Linus Jönsson1, Rickard Sandin2, Mattias Ekman3, Joakim Ramsberg3, Claudie Charbonneau4, Xin Huang5, Bengt Jönsson6, Milton C Weinstein7, Michael Drummond8. 1. OptumInsight AB, Klarabergsviadukten, Stockholm, Sweden. Electronic address: linus.jonsson@ki.se. 2. Global Health Economics and Outcomes Research, Pfizer Oncology, Sollentuna, Sweden. 3. OptumInsight AB, Klarabergsviadukten, Stockholm, Sweden. 4. Global Outcomes Research, Specialty Care BU, Pfizer, Paris, France. 5. Oncology Clinical Development, San Diego, CA, USA. 6. Stockholm School of Economics, Stockholm, Sweden. 7. Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA; OptumInsight, Medford, MA, USA. 8. Centre for Health Economics, University of York, York, North Yorkshire, UK.
Abstract
BACKGROUND: Offering patients in oncology trials the opportunity to cross over to active treatment at disease progression is a common strategy to address ethical issues associated with placebo controls but may lead to statistical challenges in the analysis of overall survival and cost-effectiveness because crossover leads to information loss and dilution of comparative clinical efficacy. OBJECTIVES: We provide an overview of how to address crossover, implications for risk-effect estimates of survival (hazard ratios) and cost-effectiveness, and how this influences decisions of reimbursement agencies. Two case studies using data from two phase III sunitinib oncology trials are used as illustration. METHODS: We reviewed the literature on statistical methods for adjusting for crossover and recent health technology assessment decisions in oncology. RESULTS: We show that for a trial with a high proportion of crossover from the control arm to the investigational arm, the choice of the statistical method greatly affects treatment-effect estimates and cost-effectiveness because the range of relative mortality risk for active treatment versus control is broad. With relatively frequent crossover, one should consider either the inverse probability of censoring weighting or the rank-preserving structural failure time model to minimize potential bias, with choice dependent on crossover characteristics, trial size, and available data. A large proportion of crossover favors the rank-preserving structural failure time model, while large sample size and abundant information about confounding factors favors the inverse probability of censoring weighting model. When crossover is very infrequent, methods yield similar results. CONCLUSIONS: Failure to correct for crossover may lead to suboptimal decisions by pricing and reimbursement authorities, thereby limiting an effective drug's potential.
BACKGROUND: Offering patients in oncology trials the opportunity to cross over to active treatment at disease progression is a common strategy to address ethical issues associated with placebo controls but may lead to statistical challenges in the analysis of overall survival and cost-effectiveness because crossover leads to information loss and dilution of comparative clinical efficacy. OBJECTIVES: We provide an overview of how to address crossover, implications for risk-effect estimates of survival (hazard ratios) and cost-effectiveness, and how this influences decisions of reimbursement agencies. Two case studies using data from two phase III sunitinib oncology trials are used as illustration. METHODS: We reviewed the literature on statistical methods for adjusting for crossover and recent health technology assessment decisions in oncology. RESULTS: We show that for a trial with a high proportion of crossover from the control arm to the investigational arm, the choice of the statistical method greatly affects treatment-effect estimates and cost-effectiveness because the range of relative mortality risk for active treatment versus control is broad. With relatively frequent crossover, one should consider either the inverse probability of censoring weighting or the rank-preserving structural failure time model to minimize potential bias, with choice dependent on crossover characteristics, trial size, and available data. A large proportion of crossover favors the rank-preserving structural failure time model, while large sample size and abundant information about confounding factors favors the inverse probability of censoring weighting model. When crossover is very infrequent, methods yield similar results. CONCLUSIONS: Failure to correct for crossover may lead to suboptimal decisions by pricing and reimbursement authorities, thereby limiting an effective drug's potential.