IDEAS home Printed from https://ideas.repec.org/p/ehl/lserod/31520.html
   My bibliography  Save this paper

Adjusting for selection effects in epidemiologic studies: why sensitivity analysis is the only “solution”

Author

Listed:
  • Geneletti, Sara
  • Mason, Alexina
  • Best, Nicky

Abstract

No abstract is available for this item.

Suggested Citation

  • Geneletti, Sara & Mason, Alexina & Best, Nicky, 2011. "Adjusting for selection effects in epidemiologic studies: why sensitivity analysis is the only “solution”," LSE Research Online Documents on Economics 31520, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:31520
    as

    Download full text from publisher

    File URL: http://eprints.lse.ac.uk/31520/
    File Function: Open access version.
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. James M. Robins & Dianne M. Finkelstein, 2000. "Correcting for Noncompliance and Dependent Censoring in an AIDS Clinical Trial with Inverse Probability of Censoring Weighted (IPCW) Log-Rank Tests," Biometrics, The International Biometric Society, vol. 56(3), pages 779-788, September.
    2. Rebecca M. Turner & David J. Spiegelhalter & Gordon C. S. Smith & Simon G. Thompson, 2009. "Bias modelling in evidence synthesis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(1), pages 21-47, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Morrissey, Karyn & Kinderman, Peter & Pontin, Eleanor & Tai, Sara & Schwannauer, Mathias, 2016. "Web based health surveys: Using a Two Step Heckman model to examine their potential for population health analysis," Social Science & Medicine, Elsevier, vol. 163(C), pages 45-53.
    2. David McConnell & Conor Hickey & Norma Bargary & Lea Trela-Larsen & Cathal Walsh & Michael Barry & Roisin Adams, 2021. "Understanding the Challenges and Uncertainties of Seroprevalence Studies for SARS-CoV-2," IJERPH, MDPI, vol. 18(9), pages 1-19, April.
    3. Glenn W. Harrison, 2017. "Behavioral responses to surveys about nicotine dependence," Health Economics, John Wiley & Sons, Ltd., vol. 26(S3), pages 114-123, December.
    4. Claire Keeble & Stuart Barber & Graham Richard Law & Paul D. Baxter, 2013. "Participation Bias Assessment in Three High-Impact Journals," SAGE Open, , vol. 3(4), pages 21582440135, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rachel Axelrod & Daniel Nevo, 2023. "A sensitivity analysis approach for the causal hazard ratio in randomized and observational studies," Biometrics, The International Biometric Society, vol. 79(3), pages 2743-2756, September.
    2. Yanyao Yi & Ting Ye & Menggang Yu & Jun Shao, 2020. "Cox regression with survival‐time‐dependent missing covariate values," Biometrics, The International Biometric Society, vol. 76(2), pages 460-471, June.
    3. Michael Rosenblum & Nicholas P. Jewell & Mark van der Laan & Stephen Shiboski & Ariane van der Straten & Nancy Padian, 2009. "Analysing direct effects in randomized trials with secondary interventions: an application to human immunodeficiency virus prevention trials," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(2), pages 443-465, April.
    4. Douglas E. Schaubel & Guanghui Wei, 2011. "Double Inverse-Weighted Estimation of Cumulative Treatment Effects Under Nonproportional Hazards and Dependent Censoring," Biometrics, The International Biometric Society, vol. 67(1), pages 29-38, March.
    5. Romin Pajouheshnia & Noah A. Schuster & Rolf H. H. Groenwold & Frans H. Rutten & Karel G. M. Moons & Linda M. Peelen, 2020. "Accounting for time‐dependent treatment use when developing a prognostic model from observational data: A review of methods," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 74(1), pages 38-51, February.
    6. Greg DiRienzo, 2004. "Nonparametric Comparison of Two Survival-Time Distributions in the Presence of Dependent Censoring," Harvard University Biostatistics Working Paper Series 1000, Berkeley Electronic Press.
