Nicholas R Latimer1, Chris Henshall2, Uwe Siebert3, Helen Bell4. 1. School of Health and Related Research (ScHARR),University of Sheffieldn.latimer@sheffield.ac.uk. 2. Brunel University London. 3. UMIT - University for Health Sciences,Medical Informatics and Technology Oncotyrol - Center for Personalized Cancer Medicine Harvard T.H. Chan School of Public Health and Massachusetts General Hospital,Harvard Medical School. 4. School of Health and Related Research (ScHARR),University of Sheffield.
Abstract
OBJECTIVES: Treatment switching refers to the situation in a randomized controlled trial where patients switch from their randomly assigned treatment onto an alternative. Often, switching is from the control group onto the experimental treatment. In this instance, a standard intention-to-treat analysis does not identify the true comparative effectiveness of the treatments under investigation. We aim to describe statistical methods for adjusting for treatment switching in a comprehensible way for nonstatisticians, and to summarize views on these methods expressed by stakeholders at the 2014 Adelaide International Workshop on Treatment Switching in Clinical Trials. METHODS: We describe three statistical methods used to adjust for treatment switching: marginal structural models, two-stage adjustment, and rank preserving structural failure time models. We draw upon discussion heard at the Adelaide International Workshop to explore the views of stakeholders on the acceptability of these methods. RESULTS: Stakeholders noted that adjustment methods are based on assumptions, the validity of which may often be questionable. There was disagreement on the acceptability of adjustment methods, but consensus that when these are used, they should be justified rigorously. The utility of adjustment methods depends upon the decision being made and the processes used by the decision-maker. CONCLUSIONS: Treatment switching makes estimating the true comparative effect of a new treatment challenging. However, many decision-makers have reservations with adjustment methods. These, and how they affect the utility of adjustment methods, require further exploration. Further technical work is required to develop adjustment methods to meet real world needs, to enhance their acceptability to decision-makers.
OBJECTIVES: Treatment switching refers to the situation in a randomized controlled trial where patients switch from their randomly assigned treatment onto an alternative. Often, switching is from the control group onto the experimental treatment. In this instance, a standard intention-to-treat analysis does not identify the true comparative effectiveness of the treatments under investigation. We aim to describe statistical methods for adjusting for treatment switching in a comprehensible way for nonstatisticians, and to summarize views on these methods expressed by stakeholders at the 2014 Adelaide International Workshop on Treatment Switching in Clinical Trials. METHODS: We describe three statistical methods used to adjust for treatment switching: marginal structural models, two-stage adjustment, and rank preserving structural failure time models. We draw upon discussion heard at the Adelaide International Workshop to explore the views of stakeholders on the acceptability of these methods. RESULTS: Stakeholders noted that adjustment methods are based on assumptions, the validity of which may often be questionable. There was disagreement on the acceptability of adjustment methods, but consensus that when these are used, they should be justified rigorously. The utility of adjustment methods depends upon the decision being made and the processes used by the decision-maker. CONCLUSIONS: Treatment switching makes estimating the true comparative effect of a new treatment challenging. However, many decision-makers have reservations with adjustment methods. These, and how they affect the utility of adjustment methods, require further exploration. Further technical work is required to develop adjustment methods to meet real world needs, to enhance their acceptability to decision-makers.
Authors: Tasos Papanikos; John R Thompson; Keith R Abrams; Nicolas Städler; Oriana Ciani; Rod Taylor; Sylwia Bujkiewicz Journal: Stat Med Date: 2020-01-28 Impact factor: 2.373
Authors: Matthew Dodd; Katherine Fielding; James R Carpenter; Jennifer A Thompson; Diana Elbourne Journal: BMJ Open Date: 2022-01-12 Impact factor: 2.692