Clouding the Debate on Clinical Trials: Pediatric Edition

I would like to propose a rule for clinical trial benchmarks. This rule may appear so blindingly obvious that I run the risk of seeming simple-minded and naïve for even bringing it up.

The rule is this: if you’re going to introduce a benchmark for clinical trial design or conduct, explain its value.

Are we not putting enough resources into pediatric research, or have we over-incentivized risky experimentation on a vulnerable population?  This is a critically important question in desperate need of more data and thoughtful analysis.

That’s it.  Just a paragraph explaining the rationale of why you’ve chosen to measure what you’re measuring.  Extra credit if you compare it to other benchmarks you could have used, or consider the limitations of your new metric.

I would feel bad for bringing this up, were it not for two recent articles in major publications that completely fail to live up to this standard. I’ll cover one today and one tomorrow.

The first is a recent article in Pediatrics, Pediatric Versus Adult Drug Trials for Conditions With High Pediatric Disease Burden, which has received a fair bit of attention in the industry -- mostly due to Reuters uncritically recycling the authors’ press release. 

It’s worth noting that the claim made in the release title, "Drug safety and efficacy in children is rarely addressed in drug trials for major diseases", is not at all supported by any data in the study itself. However, I suppose I can live with misleading PR.  What is frustrating is the inadequacy of the measures the authors use in the actual study, and the complete lack of discussion about them.

To benchmark where pediatric drug research should be, they use the proportion of total "burden of disease" borne by children.   Using WHO estimates, they look at the ratio of burden (measured, essentially, in years of total disability) between children and adults.  This burden is further divided into high-income countries and low/middle-income countries.

This has some surface plausibility, but presents a host of issues.  Simply looking at the relative prevalence of a condition does not really give us any insights into what we need to study about treatment.  For example: number 2 on the list for middle/low income diseases is diarrheal illness, where WHO lists the burden of disease as 90% pediatric.  There is no question that diarrheal diseases take a terrible toll on children in developing countries.  We absolutely need to focus resources on improving prevention and treatment: what we do not particularly need is more clinical trials.  As the very first bullet on the WHO fact sheet points out, diarrheal diseases are preventable and treatable.  Prevention is mostly about improving the quality of water and food supplies – this is vitally important stuff, but it has nothing to do with pharmaceutical R&D.

In the US, the NIH’s National Institute for Child Health and Human Development (NICHD) has a rigorous process for identifying and prioritizing needs for pediatric drug development, as mandated by the BPCA.  It is worth noting that only 2 of the top 5 diseases in the Pediatrics article make the cut among the 41 highest-priority areas in the NICHD’s list for 2011.

(I don’t even think the numbers as calculated by the authors are even convincing on their own terms:  3 of the 5 "high burden" diseases in wealthy countries – bipolar, depression, and schizophrenia – are extremely rare in very young children, and only make this list because of their increasing incidence in adolescence.  If our objective is to focus on how these drugs may work differently in developing children, then why wouldn’t we put greater emphasis on the youngest cohorts?)

Of course, just because a new benchmark is at odds with other benchmarks doesn’t necessarily mean that it’s wrong.  But it does mean that the benchmark requires some rigorous vetting before its used.  The authors make no attempt at explaining why we should use their metric, except to say it’s "apt". The only support provided is a pair of footnotes – one of those, ironically, is to this article from 1999 that contains a direct warning against their approach:

Our data demonstrate how policy makers could be misled by using a single measure of the burden of disease, because the ranking of diseases according to their burden varies with the different measures used.

If we’re going to make any progress in solving the problems in drug development – and I think we have a number of problems that need solving – we have got to start raising our standards for our own metrics.

Are we not putting enough resources into pediatric research, or have we over-incentivized risky experimentation on a vulnerable population? This is a critically important question in desperate need of more data and thoughtful analysis. Unfortunately, this study adds more noise than insight to the debate.

Tomorrow In a couple weeks, I’ll cover the allegations about too many trials being too small. [Update: "tomorrow" took a little longer than expected. Follow up post is here.]