    7. Tala Al-Rousan & Jeffrey A Sparks & Mary Pettinger & Rowan Chlebowski & JoAnn E Manson & Andrew M Kauntiz & Robert Wallace, 2018. "Menopausal hormone therapy and the incidence of carpal tunnel syndrome in postmenopausal women: Findings from the Women’s Health Initiative," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-15, December.
    8. A. G. DiRienzo, 2003. "Nonparametric Comparison of Two Survival-Time Distributions in the Presence of Dependent Censoring," Biometrics, The International Biometric Society, vol. 59(3), pages 497-504, September.
    9. McCandless Lawrence C., 2012. "Meta-Analysis of Observational Studies with Unmeasured Confounders," The International Journal of Biostatistics, De Gruyter, vol. 8(2), pages 1-31, January.
    10. Shuxi Zeng & Elizabeth C. Lange & Elizabeth A. Archie & Fernando A. Campos & Susan C. Alberts & Fan Li, 2023. "A Causal Mediation Model for Longitudinal Mediators and Survival Outcomes with an Application to Animal Behavior," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 28(2), pages 197-218, June.
    11. K. M. Rhodes & J. Savović & R. Elbers & H. E. Jones & J. P. T. Higgins & J. A. C. Sterne & N. J. Welton & R. M. Turner, 2020. "Adjusting trial results for biases in meta‐analysis: combining data‐based evidence on bias with detailed trial assessment," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(1), pages 193-209, January.
    12. S. Dias & N. J. Welton & V. C. C. Marinho & G. Salanti & J. P. T. Higgins & A. E. Ades, 2010. "Estimation and adjustment of bias in randomized evidence by using mixed treatment comparison meta‐analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(3), pages 613-629, July.
    13. Shen, Pao-sheng, 2009. "An inverse-probability-weighted approach to the estimation of distribution function with doubly censored data," Statistics & Probability Letters, Elsevier, vol. 79(9), pages 1269-1276, May.
    14. Giancarlo MANZI & Pier Alda FERRARI, "undated". "Statistical methods for evaluating satisfaction with public services Abstract: Contrary to private enterprises, public enterprises can be unaware of the impact of their performance when providing serv," CIRIEC Working Papers 1404, CIRIEC - Université de Liège.
    15. Chiu-Hsieh Hsu & Jeremy Taylor & Susan Murray, 2004. "Survival Analysis USing Auxiliary Variables Via Nonparametric Multiple Imputation," The University of Michigan Department of Biostatistics Working Paper Series 1026, Berkeley Electronic Press.
    16. Shen, Pao-sheng, 2010. "Semiparametric estimation of survival function when data are subject to dependent censoring and left truncation," Statistics & Probability Letters, Elsevier, vol. 80(3-4), pages 161-168, February.
    17. Susmita Datta & Jennifer Le-Rademacher & Somnath Datta, 2007. "Predicting Patient Survival from Microarray Data by Accelerated Failure Time Modeling Using Partial Least Squares and LASSO," Biometrics, The International Biometric Society, vol. 63(1), pages 259-271, March.
    18. Qingxia Chen & Fan Zhang & Ming-Hui Chen & Xiuyu Julie Cong, 2020. "Estimation of treatment effects and model diagnostics with two-way time-varying treatment switching: an application to a head and neck study," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(4), pages 685-707, October.
    19. Bella Vakulenko‐Lagun & Jing Qian & Sy Han Chiou & Nancy Wang & Rebecca A. Betensky, 2022. "Nonparametric estimation of the survival distribution under covariate‐induced dependent truncation," Biometrics, The International Biometric Society, vol. 78(4), pages 1390-1401, December.
    20. 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.

    More about this item

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ehl:lserod:31520. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: LSERO Manager (email available below). General contact details of provider: https://edirc.repec.org/data/lsepsuk.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.