tooluniverse-epidemiological-analysis

Define the PECO question first, then load data with pandas, build Table 1 descriptive statistics, and fit sequential regression models with statsmodels. Report unadjusted and adjusted odds ratios with 95% confidence intervals, then run sensitivity analyses.

Use smf.logit with a formula adding confounders stepwise: exposure only, then demographics, then clinical factors. Exponentiate coefficients with numpy to get odds ratios and compare across models to assess confounding.

Use smf.ols for continuous outcomes, smf.logit for binary outcomes, OrderedModel for ordinal categories, CoxPHFitter from lifelines for time-to-event data, and smf.poisson or negativebinomial for count data.

Yes, it notes that surveys such as NHANES, BRFSS, and MEPS require sampling weights for valid inference. Weighted regression can be performed with statsmodels.stats.weightstats or the linearmodels package.

Cross-sectional designs cannot establish temporality, so causal claims are not supported. The workflow requires stating this explicitly, along with residual confounding, recall bias in self-reported exposures, and non-response bias.

Compute variance inflation factors with variance_inflation_factor from statsmodels on the covariate matrix. VIF above 5 is concerning and above 10 indicates severe multicollinearity requiring variable removal or combination.