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

  • Anand Hari , Edakkalathoor George Jinto , Divya Dennis , Kumarapillai Mohanan Nair Jagathnath Krishna EMAIL logo , Preethi S. George , Sivasevan Roshni and Aleyamma Mathew
Published/Copyright: May 13, 2024
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Statistical Applications in Genetics and Molecular Biology
From the journal Statistical Applications in Genetics and Molecular Biology Volume 23 Issue 1

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
Corresponding author: Kumarapillai Mohanan Nair Jagathnath Krishna, Division of Cancer Epidemiology and Biostatistics, Regional Cancer Centre, Thiruvananthapuram, Kerala, India, E-mail:

Funding source: Indian council for Medical research-Department of Health Research, Government of India

Award Identifier / Grant number: R.11012/03/2021-GIA/HR

Acknowledgment

The authors are grateful to the parent institution for the support given to carry out this work. The authors are also thankful to the unknown referees for the critical comments which helped in improving the manuscript.

  1. Research ethics: The local Institutional Scientific Review committee with IRB No.12/2018/13, deemed the study exempted from human ethics committee review.

  2. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Competing interests: Authors state no conflict of interest.

  4. Research funding: The authors acknowledge the funding received from Indian Council for Medical Research – Department of Health Research, Government of India with award no. R.11012/03/2021-GIA/HR.

  5. Data availability: Not applicable.

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Received: 2023-10-21
Accepted: 2024-04-23
Published Online: 2024-05-13

© 2024 Walter de Gruyter GmbH, Berlin/Boston

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https://doi.org/10.1515/sagmb-2023-0038
Keywords for this article
joint model; longitudinal submodel; Gauss–Hermite; pseudo-adaptive Gauss–Hermite; accelerated failure time model; B-spline model

Articles in the same Issue

  1. Frontmatter
  2. Research Articles
  3. Empirically adjusted fixed-effects meta-analysis methods in genomic studies
  4. A CNN-CBAM-BIGRU model for protein function prediction
  5. A heavy-tailed model for analyzing miRNA-seq raw read counts
  6. Flexible model-based non-negative matrix factorization with application to mutational signatures
  7. Choice of baseline hazards in joint modeling of longitudinal and time-to-event cancer survival data
  8. Assessing the feasibility of statistical inference using synthetic antibody-antigen datasets
  9. A global test of hybrid ancestry from genome-scale data
  10. Integrative pathway analysis with gene expression, miRNA, methylation and copy number variation for breast cancer subtypes
  11. Bayesian LASSO for population stratification correction in rare haplotype association studies
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