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Evidence · Trials · continued

Adjudicated events and why the definition matters — the long version posts 61–90

This is a continuation of a long topic, addressed by post number rather than by page. Start at post 1.

HE
h.espinozaTL2 Moderator23 Mar 2025#61
dietitian_hollis, post #8: 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. Go to post

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.

0 likes in reply to #8 16mo
ST
slow_titratorTL2Regular24 Mar 2025#62

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.

20 likes 16mo
RM
r.mensahTL2 Moderator24 Mar 2025#63

Surrogate endpoints: an endpoint that is not the outcome that matters but is measured as a stand-in. HbA1c is a surrogate for long-term glucose control and the short-term complications it prevents. Weight loss is a surrogate for metabolic health and long-term outcomes. Surrogates are useful but not identical to the endpoint that matters.

9 likes 16mo
YM
y.mensahTL3Wiki editor25 Mar 2025#64

This follows post #61 rather than contradicting it.

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.

2 likes 16mo
PD
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.

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.

29 likes in reply to #1 16mo
BJ
b.jankowiakTL3Regular26 Mar 2025#66

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.

14 likes 16mo
BW
b.wikstromTL2 Moderator27 Mar 2025#67

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.

5 likes 16mo
BE
bench_entryTL3Regular27 Mar 2025 · edited#68
s.stavrianos, post #16: Two things before anyone answers the substance. First, the context in the first post is clear and specific. Second, the question is framed so that an answer can actually address it. Both are the norm here and both matter more than they sound. Go to post

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.

0 likes in reply to #16 16mo
ER
e.roosTL2 Moderator28 Mar 2025#69

Two things before anyone answers the substance.

First, the context in the first post is clear and specific. Second, the question is framed so that an answer can actually address it. Both are the norm here and both matter more than they sound.

21 likes 16mo
BS
buffer_sheetTL3Regular29 Mar 2025#70
dietitian_hollis, post #8: 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. Go to post

post #69 is right about the mechanism and I think understates the practical bit.

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.

9 likes in reply to #8 16mo
EL
endpoint_lineTL3Regular29 Mar 2025#71
DSakamoto, post #18: Coming back to post #16, because the follow-up matters more than the original answer. 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… Go to post

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.

0 likes in reply to #18 16mo
IG
in.guerreroTL2 Moderator30 Mar 2025#72

The estimand: what the trial set out to estimate. Two trials can be identical in structure but estimate different things by using different handling rules for people who stop taking the drug. Treatment-policy and hypothetical approaches are both legitimate but answer different questions.

3 likes 16mo
R
RidgewayTL3Regular30 Mar 2025#73

post #72 is right about the mechanism and I think understates the practical bit.

Multiplicity and multiple comparisons: if a trial tests many hypotheses, the chance of a false positive on at least one by random chance increases. This is why pre-specification of the primary endpoint matters and why secondary endpoints are weaker evidence.

10 likes 16mo
ID
i.dumitruTL2 Moderator31 Mar 2025#74
e.ferreira, post #2: the opening post answers the question as asked. The question underneath it is different. 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. Go to post

Worth separating two things that post #70 runs together.

Practical note that does not fit anywhere else. Whatever you conclude from this topic, write down what you did and when. The single most useful thing in your own records is not any individual result; it is that they are dated and consecutive.

22 likes in reply to #2 16mo
EF
erratum_fileTL3Regular1 Apr 2025#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.

0 likes 16mo
IG
i.grimaldiTL21 Apr 2025#76
CP
citation_peakTL3Regular2 Apr 2025#77

post #76 answers the question as asked. The question underneath it is different.

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.

6 likes 16mo
AC
a.cabreraTL2 Moderator2 Apr 2025#78
bench_notes, post #10: 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. Go to post

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.

16 likes in reply to #10 16mo
KR
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
SI
s.ivaturiTL2 Moderator4 Apr 2025#80
i.wojcik, post #21: The estimand: what the trial set out to estimate. Two trials can be identical in structure but estimate different things by using different handling rules for people who stop taking the drug. Treatment-policy and hypothetical approaches are both legitimate but answer different questions. Go to post

Two things before anyone answers the substance.

First, the context in the first post is clear and specific. Second, the question is framed so that an answer can actually address it. Both are the norm here and both matter more than they sound.

0 likes in reply to #21 16mo
P
PSkarbekTL3Regular4 Apr 2025#81

Coming back to post #79, because the follow-up matters more than the original answer.

Two things before anyone answers the substance.

First, the context in the first post is clear and specific. Second, the question is framed so that an answer can actually address it. Both are the norm here and both matter more than they sound.

0 likes 16mo
KH
k.haddadTL2 Moderator5 Apr 2025#82

Picking up post #79: that is the part I would want checked first.

Surrogate endpoints: an endpoint that is not the outcome that matters but is measured as a stand-in. HbA1c is a surrogate for long-term glucose control and the short-term complications it prevents. Weight loss is a surrogate for metabolic health and long-term outcomes. Surrogates are useful but not identical to the endpoint that matters.

18 likes 16mo
BM
buffer_marginTL3Regular5 Apr 2025#83

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.

4 likes 16mo
AH
a.hartmannTL2 Moderator6 Apr 2025#84
y.mensah, post #64: This follows post #61 rather than contradicting it. 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. 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.

0 likes in reply to #64 16mo
EC
excursion_checkTL3Regular7 Apr 2025#85

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.

26 likes 16mo
MA
m.agyemanTL2 Moderator7 Apr 2025#86

This follows post #83 rather than contradicting it.

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.

12 likes 16mo
TT
taper_tableTL3Regular8 Apr 2025#87

Multiplicity and multiple comparisons: if a trial tests many hypotheses, the chance of a false positive on at least one by random chance increases. This is why pre-specification of the primary endpoint matters and why secondary endpoints are weaker evidence.

2 likes 16mo
TV
t.verhoevenTL2 Moderator8 Apr 2025#88
j.asante, post #58: 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. Go to post

For anyone arriving from a search: the marked solution above is the direct answer, and the replies underneath it add the caveats that make it safe to use.

0 likes in reply to #58 16mo
H
HRouhaniTL1Member9 Apr 2025#89

The estimand: what the trial set out to estimate. Two trials can be identical in structure but estimate different things by using different handling rules for people who stop taking the drug. Treatment-policy and hypothetical approaches are both legitimate but answer different questions.

19 likes 16mo
EM
e.mensaTL2 Moderator9 Apr 2025 · edited#90

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.

8 likes 16mo