Alert & Oriented

An article by Diamond and Kaul in the American Journal of Cardiology outlines additional difficulties for the science of medical predictions.

Some of the problems related to HTE have been outlined by Kravitz et al.,  as we have previously seen, and Kent et al. offered a proposal to mitigate them.  But as we noted, the proposal by Kent et al. assumes a treatment that provides fixed relative risk reduction across the range of risks.  This is displayed in Panel A in the AJC paper:

The thin line is the hypothetical baseline risk-outcome relationship, the bold line is the post-treatment risk-outcome relationship.  In Panel A, the relative risk reduction is constant.  As one moves from lower risk to higher risk, the absolute benefit (vertical difference between the lines) increases.Continue reading “Heterogeneity of treatment effect: additional difficulties”

I am very grateful to Dr. James Gaulte for pointing out the paper by Kravitz et al. on the vexing problem of heterogeneity of treatment effects (HTE).  The article indeed provides a very clear and comprehensive explanation of the issues, and it is apparent that the proposal by Kent et al. discussed in the last post was aimed at addressing some of the challenges described by Kravitz et al.  I agree with Dr. Gaulte that the article should be recommended reading for medical students (and we’re all medical students…), but I will present here additional comments about the specifics of the paper, as well as the general philosophy that the authors seem to espouse.  Interested readers are encouraged to review the paper first.Continue reading “More on HTE”

I mentioned in the last post a recent proposal by Kent et al. that would improve clinical trial reporting and—in the authors’ words—lead to “actionable” clinical trial results.  The proposal addresses “heterogeneity of treatment effect (HTE)” and the relationship of that effect to the baseline risk of clinical trial enrollees.

HTE is the technical term used to reflect the fact that clinical trials (other than N-of-1 trials) are designed to reach a conclusion about an overall treatment effect, usually reported as relative or absolute risk reduction for the treated population when compared to the control group.  Clinical trial results tell nothing specific about an individual subject’s response to treatment.

Clearly, not everyone enrolled in the treatment arm of a given clinical trial benefits from (or is harmed by) the intervention.  Attempts to sort out who benefits most or least is usually done by dividing the trial population into subgroups, one characteristic at a time (eg. male vs female, old vs. young, hypertensive vs. normotensive, etc.), and by performing a statistical analysis on each subgroup.  Traditional subgroup analyses, however, are fraught with many well known limitations and are not practically useful to help identify those likely to benefit.

In this proposal, Continue reading “Dealing with variable risk”

A “reader’s comment” on an opinion piece by William C. Roberts in the American Journal of Cardiology, kindly published by the editor.  Roberts’ editorial focuses on the large body of evidence favoring the cholesterol hypothesis and laments what he considers the distracting effect of complementary hypotheses (eg. inflammation, “multifactorial” nature of atherosclerosis, etc.).  I offer a few points regarding the difficulties of dealing with strictly risk-based diagnoses that rely on arbitrary cut-off numbers and have no defining pathological correlates.  Unfortunately not free online, but I’m happy to provide reprints upon request.