How predictive is a Phase 2 dose-response for a Phase 3 result?

The clinician who ordered the panel was not concerned; I would still like to understand it.

This is one of those things that everyone repeats and nobody derives.

This matters practically, not just academically, because it changes what I would do next.

Why does this happen, and what would falsify the usual explanation?

The hazard ratio is the relative effect. What changes decisions is the absolute effect, and converting between them requires the event rate in the control arm, which is usually in the same table and rarely in the abstract.

A network meta-analysis can rank agents that were never compared directly, but only under a transitivity assumption — that the trials being linked are similar enough in population, duration and endpoint definition for the indirect comparison to hold. In this field that assumption is often visibly violated, which is why indirect rankings should be read as hypotheses.

Headline results, principal programmes

Specifically, apoB and LDL-C disagree because they measure different things: LDL-C is the cholesterol mass carried in the LDL fraction, ApoB is a count of atherogenic particles. Small dense particles carry less cholesterol each, so a person with many small particles has a concordantly higher ApoB than their LDL-C suggests. When they disagree, ApoB is the better risk marker.

One qualification: a trial that demonstrates an endpoint at a given dose has demonstrated it at that dose. Extrapolating the endpoint down the dose ladder is an assumption, not a finding.

None of this replaces a clinician who can see the whole picture, and the whole picture is usually where the answer is.

Put another way, the confidence interval is the informative part. A point estimate with an interval spanning no effect is a different object from the same point estimate with a tight interval, and the abstract presents them identically.

The early fall in estimated glomerular filtration rate on treatment is haemodynamic rather than structural. Reduced intraglomerular pressure lowers the filtration rate acutely and preserves the glomerulus chronically — the same pattern seen with renin-angiotensin blockade and with SGLT2 inhibition. A dip of a few millilitres per minute in the first weeks, followed by a shallower long-term slope, is the desired trajectory, not a warning sign.

A fasting lipid panel drawn during rapid weight loss reads oddly for a mechanical reason: mobilised adipose tissue delivers free fatty acids to the liver, and hepatic triglyceride export rises. Triglycerides can transiently increase while the person is doing exactly the right thing. Draw the panel when weight has been stable for a few weeks if you want an interpretable number.

Worth being explicit that this is interpretation of published data and not medical advice. Laboratory results belong in a conversation with whoever ordered them.

Convert everything to an absolute effect before you compare two interventions. Relative effects are not comparable across different baseline risks.

In practice, this is a question about what the trial was designed to answer, and the honest response is that it was not designed to answer this.

Creatinine is a muscle-derived metabolite, so a substantial loss of lean mass lowers serum creatinine and mathematically raises estimated GFR without anything happening to the kidney. If you have lost twenty kilograms, your creatinine-based eGFR is flattering you. Cystatin C is not muscle-dependent and is the measure to use when the two disagree.

It helps to be literal here: estimated average glucose from HbA1c: eAG in mg/dL = 28.7 × A1c − 46.7, or in mmol/L, 1.59 × A1c − 2.59. An A1c of 6.5 per cent is therefore about 140 mg/dL or 7.8 mmol/L. The relationship is a population regression, so an individual can sit well off the line.

SURMOUNT-4 randomised participants after an open-label lead-in to continued tirzepatide or placebo, and the withdrawal arm regained a substantial proportion of the lost weight over the following year[1].

The limitation is that surrogate endpoints and hard endpoints have come apart before in metabolic medicine, so a favourable biomarker is a reason for optimism rather than a conclusion.

Read the confidence interval, read the estimand, and compute the absolute effect yourself. It takes two minutes and it changes how the result feels.

edited 1 Jun 2026 by p_mkhize — added the citation requested in comments

A single laboratory value is a point on a noisy curve. What you want is a trend across at least three draws under comparable conditions, and "comparable" is doing a lot of work in that sentence.

HbA1c is a weighted average, not a flat one: roughly half the signal comes from the most recent month. That is why a value drawn six weeks after a change already reflects most of the effect, and why a value drawn during rapid haematological turnover reflects something other than glycaemia.

SELECT reported a hazard ratio of 0.80 (95% CI 0.72–0.90) for the primary composite major adverse cardiovascular event endpoint with semaglutide 2.4 mg in overweight or obese adults with established cardiovascular disease and without diabetes[1].

The papers are readable. Read the paper rather than the summary of the paper, especially where the summary is enthusiastic.

Read the estimand before the effect size. Almost every apparent contradiction between two published figures from the same trial resolves once you notice that one is a trial-product estimand and the other is a treatment-policy estimand.

Absolute risk reduction, worked: if the control-arm event rate is 8.0 per cent over the follow-up period and the hazard ratio is 0.80, the treated rate is approximately 6.4 per cent, the absolute risk reduction is 1.6 percentage points, and the number needed to treat is 1 ÷ 0.016 ≈ 63 over that period. A 20 per cent relative reduction and a number needed to treat of 63 are the same finding stated two ways, and only one of them sounds impressive.

FLOW tested a composite renal endpoint — kidney failure, sustained 50 per cent eGFR decline, or renal or cardiovascular death — in type 2 diabetes with chronic kidney disease, and was stopped early for efficacy[1].

If the trend across three draws is flat, the difference between draws one and two was noise. Most of what people react to is noise.

edited 23 Jun 2026 by plate_count_9k — clarified the distinction between purity and content