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J Clin Epidemiol. 2017 Jan;81:51-55.e2. doi: 10.1016/j.jclinepi.2016.08.006. Epub 2016 Aug 24.

A simple method for analyzing matched designs with double controls: McNemar's test can be extended.

Author information

1
Department of Medicine, University of Toronto, Toronto, Ontario, Canada; Evaluative Clinical Sciences Program, Sunnybrook Research Institute, Toronto, Ontario, Canada; Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada; Division of General Internal Medicine, Sunnybrook Health Sciences Centre, G-151, 2075 Bayview Avenue, Toronto, Ontario M4N 3M5, Canada; Center for Leading Injury Prevention Practice Education & Research, Toronto, Ontario, Canada. Electronic address: dar@ices.on.ca.
2
Department of Statistics, Stanford University, Stanford, CA, USA.

Abstract

OBJECTIVES:

To introduce a new analytic approach for matched studies, where exactly two controls are linked to each case (double controls rather than solitary controls). The intent is to extend McNemar's test for one-to-two matching (instead of one-to-one matching) when evaluating binary predictors and outcomes.

STUDY DESIGN AND SETTING:

We review McNemar's approach for analyzing matched data, demonstrate the Mantel-Haenszel approach for integrating two overlapping McNemar's estimates, review conditional logistic regression as an alternative analytic approach, and introduce a new method that yields a visual display and easy verification.

RESULTS:

We illustrate the new approach with real data testing the association between overcast weather and the risk of a life-threatening traffic crash (n = 6,962). We show that results from the new approach agree closely with conditional logistic regression and are sufficiently simple as to be computed on a handheld calculator. We further validate the approach by conducting simulations when a positive association was predefined and when a null association was predefined.

CONCLUSION:

The new approach provides a feasible, simple, and efficient method for analyzing matched designs with double controls.

KEYWORDS:

Case-only design; Crossover study; Matched pairs; Risk perception; Self-matching; Traffic accident

PMID:
27565976
DOI:
10.1016/j.jclinepi.2016.08.006
[Indexed for MEDLINE]

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