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Regulatory agencies, professional societies, and clinical trialists commonly base judgments of treatment benefit on separate assessments of efficacy and safety. When separate assessments were compared with an integrated assessment using a kinetic model of a hypothetical randomized trial of antiplatelet agents in patients with acute coronary syndrome, the former showed treatment A to be superior to treatment B, whereas the latter showed treatment B to be superior to treatment A. In conclusion, comparative judgments regarding the balance between efficacy and safety depend on the model chosen for analysis; kinetic models are particularly suited to the integrated assessment of efficacy and safety relative to regulatory decisions, public policy, guideline development, and clinical care.

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Although randomized trials and observational studies are used as the evidentiary basis of clinical practice guidelines, they are not always in agreement. Limitations in the process of randomization in the former and the selective referral of patients for treatment as a consequence of clinical "risk stratification" in the latter are underappreciated causes for these disagreements. As a result, neither is guaranteed to correctly quantify treatment benefit. This essay reviews the operational differences between these alternative evidentiary sources and shows how these differences can affect individual clinical decisions, population-based practice guidelines, and national health policy. In conclusion, the process of evidence-based medicine can be improved by independent agencies charged with the responsibility to identify and resolve these differences.

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The current paradigm of primary prevention in cardiology uses traditional risk factors to estimate future cardiovascular risk. These risk estimates are based on prediction models derived from prospective cohort studies and are incorporated into guideline-based initiation algorithms for commonly used preventive pharmacologic treatments, such as aspirin and statins. However, risk estimates are more accurate for populations of similar patients than they are for any individual patient. It may be hazardous to presume that the point estimate of risk derived from a population model represents the most accurate estimate for a given patient. In this review, we exploit principles derived from physics as a metaphor for the distinction between predictions regarding populations versus patients. We identify the following: (1) predictions of risk are accurate at the level of populations but do not translate directly to patients, (2) perfect accuracy of individual risk estimation is unobtainable even with the addition of multiple novel risk factors, and (3) direct measurement of subclinical disease (screening) affords far greater certainty regarding the personalized treatment of patients, whereas risk estimates often remain uncertain for patients. In conclusion, shifting our focus from prediction of events to detection of disease could improve personalized decision-making and outcomes. We also discuss innovative future strategies for risk estimation and treatment allocation in preventive cardiology.

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The conventional analysis of a typical clinical trial focuses on the time to occurrence of the first among a composite set of alternative events such as death or nonfatal myocardial infarction. Subsequent recurrent events are thereby excluded from consideration to ensure that all the observations were mutually exclusive of each other. Thus, not all events occurring during follow-up will be analyzed. Consequently, some investigators are now reporting additional analyses of previously published trials based on a naive comparison of the total number of events-first events plus recurrent events-and are recommending that these additional analyses be routinely conducted in future trials. We have summarized the potential limitations of this proposal and suggest other methods to analyze recurrent events, with a particular focus on kinetic modeling. The application of this approach to several previously published trials illustrates its facility to help elucidate the causal mechanisms underlying empirical demonstrations of safety and efficacy.

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Published reports of randomized clinical trials tend to emphasize the statistical significance of the treatment effect (p values) rather than its magnitude (effect size), although the clinical importance of the evidence depends more on the latter than on the former. We, therefore, compared the standard measures of effect size (relative and absolute risk reduction) and nonstandard composites of these measures (the product of the relative and absolute risk reductions and information content) with conventional assessments of statistical significance for 100 trials published in The New England Journal of Medicine. The p values were reported for 100% of the trials, relative risk reductions for 89%, and absolute risk reductions for 39%. Only 35% of trials reported both standard measures, and none reported either of the nonstandard measures. The standard measures correlated weakly (unexplained variance 77%). In contrast, the nonstandard measures correlated highly (unexplained variance 1.3%) but correlated weakly with statistical significance (unexplained variance 83%). Consequently, 25% of the trial results were adjudged "clinically unimportant" despite being "statistically significant." In conclusion, our results have shown that composite measures of effect size communicate the clinical importance of trial results better than do conventional assessments of risk reduction and statistical significance.

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Risk stratification is a mainstay of current cardiovascular care. Its practical relevance to therapeutic decision making depends, however, on the often unverified assumption that higher risk patients experience greater treatment benefit. The truth of this assumption depends, in turn, on the particular set of variables in the putative risk prediction model, the pathophysiology of the underlying disease, and the associated goal(s) of therapy. If the operative set of risk predictors is incomplete (ignoring variables affected by treatment) or inconsistent (including variables unaffected by treatment), this will influence the relation between pretreatment risk and post-treatment benefit in complex ways having material clinical consequences. In conclusion, the clinical appropriateness of risk stratification must not be assumed. Instead, risk stratification guidelines specific to a particular disease and a particular treatment should be founded on prospective empiric validation.

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The JUPITER trial is widely hailed as a landmark trial that has the potential to dramatically change the landscape of primary prevention of cardiovascular disease. Like most trials, however, it is not without its limitations. We address some of the common myths and misunderstandings that are underscored by the JUPITER trial. First, by its intentional and ill-advised exclusion of patients with low levels of high-sensitivity C-reactive protein (hsCRP), it is not possible to assess whether baseline hsCRP modifies treatment response to statins or whether it identifies patients most likely to benefit from statin therapy. Second, by stopping the trial early, one cannot rule out the possibility that the treatment benefit was overestimated and risk was underestimated, thereby precluding a reliable estimate of benefit/risk. Finally, as a consequence of early stopping, it is not possible to reliably assess the cost-effectiveness of primary prevention with rosuvastatin. Given these limitations, the attendant societal health policy implications remain largely unknown.

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Practitioners and investigators often view clinical trials from very different perspectives-the former in terms of individuals and the latter in terms of groups. The following whimsical dialogue highlights the philosophical foundations of these contrasting perspectives and illustrates their potential impact on patient care and public policy. The title alludes to a piece by Lewis Carroll regarding Zeno's paradox, purportedly proving that the fleet-footed Achilles cannot outrun the plodding Tortoise in a foot race.

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