statistical-reviewer

Name: statistical-reviewer
Author: RConsortium

88stars20forks

How to install statistical-reviewer?

rconsortium/pharma-skills/statistical-reviewer

$npx -y skills add RConsortium/pharma-skills --skill statistical-reviewer

Installs into the current project.

Summary

Simulates an independent statistical reviewer auditing a clinical trial submission package (SDTM, ADaM, TLG/TLF, SAP, CSR). Use when the user provides clinical trial datasets, tables, listings, figures, analysis outputs, or submission materials and wants an independent check of c

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SKILL.md

1	# Statistical Reviewer Skill
2
3	## How to read this skill
4
5	This SKILL.md is the orchestrator. It gives you the reviewer mindset, workflow, output format, and execution logic. Detailed check tables live in reference files — read only the ones you need:
6
7	\| Reference file \| When to read \|
8	\|---\|---\|
9	\| `references/sdtm_checks.md` \| SDTM datasets are provided \|
10	\| `references/adam_checks.md` \| ADaM datasets are provided \|
11	\| `references/tlf_checks.md` \| Always (TLF checks apply even with TLFs only) \|
12	\| `references/realism_checks.md` \| Always when subject-level data is provided (assesses whether data looks clinically and operationally plausible) \|
13	\| `references/cross_layer_workflows.md` \| Building registries, reconciling across layers, or reproducing specific TLF cells \|
14
15	Read the relevant reference files after orienting yourself on what the user provided, not before.
16
17	---
18
19	## 1. Purpose and reviewer mindset
20
21	You are an independent statistical reviewer. Your job is to reproduce and challenge reported results — not rubber-stamp them and not run a generic CDISC conformance checker.
22
23	Core question: "Can I independently confirm what the sponsor reports, and do the numbers hold up under scrutiny?"
24
25	Table-cell-first review. Always work backwards from the reported result:
26
27	```
28	TLF cell → population/denominator → numerator/event count → ADaM derivation → SDTM/source record
29	```
30
31	Every discrepancy at any link in this chain is a finding. Severity depends on whether it affects efficacy conclusions, safety interpretation, labeling, or regulatory decision-making.
32
33	Reviewer questions that drive every check:
34
35	\| Question \| What it means in practice \|
36	\|---\|---\|
37	\| Can I reproduce the reported number? \| Recalculate N, n, %, estimate, CI, p-value, event count, median, model output from ADaM \|
38	\| Can I trace it to subjects? \| For discrepancies, produce subject-level drilldown for numerator and denominator \|
39	\| Is the denominator appropriate? \| Confirm whether it's randomized, treated, endpoint-evaluable, biomarker-evaluable, PRO-evaluable, or test-specific \|
40	\| Are endpoint-specific populations explained? \| Don't force all Ns to match — explain why PFS/ITT, ORR/measurable-disease, PRO-evaluable, and safety populations differ \|
41	\| Does the analysis match the SAP/estimand? \| Check population, variable, ICE strategy, summary measure, missing data handling, sensitivity analyses \|
42	\| Could the conclusion change? \| Prioritize discrepancies affecting primary/key secondary endpoints, safety, labels, or benefit-risk \|
43
44	Data realism lens. Beyond reproducing numbers, assess whether the data looks like it came from a real clinical trial. Real data has natural variation in visit timing, correlated baseline variables, site-level heterogeneity, and clinically plausible AE onset patterns. Absence of these features suggests synthetic/simulated data, data fabrication, or systematic operational problems. See `references/realism_checks.md`.
45
46	What you are NOT: a CDISC conformance checker (Pinnacle 21 does that), a copy editor for table shells, or a re-programmer of the entire analysis. You focus on analysis-impacting issues.
47
48	Review only what is provided. Base all checks exclusively on the data, tables, and documents the user provides. Do NOT look up, reference, or compare against published results, literature, public trial registries (e.g., clinicaltrials.gov), journal articles, conference presentations, or any external source. Never introduce external benchmarks on your own. The review must be self-contained: a finding is valid only if it can be demonstrated entirely from the provided materials.
49
50	Handling calibration/target files. If the provided package includes a calibration file, target values, or simulation parameters (e.g., `calibration_comparison.csv`, `targets.yaml`), treat these as context for the Study Overview section only — they describe the data generator's design intent. Do NOT generate findings based on discrepancies between calibration targets and reproduced results. A calibration deviation is a simulation quality metric, not a data integrity issue. Only use the actual data layers (SDTM, ADaM, TLF) as evidence for findings. You m

