bio-clinical-biostatistics-trial-reporting

What this skill does

## Version Compatibility

Reference examples tested with: tableone 0.9+, statsmodels 0.14+, scikit-learn 1.4+, pandas 2.1+, numpy 1.26+. R packages cited (essential for current regulatory work): mmrm 0.3+ (Roche/openpharma), rbmi 1.5+ (Roche/Bayer via insightsengineering), gMCP, RBesT.

Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- R: `packageVersion('<pkg>')` then `?function_name`

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

# Trial Reporting Under CONSORT 2025 + ICH E9(R1)

**"Prepare a clinical trial statistical report"** -> Define the estimand explicitly per ICH E9(R1); execute a covariate-adjusted primary analysis targeting the right summary measure; pre-specify the missing-data strategy and run regulatory-grade sensitivity analyses; structure the output per CONSORT 2025 and the new SPIRIT 2025 alignment.

## The Single Most Important Methodological Shift -- The Estimand Comes First

**Kahan, Cro, Li, Harhay 2023 *Am J Epidemiol* 192:987 ("Eliminating Ambiguous Treatment Effects Using Estimands"):** 98% of published trial reports do not describe what the reported treatment effect represents. 54% of trials: impossible to deduce the estimand from reported methods. In 74% of trials submitted for regulatory approval 1996-2017, "what-if" hypothetical effects were used but only 2 trials explained this.

**The framework:** ICH E9(R1) Addendum (November 2019, EMA effective Feb 2020, FDA May 2021) defines an estimand as the precise specification of what is being estimated, via five attributes:

1. **Treatment condition** -- what is being compared
2. **Population** -- which patients
3. **Endpoint** -- which variable
4. **Population-level summary measure** -- mean diff, OR, HR, RD
5. **Intercurrent-event (ICE) handling strategy** -- one of five

**The order is non-negotiable:** specify the estimand BEFORE choosing the statistical method. Choosing MMRM and retrofitting the estimand to match is the canonical error.

## The Five Intercurrent-Event Strategies

| Strategy | What it does | Typical implementation | Identification cost | Regulatory pattern |
|----------|--------------|------------------------|---------------------|---------------------|
| **Treatment policy** | Include all data regardless of ICE | ANCOVA on observed value (retrieved-dropout data); ITT-like | Trivially identified; needs full follow-up regardless of ICE | FDA-preferred default for cardio/HF/CV-safety; Fleming 2025 endorsement |
| **Hypothetical** | What would have been observed had ICE not occurred | MMRM under MAR; g-computation; IPCW; reference-based MI under MAR | Sequential ignorability (causal); MAR (missing-data shorthand) | Heavy use in CNS, diabetes, respiratory; EMA more accepting than FDA |
| **Composite** | Incorporate ICE into endpoint | Death = non-responder; PFS = composite of progression OR death; MACE | Identified from observed data; embeds a ranking choice | Standard in oncology PFS; acceptable when ICE has clinical signal |
| **While-on-treatment** | Use only pre-ICE values | Censor at discontinuation (for TTE); analyse last pre-ICE value (for repeated measures) | Estimates conditional quantity | Safety endpoints (AE rate per time on drug); FDA cautious for efficacy |
| **Principal stratum** | Confine to latent stratum (e.g. tolerators) | Bayesian estimation under monotonicity/principal ignorability | Latent membership; unverifiable assumptions | Rare as primary; some oncology/vaccine acceptance |

**Postdoc reading list:**

- Bornkamp, Rufibach, Lin, Liu, Mehrotra, Roychoudhury, Schmidli, Shentu, Wolbers 2021 *Pharm Stat* 20:737 — principal stratum critique
- Olarte Parra, Bartlett, Daniel 2022 *Stat Biopharm Res* — proves MMRM-MAR IS a hypothetical estimator under specific causal assumptions
- Permutt 2016 *Stat Med* 35:2865; 2020 *Stat Biopharm Res* 12:45 — FDA Missing Data Working Group taxonomy; "do covariates change the estimand?"
- Fleming, Carroll, Wittes et al 2025 *Stat Med* — argue treatment policy is the only strategy preserving randomisation; critique hypothetical
- Morris 2026 *Stat Med* — causal-perspective comment on Fleming
- Lipkovich, Ratitch, Mallinckrodt 2020 *Stat Biopharm Res* — Rubin causal model connection

