Early prediction of median survival among a large AIDS surveillance cohort

The data consisted of 96,373 individuals who were reported to the HIV/AIDS Reporting System of the California Department of Health Services Office of AIDS as of December 31, 1996. We defined cohorts based on quarter year of diagnosis (e.g., the "931" cohort consists of individuals diagnosed with AIDS in the first quarter of 1993). We used early quantiles (estimated using the Inverse Probability of Censoring Weighted estimator) of the survival distribution to estimate median survival by assuming a linear relationship between the earlier quantiles and median survival. From this model, median survival was predicted for cohorts for which a median could not be estimated empirically from the available data. This prediction was compared with the actual medians observed when using updated survival data reported at least five years later.Using the 0.15 quantile as the predictor and the data available as of December 31, 1996, we were able to predict the median survival of four cohorts (933, 934, 941, and 942) to be 34, 34, 31, and 29 months. Without this approach, there were insufficient data with which to make any estimate of median survival. The actual median survival of these four cohorts (using data as of December 31, 2001) was found to be 32, 40, 46, and 80 months, suggesting that the accuracy for this approach requires a minimum of three years to elapse from diagnosis to the time an accurate prediction can be made.The results of this study suggest that early and accurate prediction of median survival time after AIDS diagnosis may be possible using early quantiles of the survival distribution. The methodology did not seem to work well during a period of significant change in survival as observed with highly active antiretroviral treatment, but results suggest that it may work well in a time of more gradual improvement in survival.Since the beginning of the AIDS epidemic, the prediction of trends in survival after an AIDS diagnosis has been important for planning health c