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Treatment Switching and Overall Survival in Oncology
PSI Conference 2014 Euan Macpherson AstraZeneca Acknowledgements: Claire Watkins
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Disclaimer Euan Macpherson is an employee of AstraZeneca LP. The views and opinions expressed herein are my own and cannot and should not necessarily be construed to represent those of AstraZeneca or its affiliates. Euan Macpherson | May 2014
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Background Payer Requirements, Regulatory Requirements, Clinical Trial Design Payer Requirements Regulatory & Trial Design Requirements Quicker conclusion Shorter and Smaller Clinical Trial Progression Free Survival Enhanced Quality of Life Overall Survival Benefit Often, this is an acceptable endpoint for approval This is what payers really want Most likely with some evidence of this as well From a regulatory approval point of view these factors are also attractive Euan Macpherson | May 2014
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Demonstrating Overall Survival Benefit in Oncology Clinical Trials
Challenges Overall survival is a hard, indisputable endpoint BUT duration may be too long to be practical active treatment received as a later line of therapy – spontaneous crossover or switching dilution of any real treatment effect Euan Macpherson | May 2014
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Progression Free Survival
A Case Study in NSCLC Experimental Therapy Standard of Care (SOC) Randomisation Progression Free Survival Overall Survival Available later line treatments already include the experimental therapy and other agents with same mechanism of action Euan Macpherson | May 2014
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ITT OS Results HR: 0.78 95% CI (0.50, 1.20) P-value = 0.26
49% SOC patients subsequently received a treatment of same mechanism of action as Experimental Therapy ITT estimate of OS effect of Experimental vs SOC likely to be confounded by switch treatment Question of interest: to estimate overall survival benefit of experimental therapy if later therapy with same mechanism of action had not been available to SOC patients Euan Macpherson | May 2014
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Simple Analysis Methods To Adjust for Treatment Switch
Disadvantages Method Disadvantage Intention To Treat analysis Compares efficacy in treatment groups as randomised but if experimental treatment efficacious in later line therapy, analysis will be biased in favour of control arm Exclude switchers from control arm Assumes control arm switchers and non-switchers have the same prognosis – i.e. there is no confounding between treatment switch and survival, an unlikely assumption leading to bias. Breaks randomisation, post-randomisation selection of treatment groups. Censor switchers at time of switch Standard analysis assumes censoring is independent of the outcome, an unlikely assumption, leading to bias Time-varying covariate for treatment or switch Also relies on the no unmeasured confounders assumption, unlikely to hold, leading to bias. Euan Macpherson | May 2014
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Case Study Survival Data
Control Data By Switch Status CAUTION: COMPARISONS NOT ON A RANDOMISED BASIS - SUBJECT TO SELECTION BIAS Non -Switch Switch Experimental Direct treatment comparisons for non-switchers versus Experimental are not on a randomised basis Hazard ratios would be subject to selection bias and not directly interpretable Euan Macpherson | May 2014
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Complex Methods Introduction 2 methods will be briefly described here
More sophisticated methods required to overcome the limitations of simple, naive methods 2 methods will be briefly described here Rank Preserving Structural Failure Time Model (RPSFTM) Inverse Probability of Censoring Weighting (IPCW) Euan Macpherson | May 2014
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Rank Preserving Structural Failure Time
Complex Methods (1) Rank Preserving Structural Failure Time What would survival have been if active therapy not available? Observed Overall Survival Time Off Active Therapy Time On Active Therapy Multiply this by acceleration factor (< 1 if active therapy extends life ) Choose factor to balance survival across both treatment arms Time Off Active Therapy Accelerated Survival Counterfactual Overall Survival Compare counterfactual from control vs actual OS from active Assumption: Active treatment effect constant regardless of when given Arms balanced due to randomisation Euan Macpherson | May 2014
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Rank Preserving Structural Failure Time
Re-censoring Assumption of non-informative censoring in time-to-event analysis Whether a patient switches or survives long enough to be censored both dependent on prognosis Without recensoring, argument that patients who actually received less active treatment after switching and died will be over-represented and cause bias Re-censoring can lead to loss of many events in RPSFT analysis See Korhonen et al for strategies to mimimise this when planning a study Euan Macpherson | May 2014
