Nonparametric Estimation of Transition Probabilities for a General Progressive Multi-State Model Under Cross-Sectional Sampling

Nonparametric estimation of the transition probability matrix of a progressive multi-state model is considered under cross-sectional sampling. Two different estimators adapted to possibly right-censored and left-truncated data are proposed. The estimators require full retrospective information befor...

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Bibliographic Details
Published in:Biometrics Vol. 74; no. 4; pp. 1203 - 1212
Main Authors: de Uña-Álvarez, Jacobo, Mandel, Micha
Format: Journal Article
Language:English
Published: United States Wiley-Blackwell 01.12.2018
Blackwell Publishing Ltd
Subjects:
Acute Disease - mortality
Acute Disease - therapy
Biased data
BIOMETRIC METHODOLOGY
biometry
Biometry - methods
Computer Simulation
Cross-Sectional Studies
Estimators
Humans
Illness‐death model
Intensive Care Units
Inverse weighting
Left truncation
Monte Carlo simulation
Multi‐state models
Nonparametric statistics
Oversampling
patients
probability
Regression analysis
Sampling
Statistics as Topic - methods
Survival
Time Factors
Transition probabilities
Left truncation
Multi-state models
Biased data
Illness-death model
Inverse weighting
ISSN:0006-341X, 1541-0420, 1541-0420
Online Access:Get full text
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Summary:Nonparametric estimation of the transition probability matrix of a progressive multi-state model is considered under cross-sectional sampling. Two different estimators adapted to possibly right-censored and left-truncated data are proposed. The estimators require full retrospective information before the truncation time, which, when exploited, increases efficiency. They are obtained as differences between two survival functions constructed for sub-samples of subjects occupying specific states at a certain time point. Both estimators correct the oversampling of relatively large survival times by using the left-truncation times associated with the cross-sectional observation. Asymptotic results are established, and finite sample performance is investigated through simulations. One of the proposed estimators performs better when there is no censoring, while the second one is strongly recommended with censored data. The new estimators are applied to data on patients in intensive care units (ICUs).
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ISSN:0006-341X
1541-0420
1541-0420
DOI:10.1111/biom.12874