Pharmacy & Therapeutics - December 2008 - (Page 703)

Interpreting Estimates of Treatment Effects and is negative if a treatment is harmful. The main advantage of the NNT is its straightforward clinical interpretation. It also can be easily used to weigh the costs and benefits of treatment. In this framework, the cost of achieving one successful treatment is not only the amount incurred for the patient who improved; the cost also includes the amount incurred in treating patients who do not improve. Therefore, if the cost of a single therapy is C, the cost of achieving one successful treatment is C * NNT. If the costs of two therapies are C1 and C2 and their NNTs are NNT1 and NNT2, the relative cost of the first treatment, compared with the second, is: C1 NNT1 C 2 NNT2 (5) As Equation 5 indicates, doubling the NNT has the same effect as doubling the cost of the treatment. EFFECT MAGNITUDE: RELATIVE MEASURES Relative Risk A simple measure of effect magnitude would be to use the percentage of patients who improve in the treated group as a comparative index of efficacy in different studies; however, this would be wrong. Although statistics involving percentages of improvement are easy to understand, they cannot be interpreted meaningfully without referring to the percentage of improvement observed in a placebo group, especially when improvement is defined in an arbitrary manner such as a 30% reduction in a symptom outcome score. One solution to this problem is to express drug–placebo differences in improvement as the relative risk for improvement, which is computed as the ratio of the percentage of patients who improved in the treatment group divided by the percentage of patients who improved in the placebo group: relative risk for improvement = percentage improved with treatment percentage improved with placebo ment that can be attributed to the treatment. An odds ratio can also have an associated CI to allow one to decide on the reliability of the comparison. Because thinking about “percentage improvement” is more intuitive than thinking about “odds of improvement,” most people find it easier to understand and interpret the relative risk for the improvement statistic compared with the odds ratio. However, relative risk has an interpretive disadvantage, which is best explained with an example. Suppose a new treatment has a 40% improvement rate compared with a standard treatment (at 10%). The relative risk for improvement is 40/10, so the new treatment seems to be four times better than the standard treatment; however, the treatment failure rates would be 60% for the new treatment and 90% for the standard treatment. The relative risk for failure is 90/60, which indicates that the old treatment is 1.5 times worse than the new treatment. As this example shows, estimating relative risk depends on whether one examines improvement or failure to improve. In contrast, this problem of interpretation is not present for the odds ratio. DISCUSSION Table 1 summarizes measures of magnitude of effect. Even though using a measure of effect magnitude to compare the efficacy of different treatments is clearly an advance beyond qualitative comparisons of different studies, it would be a mistake to compare magnitudes of effect between studies without acknowledging the main limitation of this method. The computation of effect measures makes sense only if we are certain that the studies being compared are reasonably similar in any study design features that might increase or decrease the effect size. The usefulness of comparing measures of effect between studies is questionable if the studies differ substantially on design features that might plausibly influence differences between drug and placebo. For example, if a study of drug A used double-blind methodology and found a smaller magnitude of effect than a study of a drug B that was not blinded, we could not be sure whether the difference in magnitude of effect was a result of differences in drug efficacy or differences in methodology. If endpoint outcome measures differ dramatically among studies, that could also lead to spurious results. As another example, if one study used a highly reliable and well-validated outcome measure and the other used a measure of questionable reliability and validity, comparing measures of effect would not be meaningful. Meta-analyses of effect measures need to either adjust for such differences or to exclude studies with clearly faulty methodology. Effect magnitude can be useful in making treatment and formulary decisions, but clinicians and managed care organizations must consider whether superiority of effect for a treatment reported from one or more studies would apply to all types of studies. The randomized, controlled trials from which effect measures are often derived might not reflect real-world conditions of clinical practice. For example, patients enrolled in randomized, controlled trials typically are subject to inclusion and exclusion criteria that would affect the clinical composition of the sample. continued on page 710 Vol. 33 No. 12 • December 2008 • (6) Although it seems odd to view improvement as a “risk,” the term is widely used in the sense of probability rather than risk of a negative outcome. Relative risk is easily interpreted: it is the number of times more likely a patient is to improve with treatment compared with placebo. Therefore, a relative risk for improvement of 10 would mean that 10 treated patients improved for every untreated patient. Odds Ratio As its name indicates, the odds ratio is computed as the ratio of two odds: the odds of improvement with treatment and the odds of improvement with placebo. The formula is: odds ratio = odds of improvement with treatment odds of improvement with placebo (7) In Equation 7, the odds of improving with the drug are the ratio of the percentage of patients improved with treatment to the percentage not improved with treatment. The odds of improvement with placebo are computed in a similar manner. The odds ratio indicates the increase in the odds for improve- P&T® 703

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Pharmacy & Therapeutics - December 2008 Contents Editorial Medication Errors Prescription: Washington New Drugs/Drug News/ New Medical Devices Drug Forecast Interpreting Estimates of Treatment Effects Chemotherapy Foundation Symposium XXVI: Innovative Cancer Therapy for Tomorrow Third Annual Onmark National Payor/Provider Forum Fourth Annual Ophthalmic Drug Development and Delivery Summit Classification and Pharmacological Management Of Obesity CE Test and Forms Season’s Greetings: Thanks to Our Readers and Reviewers

Pharmacy & Therapeutics - December 2008

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