Friday, May 29, 2009

Comparative Effectiveness Research--Blessing or Curse for Life Sciences Industry?

We welcome a new author to our blog, Dr. Joel Studebaker who discusses an issue that is certain to have an impact on the entire Life Sciences Industry assuming the Obama administration is able to get this legislation passed. Joel's biography is appended the end of this article.

Comparative Effectiveness Research:

The $1.1 billion in funding for comparative effectiveness research (CER) in the federal stimulus plan has considerable significance for the pharmaceutical and medical device industries. This funding will support studies of the effectiveness of alternative therapies – pharmaceutical, device, or medical procedure – for particular medical conditions. The studies will focus on outcomes like improvements in patient status or adverse effects but they will not currently consider the costs of treatment. Proponents of CER assert that it provides opportunities to control health care costs without sacrificing quality and to give physicians systematic, unbiased data about outcomes for competing therapies. Opponents believe the results will determine which therapies receive reimbursement from government and private insurance and thus effectively dictate medical decisions to physicians. In addition, there is concern that CER studies may lead to studies that do consider cost, with the result that treatments that are effective but relatively expensive will not qualify for reimbursement. In the United Kingdom, the effectiveness studies of the National Institute for Health (NIH) and Clinical Excellence (NICE) currently take cost into account.

Clinical trials represent the most rigorous approach to CER, and pharmaceutical companies sometimes publish the results of trials demonstrating that their products produce better outcomes than competing alternatives. The expense of clinical trials limits the number of patients they can include and the length of time they can cover, however. A second approach is to compare the reported results of clinical trials of products aimed at the same clinical condition. The major challenges in this approach are that different studies may use different methods and significantly different patient populations. A third approach is to analyze data available in medical and pharmacy claims. The data in medical claims submitted to insurers, managed care organizations, and government programs includes codes identifying the patient’s diagnoses, the procedures carried out, and the provider for each claim. Pharmacy claims submitted to pharmacy benefit managers provide data on drugs a patient is taking. Some private plans and government programs cover millions of members, and thus they have data on larger populations than a clinical trial can enroll. When membership in these programs is relatively stable over time, it’s possible to study long term effects. Large populations also make it possible to study sub-populations, preserving a measure of personalized medicine in CER.

Referring to studies based on claim records, a 2007 paper from the Congressional Budget Office noted that “A central difficulty in such studies, however, is accounting for the differences in patients’ health status that play a role in determining which treatment they get… Insurance claims typically do not include any information about health status.” To overcome this difficulty, one may use a software package that classifies individuals by health status on the basis of their claims. One should recognize, however, that there are limitations to using claims for CER. Unlike a clinical trial, a study of claim data lacks demographic data beyond age and gender, results from laboratory tests (though diagnoses may reflect laboratory results), or medical charts. Precedents for using claim data in comparative research include a Lilly study of cost effectiveness for the antipsychotic olanzapine and a Pfizer study comparing patients taking Lipitor® to patients taking Merck’s Zocor.

Currently, it’s not clear what new insights future CER activity will produce or what impact they will have on health care. It is possible that a body similar to NICE will come into existence in the US or that the FDA will begin to consider CER comparing products submitted for approval to products already on the market. One observer has suggested that CER results may eventually be the only way for a particular product to succeed in the marketplace in competition with less expensive alternatives.

Dr. Joel Studebaker's – Biography

After finishing graduate school, Joel Studebaker began his career at the IBM Watson Research Center in Yorktown Heights, NY. He then moved to IBM Biomedical Systems, a small division that made centrifuges for separating blood into components, where he established the laboratory for chemistry and hematology. After IBM sold that division, he worked on an IBM project at Princeton University for three years and then worked with Larry Rothman at the IBM Engineering/Scientific support center and the Pharmaceutical Industry Center.

Since leaving IBM in 1992, he has worked as a developer and project manager in databases and software development for several small systems integration firms. His pharmaceutical/biotech experience has included two tours of duty with the American Red Cross Blood Banks, an assignment managing the discovery software support group at J&J PRD in Raritan, NJ, and a position as Associate Director of Informatics at Orchid BioSciences. More recently, he has worked in medical and pharmacy claim analysis at CareAdvantage and Integr-eCare.

His current interests include comparative effectiveness research and single nucleotide polymorphisms in personalized medicine. He holds a BS in chemistry from Stanford and a PhD in chemical physics from Harvard.

1 comment:

David Avitabile said...

Having lived and worked in healthcare communications in the United Kingdom for 8 years (during which NICE was introduced), I think comparative effectiveness is really the only way you can have any kind of workable universal healthcare system. The challenge for companies becomes how best to communicate cost benefit. And this doesn't always mean that the cheapest drugs win. In fact NICE has and will continue to make funding decisions in favor of more expensive drugs--if the data is there to support efficacy and if the outcomes benefits are clear.