Distribution of symptom change

Explore the executed results

Selection switches exact precomputed results. It changes the figure, denominators, table, summary and current-selection downloads together. No analysis runs in this browser.

Python · All participants · 34 computed rows. Review population counts and exact values in the table below.

Image unavailable. Use the numerical table and downloads below.

CSV · all three strata (overlapping; do not sum)

Values behind this selection

The table scrolls within this panel. All computed rows are included in the current-selection CSV and JSON.

Display rounded to three decimals. Downloaded CSV/JSON retains analysis precision. “Unavailable” is not zero.

Why this figure, and what it estimates

An empirical cumulative distribution keeps every complete-pair change without histogram bins or a density bandwidth. At any x, read the fraction whose change is at most x. Negative values mean less burden on this invented scale.

Analysis contract

Week-12 minus baseline Fatigue, among complete pairs in each arm and selected sex stratum. SciPy stats.ecdf and R stats::ecdf are run independently. Ties share a jump. No confidence band, imputation, censoring adjustment or multiplicity claim.

Assigned fictional arms; baseline = scheduled Week 0. No visit windows or treatment switching. Subjects are independent; domain scores are invented 0–100 points. Missing outcomes stay missing; missing scheduled rows are rejected.

Full input, units, missingness, interval, rounding and tolerance specification

Do not overinterpret

This describes observed complete pairs, not the randomized-population treatment effect. Missing pairs are not censored observations. A taller curve at a negative threshold does not establish clinical benefit or significance.

Alternative views

Use an adjusted mean treatment contrast for inference; use a quantile plot to emphasize the middle and tails. A histogram may be more familiar but depends on bins.

Read, reproduce, then adapt

Four-step review guide · Compare complementary figures

Select Female, then inspect complete-pair and roster counts. At change zero, count the exported raw pairs with change <= 0 and divide by the complete-pair n. Explain why the answer cannot be called a responder rate without a prespecified meaningful threshold.

1. Open the selected CSV and reconcile counts before comparing outcomes.
2. Run the minimal example locally, then inspect the native Figure or ggplot object.
3. For your own authorized data, explicitly map subject, arm, scheduled time and units. Keep expected missing visits as rows, document exclusions, and revise the contract for your trial design.
4. Run invalid-input and known-answer checks before comparing R/Python outputs.

Cross-domain adaptation notes and clinical coverage matrix

Executed source and separate QC layers

Original author: Jaime Yan. Personal noncommercial use only. License · Required citation · Upstream attribution

Python reusable implementation · R reusable implementation · Complete reproducible source bundle

Extract the complete bundle and run these commands from its root. Recorded environments: Python · R.

python examples/extensions.py ecdf All
Rscript --vanilla examples/extensions.R ecdf All
python scripts/render_extensions.py
Rscript --vanilla scripts/render_extensions.R
Rscript --vanilla scripts/verify_extensions.R
python scripts/verify_extensions.py

Numerical validation

670 values across three templates; max absolute difference 5.329e-14. Tolerance 1e-9 absolute/relative; counts exact.

Data QC

Keys, schedules, units, finite ranges, empty and sparse populations tested.

Export QC

CSV/JSON selection, parsed SVG, PNG dimensions, readable PDF text and Python text bounds checked.

Package-risk assessment

Not run. Numerical agreement is not package-risk or regulatory validation.

Browser evidence

Recorded separately in the local delivery report; no accessibility certification claimed.

Machine-readable numerical QC · Source, input and artifact hashes

Input: the library's original synthetic generator, seed 20260921, 36 fictional participants. Separate from CDISC Pilot. The deterministic missingness mechanism is described in the contract. No patient or employer records.