Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Chen Q

PROVIDER: S-EPMC7483904 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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

Chen Qingxia Q Zhang Fan F Chen Ming-Hui MH Cong Xiuyu Julie XJ

Lifetime data analysis 20200303 4

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 e ...[more]

PMID: 32125594

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Project description:In oncology trials, control group patients often switch onto the experimental treatment during follow-up, usually after disease progression. In this case, an intention-to-treat analysis will not address the policy question of interest - that of whether the new treatment represents an effective and cost-effective use of health care resources, compared to the standard treatment. Rank preserving structural failure time models (RPSFTM), inverse probability of censoring weights (IPCW) and two-stage estimation (TSE) have often been used to adjust for switching to inform treatment reimbursement policy decisions. TSE has been applied using a simple approach (TSEsimp), assuming no time-dependent confounding between the time of disease progression and the time of switch. This is problematic if there is a delay between progression and switch. In this paper we introduce TSEgest, which uses structural nested models and g-estimation to account for time-dependent confounding, and compare it to TSEsimp, RPSFTM and IPCW. We simulated scenarios where control group patients could switch onto the experimental treatment with and without time-dependent confounding being present. We varied switching proportions, treatment effects and censoring proportions. We assessed adjustment methods according to their estimation of control group restricted mean survival times that would have been observed in the absence of switching. All methods performed well in scenarios with no time-dependent confounding. TSEgest and RPSFTM continued to perform well in scenarios with time-dependent confounding, but TSEsimp resulted in substantial bias. IPCW also performed well in scenarios with time-dependent confounding, except when inverse probability weights were high in relation to the size of the group being subjected to weighting, which occurred when there was a combination of modest sample size and high switching proportions. TSEgest represents a useful addition to the collection of methods that may be used to adjust for treatment switching in trials in order to address policy-relevant questions.

Dataset Information

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

Publications

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

Similar Datasets

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