Open Access Research article

Early prediction of median survival among a large AIDS surveillance cohort

Wayne TA Enanoria1, Alan E Hubbard2, Mark J van der Laan2, Mi Chen3, Juan Ruiz4 and John M Colford1*

Author Affiliations

1 Division of Epidemiology, School of Public Health, University of California at Berkeley, Berkeley, California, USA

2 Division of Biostatistics, School of Public Health, University of California at Berkeley, Berkeley, California, USA

3 Centers for Disease Control and Prevention, Atlanta, Georgia, USA

4 California Department of Health Services, Office of AIDS, Sacramento, California, USA

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BMC Public Health 2007, 7:127  doi:10.1186/1471-2458-7-127

Published: 27 June 2007

Abstract

Background

For individuals with AIDS, data exist relatively soon after diagnosis to allow estimation of "early" survival quantiles (e.g., the 0.10, 0.15, 0.20 and 0.30 quantiles, etc.). Many years of additional observation must elapse before median survival, a summary measure of survival, can be estimated accurately. In this study, a new approach to predict AIDS median survival is presented and its accuracy tested using AIDS surveillance data.

Methods

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.

Results

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.

Conclusion

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.