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Why does comparative effectiveness research fall on deaf ears?

March 7, 2012 in CER | 1 comment

Many of the best practices discovered in the research community are never implemented in practice.  Why is this the case?  One reason is that physicians are overloaded with information and it is costly to cull the literature for best practice information.  This is especially true when best practices are not clearly identified or change over time.

However, there is another reason why physicians would ignore best practices research: money.

“If you’re a radiologist and work in a hospital complex that can offer targeted proton beams from a $100 million cyclotron to treat prostate cancer (there’s more than a half dozen already up and operating in the U.S.), are you really interested in a scientific study that definitively determines whether that, drug therapy or simply ‘watchful waiting’ is the best course for treating that slow-growing tumor?”

The answer is ‘no’.  In the UK, National Institute for Health and Clinical Excellence (NICE) uses comparative effectiveness research to determine which treatments are covered by the public health insurance.  In the U.S., Patient-Centered Outcomes Research Institute (PCORI) aims to do the same thing but without the ability to affect payment policy.  Thus, PCORI is doomed to be a near useless body.

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Setting FDA Standards

July 5, 2011 in CER, Pharmaceuticals, Regulation | 1 comment

To what standard should the FDA hold new drugs?  The FDA has a number of choices.  Drugs companies could be required to prove that the drugs they make:

  • Do no harm.
  • Are more effective than placebos
  • Are more effective than existing drugs
  • Are more cost-effective than existing drugs, or
  • Are both more effective and more cost effective than existing drugs.

For my money, I believe the standard should be the first and second ones.  The drug company should simply have to show that the drug does no harm and is more effective than placebos.

Due to asymmetric information, however, the FDA could require the drug companies to compare their drug’s effectiveness against existing treatments or gauge the cost-effectiveness of the treatment.  Although these effectiveness and cost-effectiveness tests need not affect drug approval, insurance plans could use this information to determine if they should cover the drugs.

GoozNews has some interesting commentary regarding calls for the FDA to perform Stage III CER testing.

“I do not support the position of advocates like former New England Journal of Medicine editor Marcia Angell who think new drugs should have to be proven better than what exists before they are approved. If companies want to bring comparable therapies to market, that’s their business. It may even be the case that some me-too drugs work in some sub-populations, but not in others. So if one drug fails to achieve lower cholesterol, or offer arthritis pain relief, for instance, the doctor can switch her patient to the newer drug. But if the new drug is not proven to be better than what exists in a large Phase III trial, then physicians, patients and payers will have the information they need to insist that people start on the cheapest, comparably effective medicine that is available. For most drug classes, that will mean a generic.“

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Patient Heterogeneity and Comparative Effectiveness Research

August 9, 2010 in CER | No comments

According to Kathleen Lohr, the most pressing issues for comparative effectiveness research (CER) include: 1) how to conduct CER for heterogeneous patient populations and 2) ways to implement longitudinal investigations to capture long-term health outcomes.  Today I will focus on measuring patient heterogeneity.  Although it makes sense to take into account differences across patients, measuring this in practice can be difficult.

A paper by Kaplan et al. (2010) discusses a number of ways to measure patient heterogeneity in practice.  The article discusses six categories of patient characteristics.  Below I present each and–where appropriate–discuss how the authors attempt to measure differences in patient characteristics.

  • Immutable characteristics: These are intrinsic factors that the patient cannot change such as demographic characteristics or genetic factors.  The authors use age, gender and race as well as education in their study.  Although one could of course get more education, few elderly receive advanced degrees.  A more appropriate measure might be highest education reached by age 30 (which would not change over time after age 30), but because this measure would likely be very similar to education overall, current education can be used as a substitute.
  • Health Profile.  This category represents the patient’s current health condition.  This can be represented by factors such as a patient’s disease burden, mental/physicial functioning and other measures.  The authors use the patient’s Total Illness Burden Index (TIBI).  A paper by Willson et al (2000) uses a Comprehensive Severity Index (CSI) to measure patient illness severity over time.  To determine physician functioning, the authors use a 10 item physical function scale (PFI-10) on the Short Form 36 of the TIBI.  Other studies have used the Functional Independence Measure (FIM) to estimate patient physical functioning. To measure the patient’s mental health and depression, the authors used a modified version of the Center for Epidemiological Studies Depression Scale (CES-D).
  • Personality Profile Measures.  A patient’s personality can affect health outcomes as well.  In Kaplanet al.  (2010) study, the authors measure whether the patient has a passive orientation to health and health care according to the Provider Dependent Health Care Orientation (PDHCO) measure.  The scale measures whether patients take a passive approach to disease management and the author claims that this measure “has been linked with poor transitions in physical functioning over time.”
  • Behavioral Profile.  This includes disease management skills and health habits (e.g., smoking, diet, exercise).
  • Medical/Treatment Context.  The category measures differences across patients in the relationship with their physician, the setting where care is given, and continuity of care.
  • Life Context.  The goal of this final category is to measure whether the patient experience a stressful life event or whether they have a significant social support system.

In the paper, the authors examine whether diabetes treatment improves glycemic control (i.e., HbA1c levels).  Only the first three categories of patient information are included in the regressions.  Unsurprisingly, the strongest predictor of differential benefits from treatment is generally the health profile.  The TIBI index has the strongest effect on how treatment affects patient outcomes, but the PF-10 physical functioning disability measure is also influential in a statistically significant manner.   Patient adherence to drug regimens, unsurprisingly, also improves the affect of treatment.

