Dialogues in health · 2026
Treatment switching in oncology evidence synthesis: a systematic review of meta-analytical practices incorporating time-dependent confounding, censoring bias, and patient-level variability.
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Limijadi Edward Kurnia Setiawan
Аннотация
BACKGROUND: Treatment switching in the oncology trials introduced time-dependent confounding and informative censoring, which complicated the unbiased estimation of treatment effects in evidence synthesis.
OBJECTIVES: To systematically assess the meta-analytical practices that are used for adjusting treatment switching in oncology, with an emphasis on handling time-dependent confounding, censoring bias, as well as patient-level variability.
METHODS: A systematic review was conducted using PRISMA 2020 guidelines. The databases searched systematically are MEDLINE, Embase, Cochrane Library and Web of Science, and the studies published between 2015 and the last search date were included (provide exact search date). The review was conducted with the reporting standards from the PRISMA 2020 statement, and because there was methodological heterogeneity, an analysis of the findings was conducted using a structured narrative synthesis. The eligible studies included randomized trials, simulation studies, observational studies, and health technology assessments, with the application of formal statistical methods to adjust for treatment switching.
RESULTS: Six studies had met the inclusion criteria. Common methods involved rank-preserving structural failure time models (RPSFTM), marginal structural models (MSMs), and inverse probability of censoring weighting (IPCW). The modeling assumptions, the specification of the model, the switching mechanisms, the method of dealing with time-dependent confounders, and the method of dealing with censoring were shown to vary across the included studies and affect the results and estimates of the treatment effects. The reporting practices were varying, and there was limited integration into the meta-analytical frameworks.
CONCLUSION: Adjustment regarding the treatment switching is methodologically complex, having no universally optimal approach. Enhanced level of transparency, methodological standardization, along with the consideration of patient-level heterogeneity, is important to improve the validity of the oncology evidence synthesis.
OBJECTIVES: To systematically assess the meta-analytical practices that are used for adjusting treatment switching in oncology, with an emphasis on handling time-dependent confounding, censoring bias, as well as patient-level variability.
METHODS: A systematic review was conducted using PRISMA 2020 guidelines. The databases searched systematically are MEDLINE, Embase, Cochrane Library and Web of Science, and the studies published between 2015 and the last search date were included (provide exact search date). The review was conducted with the reporting standards from the PRISMA 2020 statement, and because there was methodological heterogeneity, an analysis of the findings was conducted using a structured narrative synthesis. The eligible studies included randomized trials, simulation studies, observational studies, and health technology assessments, with the application of formal statistical methods to adjust for treatment switching.
RESULTS: Six studies had met the inclusion criteria. Common methods involved rank-preserving structural failure time models (RPSFTM), marginal structural models (MSMs), and inverse probability of censoring weighting (IPCW). The modeling assumptions, the specification of the model, the switching mechanisms, the method of dealing with time-dependent confounders, and the method of dealing with censoring were shown to vary across the included studies and affect the results and estimates of the treatment effects. The reporting practices were varying, and there was limited integration into the meta-analytical frameworks.
CONCLUSION: Adjustment regarding the treatment switching is methodologically complex, having no universally optimal approach. Enhanced level of transparency, methodological standardization, along with the consideration of patient-level heterogeneity, is important to improve the validity of the oncology evidence synthesis.