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Evidence · Trials

Reading a supplementary appendix and finding the interesting part

PK
p.krastevTL2 Moderator29 Sep 2024#1

Reading a supplementary appendix and finding the interesting part — setting out what I have, and where I think it stops being reliable.

I have seen FLOW (N Engl J Med, 2024) cited in support of a claim I do not think it supports, twice this month, so I would like to work through what it actually shows.

My reading is that the trial is sound for its own question and is being stretched to answer a different one. I might be wrong about that, which is why this is a topic rather than a correction.

What I would like from this discussion: someone who disagrees with me to say why, with the section of the paper they are relying on.

52 likes 22mo
EI
e.iyerTL2 Moderator3 Oct 2024#2

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

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.

14 likes 22mo
MH
ms_hollowayTL4Mass spectrometrist6 Oct 2024#3

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

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.

5 likes 22mo
NS
n.silvaTL2 Moderator9 Oct 2024#4
ms_holloway, post #3: On the opening post — agreed on the reasoning, with one qualification. 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),… Go to post

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.

0 likes in reply to #3 22mo
HO
h.oyelowoTL2Regular11 Oct 2024 · edited#5
n.silva, post #4: 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. Go to post

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.

21 likes in reply to #4 22mo
RB
r.bruunTL2 Moderator14 Oct 2024#6

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.

9 likes 21mo
SC
sourced_claimsTL316 Oct 2024#7
CC
ch.correiaTL2 Moderator18 Oct 2024#8

post #7 is right about the mechanism and I think understates the practical bit.

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.

0 likes 21mo
QL
quiet_lurkerTL2Regular20 Oct 2024#9
ch.correia, post #8: post #7 is right about the mechanism and I think understates the practical bit. 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

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.

14 likes in reply to #8 21mo
JB
j.bhattacharyaTL2 Moderator22 Oct 2024#10

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.

5 likes 21mo
KM
k.marchandTL2 Moderator24 Oct 2024#11

Picking up post #8: that is the part I would want checked first.

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 21mo
FT
fr.translation_moTL2Translator · FR26 Oct 2024#12
e.iyer, post #2: Picking up the opening post: that is the part I would want checked first. 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

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.

1 like in reply to #2 21mo
AD
a.delgadoTL2 Moderator28 Oct 2024#13

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.

6 likes 21mo
MH
m.haddadTL2Regular29 Oct 2024#14

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.

17 likes 21mo
SA
s.adebayoTL2 Moderator31 Oct 2024 · edited#15

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.

32 likes 21mo
WP
weekly_pinTL2Regular2 Nov 2024#16
quiet_lurker, post #9: 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

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 #9 21mo
HL
h.lindqvistTL2 Moderator4 Nov 2024#17

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.

3 likes 21mo
AD
appeals_deskTL3Regular5 Nov 2024#18

Worth separating two things that post #14 runs together.

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.

11 likes 21mo
DA
d.achebeTL2 Moderator7 Nov 2024#19
a.delgado, post #13: 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

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.

1 like in reply to #13 21mo
D
DKwiatkowskiTL3Regular9 Nov 2024#20

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.

6 likes 21mo
CG
c.grimaldiTL2 Moderator10 Nov 2024#21

Coming back to post #19, because the follow-up matters more than the original answer.

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 21mo
CC
c.cardosoTL2 Moderator12 Nov 2024#22

Picking up post #19: 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.

0 likes 20mo
ML
m.lehtinenTL2 Moderator14 Nov 2024#23
h.lindqvist, post #17: 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

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.

12 likes in reply to #17 20mo
NN
n.nybergTL2 Moderator15 Nov 2024#24
fr.translation_mo, post #12: 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

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.

4 likes in reply to #12 20mo
NB
n.brobergTL2 Moderator17 Nov 2024#25

I read post #23 twice before replying, because I had assumed the opposite.

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.

1 like 20mo
PW
PharmNotes_WhitfieldTL4Pharmacist18 Nov 2024#26

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 20mo
JF
j.fonsecaTL220 Nov 2024#27
DO
dr_okonkwoTL4 Moderator21 Nov 2024#28
h.lindqvist, post #17: 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

post #27 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.

7 likes in reply to #17 20mo
RF
ro.friskTL2 Moderator23 Nov 2024#29

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 20mo
DS
dr_seongTL3Physician24 Nov 2024#30
h.oyelowo, post #5: 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. 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.

18 likes in reply to #5 20mo