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Complex Methods (2): IPCW (weight non-switched times)
Control arm survival Compare to observed experimental arm survival Observed (ITT) control arm IPC weighted WEIGHT Non switchers Non switchers WEIGHT WEIGHT S Switchers Switchers WEIGHT S Key Death time Censor time Switch time S Assumption: The variables in the weight calculation fully capture all reasons for switching that are also linked to survival Weights represent how “switch-like” a patient is that has not yet switched Calculated using observational propensity score methods, vary by time and patient Euan Macpherson | May 2014 Slide by Claire Watkins
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More on Calculation of Weights
IPCW More on Calculation of Weights Propensity score model based on pooled logistic regression Analogous to time dependent cox regression All confounding factors predictive of switching and survival outcome must be included Weights for each subject at time t are basically Pseudo control population that would have been observed without switch constructed by weighting the contribution of patient not yet switched by Wi(t) Larger weight given to patients similar to switchers but not yet switched (or censored for survival) Euan Macpherson | May 2014
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Summary of Results From Complex Methods
Case Study Data Summary of Results From Complex Methods .25 .5 .75 1 Survivor function 5 10 15 20 25 Months Experimental Control ITT C RPSFTM C IPCW Observed Experimental vs Control Hazard ratio 95% CI ITT OS, all E vs all C 0.776 RPSFTM adjusted OS, all E vs all C without switch 0.36 0.062,2.095 IPCW adjusted OS, all E vs all C, without switch 0.611 0.362, 1.031 After adjusting for treatment switch, both methods suggest enhanced (numerical) treatment benefit in favour of Experimental Challenges in applying methods noted (next slide) Number at risk Month 5 10 15 20 25 Experimental 132 125 94 47 17 C ITT 129 121 83 39 C RP 88 2 C IPCW 128.97 103.32 42.42 9.51 5.20 Euan Macpherson | May 2014
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Challenges In Complex Methods To Adjust For Treatment Switch
Retrospective application of methods in the Case Study Rank Preserving Method Loss of events and power due to “recensoring” - A process to avoid biasing conclusions too much towards early events Difficulty in communicating the interpretation of this Inverse Probability of Censoring Weighting Concern that we don’t have the data on all confounders Data collection stopped at progression of disease Quality of life, tumour growth data no longer collected Missing data predictive of both switching treatment or death Upfront planning could have mitigated some issues Euan Macpherson | May 2014
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Software Rank Preserving Structural Failure Time Model
strbee package in stata Not easily applied currently in SAS, R – need for development Inverse Probability of Censoring Weighting Implemented using pooled logistic regression modelling in Stata Can be done in SAS Euan Macpherson | May 2014
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Considerations For Planning Future Studies
Define what is considered to be a crossover treatment Consider if long term treatment effect in absence of crossover is a relevant question for payers (or regulators) Assess the potential for spontaneous crossover Assess the case for built in crossover Patient perspective Payer (society) perspective Consider randomized controlled and observational trial design and analysis options to answer the relevant question Euan Macpherson | May 2014
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References Watkins C, Huang X, Latimer N, Tany Y, Wright EJ. Adjusting overall survival for treatment switches: commonly used methods and practical application. Pharm Stat Nov-Dec;12(6): Morden JP et al. Assessing methods for dealing with treatment switching in randomised controlled trials: a simulation study. BMC Medical Research Methodology 2011, 11:4 Robins JM and Finkelstein DM. Correcting for noncompliance and dependent censoring in an AIDS clinical trial with Inverse Probability of Censoring Weighted (IPCW) Log-Rank tests. Biometrics 2000 (56) Robins JM and Tsiatis A. Correcting for non-compliers in randomised trials using rank-preserving structural failure time models. Communications in Statistics - Theory and Methods 1991; 20: P.Korhonen, E. Zuber, M. Branson, N. Hollaender, N. Yateman, T. Katiskalahti, D. Lebwohl & T. Haas (2012) Correcting Overall Survival for the Impact of Crossover Via a Rank-Presevring Structural Failure Time (RPSFT) Model in the RECORD-1 Trial of Everolimus in Metastatic Renal-Cell Carcinoma, Journal of Biopharmaceutical Statistics, 22:6, Euan Macpherson | May 2014
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