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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

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Being an Informed Consumer of Drug Research

Robert E. McGrath

Fairleigh Dickinson University


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Outline

  • Obstacles to objective decision-making in pharmacotherapy

  • Review of research terminology

  • Accurately estimating drug effects

  • Utility analysis


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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)


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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.


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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.


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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


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Becoming a Critical Consumer

  • Being a critical consumer means critically evaluating research

  • Lack of access to research data

  • The Internet!


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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.


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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


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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.


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Examples

  • Odds ratio

  • Risk ratio

  • NNTN


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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.


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Schizophrenia:Abilify (aripiprazole)

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


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  • Click on For Healthcare Professionals

  • Click on Efficacy

  • Click on Symptom Improvement


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  • 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.


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  • Positive Symptoms

  • Negative Symptoms

  • General Symptoms


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  • 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


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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!


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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


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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


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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)


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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)


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Why Therapy is Better (cont’d)

  • Comparison

    • Effect size: Therapy ≥ Meds

    • Value: Meds = Therapy

    • Risks: Meds > Therapy

    • Cost: Meds ≥ Therapy

  • Therapy > Meds


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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?


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