Statistical Science

Analysis of 1 : 1 Matched Cohort Studies and Twin Studies, with Binary Exposures and Binary Outcomes

Arvid Sjölander, Anna L. V. Johansson, Cecilia Lundholm, Daniel Altman, Catarina Almqvist, and Yudi Pawitan

Full-text: Open access


To improve confounder adjustments, observational studies are often matched on potential confounders. While matched case-control studies are common and well covered in the literature, our focus here is on matched cohort studies, which are less common and sparsely discussed in the literature. Matched data also arise naturally in twin studies, as a cohort of exposure–discordant twins can be viewed as being matched on a large number of potential confounders. The analysis of twin studies will be given special attention. We give an overview of various analysis methods for matched cohort studies with binary exposures and binary outcomes. In particular, our aim is to answer the following questions: (1) What are the target parameters in the common analysis methods? (2) What are the underlying assumptions in these methods? (3) How do the methods compare in terms of statistical power?

Article information

Statist. Sci., Volume 27, Number 3 (2012), 395-411.

First available in Project Euclid: 5 September 2012

Permanent link to this document

Digital Object Identifier

Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Cohort studies likelihood matching


Sjölander, Arvid; Johansson, Anna L. V.; Lundholm, Cecilia; Altman, Daniel; Almqvist, Catarina; Pawitan, Yudi. Analysis of 1 : 1 Matched Cohort Studies and Twin Studies, with Binary Exposures and Binary Outcomes. Statist. Sci. 27 (2012), no. 3, 395--411. doi:10.1214/12-STS390.

Export citation


  • Breslow, N. E. and Day, N. E. (1980). Statistical methods in cancer research. Volume 1—The analysis of case control studies. IARC Scientific Publications No 32.
  • Brumback, B. A., Dailey, A. B., Brumback, L. C., Livingston, M. D. and He, Z. (2010). Adjusting for confounding by cluster using generalized linear mixed models. Statist. Probab. Lett. 80 1650–1654.
  • Carlin, J. B., Gurrin, L. C., Sterne, J. A. C., Morley, R. and Dwyer, T. (2005). Regression models for twin studies: A critical review. International Journal of Epidemiology 34, 1089–1099.
  • Chen, H. Y. (2007). A semiparametric odds ratio model for measuring association. Biometrics 63 413–421.
  • Cummings, P., McKnight, B. and Greenland, S. (2003). Matched cohort methods for injury research. Epidemiol Rev. 25 43–50.
  • Fitzmaurice, G. M., Laird, N. M. and Ware, J. H. (2004). Applied Longitudinal Analysis. Wiley, Hoboken, NJ.
  • Greenland, S., Robins, J. M. and Pearl, J. (1999). Confounding and collapsibility in causal inference. Statist. Sci. 14 29–46.
  • Hernán, M. A. and Robins, J. M. (2006). Estimating causal effects from epidemiological data. J. Epidemiol. Community Health 60 578–586.
  • Ingelsson, E., Lundholm, C., Johansson, A. L. and Altman, D. (2010). Hysterectomy and risk of cardiovascular disease: A population based cohort study. European Heart Journal doi:10.1093/eurheartj/ehq477.
  • Jewell, N. P. (2004). Statistics for Epidemiology. Chapman & Hall/CRC Press, Boca Raton, FL.
  • Neuhaus, J. M., Kalbfleisch, J. D. and Hauck, W. W. (1991). A comparison of cluster-specific and population-averaged approaches for analyzing correlated binary data. Int. Stat. Rev. 59 25–35.
  • Neuhaus, J. M., Kalbfleisch, J. D. and Hauck, W. W. (1994). Conditions for consistent estimation in mixed-effects models for binary matched-pairs data. Canad. J. Statist. 22 139–148.
  • Neuhaus, J. M. and Kalbfleisch, J. D. (1998). Between- and within-cluster covariate effects in the analysis of clustered data. Biometrics 54 638–645.
  • Neuhaus, J. M. and McCulloch, C. E. (2006). Separating between- and within-cluster covariate effects by using conditional and partitioning methods. J. R. Stat. Soc. Ser. B Stat. Methodol. 68 859–872.
  • Örtqvist, A. K., Lundholm, C., Carlström, E., Lichtenstein, P., Cnattingius, S. and Almqvist, C. (2009). Familial factors do not confound the association between birth weight and childhood asthma. Pediatrics 124 e737–43.
  • Rothman, K. J., Greenland, S. and Lash, T. L. (2008). Modern Epidemiology. Lippincott Williams and Wilkins, Philadelphia, PA.
  • Woodward, M. (2005). Epidemiology: Study Design and Data Analysis, 2nd ed. Chapman & Hall/CRC, Boca Raton, FL.