[Note: the Pediatrics article also uses another metric, "Percentage of Trials that Are Pediatric", that is used as a proxy for amount of research effort being done.  For space reasons, I’m not going to go into that one, but it’s every bit as unhelpful as the pediatric burden metric.]

Bourgeois FT, Murthy S, Pinto C, Olson KL, Ioannidis JP, & Mandl KD (2012). Pediatric Versus Adult Drug Trials for Conditions With High Pediatric Disease Burden. Pediatrics PMID: 22826574

How Not to Report Clinical Trial Data: a Clear Example

I know it’s not even August yet, but I think we can close the nominations for "Worst Trial Metric of the Year".  The hands-down winner is Pharmalot, for the thoughtless publication of this article reviewing "Deaths During Clinical Trials" per year in India.  We’ll call it the Pharmalot Death Count, or PDC, and its easy to explain – it's just the total number of patients who died while enrolled in any clinical trial, regardless of cause, and reported as though it were an actual meaningful number.

(To make this even more execrable, Pharmalot actually calls this "Deaths attributed to clinical trials" in his opening sentence, although the actual data has exactly nothing to do with the attribution of the death.)

In fairness, Pharmalot is really only sharing the honors with a group of sensationalistic journalists in India who have jumped on these numbers.  But it has a much wider readership within the research community, and could have at least attempted to critically assess the data before repeating it (along with criticism from "experts").

The number of things wrong with this metric is a bit overwhelming.  I’m not even sure where to start.  Some of the obvious issues here:

1. No separation of trial-related versus non-trial-related.  Some effort is made to explain that there may be difficulty in determining whether a particular death was related to the study drug or not.  However, that obscures the fact that the PDC lumps together all deaths, whether they took an experimental medication or not. That means the PDC includes:

• Patients in control arms receiving standard of care and/or placebo, who died during the course of their trial.
• Patients whose deaths were entirely unrelated to their illness (eg, automobile accident victims)

2. No base rates.  When a raw death total is presented, a number of obvious questions should come to mind:  how many patients were in the trials?  How many deaths were there in patients with similar diseases who were not in trials?  The PDC doesn’t care about that kind of context

3. No sensitivity to trial design.  Many late-stage cancer clinical trials use Overall Survival (OS) as their primary endpoint – patients are literally in the trial until they die.  This isn’t considered unethical; it’s considered the gold standard of evidence in oncology.  If we ran shorter, less thorough trials, we could greatly reduce the PDC – would that be good for anyone?

Case Study: Zelboraf

FDA: "Highly effective, more personalized therapy"

PDC: "199 deaths attributed to Zelboraf trial!"

There is a fair body of evidence that participants in clinical trials fare about the same as (or possibly a bit better than) similar patients receiving standard of care therapy.  However, much of that evidence was accumulated in western countries: it is a fair question to ask if patients in India and other countries receive a similar benefit.  The PDC, however, adds nothing to our ability to answer that question.

So, for publicizing a metric that has zero utility, and using it to cast aspersions on the ethics of researchers, we congratulate Pharmalot and the PDC.

Measuring Quality: Probably Not Easy

I am a bit delayed getting my latest post up.  I am writing up some thoughts on this recentstudy put out by ARCO, which suggests that the level of quality in clinical trials does not vary significantly across global regions.

The study has gotten some attention through ARCO’s press release (an interesting range of reactions: the PharmaTimes headline declares “Developingcountries up to scratch on trial data quality”, while Pharmalot’s headline, “WhatProblem With Emerging Markets Trial Data?”, betrays perhaps a touch more skepticism). 

And it’s a very worthwhile topic: much of the difficultly, unfortunately, revolves around agreeing on what we consider adequate metrics for data quality.  The study only really looks at one metric (query rates), but does an admirably job of trying to view that metric in a number of different ways.  (I wrote about another metric – protocol deviations – in a previous post on the relation of quality to site enrollment performance.)

I have run into some issues parsing the study results, however, and have a question in to the lead author.  I’ll withhold further comment until I head back and have had a chance to digest a bit more.