End-to-end clinical trial analysis workflow from CDISC SDTM/ADaM loading through ICH E9(R1) estimand-driven primary analysis to CONSORT 2025 regulatory-compliant reporting. Covers data preparation, FDA 2023 marginal vs conditional logistic regression, categorical tests with Boschloo, modern HTE/subgroup methods, missing-data sensitivity (MMRM, reference-based MI, Permutt tipping point), graphical multiplicity (Bretz-Maurer), survival analysis (Cox/RMST/competing risks) when applicable, and Table 1. Use when performing a complete analysis of clinical trial data.
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Reference examples tested with: statsmodels 0.14+, scipy 1.12+, tableone 0.9+, pyreadstat 1.2+, pandas 2.1+, numpy 1.26+, matplotlib 3.8+
Before using code patterns, verify installed versions match. If versions differ:
pip show <package> then help(module.function) to check signaturesIf code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
"Analyze my clinical trial data end to end" -> Load CDISC domain tables, prepare a subject-level analysis dataset, run primary statistical models, perform subgroup analyses, and generate regulatory-compliant tables and figures.
Complete workflow for clinical trial statistical analysis from raw data to publication-ready results.
Before executing any analysis step, establish the causal framework. For an RCT, randomization justifies causal interpretation of the primary analysis, but subgroup analyses and observational comparisons within the trial (e.g., adherence effects) do not inherit this protection. Key decisions requiring scientific judgment at each step: (1) data preparation -- which aggregation strategy matches the estimand, (2) covariate selection -- include confounders and prognostic factors from the SAP, exclude mediators and colliders, (3) subgroup analysis -- test only biologically motivated interactions, (4) missing data -- link DS domain reasons to the assumed mechanism before choosing a method. The workflow below provides the technical steps; the scientific reasoning at each decision point determines whether the results are valid.
CDISC Domain Files (DM, AE, EX, LB)
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[1. Data Preparation] ----> Subject-level dataset with outcomes and covariates
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[2. Table 1] ------------> Baseline characteristics by treatment arm
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[3. Primary Analysis] ---> Logistic regression with OR extraction
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[4. Categorical Tests] --> Chi-square / Fisher's exact for key associations
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[5. Subgroup Analysis] --> Interaction terms, stratified ORs, forest plot
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[6. Missing Data] -------> Multiple imputation sensitivity analysis
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Results tables and figures
Goal: Create a single subject-level analysis dataset from CDISC domain tables.
Approach: Load domain files, aggregate event-level data to one row per subject, merge on USUBJID, and code the outcome variable.
import pandas as pd
import pyreadstat
dm, _ = pyreadstat.read_xport('dm.xpt')
ae, _ = pyreadstat.read_xport('ae.xpt')
# Aggregate: did each subject have the target adverse event?
target_ae = ae[ae['AEDECOD'] == 'COVID-19']
severity_map = {'MILD': 1, 'MODERATE': 2, 'SEVERE': 3, 'LIFE THREATENING': 4, 'FATAL': 5}
target_ae['AESEV_NUM'] = target_ae['AESEV'].map(severity_map)
had_event = target_ae.groupby('USUBJID')['AESEV_NUM'].max().reset_index()
had_event.columns = ['USUBJID', 'EVENT_SEVERITY']
analysis = dm[['USUBJID', 'ARM', 'ARMCD', 'AGE', 'SEX']].merge(had_event, on='USUBJID', how='left')
analysis['HAD_EVENT'] = analysis['EVENT_SEVERITY'].notna().astype(int)
analysis['TREATMENT'] = (analysis['ARMCD'] != 'PLACEBO').astype(int)
QC Checkpoint: Verify one row per USUBJID, no unexpected duplicates, treatment arms are present and reasonably balanced.
assert analysis['USUBJID'].is_unique, 'Duplicate subjects detected'
print(analysis['ARM'].value_counts())
Goal: Summarize demographics and baseline variables by treatment arm.
Approach: Use TableOne to generate a baseline table by arm with standardized mean differences and explicit missingness. Omit the baseline p-value column: in a randomized trial any imbalance is by definition due to chance, so a baseline p-value tests a null already known to be true (Senn 1994; CONSORT 2010/2025). Report SMD for balance instead. Table-construction and export mechanics (gtsummary/tableone, Word export, gene-symbol-safe supplements) live in reporting/publication-tables.
from tableone import TableOne
columns = ['AGE', 'SEX', 'RACE']
categorical = ['SEX', 'RACE']
table1 = TableOne(analysis, columns=columns, categorical=categorical,
groupby='ARM', pval=False, smd=True, missing=True)
print(table1.tabulate(tablefmt='github'))
Interpret SMD > 0.1 as meaningful imbalance. The response to a worrying imbalance on a prognostic covariate is to adjust for it (a pre-specified ANCOVA/model covariate), not to test it.
Goal: Estimate the treatment effect on the binary outcome as an adjusted odds ratio.
Approach: Fit a logistic regression with explicit reference category and clinically relevant covariates, then exponentiate coefficients to obtain ORs.
import statsmodels.formula.api as smf
import numpy as np
model = smf.logit(
'HAD_EVENT ~ C(ARM, Treatment(reference="Placebo")) + AGE + C(SEX)',
data=analysis
).fit()
or_table = pd.DataFrame({
'OR': np.exp(model.params),
'Lower_CI': np.exp(model.conf_int()[0]),
'Upper_CI': np.exp(model.conf_int()[1]),
'p_value': model.pvalues
})
print(or_table)
print(f'McFadden pseudo-R2: {model.prsquared:.4f}')
QC Checkpoint: Verify model converged (no warnings), check for separation (coefficients > 10 or SE > 100), report pseudo-R-squared (McFadden > 0.2 is excellent; do not compare across pseudo-R2 types).
