Advertisement

Competing risks analysis of patients with osteosarcoma: a comparison of four different approaches

Authors

  • Bee-Choo Tai,

    Corresponding author
    1. National Medical Research Council, Clinical Trials & Epidemiology Research Unit, 10 College Road, Singapore 169851
    • National Medical Research Council, Clinical Trials & Epidemiology Research Unit, 10 College Road, Singapore 169851
    Search for more papers by this author
  • David Machin,

    1. National Medical Research Council, Clinical Trials & Epidemiology Research Unit, 10 College Road, Singapore 169851
    2. Institute of General Practice and Primary Care, University of Sheffield, Northern General Hospital, Herries Road, Sheffield S5 7AU, U.K.
    Search for more papers by this author
  • Ian White,

    1. London School of Hygiene & Tropical Medicine, Medical Statistics Unit, Keppel St, London WC1E 7HT, U.K.
    Search for more papers by this author
  • Val Gebski

    1. NHMRC Clinical Trials Centre, Level 5, Building F, 88 Mallett St, Locked Mail Bag 77, Camperdown NSW 2050, Australia
    Search for more papers by this author

Abstract

In failure time studies involving a chronic disease such as cancer, several competing causes of mortality may be operating. Commonly, the conventional statistical technique of Kaplan–Meier, which is only meaningfully interpreted by assuming independence of failure types and the censoring mechanism, is employed in clinical research involving competing risks data. Some authors have advocated the use of a cause-specific cumulative incidence function which takes into account the existence of other events within a competing risks framework, without making any assumption about independence. Lunn and McNeil have proposed an approach based on an extension of the Cox proportional hazards regression, which enables direct comparisons between failure types. We have extended this approach to estimate cause-specific cumulative incidence. As it is often not easy to follow competing risks methodology in the literature, this paper sets out systematically the assumptions made and the steps taken to implement four different methods of analysing competing risks data using cumulative incidence rates or the Kaplan–Meier estimates of cause-specific failure probabilities. The data obtained from a randomized trial of patients with osteosarcoma were used to compare these four approaches. As illustrated using the osteosarcoma data, the estimates of the classical Kaplan–Meier methods have larger numerical values than the cause-specific cumulative incidence. On the other hand, estimates of the cause-specific cumulative incidence rates from the conventional method and the modified Cox method are highly comparable. Copyright © 2001 John Wiley & Sons, Ltd.

Ancillary