無存活時間之資料分析

Analyzing Survival Data Without Prospective Follow-Up

陳世緯(Shih-Wei Chen)

指導教授 ： 江金倉

國立臺灣大學/理學院/應用數學科學研究所/碩士(2016年)

https://doi.org/10.6342/NTU201600523

摘要

無資料

關鍵字

發生世代抽樣 ； 盛行世代抽樣 ； 排序相關估計式 ； 單指標存活模式

並列摘要

This article develops new approaches to estimate survival parameters based on two types of survival data without collecting survival times. The first one consists of incident and prevalent covariates and the other is a prevalent cohort sample with only covariates and truncation time. Our research aims to identify the effects of covariates on a failure time through more general single-index survival regression models. Under the assumption of covariate-independent truncation, the density ratio of incident and prevalent covariates and the hazard function of an observed truncation time are shown to be monotonic functions of the single-index in the proposed survival regression models. In light of these features, the rank correlation estimation technique can be naturally applied to estimate the index coefficients. Thus, existing theoretical frameworks can be used to establish the consistency and asymptotic normality of the proposed maximum rank correlation estimators. We further conduct a series of
simulations to investigate the finite-sample performance of the estimators. In addition, our methodological ideas are illustrated by data from the National Comorbidity Survey Replicate.

並列關鍵字

incident cohort sampling ； prevalent cohort sampling ； rank correlation estimation ； single-index survival model

參考文獻

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Chan, K. C. G. (2013). Survival analysis without survival data: connecting lengthbiased and case-control data. Biometrika 100 764-770.

Chan, K. C. G., Chen, Y. Q., and Di, C. Z. (2012). Proportional mean residual life model for right-censored length-biased data. Biometrika 99 995-1000.

Chan, K. C. G. and Qin, J. (2015). Rank-based testing of equal survivorship based on cross-sectional survival data with or without prospective follow-up. Biostatistics 16 772-784.

Chan, K. C. G. and Wang, M. C. (2012). Estimating incident population distribution from prevalent data. Biometrics 68 521-531.