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Survival Analysis II

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Principles and Practice of Clinical Trials

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Abstract

Survival analysis modeling is integral to clinical trial analysis, as even in well-designed randomized trials where the primary inference is to be based on fundamental quantities such as estimated survival distributions and nonparametric tests, survival models offer additional insights and succinct treatment effect summaries. The ubiquitous Cox proportional hazards model has numerous variations and extensions to fit specific analytic needs and has become a mainstay of biomedical and clinical trial data analysis. However, other models and treatment effect metrics are increasingly available and should be adopted in cases where model assumptions are not met.

A natural extension of survival analysis pertains to the case where multiple potential causes of failure may be in effect. When these causes of failure are mutually exclusive, then competing risks observations are encountered, while in other cases, there may be multiple failures per individual. Methods that address these extensions of time to event data are needed to (a) assess of value of treatment in the presence of events that may preclude observation of the disease process of interest, (b) evaluate risks and benefits of treatment in a way that reflects patient experience, and (c) provide tools for study of factors related to different failure types and model more complex multi-event failure processes.

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Author information

Authors and Affiliations

  1. Department of Public Health Sciences, The University of Chicago, Chicago, IL, USA

    James J. Dignam

Authors
  1. James J. Dignam
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Correspondence to James J. Dignam .

Editor information

Editors and Affiliations

  1. Department of Surgery, Division of Surgical Oncology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Steven Piantadosi

  2. Department of Epidemiology, School of Public Health, Johns Hopkins University, Baltimore, MD, USA

    Curtis L. Meinert

Section Editor information

  1. Dept. of Biostatistics and Bioinformatics,Basic Science Divison, Duke University, School of Medicine, Durham, North Carolina, USA

    Stephen George

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Dignam, J.J. (2022). Survival Analysis II. In: Piantadosi, S., Meinert, C.L. (eds) Principles and Practice of Clinical Trials. Springer, Cham. https://doi.org/10.1007/978-3-319-52636-2_120

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