Being an Informed Consumer of Drug Research

1. Being an Informed Consumer of Drug Research Robert E. McGrath Fairleigh Dickinson University

2. Outline • Obstacles to objective decision-making in pharmacotherapy • Review of research terminology • Accurately estimating drug effects • Utility analysis

3. Industry Impact on Data Sources • Pharmaceutical industry funds half of all CE on medication (Holmer, 2001). CE presenters tend to be more positive about the funder’s product than presenters without support (Bowman, 1986). • Villanueva, Peiro, Librero, & Pereiro (2003): 44.1% of claims in pharmaceutical ads were not supported by the reference, most frequently because the ad recommended the drug for a patient group not treated in the study. • 87% of practice guideline authors who responded admitted pharmaceutical industry funding (Choudhry, Stelfox, & Detsky, 2002). • Industry is even a major supporter of bioethicists (Elliott, 2004)

4. Implicit Information-Gathering(Avorn, Chen, & Hartley, 1982) • Practicing physicians rated scientific sources much more important influences on prescribing than commercial sources. • Also gauged knowledge in two cases where the message about medications from the scientific literature contradicted the commercial literature. • The majority of doctors responded in a manner consistent with commercial literature.

5. The Principle of Least Effort(Haug, 1997) • When seeking information about “cutting-edge” treatments, physicians tend to choose easily available information sources, even if it is of low quality, over higher-quality sources that require more effort.

6. Personal Misestimation of Treatment Effectiveness • Cognitive Errors (Arkes, 1981) • Covariance Misestimation • Expectancies • Logical Errors: post hoc, ergo propter hoc • Natural history of the disorder • “placebo” effects

7. Becoming a Critical Consumer • Being a critical consumer means critically evaluating research • Lack of access to research data • The Internet!

8. Statistical Terminology • p: The probability of your sample outcome if the null hypothesis is true. For two groups, the probability of this sample difference between group means if the difference is 0 in the populations. For a correlation, the probability of this sample correlation, if the correlation is 0 in the population. • α: The p value at which you are willing to reject the null hypothesis that the population value = 0. The probability of rejecting the null hypothesis if the null hypothesis is true (incorrect rejection; Type I error). • The problem: Population differences or correlations rarely equal 0.

9. Statistical Terminology (cont’d) • Power (1 - β): The probability of rejecting the null hypothesis if the null hypothesis is false (incorrect rejection). A function of: • α: ↑α, ↑power • Sample size: ↑sample size, ↑power • Effect size: ↑effect size, ↑power • Effect size: The size of the difference or correlation in the population or sample. • The larger the effect, the easier it is to reject the null hypothesis (greater power) • Common measures: • d: The difference between means divided by the standard deviation • r: The standard correlation coefficient

10. More Effect Sizes • Odds ratio: Odds of improvement in the treatment group divided by odds of improvement in control group (declining in popularity) • Risk ratio: Probability of improvement in the treatment group divided by probability of improvement in control group • Number needed to treat: The number of cases needed to be treated to have one more positive outcome. Smaller is better. E.g., NNT = 4 means you will get 1 more positive outcome for meds than placebo for every 4 treated.

11. Examples • Odds ratio • Risk ratio • NNTN

12. Methodological Terminology • Last Observation Carried Forward (LOCF): An analysis in which participants’ last observation is used, even if they dropped out. All participants are included. • Observed Cases (OC): An analysis restricted to participants who completed the entire protocol • Evidence is poor that OC effects are larger (Breier & Hamilton, 1999; Kirsch, Moore, Scoboria, & Nicholls, 2002) • LOCF significance tests are more powerful. • Meta-analysis: An integration of prior research findings across studies. Focus on size of effects rather than significance.

13. Schizophrenia:Abilify (aripiprazole) • Google Abilify. Go to www.abilify.com.

14. Click on For Healthcare Professionals • Click on Efficacy • Click on Symptom Improvement

16. Google PANSS • Positive and Negative Syndrome Scale (PANSS) • Kay, Fiszbein, & Opler (1987) • 30-item scale • 16 general psychopathology symptom items • 7 positive symptom items • 7 negative symptom items • completed by the physician • Each item is scored on a 7-point severity scale • A patient with schizophrenia entering a clinical trial typically scores 91.

17. Positive Symptoms • Negative Symptoms • General Symptoms

18. After 4 weeks, Abilify reduced PANSS score by 14 (15% of 91) • Positive score only improved by 5 points • Negative score only improved by 3 points • About half of the effect had to do with general symptoms

19. Mean improvement in HAM-D score: 2.4 (LOCF)-3.5 (OC) points Mean improvement in mood score: .4 (OC) -.5 (LOCF) points Conclusion: It doesn’t take much to get this guy golfing again!

20. Why Therapy is Better • The utility (clinical significance) of an intervention is a function of three factors: • The size of the effect: ↑effect, ↑utility • The treatment’s value: ↑value, ↑utility • The costs or risks: ↑cost/risk, ↓utility • Interpreting effect sizes (Cohen, 1988) • d: small = .20; medium = .50; large = .80 • r: small = .10; medium = .30; large = .80

21. Examples of Utility Analysis • Physicians’ Aspirin Study: r = .034 (Rosenthal, 1990) • ECT (Carney et al., 2003): • d = .91 versus placebo; mean Hamilton difference 9 points • d = 1.01 versus meds; mean difference 5 points

22. Comparing Meds to Therapy • Greater risks must be offset by greater value • Lasser, Allen, Woolhandler, Himmelstein, Wolfe, & Bor (2002): Among drugs FDA approved 1975-1999, 8.2% acquired an additional black box warning; 2.9% were withdrawn • Kathol & Henn (1982): Half of serious adult overdoses involved tricyclics (dated article)

23. Comparing Meds to Therapy (cont’d) • Therapy can be at least as effective as meds • Therapy equal to or better than meds for depression, even severe (Antonuccio, Danton, & DeNelsky , 2004) • Mean d for treating cognitive problems in schizophrenia with: • Meds = .22 (Mishara & Goldberg, 2004) • Cognitive rehab = .45 (Krabbendam & Aleman, 2003) • Increasing evidence total cost for therapy is cheaper for depression (Antonuccio et al., 2004) and anxiety disorders (Heuzenroeder et al., 2004)

24. Why Therapy is Better (cont’d) • Comparison • Effect size: Therapy ≥ Meds • Value: Meds = Therapy • Risks: Meds > Therapy • Cost: Meds ≥ Therapy • Therapy > Meds

25. Being an Informed Consumer • Be aware that information may be biased, even if it comes from trustworthy sources • Monitor your own use of meds • How many are on prescription? • What are they taking? • How many are taking multiple meds? • How long are they maintained on meds? • Outcomes? • Do the results match your beliefs?