Assessing methods for dealing with treatment switching in clinical trials: A follow-up simulation study

When patients randomised to the control group of a randomised controlled trial are allowed to switch onto the experimental treatment, intention-to-treat analyses of the treatment effect are confounded because the separation of randomised groups is lost. Previous research has investigated statistical methods that aim to estimate the treatment effect that would have been observed had this treatment switching not occurred and has demonstrated their performance in a limited set of scenarios. Here, we investigate these methods in a new range of realistic scenarios, allowing conclusions to be made based upon a broader evidence base. We simulated randomised controlled trials incorporating prognosis-related treatment switching and investigated the impact of sample size, reduced switching proportions, disease severity, and alternative data-generating models on the performance of adjustment methods, assessed through a comparison of bias, mean squared error, and coverage, related to the estimation of true restricted mean survival in the absence of switching in the control group. Rank preserving structural failure time models, inverse probability of censoring weights, and two-stage methods consistently produced less bias than the intention-to-treat analysis. The switching proportion was confirmed to be a key determinant of bias: sample size and censoring proportion were relatively less important. It is critical to determine the size of the treatment effect in terms of an acceleration factor (rather than a hazard ratio) to provide information on the likely bias associated with rank-preserving structural failure time model adjustments. In general, inverse probability of censoring weight methods are more volatile than other adjustment methods

Additional file 2 of Clinico-pathologic relationships with Ki67 and its change with short-term aromatase inhibitor treatment in primary ER + breast cancer: further results from the POETIC trial (CRUK/07/015)

Additional file 2: Fig. S2. Distribution of A. log(Ki67Baseline +0.1) for all patients, B. log(Ki672week +0.1) in patients allocated control, C. log fold. change Ki67 in patients allocated control, D. log(Ki672week +0.1) in patients allocated AI and E. log fold change Ki67 in patients allocated AI. Presented separately for HER2- and HER2+ patients

Additional file 3 of Clinico-pathologic relationships with Ki67 and its change with short-term aromatase inhibitor treatment in primary ER + breast cancer: further results from the POETIC trial (CRUK/07/015)

Additional file 3: Fig. S3. Log fold change in Ki67 A. for patients allocated control by sample. type, B. for patients allocated AI by sample type and choice of AI. Presented separately for HER2- and HER2- patients

Additional file 1 of Clinico-pathologic relationships with Ki67 and its change with short-term aromatase inhibitor treatment in primary ER + breast cancer: further results from the POETIC trial (CRUK/07/015)

Additional file 1: Fig. S1. Distribution of Ki67Baseline a. Distribution of Ki67 % positive cells b. Distribution of ln(Ki67+0.1) c. Distribution of Ki67Baseline by HER2 statu

Additional file 4 of Clinico-pathologic relationships with Ki67 and its change with short-term aromatase inhibitor treatment in primary ER + breast cancer: further results from the POETIC trial (CRUK/07/015)

Additional file 4: Table S1. Univariable and multivariable linear regression results for change in Ki67 in patients allocated to control by HER2 status. Table S2. Multivariable linear regression results for change in Ki67 in patients allocated to control by HER2 status showing adjustment for sample type. Table S3. Multivariable linear regression results for change in Ki67 in patients allocated to AI by HER2 status showing adjustment for sample type and AI choice. Table S4. CCCA by AI and sample type in patients allocated AI