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Tools Reference: Statistical Modeling Skill

Core Python Packages (Computational)

This skill is primarily computational. The statistical modeling is done with Python packages, not ToolUniverse API tools.

statsmodels (Primary statistical modeling)

Function Purpose Key Parameters
smf.ols(formula, data) OLS linear regression formula (R-style), data (DataFrame)
smf.logit(formula, data) Binary logistic regression formula, data
smf.mnlogit(formula, data) Multinomial logistic formula, data
smf.mixedlm(formula, data, groups) Linear mixed-effects formula, data, groups, re_formula
smf.gee(formula, groups, data, family) GEE models formula, groups, data, family
sm.OLS(y, X) OLS (matrix interface) y (array), X (array with constant)
sm.Logit(y, X) Logistic (matrix) y (binary), X (array)
sm.MNLogit(y, X) Multinomial logistic (matrix) y (coded), X (array)
OrderedModel(y, X, distr) Ordinal logistic y (codes), X (array), distr='logit'

statsmodels diagnostics

Function Purpose Returns
het_breuschpagan(resid, exog) Heteroscedasticity test (LM, p, F, Fp)
durbin_watson(resid) Autocorrelation test DW statistic
variance_inflation_factor(X, i) Multicollinearity VIF value
shapiro(resid) Normality test (W, p)

lifelines (Survival analysis)

Class/Function Purpose Key Parameters
CoxPHFitter() Cox PH model .fit(df, duration_col, event_col)
KaplanMeierFitter() KM estimation .fit(durations, event_observed)
logrank_test(T1, T2, E1, E2) Log-rank test durations and events for 2 groups
NelsonAalenFitter() Cumulative hazard .fit(durations, event_observed)

scipy.stats (Statistical tests)

Function Purpose Returns
ttest_ind(a, b) Independent t-test (t, p)
ttest_rel(a, b) Paired t-test (t, p)
mannwhitneyu(a, b) Mann-Whitney U (U, p)
chi2_contingency(table) Chi-square test (chi2, p, dof, expected)
fisher_exact(table) Fisher's exact test (OR, p)
f_oneway(*groups) One-way ANOVA (F, p)
kruskal(*groups) Kruskal-Wallis (H, p)
wilcoxon(a, b) Wilcoxon signed-rank (W, p)
shapiro(data) Shapiro-Wilk normality (W, p)

scikit-learn (Supplementary)

Class Purpose Key Methods
LogisticRegression(multi_class='multinomial') Multinomial logistic .fit(X, y), .predict_proba(X)
StandardScaler() Feature scaling .fit_transform(X)
LabelEncoder() Label encoding .fit_transform(y)

ToolUniverse Integration Tools (Data Retrieval)

These ToolUniverse tools can be used to retrieve data before modeling:

Clinical Trial Data

Tool Parameters Returns
clinical_trials_search action="search_studies", condition, intervention, limit {total_count, studies}
get_clinical_trial_eligibility_criteria nct_ids (array), eligibility_criteria="all" [{NCT ID, eligibility_criteria}]

Drug Safety / Adverse Events

Tool Parameters Returns
FAERS_calculate_disproportionality drug_name, adverse_event {metrics: {PRR, ROR, IC}, signal_detection}
FAERS_stratify_by_demographics drug_name, adverse_event, stratify_by Stratified counts
FAERS_count_patient_reaction medicinalproduct [{term, count}]

Gene-Disease Evidence

Tool Parameters Returns
OpenTargets_target_disease_evidence ensemblId, efoId Evidence scores
OpenTargets_get_associated_targets_by_disease_efoId efoId, size {data: {disease: {associatedTargets}}}

Literature

Tool Parameters Returns
PubMed_search_articles query, max_results List of article dicts

Model Selection Decision Tree

Outcome Type?
|
|-- Continuous (numeric, wide range)
|   |-- Independent observations -> OLS (smf.ols)
|   |-- Clustered/repeated -> LMM (smf.mixedlm)
|
|-- Binary (0/1, yes/no)
|   |-- Independent observations -> Logistic (smf.logit)
|   |-- Clustered/repeated -> GEE Logistic (smf.gee)
|
|-- Ordinal (ordered categories, 3+ levels)
|   |-- Independent observations -> OrderedModel (distr='logit')
|   |-- If proportional odds violated -> Multinomial logistic
|
|-- Nominal (unordered categories, 3+ levels)
|   |-- -> Multinomial logistic (sm.MNLogit)
|
|-- Time-to-event (survival)
|   |-- With covariates -> Cox PH (CoxPHFitter)
|   |-- Descriptive / curves -> Kaplan-Meier (KaplanMeierFitter)
|   |-- Group comparison -> Log-rank test
|
|-- Count (integer, events)
|   |-- -> Poisson or Negative Binomial (smf.poisson / smf.negativebinomial)

Common Pitfalls

  1. OrderedModel coefficient sign: In statsmodels OrderedModel, positive coefficient = higher odds of being in HIGHER category (same direction as R's polr with method="logistic")
  2. Formula syntax: Use C(variable) for categorical variables in formulas, : for interaction, * for main effects + interaction
  3. Reference levels: First level alphabetically is default reference. Use C(var, Treatment(reference='level')) to set explicitly
  4. Convergence: OrderedModel may need method='bfgs' and maxiter=200+; logistic regression use maxiter=100 and disp=0
  5. Missing data: statsmodels formula API drops NA rows automatically; matrix API does not
  6. Odds ratio direction: OR > 1 = increased odds, OR < 1 = decreased odds, OR = 1 = no effect
  7. Hazard ratio direction: HR > 1 = increased hazard (worse survival), HR < 1 = decreased hazard (better survival)