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Estimating and modeling relative survival

Author

Listed:
  • Paul W. Dickman

    (Karolinska Institutet)

  • Enzo Coviello

    (ASL BT)

Abstract

When estimating patient survival using data collected by populationbased cancer registries, it is common to estimate net survival in a relative-survival framework. Net survival can be estimated using the relative-survival ratio, which is the ratio of the observed survival of the patients (where all deaths are considered events) to the expected survival of a comparable group from the general population. In this article, we describe a command, strs, for life-table estimation of relative survival. We discuss three methods for estimating expected survival, as well as the cohort, period, and hybrid approaches for estimating relative survival. We also implement a life-table version of the Pohar Perme (2012, Biometrics 68: 113–120) estimator of net survival, and we describe two methods for age standardization. We also explain how, in addition to net probabilities of death, crude probabilities of death due to cancer and due to other causes can be estimated using the method of Cronin and Feuer (2000, Statistics in Medicine 19: 1729–1740). We conclude this article with discussion and examples of modeling excess mortality using various approaches, including the full-likelihood approach (using the ml command) and Poisson regression (using the glm command with a user-specified link function). Copyright 2015 by StataCorp LP.

Suggested Citation

  • Paul W. Dickman & Enzo Coviello, 2015. "Estimating and modeling relative survival," Stata Journal, StataCorp LP, vol. 15(1), pages 186-215, March.
    • Handle: RePEc:tsj:stataj:v:15:y:2015:i:1:p:186-215
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    Citations

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    Cited by:

    1. Jonviea D. Chamberlain & Anne Buzzell & Hans Peter Gmünder & Kerstin Hug & Xavier Jordan & André Moser & Martin Schubert & Marcel Zwahlen & Martin W. G. Brinkhof, 2019. "Excess burden of a chronic disabling condition: life lost due to traumatic spinal cord injury in a Swiss population-based cohort study," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 64(7), pages 1097-1105, September.
    2. Ged Dempsey & Dan Hungerford & Phil McHale & Lauren McGarey & Edward Benison & Ben Morton, 2020. "Long term outcomes for elderly patients after emergency intensive care admission: A cohort study," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-14, October.
    3. Francisco Carrasco-Peña & Eloisa Bayo-Lozano & Miguel Rodríguez-Barranco & Dafina Petrova & Rafael Marcos-Gragera & Maria Carmen Carmona-Garcia & Josep Maria Borras & Maria-José Sánchez, 2020. "Adherence to Clinical Practice Guidelines and Colorectal Cancer Survival: A Retrospective High-Resolution Population-Based Study in Spain," IJERPH, MDPI, vol. 17(18), pages 1-15, September.
    4. Adelaida García-Velasco & Lluís Zacarías-Pons & Helena Teixidor & Marc Valeros & Raquel Liñan & M. Carmen Carmona-Garcia & Montse Puigdemont & Walter Carbajal & Raquel Guardeño & Núria Malats & Eric D, 2020. "Incidence and Survival Trends of Pancreatic Cancer in Girona: Impact of the Change in Patient Care in the Last 25 Years," IJERPH, MDPI, vol. 17(24), pages 1-22, December.

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