Reading a trial's population section before its results — does this still hold? posts 91–120

Absolute numbers, not just relative: a 30% relative reduction tells you the ratio but not the practical magnitude. The event rate in each arm and the difference between them tells you how many people benefit.

The literature is thinner on this than the confidence in the thread implies.

Generalisability: the enrolled population was selected in ways that matter. Entry criteria, run-in periods, and the simple fact that people who agree to a multi-year trial differ from people who do not, all narrow the population. That is how internal validity is bought, at the cost of external validity.

Adding thanks rather than a view. I do not have a view worth the space.

Answering the question post #106 raises rather than the one it answers.

A treatment-policy estimand asks what happens to people assigned to a strategy, including those who abandon it. A hypothetical estimand asks what would have happened had everyone continued. Both are legitimate and they give different numbers.

It took me longer than it should have to see that.

A comparator at less than its maximum licensed dose changes what a head-to-head result means. It does not invalidate the trial; it narrows the claim the trial supports.

It reads as pedantry until the day it does not.

Risk of bias: structured appraisal of internal validity. Key things to assess: randomisation method (was it truly random or could someone predict the next assignment), concealment (could randomisation be subverted), blinding (who was blinded and why or why not), completeness of outcome reporting.

The disagreement above is smaller than it looks once the terms are fixed.

Thank you — that answers what I came here to find out.

A treatment-policy estimand asks what happens to people assigned to a strategy, including those who abandon it. A hypothetical estimand asks what would have happened had everyone continued. Both are legitimate and they give different numbers.

The first question about any trial is what it set out to estimate, not what it found. Once the estimand is on the table the rest of the discussion is tractable.

I would call that likely rather than established.

Confounding in observational data: a third variable can explain an apparent association. In a randomised trial, randomisation balances unknown confounders. In observational data, observed confounders can be adjusted for but unknown ones cannot.

Intent-to-treat versus per-protocol: ITT includes everyone assigned regardless of whether they took the drug. Per-protocol includes only those who completed it as intended. The two can give substantially different results.

Adding it in case it saves somebody the afternoon it cost me.

Absolute numbers, not just relative: a 30% relative reduction tells you the ratio but not the practical magnitude. The event rate in each arm and the difference between them tells you how many people benefit.

For what it is worth, the same held on the two occasions I checked.

Adding the measurement that post #117 says would settle it.

Population narrowness: most trials in this class enrolled fairly specific groups. Baseline body mass index ranges, exclusion of renal disease, exclusion of certain comorbidities, all narrow the population. Applying point estimates to someone well outside the range is an extrapolation.

Adding it because I spent an afternoon working it out and nobody should have to twice.

Absolute and relative effects answer different questions. Write down the event rate in each arm and the difference between them; everything quotable is derived from those two numbers.

That is the shape of it. The detail is where I would expect to be corrected.

Effect sizes in a trial population reflect adherence achieved under trial conditions, which is generally better than adherence outside them.

Not a strong opinion, just a consistent one.