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An individual measure of relative survival

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  • Janez Stare
  • Robin Henderson
  • Maja Pohar

Abstract

Summary. Relative survival techniques are used to compare survival experience in a study cohort with that expected if background population rates apply. The techniques are especially useful when cause‐specific death information is not accurate or not available as they provide a measure of excess mortality in a group of patients with a certain disease. Whereas these methods are based on group comparisons, we present here a transformation approach which instead gives for each individual an outcome measure relative to the appropriate background population. The new outcome measure is easily interpreted and can be analysed by using standard survival analysis techniques. It provides additional information on relative survival and gives new options in regression analysis. For example, one can estimate the proportion of patients who survived longer than a given percentile of the respective general population or compare survival experience of individuals while accounting for the population differences. The regression models for the new outcome measure are different from existing models, thus providing new possibilities in analysing relative survival data. One distinctive feature of our approach is that we adjust for expected survival before modelling. The paper is motivated by a study into the survival of patients after acute myocardial infarction.

Suggested Citation

  • Janez Stare & Robin Henderson & Maja Pohar, 2005. "An individual measure of relative survival," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(1), pages 115-126, January.
  • Handle: RePEc:bla:jorssc:v:54:y:2005:i:1:p:115-126
    DOI: 10.1111/j.1467-9876.2005.00473.x
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    Cited by:

    1. Torben Martinussen & Christian Bressen Pipper, 2014. "Estimation of Causal Odds of Concordance using the Aalen Additive Model," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(1), pages 141-151, March.
    2. Zahra Mansourvar & Torben Martinussen, 2017. "Estimation of average causal effect using the restricted mean residual lifetime as effect measure," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(3), pages 426-438, July.
    3. Peter Cnudde & Szilard Nemes & Maziar Mohaddes & John Timperley & Göran Garellick & Kristina Burström & Ola Rolfson, 2017. "Is Preoperative Patient-Reported Health Status Associated with Mortality after Total Hip Replacement?," IJERPH, MDPI, vol. 14(8), pages 1-13, August.

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