Survival Analysis in Clinical Trials: Past Developments and Future Directions Export

@article{s, abstract = {The field of survival analysis emerged in the 20th century and experienced tremendous growth during the latter half of the century. The developments in this field that have had the most profound impact on clinical trials are the Kaplan-Meier (1958, Journal of the American Statistical Association53, 4572013481) method for estimating the survival function, the log-rank statistic (Mantel, 1966, Cancer Chemotherapy Report50, 1632013170) for comparing two survival distributions, and the Cox (1972, Journal of the Royal Statistical Society, Series B34, 1872013220) proportional hazards model for quantifying the effects of covariates on the survival time. The counting-process martingale theory pioneered by Aalen (1975, Statistical inference for a family of counting processes, Ph.D. dissertation, University of California, Berkeley) provides a unified framework for studying the small- and large-sample properties of survival analysis statistics. Significant progress has been achieved and further developments are expected in many other areas, including the accelerated failure time model, multivariate failure time data, interval-censored data, dependent censoring, dynamic treatment regimes and causal inference, joint modeling of failure time and longitudinal data, and Baysian methods.}, address = {Department of Biostatistics, Box 357232, University of Washington, Seattle, Washington 98 195, U.S.A.}, author = {Fleming, Thomas R. and Lin, D. Y.}, citeulike-article-id = {3377050}, citeulike-linkout-0 = {http://dx.doi.org/10.1111/j.0006-341X.2000.0971.x}, citeulike-linkout-1 = {http://www3.interscience.wiley.com/cgi-bin/abstract/119032462/ABSTRACT}, doi = {10.1111/j.0006-341X.2000.0971.x}, journal = {Biometrics}, keywords = {analysis, surivival}, number = {4}, pages = {971--983}, posted-at = {2008-10-05 13:31:07}, priority = {2}, title = {Survival Analysis in Clinical Trials: Past Developments and Future Directions}, url = {http://dx.doi.org/10.1111/j.0006-341X.2000.0971.x}, volume = {56}, year = {2000} }

TY - JOUR ID - s L3 - citeulike-article-id:3377050 N2 - The field of survival analysis emerged in the 20th century and experienced tremendous growth during the latter half of the century. The developments in this field that have had the most profound impact on clinical trials are the Kaplan-Meier (1958, Journal of the American Statistical Association53, 4572013481) method for estimating the survival function, the log-rank statistic (Mantel, 1966, Cancer Chemotherapy Report50, 1632013170) for comparing two survival distributions, and the Cox (1972, Journal of the Royal Statistical Society, Series B34, 1872013220) proportional hazards model for quantifying the effects of covariates on the survival time. The counting-process martingale theory pioneered by Aalen (1975, Statistical inference for a family of counting processes, Ph.D. dissertation, University of California, Berkeley) provides a unified framework for studying the small- and large-sample properties of survival analysis statistics. Significant progress has been achieved and further developments are expected in many other areas, including the accelerated failure time model, multivariate failure time data, interval-censored data, dependent censoring, dynamic treatment regimes and causal inference, joint modeling of failure time and longitudinal data, and Baysian methods. IS - 4 JF - Biometrics AD - Department of Biostatistics, Box 357232, University of Washington, Seattle, Washington 98 195, U.S.A. EP - 983 TI - Survival Analysis in Clinical Trials: Past Developments and Future Directions VL - 56 SP - 971 KW - analysis KW - surivival AU - Fleming, Thomas AU - Lin, DY PY - 2000/// UR - http://dx.doi.org/10.1111/j.0006-341X.2000.0971.x ER -