IDEAS home Printed from https://ideas.repec.org/a/bpj/ijbist/v4y2008i1n13.html
   My bibliography  Save this article

Missing Confounding Data in Marginal Structural Models: A Comparison of Inverse Probability Weighting and Multiple Imputation

Author

Listed:
  • Moodie Erica E. M.

    (McGill University)

  • Delaney Joseph A.C.

    (University of Washington)

  • Lefebvre Geneviève

    (University of British Columbia)

  • Platt Robert W

    (McGill University)

Abstract

Standard statistical analyses of observational data often exclude valuable information from individuals with incomplete measurements. This may lead to biased estimates of the treatment effect and loss of precision. The issue of missing data for inverse probability of treatment weighted estimation of marginal structural models (MSMs) has often been addressed, though little has been done to compare different missing data techniques in this relatively new method of analysis. We propose a method for systematically dealing with missingness in MSMs by treating missingness as a cause for censoring and weighting subjects by the inverse probability of missingness. We developed a series of simulations to systematically compare the effect of using case deletion, our inverse weighting approach, and multiple imputation in a MSM when there is missing information on an important confounder. We found that multiple imputation was slightly less biased and considerably less variable than the inverse probability approach. Thus, the lower variability achieved through multiple imputation makes it desirable in most practical cases where the missing data are strongly predicted by the available data. Inverse probability weighting is, however, a superior alternative to naive approaches such as complete-case analysis.

Suggested Citation

  • Moodie Erica E. M. & Delaney Joseph A.C. & Lefebvre Geneviève & Platt Robert W, 2008. "Missing Confounding Data in Marginal Structural Models: A Comparison of Inverse Probability Weighting and Multiple Imputation," The International Journal of Biostatistics, De Gruyter, vol. 4(1), pages 1-23, July.
    • Handle: RePEc:bpj:ijbist:v:4:y:2008:i:1:n:13
    DOI: 10.2202/1557-4679.1106
    as

    Download full text from publisher

    File URL: https://doi.org/10.2202/1557-4679.1106
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.2202/1557-4679.1106?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. James R. Carpenter & Michael G. Kenward & Stijn Vansteelandt, 2006. "A comparison of multiple imputation and doubly robust estimation for analyses with missing data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(3), pages 571-584, July.
    2. Heejung Bang & James M. Robins, 2005. "Doubly Robust Estimation in Missing Data and Causal Inference Models," Biometrics, The International Biometric Society, vol. 61(4), pages 962-973, December.
    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. Erica Moodie & D. Stephens, 2011. "Marginal Structural Models: unbiased estimation for longitudinal studies," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 56(1), pages 117-119, February.

