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Semiparametric inference in observational duration-response studies, with duration possibly right-censored

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  • Brent A. Johnson
  • Anastasios A. Tsiatis

Abstract

Once treatment is found to be effective in clinical studies, attention often focuses on optimum or efficacious treatment delivery. In treatment duration-response studies, the optimum treatment delivery refers to the treatment length that optimises the mean response. In many studies, the treatment length is often left to the discretion of an attending investigator or physician but may be abruptly terminated because of treatment-terminating events. Thus, a recommended treatment length often delineates a 'treatment duration policy' which prescribes that treatment be given for a specified length of time or until a treatment-terminating event occurs, whichever comes first. Estimating a functional relationship between the response and a treatment duration policy, continuously in time, is the focus of this paper. Copyright 2005, Oxford University Press.

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  • Brent A. Johnson & Anastasios A. Tsiatis, 2005. "Semiparametric inference in observational duration-response studies, with duration possibly right-censored," Biometrika, Biometrika Trust, vol. 92(3), pages 605-618, September.
    • Handle: RePEc:oup:biomet:v:92:y:2005:i:3:p:605-618
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    File URL: http://hdl.handle.net/10.1093/biomet/92.3.605
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    Cited by:

    1. Xin Lu & Brent A. Johnson, 2017. "Direct estimation for adaptive treatment length policies: Methods and application to evaluating the effect of delayed PEG insertion," Biometrics, The International Biometric Society, vol. 73(3), pages 981-989, September.
    2. Brent A. Johnson & Heather Ribaudo & Roy M. Gulick & Joseph J. Eron Jr., 2013. "Modeling Clinical Endpoints as a Function of Time of Switch to Second-Line ART with Incomplete Data on Switching Times," Biometrics, The International Biometric Society, vol. 69(3), pages 732-740, September.
    3. Shu Yang & Anastasios A. Tsiatis & Michael Blazing, 2018. "Modeling survival distribution as a function of time to treatment discontinuation: A dynamic treatment regime approach," Biometrics, The International Biometric Society, vol. 74(3), pages 900-909, September.
    4. Andrew J. Spieker & Emily M. Ko & Jason A. Roy & Nandita Mitra, 2020. "Nested g‐computation: a causal approach to analysis of censored medical costs in the presence of time‐varying treatment," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(5), pages 1189-1208, November.
    5. Xiaofei Chen & Daniel F. Heitjan & Gerald Greil & Haekyung Jeon‐Slaughter, 2021. "Estimating the optimal timing of surgery from observational data," Biometrics, The International Biometric Society, vol. 77(2), pages 729-739, June.
    6. Hao Sun & Ashkan Ertefaie & Brent A. Johnson, 2022. "Estimating mean potential outcome under adaptive treatment length strategies in continuous time," Biometrics, The International Biometric Society, vol. 78(4), pages 1503-1514, December.
    7. Liangyuan Hu & Joseph W. Hogan & Ann W. Mwangi & Abraham Siika, 2018. "Modeling the causal effect of treatment initiation time on survival: Application to HIV/TB co†infection," Biometrics, The International Biometric Society, vol. 74(2), pages 703-713, June.

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