Statistical Modeling for Biomedical Data Analysis

Comprehensive statistical modeling skill for fitting regression models, survival models, and mixed-effects models to biomedical data. Produces publication-quality statistical summaries with odds ratios, hazard ratios, confidence intervals, and p-values.

COMPUTE, DON'T DESCRIBE

Write and run Python code (via Bash) for every statistical analysis. Never describe what you "would do" — do it. Use pandas for data wrangling, statsmodels for regression, scipy for tests, and matplotlib for plots. Execute the code and report actual numbers (β, p-value, CI, N).

LOOK UP, DON'T GUESS

When uncertain about any scientific fact, SEARCH databases first rather than reasoning from memory.

Features

• Linear Regression - OLS for continuous outcomes with diagnostic tests
• Logistic Regression - Binary, ordinal, and multinomial models with odds ratios
• Survival Analysis - Cox proportional hazards and Kaplan-Meier curves
• Mixed-Effects Models - LMM/GLMM for hierarchical/repeated measures data
• ANOVA - One-way/two-way ANOVA, per-feature ANOVA for omics data
• Model Diagnostics - Assumption checking, fit statistics, residual analysis
• Statistical Tests - t-tests, chi-square, Mann-Whitney, Kruskal-Wallis, etc.

When to Use

Apply this skill when user asks:

• "What is the odds ratio of X associated with Y?"
• "What is the hazard ratio for treatment?"
• "Fit a linear regression of Y on X1, X2, X3"
• "Perform ordinal logistic regression for severity outcome"
• "What is the Kaplan-Meier survival estimate at time T?"
• "What is the percentage reduction in odds ratio after adjusting for confounders?"
• "Run a mixed-effects model with random intercepts"
• "Compute the interaction term between A and B"
• "What is the F-statistic from ANOVA comparing groups?"
• "Test if gene/miRNA expression differs across cell types"

Model Selection Decision Tree

START: What type of outcome variable?
|
+-- CONTINUOUS (height, blood pressure, score)
|   +-- Independent observations -> Linear Regression (OLS)
|   +-- Repeated measures -> Mixed-Effects Model (LMM)
|   +-- Count data -> Poisson/Negative Binomial
|
+-- BINARY (yes/no, disease/healthy)
|   +-- Independent observations -> Logistic Regression
|   +-- Repeated measures -> Logistic Mixed-Effects (GLMM/GEE)
|   +-- Rare events -> Firth logistic regression
|
+-- ORDINAL (mild/moderate/severe, stages I/II/III/IV)
|   +-- Ordinal Logistic Regression (Proportional Odds)
|
+-- MULTINOMIAL (>2 unordered categories)
|   +-- Multinomial Logistic Regression
|
+-- TIME-TO-EVENT (survival time + censoring)
    +-- Regression -> Cox Proportional Hazards
    +-- Survival curves -> Kaplan-Meier

Workflow

Phase 0: Data Validation

Goal: Load data, identify variable types, check for missing values.

CRITICAL: Identify the Outcome Variable First

Before any analysis, verify what you're actually predicting:

1. Read the full question - Look for "predict [outcome]", "model [outcome]", or "dependent variable"
2. Examine available columns - List all columns in the dataset
3. Match question to data - Find the column that matches the described outcome
4. Verify outcome exists - Don't create outcome variables from predictors

Common mistake: Question mentions "obesity" -> Assumed outcome = BMI >= 30 (circular logic with BMI predictor). Always check data columns first: print(df.columns.tolist())

import pandas as pd
import numpy as np

df = pd.read_csv('data.csv')
print(f"Observations: {len(df)}, Variables: {len(df.columns)}, Missing: {df.isnull().sum().sum()}")

for col in df.columns:
    n_unique = df[col].nunique()
    if n_unique == 2:
        print(f"{col}: binary")
    elif n_unique <= 10 and df[col].dtype == 'object':
        print(f"{col}: categorical ({n_unique} levels)")
    elif df[col].dtype in ['float64', 'int64']:
        print(f"{col}: continuous (mean={df[col].mean():.2f})")

Phase 1: Model Fitting

Goal: Fit appropriate model based on outcome type.

Use the decision tree above to select model type, then refer to the appropriate reference file for detailed code:

• Linear Regression: references/linear_models.md
• Logistic Regression (binary): references/logistic_regression.md
• Ordinal Logistic: references/ordinal_logistic.md
• Cox Proportional Hazards: references/cox_regression.md
• ANOVA / Statistical Tests: anova_and_tests.md

Quick reference for key models:

import statsmodels.formula.api as smf
import numpy as np

# Linear regression
model = smf.ols('outcome ~ predictor1 + predictor2', data=df).fit()

# Logistic regression (odds ratios)
model = smf.logit('disease ~ exposure + age + sex', data=df).fit(disp=0)
ors = np.exp(model.params)
ci = np.exp(model.conf_int())

# Cox proportional hazards
from lifelines import CoxPHFitter
cph = CoxPHFitter()
cph.fit(df[['time', 'event', 'treatment', 'age']], duration_col='time', event_col='event')
hr = cph.hazard_ratios_['treatment']

Phase 1b: ANOVA for Multi-Feature Data

When data has multiple features (genes, miRNAs, metabolites), use per-feature ANOVA (not aggregate). This is the most common pattern in genomics.

See anova_and_tests.md for the full decision tree, both methods, and worked examples.

Default for gene expression data: Per-feature ANOVA (Method B).

Phase 2: Model Diagnostics

Goal: Check model assumptions and fit quality.

Key diagnostics by model type:

• OLS: Shapiro-Wilk (normality), Breusch-Pagan (heteroscedasticity), VIF (multicollinearity)
• Cox: Proportional hazards test via cph.check_assumptions()
• Logistic: Hosmer-Lemeshow, ROC/AUC

See references/troubleshooting.md for diagnostic code and common issues.