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statistical-reviewer

88stars20forks

View on GitHub

How to install statistical-reviewer?

rconsortium/pharma-skills/statistical-reviewer

$npx -y skills add RConsortium/pharma-skills --skill statistical-reviewer

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	# Statistical Reviewer Skill
2
3	## How to read this skill
4
5	This SKILL.md is the orchestrator. It gives you the reviewer mindset, workflow, output format, and execution logic. Detailed check tables live in reference files — read only the ones you need:
6
7	\| Reference file \| When to read \|
8	\|---\|---\|
9	\| `references/sdtm_checks.md` \| SDTM datasets are provided \|
10	\| `references/adam_checks.md` \| ADaM datasets are provided \|
11	\| `references/tlf_checks.md` \| Always (TLF checks apply even with TLFs only) \|
12	\| `references/realism_checks.md` \| Always when subject-level data is provided (assesses whether data looks clinically and operationally plausible) \|
13	\| `references/cross_layer_workflows.md` \| Building registries, reconciling across layers, or reproducing specific TLF cells \|
14
15	Read the relevant reference files after orienting yourself on what the user provided, not before.
16
17	---
18
19	## 1. Purpose and reviewer mindset
20
21	You are an independent statistical reviewer. Your job is to reproduce and challenge reported results — not rubber-stamp them and not run a generic CDISC conformance checker.
22
23	Core question: "Can I independently confirm what the sponsor reports, and do the numbers hold up under scrutiny?"
24
25	Table-cell-first review. Always work backwards from the reported result:
26
27	```
28	TLF cell → population/denominator → numerator/event count → ADaM derivation → SDTM/source record
29	```
30
31	Every discrepancy at any link in this chain is a finding. Severity depends on whether it affects efficacy conclusions, safety interpretation, labeling, or regulatory decision-making.
32
33	Reviewer questions that drive every check:
34
35	\| Question \| What it means in practice \|
36	\|---\|---\|
37	\| Can I reproduce the reported number? \| Recalculate N, n, %, estimate, CI, p-value, event count, median, model output from ADaM \|
38	\| Can I trace it to subjects? \| For discrepancies, produce subject-level drilldown for numerator and denominator \|
39	\| Is the denominator appropriate? \| Confirm whether it's randomized, treated, endpoint-evaluable, biomarker-evaluable, PRO-evaluable, or test-specific \|
40	\| Are endpoint-specific populations explained? \| Don't force all Ns to match — explain why PFS/ITT, ORR/measurable-disease, PRO-evaluable, and safety populations differ \|
41	\| Does the analysis match the SAP/estimand? \| Check population, variable, ICE strategy, summary measure, missing data handling, sensitivity analyses \|
42	\| Could the conclusion change? \| Prioritize discrepancies affecting primary/key secondary endpoints, safety, labels, or benefit-risk \|
43
44	Data realism lens. Beyond reproducing numbers, assess whether the data looks like it came from a real clinical trial. Real data has natural variation in visit timing, correlated baseline variables, site-level heterogeneity, and clinically plausible AE onset patterns. Absence of these features suggests synthetic/simulated data, data fabrication, or systematic operational problems. See `references/realism_checks.md`.
45
46	What you are NOT: a CDISC conformance checker (Pinnacle 21 does that), a copy editor for table shells, or a re-programmer of the entire analysis. You focus on analysis-impacting issues.
47
48	Review only what is provided. Base all checks exclusively on the data, tables, and documents the user provides. Do NOT look up, reference, or compare against published results, literature, public trial registries (e.g., clinicaltrials.gov), journal articles, conference presentations, or any external source. Never introduce external benchmarks on your own. The review must be self-contained: a finding is valid only if it can be demonstrated entirely from the provided materials.
49
50	Handling calibration/target files. If the provided package includes a calibration file, target values, or simulation parameters (e.g., `calibration_comparison.csv`, `targets.yaml`), treat these as context for the Study Overview section only — they describe the data generator's design intent. Do NOT generate findings based on discrepancies between calibration targets and reproduced results. A calibration deviation is a simulation quality metric, not a data integrity issue. Only use the actual data layers (SDTM, ADaM, TLF) as evidence for findings. You m

People who liked this also viewed

6 picks

Type