Making the World Safe for Evidence-Based Policy: Let's Slay the Biases in Research on Value-Based Insurance Design

Fairman, Kathleen A.; Curtiss, Frederic R.

doi:10.18553/jmcp.2008.14.2.198

Cited by 11 publications

(9 citation statements)

References 15 publications

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“…In both studies by Mark et al, baseline comparisons on key variables, such as drug spending and disease-related medical expenditures, were not reported. 11,12 Although the authors controlled for baseline measures in the statistical analyses, statistical controls are not always sufficient as noted by Fairman and Curtiss (2008), and baseline differences in known measures may signal differences in unknown factors.…”

Section: Methodological Improvementsmentioning

confidence: 99%

Pharmaceutical Step-Therapy Interventions: A Critical Review of the Literature

Motheral¹

2011

JMCP

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• Nearly 60% of commercial payers reported having 1 or more step-therapy (ST) programs in 2010, making it one of the most popular pharmacy benefit management tools. However, adoption of ST is quickly outpacing understanding of its clinical, humanistic, and economic outcomes.• Previous reviews of drug cost management tools have reported drug cost savings from ST, highlighted higher discontinuation rates, and identified gaps in the literature related to inclusion of quality of care metrics. Prior critical reviews have focused primarily on the lack of inclusion of a broad range of relevant outcomes rather than on study quality, particularly internal validity. What is already known about this subjectPharmaceuticalStep-Therapy Interventions: A Critical Review of the Literature Brenda R. Motheral, RPh, MBA, PhD ABSTRACT BACKGROUND: Adoption of step therapy (ST) is quickly outpacing the market's understanding of its clinical, humanistic, and economic outcomes. The broad scope of previous reviews of drug management programs has prohibited an in-depth discussion of the ST literature specifically.

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Section: Methodological Improvementsmentioning

confidence: 99%

Pharmaceutical Step-Therapy Interventions: A Critical Review of the Literature

Motheral¹

2011

JMCP

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“…54 Chernew et al produced a similar finding in a study of copayment decreases from $5/$25/$45 to $0/$12.50/$22.50 for generic drugs, preferred brand drugs, and nonpreferred brand drugs, respectively; the absolute (percentage point) MPR change for statins was 3.39%, representing a clinically unimportant 12.4 additional days of statin therapy annually. 55,56 Results for antihypertensives (angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers), beta blockers, and diabetes drugs were similar at 9.5 to 14.7 days of therapy per year. Although noting a remarkable lack of transparency in the Chernew et al study report, Fairman and Curtiss applied national data to its results and estimated that to achieve these tiny gains in adherence the annual per member per year (PMPY) intervention costs across the entire insured population would be large, $11.53, $9.10, and $18.60, respectively, for antihypertensives, diabetes drugs, and statins.…”

Section: Still Looking For Health Outcomes In All the Wrong Places? Mmentioning

confidence: 96%

“…Although noting a remarkable lack of transparency in the Chernew et al study report, Fairman and Curtiss applied national data to its results and estimated that to achieve these tiny gains in adherence the annual per member per year (PMPY) intervention costs across the entire insured population would be large, $11.53, $9.10, and $18.60, respectively, for antihypertensives, diabetes drugs, and statins. 56 …”

Section: Still Looking For Health Outcomes In All the Wrong Places? Mmentioning

confidence: 99%

Still Looking for Health Outcomes in All the Wrong Places? Misinterpreted Observational Evidence, Medication Adherence Promotion, and Value-Based Insurance Design

Fairman¹,

Curtiss²

2009

JMCP

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Above all, the right question needs to be framed."1 To those who would argue that sophisticated statistical modeling obviates the need to consider confounding factors and alternative explanations when interpreting observational associations, Freedman's answer was simple: "The technology is relatively easy to use. … However, the appearance of methodological rigor can be deceptive."1 More contemporary guidelines for research reporting acknowledge the same understanding: when interpreting associations, multivariate modeling is often appropriate and valuable, but is still potentially vulnerable to the effects of confounding and bias and is therefore no substitute for a randomized design. 2,3,4 Lessons Learned From Confusing Association With Causation in Chasing BiomarkersCurtiss and Fairman observed in the July/August 2008 issue of JMCP that "evidence-based" interventions targeted to biomarkers frequently do not produce the end point "outcomes we love," such as reductions in hospitalization rates or mortality.5 Notably, most of the instances cited were characterized by a single common pattern: the usurping of lower-quality evidence based on observational associations with higher-quality evidence garnered from experimental testing of hypothesized causal factors.For example, the Action in Diabetes and Vascular Disease: Preterax and Diamicron Modified Release Controlled Evaluation (ADVANCE) trial was conducted because observational research had documented associations of blood glucose and HbA1c (A1c) levels with cardiovascular events. Researchers randomized 11,140 patients with type 2 diabetes to standard glucose control or intensive glucose control, targeted to achieve an A1c level of 6.5% or less.6 During a median 5 years of follow-up, the patients randomized to the intensive glucose control intervention did not have a significantly lower risk of major macrovascular events (hazard ratio [HR] = 0.94, 95% confidence interval [CI] = 0.84-1.06), cardiovascular mortality (HR = 0.88, 95% CI = 0.74-1.04), or all-cause mortality (HR = 0.93, 95% CI = 0.83-1.06), but did have an increased risk of severe hypoglycemia (2.7% intensive control vs. 1.5% standard control; HR = 1.86, 95% CI = 1.42-2.40). Similarly, the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial was initiated because of high-quality prospective observational evidence suggesting that after "adjusting for other risk factors," each 1% decrease in A1c was associated with a 21% decrease in the risk of diabetes-related mortality, a 14% decrease Naturally, there is a strong desire to substitute intellectual capital for labor. That is why investigators often try to base causal inference on statistical models.

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“…19 Only 2 studies have examined the effects of providing free health care services; results of 1 study were unclear, the other found that free services shifted costs to the health plan without improving adherence. [22][23][24] • Offering "free" products or services is an often used and powerful marketing tool. 17 • The effect of using this tool in pharmacy benefit design is unknown.…”

Section: Psychological and Behavioral Economic Researchmentioning

confidence: 99%

“…A recent highly publicized study by Chernew et al of reduction in the amount of prescription drug cost sharing included an undisclosed number of patients whose medication was provided free of charge, but its design was weak and not transparent, the results for the "free" subgroup were not reported separately, the cost of the copayment intervention was not reported at all, and the observed effects of reduced cost sharing on medication possession ratio (adherence) across all copayment levels were statistically significant but not clinically significant at 7 to 14 days per year. 23,24 5. Benefit designers must test not just objective benefit design conditions but also the effects of messaging-how those conditions are described to plan members.…”

Section: Prescription Drug Copayment Reduction Strategiesmentioning

confidence: 99%

Making the World Safe for Evidence-Based Policy: Let's Slay the Biases in Research on Value-Based Insurance Design

Cited by 11 publications

References 15 publications

Pharmaceutical Step-Therapy Interventions: A Critical Review of the Literature

Pharmaceutical Step-Therapy Interventions: A Critical Review of the Literature

Still Looking for Health Outcomes in All the Wrong Places? Misinterpreted Observational Evidence, Medication Adherence Promotion, and Value-Based Insurance Design

What Pharmacy Benefit Designers Need to Know About Perception and Reality: Never Forget the Elephant in the Pharmacy

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