Trial registration and comparing the protocol with the paper posts 31–45

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.

This follows post #30 rather than contradicting it.

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.

I read post #32 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.

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.

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.

Picking up post #34: that is the part I would want checked first.

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.

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.

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.

Worth separating two things that post #36 runs together.

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.

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.

post #41 is right about the mechanism and I think understates the practical bit.

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.

I read post #41 twice before replying, because I had assumed the opposite.

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.

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.