Goal: Test the crude association between treatment and outcome using contingency tables.
Approach: Build a 2x2 table, check expected cell counts, and choose chi-square or Fisher's exact accordingly.
from scipy.stats import chi2_contingency, fisher_exact
table = pd.crosstab(analysis['ARM'], analysis['HAD_EVENT'])
chi2, p, dof, expected = chi2_contingency(table, correction=False)
if (expected < 5).any():
_, p = fisher_exact(table.values)
print(f'Fisher exact p = {p:.4f}')
else:
print(f'Chi-square p = {p:.4f} (chi2 = {chi2:.2f}, dof = {dof})')
Goal: Test whether the treatment effect varies across pre-specified subgroups.
Approach: Fit a model with an interaction term, extract subgroup-specific ORs, adjust for multiplicity, and visualize with a forest plot.
import matplotlib.pyplot as plt
# Interaction test
interaction_model = smf.logit(
'HAD_EVENT ~ C(ARM, Treatment(reference="Placebo")) * C(SUBGROUP)',
data=analysis
).fit()
# Subgroup-specific ORs
labels, ors, lowers, uppers = [], [], [], []
for group in analysis['SUBGROUP'].unique():
sub = analysis[analysis['SUBGROUP'] == group]
sub_model = smf.logit(
'HAD_EVENT ~ C(ARM, Treatment(reference="Placebo"))',
data=sub
).fit(disp=0)
or_val = np.exp(sub_model.params.iloc[1])
ci = np.exp(sub_model.conf_int().iloc[1])
labels.append(group)
ors.append(or_val)
lowers.append(ci[0])
uppers.append(ci[1])
# Multiplicity correction for subgroup p-values
from statsmodels.stats.multitest import multipletests
sub_pvals = [smf.logit('HAD_EVENT ~ C(ARM, Treatment(reference="Placebo"))',
data=analysis[analysis['SUBGROUP'] == g]).fit(disp=0).pvalues.iloc[1]
for g in labels]
_, adjusted_pvals, _, _ = multipletests(sub_pvals, method='holm')
# Forest plot
fig, ax = plt.subplots(figsize=(8, 5))
y_pos = range(len(labels))
ax.errorbar(ors, y_pos,
xerr=[np.array(ors) - np.array(lowers), np.array(uppers) - np.array(ors)],
fmt='D', color='black', capsize=3, markersize=5)
ax.axvline(x=1.0, color='gray', linestyle='--', linewidth=0.8)
ax.set_yticks(y_pos)
ax.set_yticklabels(labels)
ax.set_xlabel('Odds Ratio (95% CI)')
ax.set_xscale('log')
plt.tight_layout()
plt.savefig('forest_plot.png', dpi=150)
QC Checkpoint: Interaction p-value reported. Multiplicity correction applied if testing multiple subgroups. Forest plot shows overall estimate for context.
Goal: Assess robustness of the primary result under the pre-specified ICE strategy with both MAR primary and MNAR sensitivity analyses.
Approach: First examine DS (Disposition) domain for differential dropout patterns; if dropout differs by arm, MAR is suspect and reference-based MI is required as primary. Otherwise, fit MMRM under MAR with Rubin's-rules pooling for continuous endpoints, or g-computation with bootstrap for binary. Always run Permutt 2016 tipping-point sensitivity in residual SD units.
from sklearn.experimental import enable_iterative_imputer
from sklearn.impute import IterativeImputer
n_imputations = 20
covariate_cols = ['AGE'] # Only impute covariates, not treatment or outcome
mi_data = analysis.dropna(subset=['HAD_EVENT', 'TREATMENT']).copy()
results = []
for i in range(n_imputations):
imputer = IterativeImputer(max_iter=10, random_state=i, sample_posterior=True)
imputed_cov = pd.DataFrame(imputer.fit_transform(mi_data[covariate_cols]),
columns=covariate_cols, index=mi_data.index)
imputed_cov['HAD_EVENT'] = mi_data['HAD_EVENT'].values
imputed_cov['TREATMENT'] = mi_data['TREATMENT'].values
model_imp = smf.logit('HAD_EVENT ~ TREATMENT + AGE', data=imputed_cov).fit(disp=0)
results.append({'coef': model_imp.params['TREATMENT'], 'se': model_imp.bse['TREATMENT']})
pooled_coef = np.mean([r['coef'] for r in results])
within_var = np.mean([r['se']**2 for r in results])
between_var = np.var([r['coef'] for r in results], ddof=1)
total_var = within_var + (1 + 1/n_imputations) * between_var
pooled_or = np.exp(pooled_coef)
pooled_ci = (np.exp(pooled_coef - 1.96 * np.sqrt(total_var)),
np.exp(pooled_coef + 1.96 * np.sqrt(total_var)))
print(f'Pooled OR: {pooled_or:.3f} ({pooled_ci[0]:.3f}-{pooled_ci[1]:.3f})')
QC Checkpoint: Compare pooled OR and CI with the complete-case primary analysis. Large discrepancies suggest missing data may not be MCAR. Document the comparison.
This pipeline covers the typical binary-endpoint RCT workflow. For specific designs, add the corresponding specialized skill:
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