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Estimating crude cumulative incidences through multinomial logit regression on discrete cause-specific hazards

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  • Ambrogi, Federico
  • Biganzoli, Elia
  • Boracchi, Patrizia

Abstract

In the presence of competing risks, the estimation of crude cumulative incidence, i.e.the probability of a specific failure as time progresses, has received much attention in the methodological literature. It is possible to estimate crude cumulative incidence starting from models defined on cause-specific hazards or to adopt regression strategies modeling directly the quantity of interest. A generalized linear model based on discrete cause-specific hazard is used to obtain the crude cumulative incidence and its asymptotic variance. The model allows inference both on cause-specific hazard and on crude cumulative incidence in the presence of time dependent effects. Standard software can be used to compute all quantities of interest. A trial of chemoprevention of leukoplakia is considered for illustrative purposes, where different patterns of risk are suspected for the different causes of treatment failure.

Suggested Citation

  • Ambrogi, Federico & Biganzoli, Elia & Boracchi, Patrizia, 2009. "Estimating crude cumulative incidences through multinomial logit regression on discrete cause-specific hazards," Computational Statistics & Data Analysis, Elsevier, vol. 53(7), pages 2767-2779, May.
    • Handle: RePEc:eee:csdana:v:53:y:2009:i:7:p:2767-2779
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    1. Gerhard Tutz, 1995. "Competing risks models in discrete time with nominal or ordinal categories of response," Quality & Quantity: International Journal of Methodology, Springer, vol. 29(4), pages 405-420, November.
    2. Anders Skrondal & Sophia Rabe-Hesketh, 2003. "Multilevel logistic regression for polytomous data and rankings," Psychometrika, Springer;The Psychometric Society, vol. 68(2), pages 267-287, June.
    3. Yu Shen & S. C. Cheng, 1999. "Confidence Bands for Cumulative Incidence Curves Under the Additive Risk Model," Biometrics, The International Biometric Society, vol. 55(4), pages 1093-1100, December.
    4. Dankmar Böhning, 1992. "Multinomial logistic regression algorithm," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 44(1), pages 197-200, March.
    5. John P. Klein & Per Kragh Andersen, 2005. "Regression Modeling of Competing Risks Data Based on Pseudovalues of the Cumulative Incidence Function," Biometrics, The International Biometric Society, vol. 61(1), pages 223-229, March.
    6. Thomas H. Scheike & Mei-Jie Zhang & Thomas A. Gerds, 2008. "Predicting cumulative incidence probability by direct binomial regression," Biometrika, Biometrika Trust, vol. 95(1), pages 205-220.
    7. Boracchi, Patrizia & Biganzoli, Elia & Marubini, Ettore, 2003. "Joint modelling of cause-specific hazard functions with cubic splines: an application to a large series of breast cancer patients," Computational Statistics & Data Analysis, Elsevier, vol. 42(1-2), pages 243-262, February.
    8. Niels Keiding & Per Kragh Andersen, 1989. "Nonparametric Estimation of Transition Intensities and Transition Probabilities: A Case Study of a Two‐State Markov Process," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 38(2), pages 319-329, June.
    9. J. P. Fine, 1999. "Analysing competing risks data with transformation models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(4), pages 817-830.
    10. Leonardo Grilli, 2005. "The random‐effects proportional hazards model with grouped survival data: a comparison between the grouped continuous and continuation ratio versions," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(1), pages 83-94, January.
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