Breslow and Cain (1988) proposed a conditional likelihood approach based on the validation set. We combine the conditional likelihoods of the validation set and the non-validation set. The proposed ...

A class of conditional logistic regression models for clustered binary data is considered. This includes the polychotomous logistic model of Rosner (1984) as a special case. Properties such as the ...

Using conditional logistic regression with adjustment for age, race and ethnic group, underlying conditions, and exposures to persons with Covid-19, we estimated vaccine effectiveness for partial ...

In a conditional logistic-regression multivariate analysis, neither ACE inhibitors nor ARBs were associated with the likelihood of SARS-CoV-2 infection.

Multivariate conditional logistic regression modeling failed to find a significant association between exposure and hospital mortality (adjusted OR 1.15, 95% CI 0.65 to 2.04) or other relevant ...

Patients with no exposure to anthracyclines served as the reference group. Magnitude of risk is expressed as odds ratio, which was obtained using conditional logistic regression adjusting for age at ...

The researchers used multivariable conditional logistic regression analyses to measure the risk of 11 individual outcomes as the composite outcome: fracture, osteoporosis, type 2 diabetes ...

Conditional logistic regression was used to assess the associations, with adjustments for various covariates and sensitivity analyses excluding participants with missing genetic data.

A multivariate conditional logistic regression analysis showed that patients with 90 or more cumulative daily colchicine doses had significant 23% lower adjusted odds of progression compared with ...