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Instrumental variable additive hazards models

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  • Jialiang Li
  • Jason Fine
  • Alan Brookhart

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  • Jialiang Li & Jason Fine & Alan Brookhart, 2015. "Instrumental variable additive hazards models," Biometrics, The International Biometric Society, vol. 71(1), pages 122-130, March.
    • Handle: RePEc:bla:biomet:v:71:y:2015:i:1:p:122-130
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    File URL: http://hdl.handle.net/10.1111/biom.12244
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    References listed on IDEAS

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    1. Tang, Man-Lai & Lee, Sik-Yum, 1998. "Analysis of structural equation models with censored or truncated data via EM algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 27(1), pages 33-46, March.
    2. Angrist, Joshua D & Evans, William N, 1998. "Children and Their Parents' Labor Supply: Evidence from Exogenous Variation in Family Size," American Economic Review, American Economic Association, vol. 88(3), pages 450-477, June.
    3. T. Loeys & E. Goetghebeur, 2003. "A Causal Proportional Hazards Estimator for the Effect of Treatment Actually Received in a Randomized Trial with All-or-Nothing Compliance," Biometrics, The International Biometric Society, vol. 59(1), pages 100-105, March.
    4. Terza, Joseph V. & Basu, Anirban & Rathouz, Paul J., 2008. "Two-stage residual inclusion estimation: Addressing endogeneity in health econometric modeling," Journal of Health Economics, Elsevier, vol. 27(3), pages 531-543, May.
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    Citations

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    Cited by:

    1. Alexander Ahammer, 2016. "How Physicians Affect Patients’ Employment Outcomes Through Deciding on Sick Leave Durations," CDL Aging, Health, Labor working papers 2016-04, The Christian Doppler (CD) Laboratory Aging, Health, and the Labor Market, Johannes Kepler University Linz, Austria.
    2. Monia Ezzalfani & Raphaël Porcher & Alexia Savignoni & Suzette Delaloge & Thomas Filleron & Mathieu Robain & David Pérol & ESME Group, 2021. "Addressing the issue of bias in observational studies: Using instrumental variables and a quasi-randomization trial in an ESME research project," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-13, September.
    3. Torben Martinussen & Stijn Vansteelandt & Eric J. Tchetgen Tchetgen & David M. Zucker, 2017. "Instrumental variables estimation of exposure effects on a time‐to‐event endpoint using structural cumulative survival models," Biometrics, The International Biometric Society, vol. 73(4), pages 1140-1149, December.
    4. Byeong Yeob Choi, 2021. "Instrumental variable estimation of truncated local average treatment effects," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-12, April.
    5. William Liu, 2023. "A Theory Guide to Using Control Functions to Instrument Hazard Models," Papers 2312.03165, arXiv.org.
    6. Andrew Ying & Eric J. Tchetgen Tchetgen, 2023. "Structural cumulative survival models for estimation of treatment effects accounting for treatment switching in randomized experiments," Biometrics, The International Biometric Society, vol. 79(3), pages 1597-1609, September.
    7. Byeong Yeob Choi & Jason P. Fine & Roman Fernandez & M. Alan Brookhart, 2022. "Alternative sensitivity analyses for regression estimates of treatment effects to unobserved confounding in binary and survival data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(3), pages 637-659, September.
    8. Alexander Ahammer, 2018. "Physicians, sick leave certificates, and patients' subsequent employment outcomes," Health Economics, John Wiley & Sons, Ltd., vol. 27(6), pages 923-936, June.
    9. Matthias Brueckner & Andrew Titman & Thomas Jaki, 2019. "Instrumental variable estimation in semi‐parametric additive hazards models," Biometrics, The International Biometric Society, vol. 75(1), pages 110-120, March.
    10. Jad Beyhum & Jean-Pierre FLorens & Ingrid Van Keilegom, 2020. "Nonparametric instrumental regression with right censored duration outcomes," Papers 2011.10423, arXiv.org.
    11. Jad Beyhum, 2021. "Two-stage least squares with a randomly right censored outcome," Papers 2110.05107, arXiv.org.
    12. Jaeun Choi & A. James O'Malley, 2017. "Estimating the causal effect of treatment in observational studies with survival time end points and unmeasured confounding," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(1), pages 159-185, January.
    13. Linbo Wang & Eric Tchetgen Tchetgen & Torben Martinussen & Stijn Vansteelandt, 2023. "Instrumental variable estimation of the causal hazard ratio," Biometrics, The International Biometric Society, vol. 79(2), pages 539-550, June.
    14. Beyhum, Jad & Florens, Jean-Pierre & Van Keilegom, Ingrid, 2020. "Nonparametric Instrumental Regression with Right Censored Duration Outcomes," TSE Working Papers 20-1164, Toulouse School of Economics (TSE).
    15. Jad Beyhum & Jean-Pierre Florens & Ingrid Van Keilegom, 2021. "A nonparametric instrumental approach to endogeneity in competing risks models," Papers 2105.00946, arXiv.org.
    16. Peng Wang & Bin Liu & Andrew Delios & Gongming Qian, 2023. "Two-sided effects of state equity: The survival of Sino–foreign IJVs," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 54(1), pages 107-127, February.

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