    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. Daniel, Rhian M. & Kenward, Michael G., 2012. "A method for increasing the robustness of multiple imputation," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1624-1643.
    2. Zhiwei Zhang & Zhen Chen & James F. Troendle & Jun Zhang, 2012. "Causal Inference on Quantiles with an Obstetric Application," Biometrics, The International Biometric Society, vol. 68(3), pages 697-706, September.
    3. Huiming Lin & Bo Fu & Guoyou Qin & Zhongyi Zhu, 2017. "Doubly robust estimation of generalized partial linear models for longitudinal data with dropouts," Biometrics, The International Biometric Society, vol. 73(4), pages 1132-1139, December.
    4. Ashkan Ertefaie & Nima S. Hejazi & Mark J. van der Laan, 2023. "Nonparametric inverse‐probability‐weighted estimators based on the highly adaptive lasso," Biometrics, The International Biometric Society, vol. 79(2), pages 1029-1041, June.
    5. Jianxuan Liu & Yanyuan Ma & Lan Wang, 2018. "An alternative robust estimator of average treatment effect in causal inference," Biometrics, The International Biometric Society, vol. 74(3), pages 910-923, September.
    6. Nguyen Nancy Duong & Zhang Li-Chun, 2020. "An Appraisal of Common Reweighting Methods for Nonresponse in Household Surveys Based on the Norwegian Labour Force Survey and the Statistics on Income and Living Conditions Survey," Journal of Official Statistics, Sciendo, vol. 36(1), pages 151-172, March.
    7. Sant’Anna, Pedro H.C. & Zhao, Jun, 2020. "Doubly robust difference-in-differences estimators," Journal of Econometrics, Elsevier, vol. 219(1), pages 101-122.
    8. Görg Holger & Marchal Léa, 2019. "Die Effekte deutscher Direktinvestitionen im Empfängerland vor dem Hintergrund des Leistungsbilanzüberschusses: Empirische Evidenz mit Mikrodaten für Frankreich," Perspektiven der Wirtschaftspolitik, De Gruyter, vol. 20(1), pages 53-69, June.
    9. Léa Marchal & Clément Nedoncelle, 2019. "Immigrants, occupations and firm export performance," Review of International Economics, Wiley Blackwell, vol. 27(5), pages 1480-1509, November.
    10. Hisaki Kono & Yasuyuki Sawada & Abu S. Shonchoy, 2016. "DVD-based Distance-learning Program for University Entrance Exams: Experimental Evidence from Rural Bangladesh," CIRJE F-Series CIRJE-F-1027, CIRJE, Faculty of Economics, University of Tokyo.
    11. Creemers, An & Aerts, Marc & Hens, Niel & Molenberghs, Geert, 2012. "A nonparametric approach to weighted estimating equations for regression analysis with missing covariates," Computational Statistics & Data Analysis, Elsevier, vol. 56(1), pages 100-113, January.
    12. Paul Frédéric Blanche & Anders Holt & Thomas Scheike, 2023. "On logistic regression with right censored data, with or without competing risks, and its use for estimating treatment effects," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(2), pages 441-482, April.
    13. Ignaciuk, Ada & Malevolti, Giulia & Scognamillo, Antonio & Sitko, Nicholas J., 2022. "Can food aid relax farmers’ constraints to adopting climate-adaptive agricultural practices? Evidence from Ethiopia, Malawi and the United Republic of Tanzania," ESA Working Papers 324073, Food and Agriculture Organization of the United Nations, Agricultural Development Economics Division (ESA).
    14. Everding, Jakob & Marcus, Jan, 2020. "The effect of unemployment on the smoking behavior of couples," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 29(2), pages 154-170.
    15. Li Liang & Greene Tom, 2013. "A Weighting Analogue to Pair Matching in Propensity Score Analysis," The International Journal of Biostatistics, De Gruyter, vol. 9(2), pages 215-234, July.
    16. McFarland, Michael J. & Geller, Amanda & McFarland, Cheryl, 2019. "Police contact and health among urban adolescents: The role of perceived injustice," Social Science & Medicine, Elsevier, vol. 238(C), pages 1-1.
    17. Fan Li & Ashley L. Buchanan & Stephen R. Cole, 2022. "Generalizing trial evidence to target populations in non‐nested designs: Applications to AIDS clinical trials," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(3), pages 669-697, June.
    18. Antonelli Joseph & Cefalu Matthew, 2020. "Averaging causal estimators in high dimensions," Journal of Causal Inference, De Gruyter, vol. 8(1), pages 92-107, January.
    19. Yukun Ma & Pedro H. C. Sant'Anna & Yuya Sasaki & Takuya Ura, 2023. "Doubly Robust Estimators with Weak Overlap," Papers 2304.08974, arXiv.org, revised Apr 2023.
    20. Delprato, Marcos & Akyeampong, Kwame, 2019. "The effect of working on students’ learning in Latin America: Evidence from the learning survey TERCE," International Journal of Educational Development, Elsevier, vol. 70(C), pages 1-1.

    More about this item

    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:bpj:ijbist:v:4:y:2008:i:1:n:13. 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: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    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.