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Being an Informed Consumer of Drug Research. Robert E. McGrath Fairleigh Dickinson University. Outline. Obstacles to objective decision-making in pharmacotherapy Review of research terminology Accurately estimating drug effects Utility analysis. Industry Impact on Data Sources.

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being an informed consumer of drug research

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
industry impact on data sources
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)
implicit information gathering avorn chen hartley 1982
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.
the principle of least effort haug 1997
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.
personal misestimation of treatment effectiveness
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
becoming a critical consumer
Becoming a Critical Consumer
  • Being a critical consumer means critically evaluating research
  • Lack of access to research data
  • The Internet!
statistical terminology
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.
statistical terminology cont d
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
more effect sizes
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.
  • Odds ratio
  • Risk ratio
  • NNTN
methodological terminology
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.
schizophrenia abilify aripiprazole
Schizophrenia:Abilify (aripiprazole)
  • Google Abilify. Go to
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!

why therapy is better
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
examples of utility analysis
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
comparing meds to therapy
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)
comparing meds to therapy cont d
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)
why therapy is better cont d
Why Therapy is Better (cont’d)
  • Comparison
    • Effect size: Therapy ≥ Meds
    • Value: Meds = Therapy
    • Risks: Meds > Therapy
    • Cost: Meds ≥ Therapy
  • Therapy > Meds
being an informed consumer
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?