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Methods

Binary logistic regression (binomial family, logit link) was applied to model 90-day survival probability. The primary exposure was treatment group (A = standard care, B = active intervention). Covariates included age (continuous, centered at median 52 years), sex (binary), baseline clinical score (continuous, centered at mean 49.1), enrollment site (8-level categorical, dummy-coded with S1 as reference), comorbidity index (Charlson, ordered count), and a pre-specified treatment × baseline_score interaction term. Complete-case analysis was conducted (n = 820 after exclusion of 22 records with missing age or baseline_score values). Model diagnostics comprised complete-separation detection, variance inflation factors (VIF), Hosmer–Lemeshow goodness-of-fit test (8 groups), bootstrapped ROC/AUC (1 000 replicates, seed 42), calibration plot, and Cook's distance influence analysis; no violations were identified (maximum VIF = 2.48; maximum Cook's D = 0.047). All analyses were conducted in Python 3.11.8 (statsmodels 0.14.1; scikit-learn 1.4.1; numpy 1.26.4).

Results

The complete-case model (n = 820; 101 events, EPP = 7.8) demonstrated adequate overall fit (AUC 0.781, 95% CI 0.737–0.826; Hosmer–Lemeshow χ²(8) = 8.43, p = 0.392; Nagelkerke R² = 0.241). The active intervention (Group B) was associated with 85% higher odds of 90-day survival compared to standard care (OR 1.85, 95% CI 1.31–2.62, p < 0.001). Among continuous predictors, higher baseline clinical score independently increased survival odds by 4.2% per point (OR 1.042, 95% CI 1.021–1.063, p < 0.001), while older age (OR 0.973 per year, 95% CI 0.958–0.988, p < 0.001) and greater comorbidity burden (OR 0.734 per CCI unit, 95% CI 0.658–0.819, p < 0.001) were associated with reductions of 2.7% and 26.6% per unit, respectively. Sex was not significantly associated with outcome (OR 0.811, 95% CI 0.570–1.154, p = 0.244). The pre-specified treatment × baseline score interaction was non-significant (OR 1.018, 95% CI 0.996–1.041, p = 0.107), providing no evidence of effect modification by baseline score.

Limitations

Complete-case analysis excluded 22 participants (2.6%) with missing baseline data; a multiple imputation sensitivity analysis (m = 20) is recommended to assess the impact on primary estimates.
The events-per-parameter ratio (EPP = 7.8) falls below the commonly recommended minimum of 10; coefficient estimates may be unstable, and replication in an independent cohort with greater event volume is advised.
Site was modeled as a fixed effect; a mixed-effects model with site as a random intercept may better account for between-site clustering and improve generalizability.
The study is observational in design; unmeasured confounders cannot be excluded and causal inference is limited.
Generalizability is restricted to clinical settings resembling the 8 enrolled hospital sites.

Recommended Next Steps

Actionable

These follow-up analyses are pre-configured based on your results. StatGuide.AI can run them for you — your data stays loaded.

Run a multiple imputation sensitivity analysis (m = 20, seed 42) to compare primary estimates against complete-case results — recommended given the 2.6% missingness exclusion.

Add multiple imputation pre-pass (m = 20, seed 42) before logistic regression. Primary estimates compared against complete-case results.

Fit a mixed logistic regression with a random intercept for site to account for between-site clustering, which is flagged in the limitations as potentially affecting generalizability.

Upgrade to mixed logistic regression (MixedLM) with random intercept for site, replacing fixed-effect site encoding.

Software Citations

Reproducibility

If submitting to a journal, cite the packages used in your analysis. We recommend the following:

Seabold, S. & Perktold, J. (2010). statsmodels: Econometric and statistical modeling with Python. 9th Python in Science Conference.

Pedregosa, F. et al. (2011). Scikit-learn: Machine Learning in Python. JMLR 12, pp. 2825–2830.

McKinney, W. (2010). Data Structures for Statistical Computing in Python. Proc. 9th Python in Science Conf.

StatGuide.AI auto-generates a software citation block for your Methods section on export.