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Using Aalen's linear hazards model to investigate time-varying effects in the proportional hazards regression model

Author

Listed:
  • David W.Hosmer

    (University of Massachusetts)

  • Patrick Royston

    (Cancer Division, MRC Clinical Trials Unit)

Abstract

In this paper, we describe a new Stata command, stlh, which estimates and tests for the significance of the time-varying regression coefficients in Aalen's linear hazards model; see Aalen (1989). We see two potential uses for this command. One may use it as an alternative to a proportional hazards or other nonlinear hazards regression model analysis to describe the effects of covariates on survival time. A second application is to use the command to supplement a proportional hazards regression model analysis to assist in detecting and then describing the nature of time-varying effects of covariates through plots of the estimated cumulative regression coefficients, with confidence bands, from Aalen's model. We illustrate the use of the command to perform this supplementary analysis with data from a study of residential treatment programs of different durations that are designed to prevent return to drug use. Copyright 2002 by Stata Corporation.

Suggested Citation

  • David W.Hosmer & Patrick Royston, 2002. "Using Aalen's linear hazards model to investigate time-varying effects in the proportional hazards regression model," Stata Journal, StataCorp LP, vol. 2(4), pages 331-350, November.
    • Handle: RePEc:tsj:stataj:v:2:y:2002:i:4:p:331-350
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    References listed on IDEAS

    as
    1. Robin Henderson & Alvin Milner, 1991. "Aalen Plots Under Proportional Hazards," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 40(3), pages 401-409, November.
    2. Odd O. Aalen & Ørnulf Borgan & Harald Fekjær, 2001. "Covariate Adjustment of Event Histories Estimated from Markov Chains: The Additive Approach," Biometrics, The International Biometric Society, vol. 57(4), pages 993-1001, December.
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    Cited by:

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    2. Frank M.H. Neffke & Martin Henning & Ron Boschma, 2012. "The impact of aging and technological relatedness on agglomeration externalities: a survival analysis," Journal of Economic Geography, Oxford University Press, vol. 12(2), pages 485-517, March.
    3. David D. Hanagal, 2021. "RETRACTED ARTICLE: Positive Stable Shared Frailty Models Based on Additive Hazards," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 13(3), pages 431-453, December.
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    5. Pandey Arvind & Hanagal David D. & Tyagi Shikhar, 2022. "Generalised Lindley shared additive frailty regression model for bivariate survival data," Statistics in Transition New Series, Polish Statistical Association, vol. 23(4), pages 161-176, December.
    6. Yun Jeong Choi & Jee Young Kim & Min Hee You, 2020. "Radius Restriction And Firms' Survival: Evidence From The Coffee Franchise Industry," Contemporary Economic Policy, Western Economic Association International, vol. 38(3), pages 496-514, July.
    7. H. Joseph Newton & Nicholas J. Cox, 2016. "The Stata Journal Editors' Prize 2016: Patrick Royston," Stata Journal, StataCorp LP, vol. 16(4), pages 815-825, December.
    8. Paul Gatabazi & Gaëtan Kabera & Jules Clement Mba & Edson Pindza & Sileshi Fanta Melesse, 2022. "Cryptocurrencies and Tokens Lifetime Analysis from 2009 to 2021," Economies, MDPI, vol. 10(3), pages 1-14, March.
    9. Molnár, D. László & Hollósné Marosi, Judit, 2015. "Az öregségi nyugdíjasok halandósága. A nyugellátási összeg, a nyugdíjazási életkor és a halandóság összefüggései Magyarországon, 2004-2012 [Mortality of old-age pensioners. Association among the am," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(12), pages 1258-1290.

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