Adjusting for treatment crossover in the MAVORIC trial: survival in advanced mycosis fungoides and Sézary syndrome.

Background: Relative overall survival (OS) estimates reported in the MAVORIC trial are potentially confounded by a high proportion of patients randomized to vorinostat switching to mogamulizumab; furthermore, vorinostat is not used in clinical practice in the UK. Methods: Three methods were considered for crossover adjustment. Survival post-crossover adjustment was compared with data from the Hospital Episode Statistics (HES) to contextualize estimates. Results: Following adjustment, the OS hazard ratio for mogamulizumab versus vorinostat was 0.42 (95% CI: 0.18, 0.98) using the method considered most appropriate based on an assessment of assumptions and comparison with HES. Conclusions: OS of mogamulizumab relative to vorinostat may be underestimated in MAVORIC due to the presence of crossover. The HES database was used to validate this adjustment.

Methods for the inclusion of real-world evidence in network meta-analysis.

BACKGROUND: Network Meta-Analysis (NMA) is a key component of submissions to reimbursement agencies world-wide, especially when there is limited direct head-to-head evidence for multiple technologies from randomised controlled trials (RCTs). Many NMAs include only data from RCTs. However, real-world evidence (RWE) is also becoming widely recognised as a valuable source of clinical data. This study aims to investigate methods for the inclusion of RWE in NMA and its impact on the level of uncertainty around the effectiveness estimates, with particular interest in effectiveness of fingolimod. METHODS: A range of methods for inclusion of RWE in evidence synthesis were investigated by applying them to an illustrative example in relapsing remitting multiple sclerosis (RRMS). A literature search to identify RCTs and RWE evaluating treatments in RRMS was conducted. To assess the impact of inclusion of RWE on the effectiveness estimates, Bayesian hierarchical and adapted power prior models were applied. The effect of the inclusion of RWE was investigated by varying the degree of down weighting of this part of evidence by the use of a power prior. RESULTS: Whilst the inclusion of the RWE led to an increase in the level of uncertainty surrounding effect estimates in this example, this depended on the method of inclusion adopted for the RWE. 'Power prior' NMA model resulted in stable effect estimates for fingolimod yet increasing the width of the credible intervals with increasing weight given to RWE data. The hierarchical NMA models were effective in allowing for heterogeneity between study designs, however, this also increased the level of uncertainty. CONCLUSION: The 'power prior' method for the inclusion of RWE in NMAs indicates that the degree to which RWE is taken into account can have a significant impact on the overall level of uncertainty. The hierarchical modelling approach further allowed for accommodating differences between study types. Consequently, further work investigating both empirical evidence for biases associated with individual RWE studies and methods of elicitation from experts on the extent of such biases is warranted.

Exploring the Impact of Treatment Switching on Overall Survival from the PROfound Study in Homologous Recombination Repair (HRR)-Mutated Metastatic Castration-Resistant Prostate Cancer (mCRPC).

BACKGROUND: In oncology trials, treatment switching from the comparator to the experimental regimen is often allowed but may lead to underestimating overall survival (OS) of an experimental therapy. OBJECTIVE: This study evaluates the impact of treatment switching from control to olaparib on OS using the final survival data from the PROfound study and compares validated adjustment methods to estimate the magnitude of OS benefit with olaparib. PATIENTS AND METHODS: The primary population from PROfound (Cohort A) was included, alongside two populations approved for treatment with olaparib by the European Medicines Agency and US Food and Drug Administration: BRCAm and Cohort A+B (excluding the PPP2R2A gene). Five methods were explored to adjust for switching: excluding or censoring patients in the control arm who receive subsequent olaparib, Rank Preserving Structural Failure Time Model (RPSFTM), Inverse Probability of Censoring Weights, and Two-Stage Estimation. RESULTS: The RPSFTM was considered the most appropriate approach for PROfound as the results were robust to sensitivity analysis testing of the common treatment effect assumption. For Cohort A, the final OS hazard ratio reduced from 0.69 (95% CI 0.5-0.97) to between 0.42 (0.18-0.90) and 0.52 (0.31-1.00) for olaparib versus control, depending on the RPSFTM selected. Median OS reduced from 14.7 months to between 11.73 and 12.63 months for control. CONCLUSIONS: The magnitude of the statistically significant (P < 0.05) survival benefit of olaparib versus control observed in Cohort A of PROfound is likely to be underestimated if adjustment for treatment switching from control to olaparib is not conducted. The RPSFTM was considered the most plausible method, although further development and validation of robust methods to estimate the magnitude of impact of treatment switching is needed.