Talking about Treatment: The Language of Populations and the Language of Individuals

In recent years, clinical decisions about treatment have been increasingly guided by the findings of randomized clinical trials. These trials use the strongest type of study design for treatment assessment. They offer the best approach to reducing bias and balancing the distribution of known or unknown patient attributes that could threaten the internal validity of the study (1). The quantitative measures of risk reduction used to report randomized clinical trials describe the collective experience of the participants in the trial. To apply the results of such trials in clinical practice, physicians must translate the concepts and measures used to describe groups of patients into a language that can inform the decisions of an individual patient. A clinical vignette illustrates the tensions inherent in this process of translation. You are caring for a 74-year-old man whose blood pressure averages approximately 175/85 mm Hg. He has type 2 diabetes mellitus that is controlled by oral medications. On the basis of the findings of a randomized clinical trial, the Systolic Hypertension in the Elderly Program (SHEP) (2), you believe that treatment with antihypertensive medication will reduce his risk for a cardiovascular event. The patient asks you, If I take this medication, how will it help me? (The Table provides data from the SHEP trial to inform your answer.) Table. Measures of Treatment Efficacy from the Systolic Hypertension in the Elderly Program (SHEP) Trial Three problems impede an accurate response to your patient's seemingly simple question. First, reports of randomized clinical trials do not usually account for individual characteristics that modify the effect of treatment (3). Second, clinicians and patients often have difficulty interpreting quantitative data about risk and risk reduction (4, 5). Finally, the results of treatment studies can be expressed, or framed, in different ways; this can influence how both physicians and patients perceive the efficacy of treatment (6). You suspect that diabetes increases your patient's risk for cardiovascular disease. He may therefore receive more benefit from treatment of hypertension. In the SHEP trial, as in most randomized clinical trials, some participants had risk factors that increased their likelihood of a cardiovascular event; 13% of enrolled patients were current smokers, 10% had a personal history of diabetes, and 5% had previous myocardial infarction (2). However, this particular trial provides only the average estimated benefit for all participants rather than an estimate that takes your patient's diabetes into account. Interpreting the Data The measures of risk reduction in a randomized clinical trial are calculated from two proportions: the proportion of patients in the control group with an adverse outcome (P C) and the proportion of patients in the treatment group with the same adverse outcome (P T). These proportions can be used to calculate two measures of treatment effect: relative risk reduction and absolute risk reduction. Formulas for both calculations are provided in the Table. The relative risk reduction is the traditional method of reporting the findings of a randomized clinical trial. To describe to your patient the reduction in all cardiovascular events observed in the SHEP trial by using the relative risk reduction, you could say, Taking antihypertensive medications for 5 years will reduce your risk for stroke or cardiac event by 30%. Because the event rate appears in both the numerator and the denominator of the relative risk reduction, it cancels out of their ratio (3). As a result, the same relative risk reduction can be observed for adverse outcomes that occur at much different rates. For example, the relative risk reduction for all adverse cardiovascular events in the SHEP trial (30%) was approximately the same as the relative risk reduction for a fatal stroke (29%), even though 12.2% of treated patients experienced some type of cardiovascular event and only 0.4% had a fatal stroke (Table). Laupacis and colleagues (7) proposed that clinical decisions about treatment should incorporate the reciprocal of the absolute risk reduction, calculated as 1/(P C P T), which they termed the number needed to treat. The number needed to treat to prevent any cardiovascular event in the SHEP trial was 19, roughly 30-fold less than the number needed to treat to prevent a fatal stroke (Table). You could describe the number needed to treat to your patient by saying, About 19 persons need to be treated for hypertension for 5 years to prevent one stroke or cardiac event. The number needed to treat has become the standard for presenting the results of randomized clinical trials in ACP Journal Club (8). Framing the Effectiveness of Treatment The relative risk reduction and number needed to treat frame therapeutic decisions differently. The number needed to treat unambiguously assumes the population perspective