Two simple approaches for validating a binary surrogate endpoint using data from multiple trials.
A surrogate endpoint is an endpoint that is observed before a true endpoint and is used to draw conclusions about the effect of intervention on true endpoint. To gauge confidence in the use of a surrogate endpoint, it must be validated. Two simple validation methods using data from multiple trials with surrogate and true endpoints are discussed: an estimation method extending previous work and new method based on hypothesis tests. The validation methods were applied to two data sets, each involving 10 randomized trials: one for patients with early colon cancer where the true endpoint was survival status at eight years and surrogate endpoint was cancer recurrence status at three years, and one for patients with advanced colorectal cancer where the true endpoint was survival status at 12 months and the surrogate endpoint was cancer recurrence status at six months. The estimation method uses the surrogate endpoint in the new trial and a model relating surrogate and true endpoints in previous trials to predict the effect of intervention on true endpoint in the new trial. For validation, each trial was successively treated as the ;new' trial and a comparison was made between predicted and observed effects of intervention on true endpoint. Performance of the surrogate endpoint was good in both data sets. The hypothesis testing method involves the z-statistic for the surrogate endpoint, which is the estimated effect of intervention on surrogate endpoint divided by its standard error. To use this z-statistic to test a null hypothesis of no effect of intervention on true endpoint, the critical value is increased above a standard level of 1.96 to a level determined by the relationships between surrogate and true endpoints in the data sets. This elevated critical value could be used for accelerated approval.
Baker
Stuart G
SG
Biometry Research Group, Division of Cancer Prevention, National Cancer Institute, Bethesda, MD 20892-7354, USA. sb16i@nih.gov
D000465:Algorithms; D015415:Biomarkers; D015179:Colorectal Neoplasms; D006801:Humans; D015201:Meta-Analysis as Topic; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data; D015203:Reproducibility of Results
D015415:Biomarkers