Solving probabilistic and statistical problems: a matter of information structure and question form The Effectiveness of a Primer to Help People Understand Risk The Role of Numeracy in Understanding the Benefit of Screening Mammography Summary-of-findings tables in Cochrane reviews improved understanding and rapid retrieval of key information. Using a Drug Facts Box to Communicate Drug Benefits and Harms Two Randomized Trials
Is the human mind inherently unable to reason probabilistically , or is it able to do so only when problems tap into a module for reasoning about natural frequencies ? We suggest an alternative possibility : naive individuals are able to reason probabilistically when they can rely on a representation of subsets of chances or frequencies . We predicted that naive individuals solve conditional probability problems if they can infer conditional probabilities from the subset relations in their representation of the problems , and if the question put to them makes it easy to consider the appropriate subsets . The results of seven studies corroborated these predictions : when the form of the question and the structure of the problem were framed so as to activate intuitive principles based on subset relations , naive individuals solved problems , whether they were stated in terms of probabilities or frequencies . Otherwise , they failed with both sorts of information . The results contravene the frequentist hypothesis and the evolutionary account of probabilistic reasoning Context Educational material s aim ed at improving people 's ability to underst and information about risk are scarce . Contribution In 2 trials , adults with high or low socioeconomic status ( SES ) were r and omly assigned to receive a primer about underst and ing risk or a general health booklet . In both SES groups , adults receiving the primer more often passed a medical data interpretation test than did those receiving the general health booklet . They also expressed greater interest in medical statistics but not greater confidence in interpreting statistics , and most rated the primer helpful or very helpful . Caution s The authors did not examine whether improved data interpretation skills affected decision-making . The Editors People face a bewildering array of medical decisions ( for example , should I be tested for BRCA1 ? Should I be screened for prostate cancer ? Should I have lumpectomy and radiation to treat my breast cancer ? ) . If people are to make informed decisions , they need to underst and risk : What is my chance of staying healthy if I undergo this intervention ? What is my chance of staying healthy if I forgo it ? What is the chance of harm ? Underst and ing risk data entails a set of skills . First , people must be able to work with probabilities and changes in probabilities , the typical measures used to communicate risk information . Next , they need to have a framework on which to organize data . In essence , this means having a sense of what additional information is needed to give meaning to a statement about probability ( conversely , how to know when such additional information is lacking ) . For example , putting a particular risk in context ( your chance of breast cancer is 1 in 8) entails knowing what outcome is being considered ( diagnosis vs. death ) , being clear about the time period ( 5 years vs. lifetime ) , and having some perspective on the magnitude of the probability ( How does the risk for a particular condition compare with other important health risks ? ) . Third , people need to be able to make some basic assessment about the quality of the evidence to know whether they can believe the numbers that they are given . Anecdotal experience and a growing body of literature document the trouble many people have in making sense of risk information ( 14 ) , which is now ubiquitous in health messages . This is not surprising : Much more effort goes into disseminating health information ( for example , drug advertisements , media reports , and decision-making aids ) than into preparing the target audiences to underst and it . To address this gap , we developed a primer to help people develop the basic skills needed to make sense of the medical risk data that surround them . In this paper , we report on 2 r and omized trials that tested the primer . We sought to learn how people would respond to the primer : Would they read it ? Would they value this knowledge ? Most important , would the primer improve patients ' abilities to interpret medical data ? Methods Design Overview To test the effect of the primer on how well people underst and risk , we conducted 2 r and omized trials in distinct population s : people with high and low socioeconomic status ( SES ) . Figure 1 shows an overview of the study design . The Committee for the Protection of Human Subjects at Dartmouth College approved this project , and the survey cover letter was considered to be informed consent ( signed consent was waived ) . Figure 1 . Overview of 2 r and omized trials in distinct population s. SES = socioeconomic status ; VA = Veterans Affairs . Setting and Participants We calculated our sample size under the most conservative conditionswhen the pooled proportion of passing scores was 50 % . We asserted that a 20-percentage point absolute difference in the proportion passing the test in the primer group versus the control group would be clinical ly important . In calculating the sample size requirements , we therefore assumed that the proportion passing would be 50 % in the control group and 70 % in the primer group . When a power of 0.8 and a 2-sided P value of 0.05 were used , 100 patients were required for each study group . Assuming that 10 % of participants would be lost to follow-up , we planned to enroll 110 patients per group in each trial . High Socioeconomic Status Trial To study the effect of the primer in a highly educated and affluent group , we recruited alumni from Dartmouth 's Community Medical School . This 9-lecture series on various health and medical topics is taught by Dartmouth faculty and guests ; is held annually in Hanover , New Hampshire , and Manchester , New Hampshire ; and has a $ 25 registration fee ( 5 ) . For this study , the program organizers mailed recruitment letters to 1138 alumni . A total of 334 people who responded to the letter were eligible ( that is , they met the age criterion of 35 to 79 years , spoke English , and had attended the Community Medical School in the past ) and were subsequently r and omly assigned . Ninety-six percent ( n= 322 ) returned a completed survey ( completion rates were not significantly different between the primer and control groups [ 95 % vs. 98 % , respectively ] ) ( P= 0.192 ) . Low Socioeconomic Status Trial To study the effect of the primer in a sample with lower income and less formal education , we recruited veterans and their families at the