because it explicitly states the number of people who must be treated and for what length of time to protect one person. Although the relative risk reduction is also a population statistic, it seems to promise a benefit that is individual, large, and immediate. In studies of hypothetical treatment decisions, physicians (9-12) and patients (13, 14) viewed treatments more favorably when benefits of these treatments were framed in terms of relative risk reduction. In a questionnaire study of 235 physicians (9), 49% of respondents reported that they would be more likely to prescribe a lipid-lowering drug with a relative risk reduction of 24% than a drug with an absolute risk reduction of 0.4%, even though both statistics were calculated from the Lipid Research Clinics Coronary Primary Prevention Trial (15). In another questionnaire study that used data from the Helsinki Heart Study (16), 88% of patients indicated that they would be willing to take a medication that caused a 34% relative risk reduction in heart attacks. However, only 31% of patients said that they would take this medication when the same information was expressed as a number needed to treat (71 persons needed to be treated for 5 years to prevent one heart attack) (14). Thus, in hypothetical cases, physicians and patients seem more likely to promote or accept treatment when it is framed in individual terms rather than in population terms. Little research has assessed the content of actual clinical conversations about risk. In one study, discussions about risk occurred in 26% of audiotaped primary care office visits (17). Only 3.4% of risk discussions were quantitative; relative risk reduction was used in those conversations. The physician usually told the patient that an adverse outcome was certain unless the patient adopted the recommended treatment; in other words, that P C =1 (17). These limited clinical data support studies of hypothetical treatment decisions (9-14) by suggesting that physicians may deliberately or unconsciously choose to discuss the benefits of a treatment by using a framing strategy that does not provide neutral information but is intended to guide the treatment choices of their patients. The Language of Populations: From Groups to Subgroups You could estimate the benefits of hypertension treatment for your patient more precisely if information were available about the subgroup of diabetic patients enrolled in the SHEP trial. A valid subgroup analysis of a randomized clinical trial identifies characteristics of prognostic importance before data analysis, then compares outcomes between treated patients and controls within the subgroup of patients who have these characteristics (18). Subgroup analyses can provide useful information to guide decision making when the subgroups are large, the subgroup analyses are specified in advance, and the statistical analysis is appropriately conservative (19, 20). Although the initial SHEP study (2) did not evaluate the effect of treatment of hypertension in persons with diabetes, a subsequent SHEP report found that the cumulative incidence of adverse cardiovascular events at 5 years among the subgroup of diabetic persons who received antihypertensive drugs was 21.4% compared with 31.5% among diabetic persons receiving placebo (21). When the formulas in the Table are used, these findings represent a relative risk reduction of 32% and a number needed to treat of 10. Thus, as we would expect to find in clinical practice, diabetic patients in the SHEP trial were at greater risk for adverse events and received more benefit from treatment of hypertension. With this additional information, you could say to your patient, On average, 19 patients need to be treated for hypertension for 5 years to prevent a stroke or cardiac event. For persons with diabetes, only 9 to 10 patients need to be treated for the same period to achieve the same benefit. In a similar way, you could use other single risk factorsor your overall assessment of your patient's risk for a cardiovascular event if he does not receive antihypertensive medicationto modify the average estimate of treatment benefit from the SHEP trial. A full discussion of this process, which is called Bayesian reasoning, is beyond the scope of this paper, but the literature contains excellent introductions (22, 23). In a busy clinical practice, neither the findings of the SHEP trial itself nor the additional data necessary to further particularize the benefits of treatment are readily available. Computerized decision support systems to provide such information in real time are being developed (24). Problems of statistical power limit the applicability of subgroup analyses in randomized clinical trials to individual decisions. The sample size for a randomized clinical trial is calculated to provide a precise estimate of the average treatment effect. Nevertheless, estimates of the relative risk reduction or the number needed to treat from any study retain statistical uncertainty, which can be