Use of Self-Matching to Control for Stable Patient Characteristics While Addressing Time-Varying Confounding on Treatment Effect: A Case Study of Older Intensive Care Patients


  • Ling Han Department of Internal Medicine, Program on Aging, Yale School of Medicine, New Haven, CT, USA
  • M. A. Pisani Department of Internal Medicine, Section of Pulmonary, Critical Care and Sleep Medicine, Yale School of Medicine, New Haven, CT, USA
  • K.L. B. Araujo Department of Internal Medicine, Program on Aging, Yale School of Medicine, New Haven, CT, USA
  • Heather G. Allore Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA



Exposure-crossover design, self-matching, confounding, causal effects, generalized estimating equation.


Exposure-crossover design offers a non-experimental option to control for stable baseline confounding through self-matching while examining causal effect of an exposure on an acute outcome. This study extends this approach to longitudinal data with repeated measures of exposure and outcome using data from a cohort of 340 older medical patients in an intensive care unit (ICU). The analytic sample included 92 patients who received ≥1 dose of haloperidol, an antipsychotic medication often used for patients with delirium. Exposure-crossover design was implemented by sampling the 3-day time segments prior (Induction) and posterior (Subsequent) to each treatment episode of receiving haloperidol. In the full cohort, there was a trend of increasing delirium severity scores (Mean±SD: 4.4±1.7) over the course of the ICU stay. After exposure-crossover sampling, the delirium severity score decreased from the Induction (4.9) to the Subsequent (4.1) intervals, with the treatment episode falling in-between (4.5). Based on a GEE Poisson model accounting for self-matching and within-subject correlation, the unadjusted mean delirium severity scores was -0.55 (95% CI: -1.10, -0.01) points lower for the Subsequent than the Induction intervals. The association diminished by 32% (-0.38, 95%CI: -0.99, 0.24) after adjusting only for ICU confounding, while being slightly increased by 7% (-0.60, 95%CI: -1.15, -0.04) when adjusting only for baseline characteristics. These results suggest that longitudinal exposure-crossover design is feasible and capable of partially removing stable baseline confounding through self-matching. Loss of power due to eliminating treatment-irrelevant person-time and uncertainty around allocating person-time to comparison intervals remain methodological challenges.


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How to Cite

Han, L., Pisani, M. A., Araujo, K. B., & Allore, H. G. (2016). Use of Self-Matching to Control for Stable Patient Characteristics While Addressing Time-Varying Confounding on Treatment Effect: A Case Study of Older Intensive Care Patients. International Journal of Statistics in Medical Research, 5(1), 8–16.



Special Issue - Methods for Estimating Treatment Effects of Persons with Multiple Chronic Conditions