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To investigate MBL and ficolin-2 as potential risk factors for SSc, matched univariate analysis was performed by running conditional logistic regression on one variable at a time with SSc as the dependent variable.
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We ran conditional logistic regressions by cause-specific hospital admissions, including simultaneously all eligible primary organic compounds, with their concentrations scaled by their IQR, and adjusted linearly for same day temperature, same day dew point, 1- to 3-day averaged temperature, and day of week.
We ran conditional logistic regression models by cause-specific hospital admission, for all dates and stratified by season.
Instead, we ran conditional logistic regression models using age in months as a matching variable, rather than individual case-to-control matchings.
To examine effect modification by sex, age (65 75, >75) or race (black, white, other races) we ran the conditional logistic regression stratified by each demographic characteristic.
As sensitivity analyses, we also ran models using conditional logistic regression with stratification by recruitment site.
By applying to some real data and using simulations, we demonstrate that conditional Poisson models were simpler to code and shorter to run than are conditional logistic analyses and can be fitted to larger data sets than possible with standard Poisson models.
In this case, the expected return is multiplied by the expected logistic return index (by the conditional logistic variance).
Odds ratios (OR) and 95% confidence intervals (CI) for breast cancer were estimated by exact conditional logistic regression.
Data were analyzed by using conditional logistic regression.
Risk factors were examined by using conditional logistic regression.
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