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Synapse
February 1, 1995Technometrics2,174 citations

Modelling for Survival Data in Medical Research

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Key Points

  • Provide a comprehensive methodological framework for analyzing time-to-event and survival data in medical research and clinical trials.
  • Details non-parametric estimation, proportional hazards modelling via the Cox regression model, and alternative parametric survival models.
  • Addresses advanced modeling challenges including time-dependent covariates, interval-censored data, and multi-state systems.
  • Outlines implementation using maximum likelihood estimation, score statistics, and specialized software macros.
  • Establishes systematic procedures for model diagnostics and checking goodness-of-fit in survival regressions.
  • Provides analytical pathways to handle complex clinical trial structures, multi-stage disease progressions, and incomplete follow-up timelines.

Abstract

Some non-parametric procedures. Modelling survival data. The Cox Regression Model. Design of clinical trials. Some other models for survival data. Model checking. Time dependent co-variates. Interval censored survival data. Multi-state survival models. Some additional topics. Use of computer software in survival analysis. Appendices: Example data sets. Maximum liklihood estimation score statistics and information. GLIM macros for survival analysis.

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Cite This Study

A 1995 study studied this question.

synapsesocial.com/papers/6a091c0cc64d0aaf94b61987https://doi.org/10.1080/00401706.1995.10485920
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