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For our analysis, we used a conditional logistic regression model to permit stratification by site.
To assess potential risk factors for SSI we used a conditional logistic regression model.
We used a conditional logistic regression to compare the AMI risk between physicians and controls.
We used a conditional logistic regression model to estimate crude and adjusted odds ratios, with 95% confidence intervals.
We then used a conditional logistic regression model to estimate excess relative risk of leukemia per gray (ERR/Gy) of radiation dose.
For non-diarrhea controls, we used a conditional logistic regression model to estimate the crude odds ratio, because these controls were matched to case-patients by hospital and date of birth (±30 days).
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Using a conditional logistic regression model, sepsis patients during the winter season with higher 25-OHD levels were more likely to develop acute lung injury (odds ratio 1.68, 95% confidence interval of 1.05 to 2.69, P = 0.03).
Univariate ORs and 95% confidence intervals (95% CI) were estimated for each potential risk factor using a conditional logistic regression model (to account for the matched design) and significance was assessed by the Wald test.
VE was defined as (1 – OR *100) as previously published [12] the adjusted odds ratios for LAIV and TIV in the vaccine stratified analysis were tested for homogeneity using a conditional logistic regression model.
Matched analyses were conducted by using a conditional logistic regression model.
We then compared exposed children to unexposed children using a conditional logistic regression model.
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