental health (n = 4), paediatrics (n = 2) and transfusion medicine (n = 1). The most common method implemented was a marginal structural model with inverse probability of treatment weighting.

CONCLUSIONS

Examples of studies which exploit RCT data to address non-randomised questions using causal inference methodology remain relatively limited, despite the growth in methodological development and increasing utilisation in observational studies. Further efforts may be needed to promote use of causal methods to address additional clinical questions within RCTs to maximise their value.