Adjusting Survival Time Estimates to Account for Treatment Switching in Randomized Controlled Trials—an Economic Evaluation Context

Latimer, Nicholas; Abrams, Keith R.; Lambert, Paul C.; Crowther, Michael J.; Wailoo, Allan; Morden, James P.; Akehurst, Ron; Campbell, Mike

doi:10.1177/0272989x13520192

Cited by 79 publications

(183 citation statements)

References 29 publications

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“…However, this approach is highly prone to selection bias, because switchers are likely to be prognostically different from nonswitchers (9)(10)(11)(12). There is a general view that this "naïve" approach should be avoided (2)(3)(4).…”

Section: Adjusting For Treatment Switching the Methodsmentioning

confidence: 99%

“…Therefore, if early analysis, or external information, suggests that clinical equipoise between the treatments under investigation is lost, there may be an ethical requirement to permit switching. The presence of switching in clinical trials creates difficulties in estimating the true effectiveness and costeffectiveness of experimental treatments (2)(3)(4). This has implications for drug manufacturers, regulators, payers, clinicians, and future patients: ultimately, a reliance on standard statistical techniques to analyze RCTs affected by treatment switching could result in effectiveness and harms being underestimated and access being denied to effective and cost-effective treatments.…”

mentioning

confidence: 99%

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Treatment Switching: Statistical and Decision-Making Challenges and Approaches

Latimer¹,

Henshall

Siebert

et al. 2016

Int J Technol Assess Health Care

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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.

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Section: Adjusting For Treatment Switching the Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

Treatment Switching: Statistical and Decision-Making Challenges and Approaches

Latimer¹,

Henshall

Siebert

et al. 2016

Int J Technol Assess Health Care

Self Cite

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“…Advanced statistical approaches such as inverse probability of censoring weighted (IPCW) marginal structural models and rank-preserving structural failure time (RPSFT) models have emerged to control for the confounding effect of crossover in OS results [13,14]. These techniques have been progressively applied in the context of health technology assessments (HTA) as they seem to overcome key fundamental limitations derived from the application of more naive analyses.…”

Section: Introductionmentioning

confidence: 99%

“…These techniques have been progressively applied in the context of health technology assessments (HTA) as they seem to overcome key fundamental limitations derived from the application of more naive analyses. Simple approaches, such as censoring or excluding patients who switch treatments, or incorporating a treatment indicator as a time-dependent covariate, are prone to substantial selection bias [10,13,15]. The more sophisticated IPCW and RPSFT techniques have important advantages but are not free from potential limitations associated with the inherent assumptions upon which their structural model is based.…”

Section: Introductionmentioning

confidence: 99%

“…It has been suggested that HTA bodies would benefit from evaluating a range of sensitivity analyses exploring various methodologies when assessing the OS benefit of new oncology interventions [13,22]. HTA bodies are making increasing use of these analyses; however, clinicians, investigators, and payers may not be as familiar with these techniques.…”

Section: Introductionmentioning

confidence: 99%

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Overall Survival Endpoint in Oncology Clinical Trials: Addressing the Effect of Crossover - The Case of Pazopanib in Advanced Renal Cell Carcinoma

Diaz

Sternberg²,

Mehmud³

et al. 2016

Oncology

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Objective: To identify the issues of using overall survival (OS) as a primary endpoint in the presence of crossover and the statistical analyses available to adjust for confounded OS due to crossover in oncology clinical trials. Methods: An indirect comparison was conducted between pazopanib and sunitinib in advanced renal cell carcinoma. Statistical adjustment methods were used to estimate the true comparative effectiveness of these treatments. Recently, a head-to-head trial comparing pazopanib and sunitinib was completed. This provided the opportunity to compare the OS treatment effect estimated for pazopanib versus sunitinib using indirect comparison and statistical adjustment techniques with that observed in the head-to-head trial. Results: Using a rank-preserving structural failure time model to adjust for crossover in the pazopanib registration trial, the indirect comparison of pazopanib versus sunitinib resulted in an OS hazard ratio (HR) of 0.97, while an unadjusted analysis resulted in an OS HR of 1.96. The head-to-head trial reported a final OS HR of 0.92 for pazopanib versus sunitinib. Conclusion: This case study supports the need to adjust for confounded OS due to crossover, which enables trials to meet ethical standards and provides decision makers with a more accurate estimate of treatment benefit.

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Adjusting for treatment switching in theMETRICstudy shows further improved overall survival with trametinib compared with chemotherapy

et al. 2016

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Trametinib, a selective inhibitor of mitogen‐activated protein kinase kinase 1 (MEK1) and MEK2, significantly improves progression‐free survival compared with chemotherapy in patients with BRAF V600E/K mutation–positive advanced or metastatic melanoma (MM). However, the pivotal clinical trial permitted randomized chemotherapy control group patients to switch to trametinib after disease progression, which confounded estimates of the overall survival (OS) advantage of trametinib. Our purpose was to estimate the switching‐adjusted treatment effect of trametinib for OS and assess the suitability of each adjustment method in the primary efficacy population. Of the patients randomized to chemotherapy, 67.4% switched to trametinib. We applied the rank‐preserving structural failure time model, inverse probability of censoring weights, and a two‐stage accelerated failure time model to obtain estimates of the relative treatment effect adjusted for switching. The intent‐to‐treat (ITT) analysis estimated a 28% reduction in the hazard of death with trametinib treatment (hazard ratio [HR], 0.72; 95% CI, 0.52–0.98) for patients in the primary efficacy population (data cut May 20, 2013). Adjustment analyses deemed plausible provided OS HR point estimates ranging from 0.48 to 0.53. Similar reductions in the HR were estimated for the first‐line metastatic subgroup. Treatment with trametinib, compared with chemotherapy, significantly reduced the risk of death and risk of disease progression in patients with BRAF V600E/K mutation–positive advanced melanoma or MM. Adjusting for switching resulted in lower HRs than those obtained from standard ITT analyses. However, CI are wide and results are sensitive to the assumptions associated with each adjustment method.

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Adjusting Survival Time Estimates to Account for Treatment Switching in Randomized Controlled Trials—an Economic Evaluation Context

Cited by 79 publications

References 29 publications

Treatment Switching: Statistical and Decision-Making Challenges and Approaches

Treatment Switching: Statistical and Decision-Making Challenges and Approaches

Overall Survival Endpoint in Oncology Clinical Trials: Addressing the Effect of Crossover - The Case of Pazopanib in Advanced Renal Cell Carcinoma

Adjusting for treatment switching in theMETRICstudy shows further improved overall survival with trametinib compared with chemotherapy

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