China's healthcare reform and clinical trials

Image by michael40001 via Flickr

Drove up to Foster City this weekend and attended the BioPacific 2010 Conference sponsored by the Chinese-American Biopharmaceutical Society (CABS). Not that long ago at a Bay Area meetup, the question of drug/device regulation in China came up, and it was clear that few people in the U.S. Biotech industry had a clear idea of the regulatory environment in People's Republic. This is unfortunate as China is projected to be the number 3 market for pharmaceuticals in 2010, and soon to surpass Japan and become number 2 a few years after.

One of the featured speakers is Mr. Ruling Song, the Executive President of China Pharma R&D Association. He had participated in the country's healthcare reform, which was first announced in 2009. The regulations and guidelines are now in place, and now it is clearer what the direction of the reform will be.

Echoing the U.S.'s sentiments, Mr. Song reports that the major focus of the government is on "Big Medicine". That is to say, targeting the most common diseases that cause the most morbidities, in particular in chronic diseases. In order to provide medications to 70% of the 1.3 billion population that the reform plans to cover, the government will rely heavily on generic medications.

This is not surprising. For the purpose of cost-control, insurance plans--public or private--have relied on off-patent medications.

What is important with public healthcare is that the government plays the dual roles of the regulator as well as the payer. In the U.S., under the rubric of comparative effectiveness research, large chunks of resources will be directed towards looking for the most cost-effective (read cheapest) treatment protocols in clinically plausible settings. This will make randomized clinical trials an order of magnitude more complex. The NIH will be awarding grants to study how best to do these complex clinical trials.

In China, the government as the payer will be directing its resources towards the development of generic medications. According to Mr. Song, government grants will also be given out to the developers of generics. The government's goal of 44 new medications--with 2 going for global sales--will also include the development of generics.

This was spoken, of course, in a room full of biotech industry professions, most of whose work involve R&D of novel biochemical entities.

In the U.S., generic medications only have to demonstrate bioequivalence to the predicate product, not pharmaceutical equivalence. Clinical trials, then, can be limited to pharmacologic, e.g., demonstration of similar absorption.

In the grand scheme of things, public money may not be a significant contributor when multinational corporations are throwing money and people at the Chinese pharma market. The outcome will depend on how strict the public healthcare plan will be, and how much competition there will be from private insurance plans.

Matching in clinical studies

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This blog post is motivated by articles that I read this month in which matching was used inappropriately in clinical studies.

First, some background. The primary purpose of randomization in clinical trials is to hope that the stochastic process of group assignment would, on average, remove the effects of confounders.

In observational studies, group assignments (e.g.., whether a person is a case, or a control) are not under the control of the researcher. To remove the effects of confounders--at least those that are known--one could adjust for it in the analysis stage. Alternatively the groups can be actively matched during the design stage.

The typical matching one thinks of is the 1-to-n case-control study, where for each case, n matching control are obtained. This is termed individual matching. Another often used matching method is frequency matching, in which the distributions of the confounder variable are matched.

However, matching in design stage must be echoed by statistical adjustment in the analysis stage.

We note that for for frequency matching, at least simple stratified analysis should be adopted. To take the extreme example, consider the Simpson's paradox. This is the phenomenon where an effect seen in all subgroups are reversed when all subgroups are considered together.