Topic summary

Adjudicated events and why the definition matters — the long version

Evidence › Trials intention to treat estimand randomised trial discontinuation & dropout

This is a generated summary. It shows the 9 most-liked posts from a topic of 147, in their original order, with the accepted answer included where one exists. It is a reading aid and it will miss nuance — the full topic is the record.

k.asanteTL2 Moderator26 Feb 2025 · edited#25

Open-label design: unblinded trials admit expectation effects. For weight-loss trials where one arm loses substantial weight and the other does not, complete blinding is impossible anyway. The unblinded nature is a limitation worth noting.

30 likes 17mo

e.kuuselaTL2 Moderator3 Mar 2025#31

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.

32 likes 17mo

d.barrosTL2 Moderator17 Mar 2025 · edited#51

Thank you for the correction. I have edited my earlier post with a note rather than silently, so the thread still makes sense to read. The error was mine and it was the kind that comes from remembering a figure instead of looking it up.

29 likes 16mo

p.dialloTL2 Moderator25 Mar 2025#65

j.steiner, post #1: Adjudicated events and why the definition matters — the long version Writing it up because I had to work it out twice and would rather nobody else did. Comparing SURPASS-4 ( Lancet , 2021) with STEP 4 ( JAMA , 2021) and finding the comparison harder than it looks. Different populations, different durations, different endpoints defined… Go to post

On post #61 — agreed on the reasoning, with one qualification.

29 likes in reply to #1 16mo

k.redgraveTL2Member3 Apr 2025#79

i.boateng, post #3: Dropout is information: high dropout rates can indicate tolerability problems or lower efficacy than the summary suggests. Where the analysis handled dropouts matters. An intention-to-treat analysis with many dropouts can give a smaller apparent effect than per-protocol analysis. Go to post

This follows post #76 rather than contradicting it.

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.

31 likes in reply to #3 16mo

SHermansenTL2Member13 Apr 2025#97

30 likes 15mo

IHollingworthTL2Member22 Apr 2025#113

t.vasquez, post #57: 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. Go to post

Worth separating two things that post #109 runs together.

Having read the exchange above, I think I was wrong earlier in this topic and I want to say so plainly rather than quietly editing.

The correction was fair and I had been repeating something I had not checked carefully enough.

31 likes in reply to #57 15mo

p.krastevTL2 Moderator26 Apr 2025#120

erratum_file, post #75: Open-label design: unblinded trials admit expectation effects. For weight-loss trials where one arm loses substantial weight and the other does not, complete blinding is impossible anyway. The unblinded nature is a limitation worth noting. Go to post

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.

32 likes in reply to #75 15mo

a.adeyemiTL2 Moderator29 Apr 2025#126

Coming back to post #124, because the follow-up matters more than the original answer.

31 likes 15mo

Read the full topic (147 posts)

This topic was referenced in

Reading a trial's population section before its resultsEvidence › Trials · 109 replies
[2026 update] How to read a forest plot, properly, from scratchEvidence › Trials · 53 replies

Promoted into the documentation commons. The content of this topic is maintained at STEP 1 — trial digest, with named maintainers and a review date. The promotion was discussed in doc review. Corrections are best raised against the document, which is the version that gets kept current.

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