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Simple Optimal Weighting of Cases and Controls in Case-Control Studies

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  • Rose Sherri

    (University of California, Berkeley)

  • van der Laan Mark J.

    (University of California, Berkeley)

Abstract

Researchers of uncommon diseases are often interested in assessing potential risk factors. Given the low incidence of disease, these studies are frequently case-control in design. Such a design allows a sufficient number of cases to be obtained without extensive sampling and can increase efficiency; however, these case-control samples are then biased since the proportion of cases in the sample is not the same as the population of interest. Methods for analyzing case-control studies have focused on utilizing logistic regression models that provide conditional and not causal estimates of the odds ratio. This article will demonstrate the use of the prevalence probability and case-control weighted targeted maximum likelihood estimation (MLE), as described by van der Laan (2008), in order to obtain causal estimates of the parameters of interest (risk difference, relative risk, and odds ratio). It is meant to be used as a guide for researchers, with step-by-step directions to implement this methodology. We will also present simulation studies that show the improved efficiency of the case-control weighted targeted MLE compared to other techniques.

Suggested Citation

  • Rose Sherri & van der Laan Mark J., 2008. "Simple Optimal Weighting of Cases and Controls in Case-Control Studies," The International Journal of Biostatistics, De Gruyter, vol. 4(1), pages 1-26, September.
    • Handle: RePEc:bpj:ijbist:v:4:y:2008:i:1:n:19
    DOI: 10.2202/1557-4679.1115
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    References listed on IDEAS

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    1. van der Laan Mark J. & Rubin Daniel, 2006. "Targeted Maximum Likelihood Learning," The International Journal of Biostatistics, De Gruyter, vol. 2(1), pages 1-40, December.
    2. van der Laan Mark J., 2006. "Statistical Inference for Variable Importance," The International Journal of Biostatistics, De Gruyter, vol. 2(1), pages 1-33, February.
    3. Paul W. Holland & Donald B. Rubin, 1988. "Causal Inference in Retrospective Studies," Evaluation Review, , vol. 12(3), pages 203-231, June.
    4. Anthony P. Morise & George A. Diamond & Robert Detrano & Marco Bobbio & Erdogan Gunel, 1996. "The Effect of Disease-prevalence Adjustments on the Accuracy of a Logistic Prediction Model," Medical Decision Making, , vol. 16(2), pages 133-142, June.
    5. van der Laan Mark J., 2008. "Estimation Based on Case-Control Designs with Known Prevalence Probability," The International Journal of Biostatistics, De Gruyter, vol. 4(1), pages 1-59, September.
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    Citations

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    Cited by:

    1. van der Laan Mark J., 2010. "Targeted Maximum Likelihood Based Causal Inference: Part I," The International Journal of Biostatistics, De Gruyter, vol. 6(2), pages 1-45, February.
    2. Rose Sherri & van der Laan Mark J., 2011. "A Targeted Maximum Likelihood Estimator for Two-Stage Designs," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-21, March.
    3. Etienne Volatier & Francesco Salvo & Antoine Pariente & Émeline Courtois & Sylvie Escolano & Pascale Tubert-Bitter & Ismaïl Ahmed, 2022. "High-Dimensional Propensity Score-Adjusted Case-Crossover for Discovering Adverse Drug Reactions from Computerized Administrative Healthcare Databases," Drug Safety, Springer, vol. 45(3), pages 275-285, March.
    4. van der Laan Mark J. & Gruber Susan, 2012. "Targeted Minimum Loss Based Estimation of Causal Effects of Multiple Time Point Interventions," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-41, May.
    5. van der Laan Mark J. & Petersen Maya & Zheng Wenjing, 2013. "Estimating the Effect of a Community-Based Intervention with Two Communities," Journal of Causal Inference, De Gruyter, vol. 1(1), pages 83-106, June.
    6. van der Laan Mark J. & Gruber Susan, 2010. "Collaborative Double Robust Targeted Maximum Likelihood Estimation," The International Journal of Biostatistics, De Gruyter, vol. 6(1), pages 1-71, May.
    7. Sherri Rose & Julie Shi & Thomas G. McGuire & Sharon-Lise T. Normand, 2017. "Matching and Imputation Methods for Risk Adjustment in the Health Insurance Marketplaces," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(2), pages 525-542, December.
    8. Noah Zaitlen & Sara Lindström & Bogdan Pasaniuc & Marilyn Cornelis & Giulio Genovese & Samuela Pollack & Anne Barton & Heike Bickeböller & Donald W Bowden & Steve Eyre & Barry I Freedman & David J Fri, 2012. "Informed Conditioning on Clinical Covariates Increases Power in Case-Control Association Studies," PLOS Genetics, Public Library of Science, vol. 8(11), pages 1-13, November.
    9. O. Saarela & L. R. Belzile & D. A. Stephens, 2016. "A Bayesian view of doubly robust causal inference," Biometrika, Biometrika Trust, vol. 103(3), pages 667-681.

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