Phase 3: Interpretation

Goal: Generate publication-quality summary.

For every result, report: effect size (OR/HR/coefficient), 95% CI, p-value, and model fit statistic. See bixbench_patterns_summary.md for common question-answer patterns.

Common BixBench Patterns

Pattern	Question Type	Key Steps
1	Odds ratio from ordinal regression	Fit OrderedModel, exp(coef)
2	Percentage reduction in OR	Compare crude vs adjusted model
3	Interaction effects	Fit A * B, extract A:B coef
4	Hazard ratio	Cox PH model, exp(coef)
5	Multi-feature ANOVA	Per-feature F-stats (not aggregate)

See bixbench_patterns_summary.md for solution code for each pattern. See references/bixbench_patterns.md for 15+ detailed question patterns.

Statsmodels vs Scikit-learn

Use Case	Library	Reason
Inference (p-values, CIs, ORs)	statsmodels	Full statistical output
Prediction (accuracy, AUC)	scikit-learn	Better prediction tools
Mixed-effects models	statsmodels	Only option
Regularization (LASSO, Ridge)	scikit-learn	Better optimization
Survival analysis	lifelines	Specialized library

General rule: Use statsmodels for BixBench questions (they ask for p-values, ORs, HRs).

Python Package Requirements

statsmodels>=0.14.0
scikit-learn>=1.3.0
lifelines>=0.27.0
pandas>=2.0.0
numpy>=1.24.0
scipy>=1.10.0

Key Principles

1. Data-first approach - Always inspect and validate data before modeling
2. Model selection by outcome type - Use decision tree above
3. Assumption checking - Verify model assumptions (linearity, proportional hazards, etc.)
4. Complete reporting - Always report effect sizes, CIs, p-values, and model fit statistics
5. Confounder awareness - Adjust for confounders when specified or clinically relevant
6. Reproducible analysis - All code must be deterministic and reproducible
7. Robust error handling - Graceful handling of convergence failures, separation, collinearity
8. Round correctly - Match the precision requested (typically 2-4 decimal places)

Reasoning Framework for Result Interpretation

Evidence Grading

Grade	Criteria	Example
Strong	p < 0.001, effect size clinically meaningful, model assumptions met	OR = 3.5 (95% CI: 2.1-5.8), p < 0.001, Hosmer-Lemeshow p > 0.05
Moderate	p < 0.05, reasonable effect size, minor assumption concerns	HR = 1.8 (95% CI: 1.1-2.9), p = 0.02, borderline PH test
Weak	p < 0.05 but wide CI, small effect, or assumption violations	OR = 1.2 (95% CI: 1.01-1.43), p = 0.04, VIF > 5 for a covariate
Insufficient	p >= 0.05, or model fails convergence/diagnostics	Non-significant coefficient with model separation warning

Interpretation Guidance

• Model diagnostics (R-squared): For OLS, R-squared > 0.7 indicates good fit in biomedical data; 0.3-0.7 is moderate. Adjusted R-squared penalizes added predictors. For logistic models, use pseudo-R-squared (McFadden > 0.2 is acceptable) and AUC (> 0.7 = acceptable, > 0.8 = good discrimination).
• AIC/BIC for model comparison: Lower is better. AIC difference > 10 between models is strong evidence for the lower-AIC model. BIC penalizes complexity more heavily than AIC, preferring simpler models. Use AIC for prediction-focused selection, BIC for inference.
• Coefficient significance thresholds: Report exact p-values rather than just significance stars. For multiple predictors, apply Bonferroni or FDR correction. A coefficient with p = 0.049 in a model with 20 predictors is likely a false positive without correction.
• Survival analysis HR interpretation: HR > 1 means increased hazard (worse outcome) for the exposed group. HR = 2.0 means twice the instantaneous risk of the event. Always verify the proportional hazards assumption -- if violated, the HR is an average over time and may be misleading. Report median survival times alongside HRs for clinical interpretability.
• Odds ratio interpretation: OR = 1.0 means no association. OR > 1 indicates increased odds. The 95% CI excluding 1.0 confirms significance. For rare outcomes, OR approximates relative risk; for common outcomes (> 10% prevalence), OR overstates the relative risk.
• Confounding assessment: Compare crude vs adjusted ORs/HRs. A change > 10% in the effect estimate after adjusting for a covariate suggests confounding by that variable.

Synthesis Questions

1. Do the model diagnostics (residual plots, Hosmer-Lemeshow, PH test) support the validity of the chosen model, or do assumption violations require alternative approaches (e.g., robust standard errors, stratified models)?
2. For adjusted models, does the inclusion of confounders change the primary effect estimate by more than 10%, indicating meaningful confounding?
3. Are the reported effect sizes (OR, HR, coefficients) clinically meaningful in addition to being statistically significant, considering the scale of the predictor and outcome?
4. When comparing nested models via AIC/BIC, does the more complex model provide substantially better fit, or is the simpler model preferred by parsimony?
5. For survival analysis, is the proportional hazards assumption met throughout the follow-up period, or do Schoenfeld residuals suggest time-varying effects?

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Completeness Checklist

Before finalizing any statistical analysis:

• Outcome variable identified: Verified which column is the actual outcome
• Data validated: N, missing values, variable types confirmed
• Multi-feature data identified: If multiple features, use per-feature approach
• Model appropriate: Outcome type matches model family
• Assumptions checked: Relevant diagnostics performed
• Effect sizes reported: OR/HR/Cohen's d with CIs
• P-values reported: With appropriate correction if needed
• Model fit as