Although the finding that individual patient characteristics affects treatment benefits is not surprising, Kaplan and co-authors present researchers with some tools to summarize these differences for a number of different patient characteristic categories.

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The Impact of Comparative Effectiveness Research on Health Care Spending

April 14, 2010 in Health Insurance, Public Policy, Quality | 3 comments

Comparative Effectiveness Research (CER), as it name suggests, compares how well different medicines treat a given disease.  Politicians claim that using CER findings can help improve quality and decrease cost.  If one treatment produces better health outcomes on average than another and also costs less, we should always make people use that treatment, right?

Not according to a working paper by Basu and Philipson (2010).  Let us assume that health outcomes for Drug A are better than the health outcomes for Drug B.  If the results from this research were released, the public’s demand for Drug A would increase and the demand for Drug B would decrease.  However, this may not save money.  The demand for Drug B will decrease since fewer people want to buy it; this will reduce expenditures.  As the demand for Drug A increases, however, the price and quantity purchased will increase.  Thus, the net effect on spending is indeterminate.

In addition, if insurance companies or the government decided to subsidize or cover the entire cost of treatment using Drug A, the demand will increase even more.

Basu and Philipson, however, assume that the marginal cost to produce a drug increases as the quantity rises (i.e., the supply curve is upward sloping).  However, because there are economies of scale in the production of pharmaceuticals, the price of Drug A could actually decrease (increasing returns to scale) or stay the same (constant returns to scale) as demand increased.

The key assumption in the above analysis is that it assumes that all people with a given disease respond in a homogeneous way to Drug A.  If two-thirds of people have better health outcomes when treated with Drug A and one-third have better health outcomes when treated with Drug B, then it may be suboptimal to cover Drug A, but not Drug B.

To prove this, Basu and Philipson look at the Clinical Antipsychotic Trials of Intervention Effectiveness project (CATIE).  They find that “if Medicaid would have eliminated coverage for the least cost-effective treatments of the CATIE trial then under homogeneous effects, it would save about 90% of the $1.3B Medicaid class sales annually in non-elderly adult patient with schizophrenia. However, taking into account the observed heterogeneity in treatment effects, it would incur a loss of health valued annually at about 98% of class spending and thus a net loss of about 8% of annual class spending.”

However, one of their key assumptions is that not covering the less effective medicines means that no patients will take the uncovered drug.  This may be a fair assumption for the Medicaid population, but not for the population at large.  There is a big distinction that needs to be made between not covering a drug (but allowing for the purchase to purchase it out of of their own pocket) and prohibiting the drug entirely.  Basu and Philipson are looking at the most extreme case where not covering the drug means, de facto, that the drug will not be taken, but this need not be the case.

What is important to take from this research, however, is that the drug that is most effective on average may not be the best drug for everyone.  One must take into account heterogeneous treatment effects when designing any insurance benefit plan.

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Comparative Effectiveness vs. Cost Effectiveness Research

March 11, 2010 in Public Policy, Quality | 6 comments

Comparative Effectiveness has been a hot topic in health services research. According to a recent article in the New England Journal of Medicine, “the American Recovery and Reinvestment Act of 2009 authorizes the expenditure of $1.1 billion to conduct research comparing ‘clinical outcomes, effectiveness, and appropriateness of items, services, and procedures that are used to prevent, diagnose, or treat diseases, disorders, and other health conditions.’”

Comparative effectiveness compares how effective various medical treatments improve health outcomes.  This sounds like the course we want to take.  Most policymakers laud the health benefits of comparative effectiveness research, but some people claim that comparative effectiveness research can also save cost.

This is most easily seen in the case where a treatment is completely ineffective.  If research can prove a treatment is ineffective, then insurers could save a lot of money by not covering this type of treatment.  This is especially true if the treatment is expensive.

However, comparative effectiveness treatment could also increase cost.  Assume that there are two treatment currently in use: Treatment A and Treatment B.  Let us say that treatment A costs $1,000 and has a 90% cure rate and Treatment B costs $10,000 and has a 95% cure rate.  According to comparative effectiveness research, we should always use Treatment B.  Yet this would significantly increase costs.

Most health economists argue that cost effectiveness research is provides a better way to improve health and decrease cost.  In the example above, should we cover Treatment B?  The answer is likely yes if this is a very serious disease (e.g., cancer) but likely not if the disease is less serious (e.g., the common cold).  Some readers may believe insurers should always cover Treatment B no matter what.  However, would you be willing to pay increased premiums that would occur if treatment B were covered?  Would you feel the same way if Treatment B cost $100,000?  or $10 million?  What if the cure rate was only 90.1%?

At some point, there must be a trade-off between cost and benefit.  Admittedly, these are very difficult decisions in practice, but because there are limited healthcare resources, we must ration care.  Yes, I said it, we must ration care.  I’ve said this before.  This rationing can take many forms: the scope of what your insurance company (or Medicare) will cover, waiting lines, or increased prices you must pay out of pocket for medical services.  The government wants to avoid making these tough choices because it is politically unpopular.  Politicians don’t want to be labelled  the sentator who “killed Grandma” or “instituted a death panel.”  But to truly decrease cost and improve quality, cost effectiveness rather than comparative effectiveness is the prescription we need.

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