David A Schoenfeld1, Dianne M Finkelstein2. 1. Massachusetts General Hospital and Harvard University, Biostatistics Unit, Boston, MA, USA. 2. Massachusetts General Hospital and Harvard University, Biostatistics Unit, Boston, MA, USA dfinkelstein@partners.org.
Abstract
BACKGROUND: For a potentially lethal chronic disease like cancer, it is often infeasible to compare treatments on the basis of overall survival, so a combined outcome such as progression-free survival (which is the time from randomization to progression or death) has become an acceptable primary endpoint. The rationale of using an efficacy measure that is dominated by the time to progression is that an effective treatment will delay progression and when treatment is stopped at progression, the effect of treatment after this time is small. However, often trials that show a significant benefit for delaying progression but not on overall survival are not universally viewed as providing convincing evidence that the drug should become the standard of care. METHODS: We propose that when there is a significant treatment effect of delaying progression, a Bayesian analysis of overall survival should be undertaken. We suggest using a joint piecewise exponential model, where the treatment effect on the hazard for progression and for death after progression is captured through two distinct parameters. We develop a plot of the overall survival advantage of the new therapy versus the prior distribution of the relative hazard for death after progression. This plot can augment the discussion about whether the new treatment is beneficial on survival. RESULTS: In the example of an early breast cancer trial for which a new treatment significantly delayed disease recurrence, our Bayesian analysis showed that with very reasonable assumptions on the effects of treatment after recurrence, there is a high probability that the new treatment improves overall survival. CONCLUSION: For a clinical trial for which treatment delays progression, the proposed method can improve the interpretability of the survival comparison using data from the study.
BACKGROUND: For a potentially lethal chronic disease like cancer, it is often infeasible to compare treatments on the basis of overall survival, so a combined outcome such as progression-free survival (which is the time from randomization to progression or death) has become an acceptable primary endpoint. The rationale of using an efficacy measure that is dominated by the time to progression is that an effective treatment will delay progression and when treatment is stopped at progression, the effect of treatment after this time is small. However, often trials that show a significant benefit for delaying progression but not on overall survival are not universally viewed as providing convincing evidence that the drug should become the standard of care. METHODS: We propose that when there is a significant treatment effect of delaying progression, a Bayesian analysis of overall survival should be undertaken. We suggest using a joint piecewise exponential model, where the treatment effect on the hazard for progression and for death after progression is captured through two distinct parameters. We develop a plot of the overall survival advantage of the new therapy versus the prior distribution of the relative hazard for death after progression. This plot can augment the discussion about whether the new treatment is beneficial on survival. RESULTS: In the example of an early breast cancer trial for which a new treatment significantly delayed disease recurrence, our Bayesian analysis showed that with very reasonable assumptions on the effects of treatment after recurrence, there is a high probability that the new treatment improves overall survival. CONCLUSION: For a clinical trial for which treatment delays progression, the proposed method can improve the interpretability of the survival comparison using data from the study.
Authors: Paul E Goss; James N Ingle; Silvana Martino; Nicholas J Robert; Hyman B Muss; Martine J Piccart; Monica Castiglione; Dongsheng Tu; Lois E Shepherd; Kathleen I Pritchard; Robert B Livingston; Nancy E Davidson; Larry Norton; Edith A Perez; Jeffrey S Abrams; Patrick Therasse; Michael J Palmer; Joseph L Pater Journal: N Engl J Med Date: 2003-10-09 Impact factor: 91.245