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

de Uña-Álvarez, Jacobo; Mandel, Micha

Abstract:

Nonparametric estimation of the transition probability matrix of a progressive multi-state model is consideredunder cross-sectional sampling. Two different estimators adapted to possibly right-censored and left-truncated data are pro-posed. The estimators require full retrospective information before the truncation time, which, when exploited, increasesefficiency. They are obtained as differences between two survival functions constructed for sub-samples of subjects occupyingspecific states at a certain time point. Both estimators correct the oversampling of relatively large survival times by usingthe left-truncation times associated with the cross-sectional observation. Asymptotic results are established, and finite sampleperformance 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 inintensive care units (ICUs).

Year:

2018

Type of Publication:

Article

Keywords:

Biased data; Illness-death model; Inverse weighting; Left truncation; Multi-state models

Journal:

Biometrics

Volume:

74

Number:

4

Pages:

1203-1212

Month:

December

Note:

Q2 35/123 h-index 1,524 (JCR2017)

DOI:

https://doi.org/10.1111/biom.12874