A Semi-Parametric Survival Extrapolation Method: Model Validation Using RERF Cohort

• Main Authors: Yu-Chieh Cheng, 鄭宇傑
• Other Authors: 方啟泰
• Format: Others
• Language: en_US
• Published: 2011
• Online Access: http://ndltd.ncl.edu.tw/handle/74322071472277291903

碩士 === 國立臺灣大學 === 流行病學與預防醫學研究所 === 99 === Background How long can a human immunodeficiency virus (HIV)-infected patient live is a crucial question, especially for the evaluation of the cost-effectiveness of medical interventions. A semi-parametric survival extrapolation method has been developed based on a logit survival ratio W between a patient cohort and a reference population. If the excess hazard of a specific disease/exposure remains constant, then the logit survival ratio curve will converge to a straight line over time, which allows linear extrapolation to estimate survival beyond the follow-up time. The accuracy of short-term projection has been validated, while the validity and accuracy of life-long projection remains unclear. Method and Principal Findings We used a subset of the Life Span Study (LSS) cohort, which comprised atomic bomb survivors from Hiroshima and Nagasaki and is one of the longest follow-up data cohorts in the world. With this dataset, we tested the validity and accuracy of life-long semi-parametric extrapolation as well as developed mathematical criteria for applying this method to data of limited sample size. We first proved the biological premise that disease/exposure is associated with premature mortality when compared with age- and gender-matched general populations, which is mathematically equivalent to a negative slope in the logit W plot at all times. In addition, we developed a slope-time diagnostic plot. Using those cohort members with >1000 mGy radiation exposure as the index group, we found that (1) the logit W curve continued to converge toward zero at the end of a 48-year follow-up, which indicated that extrapolation based on the right end of the curve should provide a more accurate estimate than that based on the central part of the curve; (2) the slope of the logit W curve can have large random variation if the length of time used for regression is short, such as 6 months, and the diagnostic plot allows users to select the shortest time length that provides a stable slope estimation; (3) a 38-year extrapolation from the end of the 10 year (1950–1960) follow-up data, using the above-stated criteria to select the length of time and the time period for regression, yielded an accurate projection in comparison with the actual 38-year follow-up (1960–1998) data if the cohort members without radiation exposure were used as the source of reference. If the 1960 life table is used as the source of reference, then the projection will underestimate the true long-term survival due to the discrepancy between the time period and the cohort life expectancy. Conclusion Long-term semi-parametric survival extrapolation can be valid and accurate. The recommended steps in applying this methodology are as follows: 1.Create a logit W(t) plot from the follow-up data. 2.Create Slope-Time diagnostic plots using different lengths of time (for example: 6, 12, 24, 36, and 48 months) for regression. Select the shortest length of time that provides stable slope estimation without significant random variations. 3.For the selected diagnostic plot, exclude the time periods with positive slopes and find the time period as close to the end of the follow-up time as possible to obtain the best slope estimate for extrapolation.