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Modeling of the cure fraction in survival studies

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  • Paul C. Lambert

    (Centre for Biostatistics and Genetic Epidemiology, University of Leicester)

Abstract

Cure models are a special type of survival analysis model where it is assumed that there are a proportion of sub jects who will never experience the event and thus the survival curve will eventually reach a plateau. In population-based cancer studies, cure is said to occur when the mortality (hazard) rate in the diseased group of individuals returns to the same level as that expected in the general population. The cure fraction is of interest to patients and a useful measure to monitor trends and differences in survival of curable disease. I will describe the strsmix and strsnmix commands, which fit the two main types of cure fraction model, namely, the mixture and nonmixture cure fraction models. These models allow incorporation of the expected background mortality rate and thus enable the modeling of relative survival when cure is a possibility. I give an example to illustrate the commands. Copyright 2007 by StataCorp LP.

Suggested Citation

  • Paul C. Lambert, 2007. "Modeling of the cure fraction in survival studies," Stata Journal, StataCorp LP, vol. 7(3), pages 351-375, September.
    • Handle: RePEc:tsj:stataj:v:7:y:2007:i:3:p:351-375
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    References listed on IDEAS

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    1. Schmidt, Peter & Witte, Ann Dryden, 1989. "Predicting criminal recidivism using 'split population' survival time models," Journal of Econometrics, Elsevier, vol. 40(1), pages 141-159, January.
    2. Tsodikov A.D. & Ibrahim J.G. & Yakovlev A.Y., 2003. "Estimating Cure Rates From Survival Data: An Alternative to Two-Component Mixture Models," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 1063-1078, January.
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    Cited by:

    1. P. C. Lambert & P. W. Dickman & C. L. Weston & J. R. Thompson, 2010. "Estimating the cure fraction in population‐based cancer studies by using finite mixture models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(1), pages 35-55, January.
    2. Kiron Chatterjee & Kang-Rae Ma, 2009. "Time taken for residents to adopt a new public transport service: examining heterogeneity through duration modelling," Transportation, Springer, vol. 36(1), pages 1-25, January.
    3. Eva Beaujouan & Anne Solaz, 2013. "Racing Against the Biological Clock? Childbearing and Sterility Among Men and Women in Second Unions in France," European Journal of Population, Springer;European Association for Population Studies, vol. 29(1), pages 39-67, February.
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    5. Edith Gray & Ann Evans & Jon Anderson & Rebecca Kippen, 2010. "Using Split-Population Models to Examine Predictors of the Probability and Timing of Parity Progression," European Journal of Population, Springer;European Association for Population Studies, vol. 26(3), pages 275-295, August.
    6. Song, Shige & Wang, Wei & Hu, Peifeng, 2009. "Famine, death, and madness: Schizophrenia in early adulthood after prenatal exposure to the Chinese Great Leap Forward Famine," Social Science & Medicine, Elsevier, vol. 68(7), pages 1315-1321, April.
    7. Therese M.-L. Andersson & Paul C. Lambert, 2012. "Fitting and modeling cure in population-based cancer studies within the framework of flexible parametric survival models," Stata Journal, StataCorp LP, vol. 12(4), pages 623-638, December.
    8. Ewa Cukrowska-Torzewska & Magdalena Grabowska, 2023. "The sex preference for children in Europe: Children’s sex and the probability and timing of births," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 48(8), pages 203-232.
    9. Tien Vu, 2014. "One male offspring preference: evidence from Vietnam using a split-population model," Review of Economics of the Household, Springer, vol. 12(4), pages 689-715, December.

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