Practical aspects of handling treatment switching in randomized clinical trials
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1 Practical aspects of handling treatment switching in randomized clinical trials BBS Spring Seminar Viktoriya Stalbovskaya, Novartis Basel, 28 April 2016
2 NVS Oncology Treatment Switching Guideline Team Valentine Jehl Gaelle Saint-Hilary Santosh Sutradhar Zhongwen Tang Simon Wandel Nigel Yateman Viktoriya Stalbovskaya 2 BBS Spring Seminar V Stalbovskaya 28 April 2016 Practical aspects of treatment switching Business Use Only
3 Outline Motivating example Time-dependent confounding Rank preserving structured failure time model Inverse probability weighting General recommendations Recent publications and existing software Acknowledgements References 3 BBS Spring Seminar V Stalbovskaya 28 April 2016 Practical aspects of treatment switching Business Use Only
4 Motivating example Everolimus in mrcc (RECORD-1) Phase III study of everolimus in metastatic renal cell carcinoma Motzer et al (2008, 2010) Double-blind, multicenter study with patients randomized to receive either everolimus (n = 277) or placebo (n = 139) Primary endpoint PFS (HR=0.30, 95% CI , p<0.0001) Regulatory approval based on PFS Protocol allowed crossover from placebo to everolimus upon progression 4 BBS Spring Seminar V Stalbovskaya 28 April 2016 Practical aspects of treatment switching Business Use Only
5 Motivating example Everolimus in mrcc (RECORD-1) ~80% of placebo randomized patients switched to everolimus ITT analysis of OS: HR=0.87, 95% CI: , p-value=0.162 ITT analysis provides a valid assessment of the treatment policy What about assessment of treatment effect of everolimus on OS if placebo patients never received everolimus? 5 BBS Spring Seminar V Stalbovskaya 28 April 2016 Practical aspects of treatment switching Business Use Only
6 Time-dependent confounding Covariates Treatment Outcome (survival) 1 Effect of interest 2 Confounding effect on occurrence of the outcome 3 Confounding effect on future exposure to treatment 6 BBS Spring Seminar V Stalbovskaya 28 April 2016 Practical aspects of treatment switching Business Use Only
7 Rank preserving structural failure time (RPSFT) model Estimate the survival time gained/lost by receiving active treatment (i.e. either randomized or cross-over active treatment) Main assumption: treatment is acting by multiplying survival time by a given factor once patient starts receiving active treatment (transparent but un-testable assumption) Multiplicative factor interpreted as relative increase/decrease in survival if one took active treatment compared to taking control It works by reconstructing the survival duration of patients, as if they had never received active treatment 7 BBS Spring Seminar V Stalbovskaya 28 April 2016 Practical aspects of treatment switching Business Use Only
8 RPSFT acceleration factor exp( Ψ) exp( Ψ) Treatment multiplies life by acceleration factor exp( Ψ) Ψ<0 time on experimental therapy extends life compared to control Ψ>0 time on control therapy extends life compared to experimental 8 BBS Spring Seminar V Stalbovskaya 28 April 2016 Practical aspects of treatment switching Business Use Only
9 RPSFT G-estimation for psi (RECORD-1) p-value Test statistic Ψ = 0.66, 95% CI ( 2.16,0.69) 9 BBS Spring Seminar V Stalbovskaya 28 April 2016 Practical aspects of treatment switching Business Use Only
10 Programming implementation for RPSFT Data preparation Data transformation and Statistical analysis Time to event dataset Covariates: stratification, baseline, exposure on treatment / treatment group approach Combined dataset G-estimation procedure Specify range of psi values and grid size For each psi value calculate U, C and test statistic Select psi for test statistic 0 Dataset with original and recensored survival time, estimated psi value Cox regression to estimate HR and recalculate CI Output: - Crossover corrected HR - KM plot - Psi value with CI - Patient level listing 10 BBS Spring Seminar V Stalbovskaya 28 April 2016 Practical aspects of treatment switching Business Use Only
11 RPSFT model results for RECORD-1 The corrected results used reconstructed survival time for all control arm patients 11 BBS Spring Seminar V Stalbovskaya 28 April 2016 Practical aspects of treatment switching Business Use Only
12 Inverse probability weighting (IPW) approach Model-based method that reweights control arm patients using propensity score methods. Treatment effect is expressed on the hazard ratio scale and estimated using weighted Cox model. Main assumption: no unmeasured confounders (all factors influencing crossover and survival are included in the model), non-testable 12 BBS Spring Seminar V Stalbovskaya 28 April 2016 Practical aspects of treatment switching Business Use Only