## Decision Tree for Estimand Selection

| Trial scenario | Recommended estimand strategy | Why |
|----------------|-------------------------------|-----|
| Continuous endpoint, monotone missingness, MAR plausible | Hypothetical via MMRM (mmrm + KR) | Standard FDA-favoured MAR analysis; cite Mallinckrodt 2008/2014 |
| Continuous endpoint, ICE = treatment discontinuation, sponsor wants effectiveness | Treatment policy via retrieved-dropout MI | If post-ICE data available; ITT-respecting |
| Continuous endpoint, treatment-policy primary with ICE-related missingness | Hybrid: J2R for discontinuation ICEs, MMRM-MAR for other missingness | Aprocitentan precedent; de facto FDA standard 2024-2025 |
| Binary endpoint, RCT, FDA 2023-compliant | Marginal RD via g-computation; conditional OR supportive | See clinical-biostatistics/logistic-regression for g-computation |
| Oncology OS with crossover | Treatment policy as primary; hypothetical (RPSFT/IPCW) as sensitivity | Sotorasib CodeBreaK 200 precedent |
| Oncology PFS | Treatment policy with composite for subsequent therapy ICE | Lewis 2023 framework; Fleming 2025 |
| Weight management / chronic disease | Retrieved-dropout MI (Wegovy STEP precedent); J2R supportive | FDA 2025 obesity draft guidance explicitly endorses |
| Long-term safety endpoint | While-on-treatment for rate; treatment policy for cumulative incidence | Standard ICH E2A practice |
| AlloSCT in hematologic oncology | Composite "event-free survival" treating alloSCT as event | Rufibach 2020; "no alloSCT" hypothetical is clinically meaningless |
| Symptomatic palliative endpoint with high dropout | Composite with worst-rank for dropouts (Permutt trimmed means) | Permutt 2017 *Pharm Stat* 16:20 |

## MMRM -- The FDA-Favoured MAR Analysis

**Mallinckrodt 2008/2014, codified in DIA Scientific Working Group "three pillars" doctrine:** for continuous longitudinal endpoints under monotone (or near-monotone) MAR, an MMRM with treatment + visit + treatment-by-visit + baseline + baseline-by-visit, unstructured (UN) within-subject covariance, REML, contrast at the primary timepoint -- is the consistent and FDA-preferred analysis. LOCF is biased even under MCAR because it discards imputation uncertainty and assumes a flat post-withdrawal trajectory.

### The mmrm R package (Roche / openpharma)

```r
library(mmrm)
fit <- mmrm(
    formula = change_from_baseline ~ baseline + arm * visit + us(visit | subject),
    data = trial_data,
    method = "Kenward-Roger",  # or "Satterthwaite", "Kenward-Roger-Linear"
    reml = TRUE
)
summary(fit)  # treatment-by-visit contrast at primary timepoint
```

**The Kenward-Roger flavour question:** `method = "Kenward-Roger"` uses full second-order Kenward-Roger (Kenward-Roger 1997 *Biometrics* 53:983), which inflates SE for fixed-effect contrasts using an adjusted covariance estimator with second-order Taylor terms. **`method = "Kenward-Roger-Linear"` drops the second-order Cholesky-derivative term to match SAS PROC MIXED bit-for-bit.** Most submissions use Kenward-Roger-Linear to maintain SAS-R reproducibility.

### Convergence-vs-correctness trade-off

Unstructured (UN) covariance has p(p+1)/2 parameters for p visits. With ~30-50 patients per arm by week 12 and 6+ visits, UN can fail to converge. The industry-standard fallback hierarchy per pre-specified SAP:

1. UN with KR (preferred)
2. UN with Satterthwaite (if KR fails)
3. Heterogeneous Toeplitz (k+1 parameters)
4. AR(1) with heterogeneous variances
5. CS with heterogeneous variances (last resort)

Each step down imposes m