||Survival Estimation Using Splines.
Whittemore, A. S. ;
Keller., J. B. ;
||Stanford Univ., CA. Dept. of Family, Community and Preventive Medicine.;Health Effects Research Lab., Research Triangle Park, NC.
Mathematical models ;
Spline functions ;
Maximum likelihood estimates ;
Mean square values
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||A nonparametric maximum likelihood procedure is given for estimating the survivor function from right-censored data. It approximates the hazard rate by a simple function such as a spline, with different approximations yielding different estimators. A special case is that proposed by Nelson (1969, Journal of Quality Technology 1,27-52) and Altshuler (1970, Mathematical Biosciences 6, 1-11). The estimators are uniformly consistent and have the same asymptotic weak convergence properties as the Kaplan-Meier (1958, Journal of the American Statistical Association 53, 457-481) estimator. However, in small and in heavily censored samples, the simplest spline estimators have uniformly smaller mean squared error than do the Kaplan-Meier and Nelson-Altshuler estimators. The procedure is extended to estimate the baseline hazard rate and regression of coefficients in the Cox (1972, Journal of the Royal Statistical Society, Series B 34, 187-220) proportional hazards model and is illustrated using experimental carcinogenesis data.
||Pub. in Biometrics, v42 p495-506 Sep 86. Sponsored by Health Effects Research Lab., Research Triangle Park, NC.
|NTIS Title Notes
||Reprint: Survival Estimation Using Splines.
||PC A03/MF A01