***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
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    Input: BACKGROUND Medical practice patterns change in response to a variety of stimuli , one of which may be the publication of the results of r and omized clinical trials . We assessed the temporal association between the publication of clinical trials on myocardial infa rct ion and changes in treatment practice s for this disorder . METHODS We analyzed the use of aspirin before and after myocardial infa rct ion and that of calcium antagonists after myocardial infa rct ion in 2231 survivors of myocardial infa rct ion enrolled in the Survival and Ventricular Enlargement ( SAVE ) study over a three-year period ( from January 1987 through January 1990 ) . The proportion of patients using these treatments was analyzed before and after the publication date s of three clinical trials : the Physicians ' Health Study , published in January 1988 , which supported the use of aspirin to prevent a first myocardial infa rct ion ; the Second International Study of Infa rct Survival ( ISIS-2 ) , published in August 1988 , which supported the use of aspirin after myocardial infa rct ion ; and the Multicenter Diltiazem Postinfa rct ion Trial , published in August 1988 , which reported a deleterious effect of diltiazem in some patients after myocardial infa rct ion . RESULTS The use of aspirin before myocardial infa rct ion increased from 16.2 percent to 23.9 percent between January 1987 and January 1990 ( P less than 0.001 ) . Enrollment in the study after the publication of the Physicians ' Health Study independently predicted aspirin use before myocardial infa rct ion ( odds ratio , 1.43 ; 95 percent confidence interval , 1.11 to 1.85 ) . The use of aspirin after myocardial infa rct ion increased from 38.8 percent to 71.9 percent ( P less than 0.001 ) during the three-year study period . Enrollment in the study after the publication of ISIS-2 independently predicted the use of aspirin after myocardial infa rct ion ( odds ratio , 2.28 ; 95 percent confidence interval , 1.89 to 2.76 ) . The use of calcium antagonists after myocardial infa rct ion decreased from 57.1 percent to 33.1 percent ( P less than 0.001 ) during the study period . Enrollment in the study after the publication of the Multicenter Diltiazem Postinfa rct ion Trial independently predicted the use of calcium antagonists after myocardial infa rct ion ( odds ratio , 0.47 ; 95 percent confidence interval , 0.39 to 0.57 ) . CONCLUSIONS These observations suggest that r and omized clinical trials have a measurable influence on medical practice patterns In recent years , clinical decisions about treatment have been increasingly guided by the findings of r and omized 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 r and omized 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 r and omized 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 r and omized 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 r and omized 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 infa rct ion ( 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 r and omized 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 r and omized 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 st and ard for presenting the results of r and omized 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 question naire 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 question naire 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 , discussion s about risk occurred in 26 % of audiotaped primary care office visits ( 17 ) . Only 3.4 % of risk discussion s 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 Population s : 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 r and omized 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 introduction s ( 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 r and omized clinical trials to individual decisions . The sample size for a r and omized 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 In the third of a series of four articles the authors show the calculation of measures of association and discuss their usefulness in clinical decision making . From the rates of death or other " events " in experimental and control groups in a clinical trial , we can calculate the relative risk ( RR ) of the event after the experimental treatment , expressed as a percentage of the risk without such treatment . The absolute risk reduction ( ARR ) is the difference in the risk of an event between the groups . The relative risk reduction is the percentage of the baseline risk ( the risk of an event in the control patients ) removed as a result of therapy . The odds ratio ( OR ) , which is the measure of choice in case-control studies , gives the ratio of the odds of an event in the experimental group to those in the control group . The OR and the RR provide limited information in reporting the results of prospect i ve trials because they do not reflect changes in the baseline risk . The ARR and the number needed to treat , which tells the clinician how many patients need to be treated to prevent one event , reflect both the baseline risk and the relative risk reduction . If the timing of events is important -- to determine whether treatment extends life , for example -- survival curves are used to show when events occur over time The results of a r and omized clinical trial can be reported using relative and /or absolute estimators of treatment effect . These various measures convey different information , and the choice can influence the physician 's appreciation of the size of treatment effect and , subsequently , treatment decisions . We compare the estimators with respect to the clinical ly relevant information conveyed to physicians , and identify which clinical questions can and can not be answered directly by each . We also identify opportunities for misinterpretation when one estimator is substituted for another , or when an estimator is mislabeled . Clinical ly important questions are addressed most directly by reporting both relative and absolute effects using relative risk and its complement , relative risk reduction , and risk difference and its reciprocal , number needed to treat . This is true of estimates of treatment effect derived from a single trial and also from meta- analysis of a group of trials . Because the control group 's risk affects the numerical 
    Output:
    
In simple clinical scenarios , results expressed in relative risk reduction or gain terms were viewed most positively by doctors .
: While a framing effect may exist , particularly when results are presented in terms of proportional or absolute measures of gain or loss , it appears highly susceptible to modification , and even neutralization , by other factors that influence doctors ’ decision making .