Why do two papers on ESSENCE report different headline figures?

My laboratory results are from the same laboratory each time, drawn fasting, which I gather matters.

I keep seeing this stated as a fact with no explanation attached, and unexplained facts make me suspicious.

My background is quantitative but not chemical, so I can follow an equation more easily than a hand-wave.

Is the standard explanation correct, and if so, what is the evidence for it?

The honest answer here is that the published evidence supports part of the claim and is silent on the rest, and it is worth being precise about which part is which.

Placebo arms in this class are not nothing. Lifestyle-intervention placebo arms in the major obesity trials commonly lose two to three per cent of body weight, so an active-arm figure quoted without its comparator overstates the drug effect by roughly that much.

To be exact about it, trial populations are selected. Exclusion criteria in this class routinely remove people with significant renal impairment, prior pancreatitis and unstable psychiatric illness, which is exactly the population the results are then quoted for.

If a claim cannot be traced to a named trial with a named endpoint, treat it as a claim rather than as evidence.

Start with what the trial was powered for. Everything else in the publication is secondary, exploratory, or a subgroup, and those three words mean three different things.

Confidence intervals matter more than point estimates when two trials disagree. Two studies reporting fifteen and twenty per cent whose intervals overlap heavily have not disagreed about anything.

Non-inferiority and superiority designs are not interchangeable. A non-inferiority result says the new agent is not meaningfully worse against a pre-specified margin — it does not say it is as good, and it certainly does not say it is better.

One qualification: absence of a signal in a trial of this size is not evidence of absence for a rare event. It is evidence that the event is rarer than the trial could detect.

When two sources disagree, the answer is almost always in the methods section of the one you have not read.

The short version: the effect is real, the magnitude depends on the population, and the population is usually the part that gets dropped when a result is quoted second-hand.

Intention-to-treat and per-protocol analyses answer different questions. ITT asks what happens if you offer the treatment; per-protocol asks what happens if it is taken as directed. The gap between the two is a measure of how tolerable the protocol was.

The caveat is that trial evidence is about licensed product administered under supervision. None of it transfers automatically to research-grade material of unverified content.

Read the protocol and the statistical analysis plan if the result matters to you. Both are usually published alongside.

Before comparing two trials, check whether they share an endpoint definition. Frequently they do not, and the numbers then are not comparable in any sense.

Duration decides what can be seen. A 68-week trial can measure weight and glycaemia; it cannot measure anything whose event rate is one per cent per year without enrolling tens of thousands.

Meta-analyses in this area are dominated by whichever trial contributed the most participants, so read the forest plot rather than the summary estimate.

Quote the interval alongside the estimate and half the disagreements on this site would not start.