bio-workflows-clinical-trial-pipeline

What this skill does

## Version Compatibility

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:
- Python: `pip show <package>` then `help(module.function)` to check signatures

If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.

# Clinical Trial Analysis Pipeline

**"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.

### Scientific Reasoning Framework

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.

## Workflow Overview

```
CDISC Domain Files (DM, AE, EX, LB)
    |
    v
[1. Data Preparation] ----> Subject-level dataset with outcomes and covariates
    |
    v
[2. Table 1] ------------> Baseline characteristics by treatment arm
    |
    v
[3. Primary Analysis] ---> Logistic regression with OR extraction
    |
    v
[4. Categorical Tests] --> Chi-square / Fisher's exact for key associations
    |
    v
[5. Subgroup Analysis] --> Interaction terms, stratified ORs, forest plot
    |
    v
[6. Missing Data] -------> Multiple imputation sensitivity analysis
    |
    v
Results tables and figures
```

## Step 1: Data Preparation

**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.

```python
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.

```python
assert analysis['USUBJID'].is_unique, 'Duplicate subjects detected'
print(analysis['ARM'].value_counts())
```

## Step 2: Table 1 Baseline Characteristics

**Goal:** Summarize demographics and baseline variables by treatment arm.

**Approach:** Use TableOne to generate a publication-ready table with p-values and standardized mean differences.

```python
from tableone import TableOne

columns = ['AGE', 'SEX', 'RACE']
categorical = ['SEX', 'RACE']
table1 = TableOne(analysis, columns=columns, categorical=categorical,
                  groupby='ARM', pval=True, smd=True, missing=True)
print(table1.tabulate(tablefmt='github'))
```

Interpret SMD > 0.1 as meaningful imbalance rather than relying on p-values, which test whether randomization worked (a known mechanism, not a hypothesis).

## Step 3: Primary Analysis -- Logistic Regression

**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.

```python
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).

## Step 4: Categorical Tests

**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.

```python
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})')
```

## Step 5: Subgroup Analysis

**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.

```python
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.

## Step 6: Missing Data Sensitivity Analysis (per ICH E9(R1) and clinical-biostatistics/missing-data-sensitivity)

**Goal:** Assess robustness of the primary result under the pre-specified ICE strategy with both MAR primary and MNAR sensi