A nonparametric maximum likelihood approach for survival data with observed cured subjects, left truncation and right-censoring

Jue Hou, Christina D. Chambers and Ronghui Xu ()
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Jue Hou: University of California
Christina D. Chambers: University of California
Ronghui Xu: University of California

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2018, vol. 24, issue 4, No 9, 612-651

Abstract: Abstract We consider observational studies in pregnancy where the outcome of interest is spontaneous abortion (SAB). This at first sight is a binary ‘yes’ or ‘no’ variable, albeit there is left truncation as well as right-censoring in the data. Women who do not experience SAB by gestational week 20 are ‘cured’ from SAB by definition, that is, they are no longer at risk. Our data is different from the common cure data in the literature, where the cured subjects are always right-censored and not actually observed to be cured. We consider a commonly used cure rate model, with the likelihood function tailored specifically to our data. We develop a conditional nonparametric maximum likelihood approach. To tackle the computational challenge we adopt an EM algorithm making use of “ghost copies” of the data, and a closed form variance estimator is derived. Under suitable assumptions, we prove the consistency of the resulting estimator which involves an unbounded cumulative baseline hazard function, as well as the asymptotic normality. Simulation results are carried out to evaluate the finite sample performance. We present the analysis of the motivating SAB study to illustrate the advantages of our model addressing both occurrence and timing of SAB, as compared to existing approaches in practice.

Keywords: Cure rate model; EM algorithm; Ghost copy (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s10985-017-9415-2

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