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. 2021 Mar 9;190(9):1859–1866. doi: 10.1093/aje/kwab056

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© The Author(s) 2021. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com

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Bias of conditional logistic regression (CLR) and unconditional logistic regression (ULR) when caliper size increases. A) Scenario 1 with rare outcome but we set , and we defined 6 different matching calipers (d = 1, 2, 3, 4, 5, 6). B) Scenario 3 with rare outcome, c(X) has a nonlinear shape. C) Scenario 6 with common outcome, has quadratic terms in both the population and exposure models. D) Scenario 5 with rare outcome, and have interaction. L, linear term of the matching factor(s); SP, restricted cubic spline transformation of the matching factor(s).

Inline graphic — Bias of conditional logistic regression (CLR) and unconditional logistic regression (ULR) when caliper size increases. A) Scenario 1 with rare outcome but we set , and we defined 6 different matching calipers (d = 1, 2, 3, 4, 5, 6). B) Scenario 3 with rare outcome, c(X) has a nonlinear shape. C) Scenario 6 with common outcome, has quadratic terms in both the population and exposure models. D) Scenario 5 with rare outcome, and have interaction. L, linear term of the matching factor(s); SP, restricted cubic spline transformation of the matching factor(s).