Donald A Redelmeier1, Robert J Tibshirani2. 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.
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.
Authors: Alireza Samiei; David W Gjertson; Sanaz Memarzadeh; Gottfried E Konecny; Neda A Moatamed Journal: Diagn Pathol Date: 2022-09-14 Impact factor: 3.196