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Being an Informed Consumer of Drug Research

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Being an Informed Consumer of Drug Research

Robert E. McGrath

Fairleigh Dickinson University

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

- 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)

- 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.

- 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.

- Cognitive Errors (Arkes, 1981)
- Covariance Misestimation
- Expectancies

- Logical Errors: post hoc, ergo propter hoc
- Natural history of the disorder
- “placebo” effects

- Being a critical consumer means critically evaluating research
- Lack of access to research data
- The Internet!

- 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.

- 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

- 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.

- Odds ratio
- Risk ratio
- NNTN

- 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.

- Google Abilify. Go to www.abilify.com.

- Click on For Healthcare Professionals
- Click on Efficacy
- Click on Symptom Improvement

- 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.

- Positive Symptoms
- Negative Symptoms
- General Symptoms

- 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

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!

- 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

- 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

- 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)

- 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)

- Comparison
- Effect size: Therapy ≥ Meds
- Value: Meds = Therapy
- Risks: Meds > Therapy
- Cost: Meds ≥ Therapy

- Therapy > Meds

- 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?