Choice of baseline hazards in joint modeling of longitudinal and time-to-event cancer survival data

Hari Anand, Jinto Edakkalathoor George, Dennis Divya, Krishna Kumarapillai Mohanan Nair Jagathnath (), George Preethi S., Roshni Sivasevan and Mathew Aleyamma
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Hari Anand: 29384 Division of Cancer Epidemiology and Biostatistics, Regional Cancer Centre , Thiruvananthapuram, Kerala, India
Jinto Edakkalathoor George: 29384 Division of Cancer Epidemiology and Biostatistics, Regional Cancer Centre , Thiruvananthapuram, Kerala, India
Dennis Divya: 29384 Division of Cancer Epidemiology and Biostatistics, Regional Cancer Centre , Thiruvananthapuram, Kerala, India
Krishna Kumarapillai Mohanan Nair Jagathnath: 29384 Division of Cancer Epidemiology and Biostatistics, Regional Cancer Centre , Thiruvananthapuram, Kerala, India
George Preethi S.: 29384 Division of Cancer Epidemiology and Biostatistics, Regional Cancer Centre , Thiruvananthapuram, Kerala, India
Roshni Sivasevan: Department of Radiation Oncology, 29384 Regional Cancer Centre , Thiruvananthapuram, Kerala, India
Mathew Aleyamma: 29384 Division of Cancer Epidemiology and Biostatistics, Regional Cancer Centre , Thiruvananthapuram, Kerala, India

Statistical Applications in Genetics and Molecular Biology, 2024, vol. 23, issue 1, 13

Abstract: Longitudinal time-to-event analysis is a statistical method to analyze data where covariates are measured repeatedly. In survival studies, the risk for an event is estimated using Cox-proportional hazard model or extended Cox-model for exogenous time-dependent covariates. However, these models are inappropriate for endogenous time-dependent covariates like longitudinally measured biomarkers, Carcinoembryonic Antigen (CEA). Joint models that can simultaneously model the longitudinal covariates and time-to-event data have been proposed as an alternative. The present study highlights the importance of choosing the baseline hazards to get more accurate risk estimation. The study used colon cancer patient data to illustrate and compare four different joint models which differs based on the choice of baseline hazards [piecewise-constant Gauss–Hermite (GH), piecewise-constant pseudo-adaptive GH, Weibull Accelerated Failure time model with GH & B-spline GH]. We conducted simulation study to assess the model consistency with varying sample size (N = 100, 250, 500) and censoring (20 %, 50 %, 70 %) proportions. In colon cancer patient data, based on Akaike information criteria (AIC) and Bayesian information criteria (BIC), piecewise-constant pseudo-adaptive GH was found to be the best fitted model. Despite differences in model fit, the hazards obtained from the four models were similar. The study identified composite stage as a prognostic factor for time-to-event and the longitudinal outcome, CEA as a dynamic predictor for overall survival in colon cancer patients. Based on the simulation study Piecewise-PH-aGH was found to be the best model with least AIC and BIC values, and highest coverage probability(CP). While the Bias, and RMSE for all the models showed a competitive performance. However, Piecewise-PH-aGH has shown least bias and RMSE in most of the combinations and has taken the shortest computation time, which shows its computational efficiency. This study is the first of its kind to discuss on the choice of baseline hazards.

Keywords: joint model; longitudinal submodel; Gauss–Hermite; pseudo-adaptive Gauss–Hermite; accelerated failure time model; B-spline model (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1515/sagmb-2023-0038

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