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The Need for Risk Stratification

David M. Kent, MD, MS; Rodney A. Hayward, MD

JAMA. 2007;298:1209-1212.

	Since this article does not have an abstract, we have provided the first 150 words of the full text and any section headings.

There is growing awareness that the results of randomized clinical trials might not apply in a straightforward way to individual patients, even those within the trial. Although randomization theoretically ensures the comparability of treatment groups overall, there remain important differences between individuals in each treatment group that can dramatically affect the likelihood of benefiting from or being harmed by a therapy.^1-4 Averaging effects across such different patients can give misleading results to physicians who care for individual, not average, patients.

The limitations of subgroup analyses—the conventional means for exploring differences in treatment effect based on patient characteristics—are well-appreciated.^5-7 Because patients in trials, as in clinical practice, have many attributes that can affect the likelihood of treatment being beneficial or harmful, exploring each of these attributes "one variable at a time" (eg, male vs female, old vs young) risks spurious false-positive subgroup . . . [Full Text of this Article]

Two Illustrative Cases

Substantial Variation of Individual Baseline Risk Within a Clinical Trial Is Common, and Often Extreme, Almost Ensuring Marked Variation of the Absolute Treatment Benefit Across Individuals

Baseline Risk Is Typically Highly Skewed, Ensuring That the Average Risk (and Average Treatment Effect) Observed in the Summary Results of the Trial Will Be Different From That in the Typical Patient

When There Is Substantial Variation in Baseline Risk Across Study Patients, the Presence of Even a Small Degree of Treatment-Related Harm Ensures Variation in the Net Relative Risk Reduction (Unless the Risk of Treatment-Related Harm Is Highly Correlated With Outcome Risk)

Conventional Subgroup Analyses Are Typically Inadequate to Detect These Large and Clinically Important Differences in Treatment Effect Among Patients When Multiple Factors Determine Risk

Risk-Stratified Analyses Greatly Increase the Power of Detecting These Differences in Treatment Effect

Author Affiliations: Institute for Clinical Research and Health Policy Studies, Tufts-New England Medical Center, Boston, Massachusetts (Dr Kent); and Veterans Affairs Ann Arbor Health Services Research and Development Service Center of Excellence and Department of Internal Medicine, University of Michigan, Ann Arbor (Dr Hayward).

RELATED LETTERS

Using Clinical Trial Summary Results to Establish Quality Measures
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JAMA. 2007;298(23):2740-2741.
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Using Clinical Trial Summary Results to Establish Quality Measures—Reply
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JAMA. 2007;298(23):2741.
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