Efficient algorithms for survival data with multiple outcomes using the frailty model

• Published in: Statistical methods in medical research Vol. 32; no. 1; pp. 118 - 132
• Main Authors: Huang, Xifen, Xu, Jinfeng, Zhou, Yunpeng
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.01.2023; Sage Publications Ltd
• Subjects: Algorithms; Alzheimer Disease - diagnostic imaging; Alzheimer Disease - psychology; Alzheimer's disease; Cognition; Cognitive ability; Cognitive Dysfunction - diagnostic imaging; Cognitive Dysfunction - psychology; Cognitive impairment; Dementia; Frailty; Homogeneity; Humans; Impairment; Medical imaging; Neuroimaging; Random effects; Regularization; Simulation; Survival; minorization–maximization algorithm; Alternating direction method of multipliers; sparsity; the frailty model; homogeneity pursuit; Alzheimer’s Disease Neuroimaging Initiative
• ISSN: 0962-2802; 1477-0334; 1477-0334
• DOI: 10.1177/09622802221133554

Survival data with multiple outcomes are frequently encountered in biomedical investigations. An illustrative example comes from Alzheimer’s Disease Neuroimaging Initiative study where the cognitively normal subjects may clinically progress to mild cognitive impairment and/or Alzheimer’s disease dementia. Transition time from normal cognition to mild cognitive impairment and that from mild cognitive impairment to Alzheimer’s disease are expected to be correlated within subjects and the dependence is often accommodated by the frailty (random effects). Estimation in the frailty model unavoidably involves multiple integrations which may be intractable and hence leads to severe computational challenges, especially in the presence of high-dimensional covariates. In this paper, we propose efficient minorization–maximization algorithms in the frailty model for survival data with multiple outcomes. The alternating direction method of multipliers is further incorporated for simultaneous variable selection and homogeneity pursuit via regularization and fusion. Extensive simulation studies are conducted to assess the performance of the proposed algorithms. An application to the Alzheimer’s Disease Neuroimaging Initiative data is also provided to illustrate their practical utilities.