Simple and Fast Overidentified Rank Estimation for Right-Censored Length-Biased Data and Backward Recurrence Time

Length-biased survival data subject to right-censoring are often collected from a prevalent cohort. However, informative right censoring induced by the sampling design creates challenges in methodological development. While certain conditioning arguments could circumvent the problem of informative c...

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Bibliographic Details
Published inBiometrics Vol. 74; no. 1; pp. 77 - 85
Main Authors Sun, Yifei, Chan, Kwun Chuen Gary, Qin, Jing
Format Journal Article
LanguageEnglish
Published England Wiley-Blackwell 01.03.2018
Blackwell Publishing Ltd
Subjects
algorithms
Asymptotic properties
Backward and forward recurrence time
BIOMETRIC METHODOLOGY
biometry
Computer Simulation
dementia
Dementia - mortality
Dementia disorders
equations
Estimation
Generalized method of moments
Humans
Mathematical models
Models, Statistical
Recurrence
Regression analysis
Sampling designs
Survival Analysis
Time Factors
Treatment Failure
Weighted log‐rank estimating equation
Backward and forward recurrence time
Weighted log-rank estimating equation
Generalized method of moments
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Summary:Length-biased survival data subject to right-censoring are often collected from a prevalent cohort. However, informative right censoring induced by the sampling design creates challenges in methodological development. While certain conditioning arguments could circumvent the problem of informative censoring, related rank estimation methods are typically inefficient because the marginal likelihood of the backward recurrence time is not ancillary. Under a semiparametric accelerated failure time model, an overidentified set of log-rank estimating equations is constructed based on the left-truncated rightcensored data and backward recurrence time. Efficient combination of the estimating equations is simplified by exploiting an asymptotic independence property between two sets of estimating equations. A fast algorithm is studied for solving nonsmooth, non-monotone estimating equations. Simulation studies confirm that the overidentified rank estimator can have a substantially improved estimation efficiency compared to just-identified rank estimators. The proposed method is applied to a dementia study for illustration.
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ISSN:0006-341X
1541-0420
1541-0420
DOI:10.1111/biom.12727