A SIMPLE WAY TO ESTIMATE THE MEDIAN TIME AND COMPARE SURVIVAL DISTRIBUTIONS IN ANALGESIC TRIALS UNDER INFORMATIVE CENSORING

Abstract

We discuss the problem of estimating the median time and comparison of survival curves when data are nonrandomly censored in analgesic trials. In these trials patients experience post-surgical pain at the time of randomization. Time to onset of analgesia is measured by patient-administered stopwatches. An effective analgesic is one for which the median time to onset is “short.” The study design allows patients to remedicate if their pain persists, and this remedication prior to pain relief censors the time-to-onset measures. The time to onset for patients who remedicate is nonrandomly censored. Assuming noninformative censoring yields misleading results with the Kaplan-Meier method (for estimation of median time) and the log-rank test (for comparison of survival curves). This assumption can also obscure the superior effect of an effective analgesic over an ineffective one. We propose a simple and intuitive way to handle the nonrandomly censored data in analgesic trials in order to (a) estimate the median time to pain relief and (b) compare the survival distributions between treatments. The method proposed is applied to data collected from an acute pain clinical trial, and the results are discussed.