Nonparametric estimation from cross-sectional survival data

Non-Parametric Estimation in Survival Models Germ´an Rodr´ıguez [email protected] Spring, ; revised Spring We now discuss the analysis of survival data without parametric assump-tions about the form of the distribution. 1 One Sample: Kaplan-Meier Our first topic is non-parametric estimation of the survival function. If the. Nov 06,  · Abstract. Survival data from prevalent cases collected under a cross-sectional sampling scheme are subject to left-truncation. When fitting an additive hazards model to left-truncated data, the conditional estimating equation method (Lin & Ying, ), obtained by modifying the risk sets to account for left-truncation, can be very inefficient, as the marginal likelihood of the truncation times Cited by: Wang M-C. Nonparametric estimation from cross-sectional survival data. J Am Statist Assoc. ; – Wang M-C, Brookmeyer R, Jewell N. Statistical models for prevalent cohort JudgeLink.org by:

Nonparametric estimation from cross-sectional survival data

Wang, M.-C. () Nonparametric Estimation from Cross-Sectional Survival Data. Journal of the American Statistical Association, 86, When survival data arise from prevalent cases ascertained through a cross-sectional study, it is well known that the survivor function corresponding to these data is length biased and different. Cross-sectional sampling of survival data implies that one is only able to observe lifetimes corresponding to individuals “in progress” at a given time point t 0 (the cross-section date). That is, the individuals entering the sample are those who have already experienced the initiation of an event prior to time t JudgeLink.org by: Nonparametric Estimation from Cross-Sectional Survival Data Article in Journal of the American Statistical Association 86() · March with Reads DOI: / Wang M-C. Nonparametric estimation from cross-sectional survival data. J Am Statist Assoc. ; – Wang M-C, Brookmeyer R, Jewell N. Statistical models for prevalent cohort JudgeLink.org by: In nonparametric estimation, E(Y|x) is assumed to satisfy smoothness conditions . based on the ratio of a generalized cross-validation error estimate to PSE. The simplest situation encountered in survival analysis is the nonparametric estimation of a survival ( Section III) or Kalbfleisch and Prentice ( : p. The nonparametric estimation of the distribution function of X was first studied, in this when the distribution of (T,δ) has to be estimated from a cross-sectional. A survey of nonparametric methods (methods which make no distributional assumptions useful for estimating recruit survival from cross-sectional data. This paper considers survival data arising from length-biased sampling, where Keywords: Backward and forward recurrence time, Cross-sectional sampling. Nonparametric Estimation From Cross-Sectional. Survival Data. MEI-CHENG WANG*. In many follow-up studies survival data are often observed according to a.

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