13 Inverse probability weighted analysis (IPW) Patient in the control arm with crossover w 1 w 2 w n w e Control IPCW (weighted censoring at crossover) IPTW (weighted time-varying) Experimental weight treatment weighted analysis Patient in the control arm without crossover w 1 w 2 w n Control IPCW (weighted censoring at crossover) weight treatment weighted analysis IPTW (weighted time-varying) Patient in the experimental arm 1 Experimental IPCW (weighted censoring at crossover) weight treatment weighted analysis IPTW (weighted time-varying) 13 BBS Spring Seminar V Stalbovskaya 28 April 2016 Practical aspects of treatment switching Business Use Only
14 Programming implementation for IPW Data preparation Data transformation Statistical analysis Time to event dataset Split time into time intervals Subset Control arm patients Set weight for experimental arm patients to 1 Dataset with covariates (baseline, timevarying) Impute missing values for time-var covariates Calculate probabilities of remaining untreated Datasets with weights per time interval per patient Format variables for the analysis macros Combined time to event dataset with covariates Datasets with probabilities per time interval Calculate weights for various models Datasets with weights Weighted Cox regression Output: - Crossover corrected HR, p-value - Weighted KM plot - Statistics for weights - Patient level listing 14 BBS Spring Seminar V Stalbovskaya 28 April 2016 Practical aspects of treatment switching Business Use Only
15 IPCW application to RECORD-1 Model 4: best model fit (AIC) 15 BBS Spring Seminar V Stalbovskaya 28 April 2016 Practical aspects of treatment switching Business Use Only
16 General recommendations Points to consider Describe the treatment switching mechanism When was switching permitted? Patient-level or study-level Why did switching occur? Post-progression switch, switch based on study milestone Quantify the extent of switching and characterize the population of switchers Number and percentage of patients who switched, proportion of total exposure and/or follow up time that was affected by switch Timing of treatment switching in a form of Kaplan-Meier plot estimated median, range Baseline characteristics for switchers Select method to account for treatment switching, implementation and description of results Consider feasibility of the underlying assumptions for each method Identify potential sources of bias and describe them for the method selected Ideally, provide a document with the details of the model(s) 16 BBS Spring Seminar V Stalbovskaya 28 April 2016 Practical aspects of treatment switching Business Use Only
17 Recent publications and software Harvard School of Public Health Software for MSM, SNM, etc (SAS) Causal inference book by Robins and Hernan Parts of the book and the code available (in draft) Medical Research Council Software for RPSFT (STATA) 17 BBS Spring Seminar V Stalbovskaya 28 April 2016 Practical aspects of treatment switching Business Use Only
18 Acknowledgments Mike Branson Bee Chen Beat Neuenschwander All the reviewers 18 BBS Spring Seminar V Stalbovskaya 28 April 2016 Practical aspects of treatment switching Business Use Only
19 References Korhonen P, Malangone E, Sherman S et al. Overall survival (OS) of metastatic renal cell carcinoma (mrcc) patients corrected for crossover using inverse probability of censoring weights (IPCW) and rank preserving structural failure time (RPSFT) models: Two analyses from the RECORD-1 trial. J Clin Oncol 28:15s, 2010 (suppl; abstr 4595) Korhonen P, Zuber E, Branson M et al. Correcting overall survival for the impact of crossover via a rank-preserving structural failure time (RPSFT) model in the RECORD- 1 trial of everolimus in metastatic renal-cell carcinoma. J Biopharm Stat 2012 Latimer NR, Abrams KR, Lambert PC et al. Adjusting survival time estimates to account for treatment switching in randomized controlled trials--an economic evaluation context: methods, limitations, and recommendations. Med Decis Making 2014 Motzer RJ, Escudier B, Oudard S et al. Efficacy of everolimus in advanced renal cell carcinoma: a double-blind, randomised, placebo-controlled phase III trial. Lancet 2008 Motzer, R., Escudier, B., Oudard, S., et al. (2010). Phase 3 trial of everolimus for metastatic renal cell carcinoma: Final results and analysis of prognostic factors. Cancer 116: BBS Spring Seminar V Stalbovskaya 28 April 2016 Practical aspects of treatment switching Business Use Only
20 Back up 20 BBS Spring Seminar V Stalbovskaya 28 April 2016 Practical aspects of treatment switching Business Use Only
21 RPSFT Artificial censoring algorithm An additional algorithm ( artificial-censoring ) allows to maintain the assumption of independent random censoring required for unbiased estimation The artificial censoring algorithm works by shrinking the total follow-up time (time between randomization to analysis cut-off date) for all patients regardless of randomization group or treatment received Therefore every patient censored in the ITT analysis remains censored with duration equal or shorter to the original one; in addition, patients with an event in the original analysis may become censored via the artificial-censoring algorithm 21 BBS Spring Seminar V Stalbovskaya 28 April 2016 Practical aspects of treatment switching Business Use Only
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