White River Junction Veterans Affairs ( VA ) Medical Center , White River Junction , Vermont , by posting study advertisements in waiting areas of the outpatient clinic . A total of 221 people who responded to the advertisement were eligible ( that is , they met the age criteria of 35 to 79 years , spoke English , and were a veteran or the family member of a veteran who was enrolled in a VA clinic ) and were subsequently r and omly assigned . Ninety percent ( n= 200 ) returned completed surveys ( completion rates were significantly lower in the primer group than in the control group [ 85 % vs. 96 % , respectively ] ) ( P= 0.005 ) . R and omization and Interventions The letter and the advertisement asked people to participate in a research study to learn how to better give people health information . We did not mention our interest in enhancing quantitative skills . People who responded to the recruitment letter ( high SES group ) or to the advertisement ( low SES group ) were first interviewed to confirm eligibility . We only accepted 1 participant per household . Within each trial , we assigned participants on an individual basis to receive either the primer or control booklet . A list of r and om numbers ( created by using a r and om-number generator ) was given to a research assistant and was used to determine assignments ( r and omization was not stratified or blocked within each trial ) . The research assistant had access to participants ' characteristics . The investigators did not have access to the assignment list , and the deidentified code was only revealed after recruitment and completion of data collection . The appropriate booklet and a survey were mailed or given in person to participants . The survey included the major outcomes that will be described in the next section . To make the survey seem relevant to the control group , it also included additional questions about facts presented in the health booklet ( we did not analyze responses to these questions ) . All participants were asked to read the booklet and return the completed survey within 2 weeks using the self-addressed stamped envelope included with the survey . Reminder letters were sent to nonresponders . Participants who returned surveys were given their choice of a $ 25 gift certificate to a local bakery , restaurant , bookstore , or large retail store . We recruited participants from October 2004 through August 2005 . Primer Group The participants in the primer group received the booklet entitled Know Your Chances : Underst and ing Health Statistics . The goal of the primer is to teach people how to underst and risk messages and health statistics . The first part of the primer teaches people how to underst and disease risk by using the example of colon cancer . The second part focuses on how to underst and the benefits and harms of interventions by using a Zocor direct-to-consumer advertisement for secondary heart disease prevention . Figure 2 shows the summary of main concepts that were taught , which is excerpted from the booklet . The contents of the primer and many of the examples have been developed and revised over years of teaching and through focus groups with people across a diverse socioeconomic spectrum . Figure 2 . Key concepts taught in the primer , excerpted from the final pages . Because many people are intimi date d by numbers and statistics , we worked hard to make the primer inviting and nonthreatening by liberal use of cartoons and figures ; by working through examples , separating the most technical material into optional learn more boxes ; and by providing readers with quizzes ( with answers ) to assess their mastery of the material as they read the primer . Figure 3 shows sample pages from the primer . Most of the primer is written at the eighth- grade or lower reading level ( 6 ) and is a color document approximately 80 pages in length . Figure 3 . Excerpts of primer . Control Group The control group received a 70-page booklet entitled The Pocket Guide for Good Health for Adults , which is published by the U.S. Department of Health and Human Services Agency for Health Care Research and Quality ( 7 ) . We chose this booklet because the length is similar to that of the primer , it is written at a similar reading level ( eighth grade or lower ) ( 6 ) , and it contains general information about risk and reducing risk by following recommended prevention or screening activities . However , it does not include training on how to interpret quantitative information . Measurement and Outcomes All outcome measures were assessed in the survey . Primary Outcome The primary outcome was a measure of participants ' abilities to interpret medical statistics . To measure this outcome , we developed the 18-item data interpretation test during the same time Patients are increasingly being exposed to quantitative information about risks for disease and benefits of treatment . Many authors [ 1 - 6 ] believe that the use of such information is an important component of informed decision making ; others cl aim that only patients given such information can make truly informed choices . This position is reflected in the recent decision of the National Institutes of Health Consensus Panel not to make a recommendation about screening mammography for women aged 40 to 49 years but to advocate that these women make their own decisions about screening on the basis of their personal evaluation of risks and benefits [ 7 ] . This strategy , however , is based on the assumption that patients underst and quantitative information . Research in this area has largely focused on how simple changes in the format of numerical information can influence choices , a concept referred to as framing [ 8 - 20 ] . But just as it is premature to worry about wording before knowing whether patients can read , it may be premature to worry about framing before knowing whether patients can underst and and manipulate numbers . In fact , evidence suggests that many persons do not work well with numbers [ 21 , 22 ] . It is likely that quantitative information is only meaningful to the extent that patients have some facility with basic probability and numerical concepts , a construct called numeracy . To learn more about the role of numeracy in communicating information about risk , we studied women 's comprehension of messages about mammography . Our goal was to underst and how numeracy affects women 's ability to gauge the benefit of mammography after receiving quantitative information . We provided women with typical risk reduction expressions about mammography , framed in relative and absolute terms , to study how well they could make use of such information . We hypothesized that the ability to use quantitative risk information would be related to the level of numeracy . Methods Study Design and Sample In December 1995 , we drew a simple r and om sample of 500 women from a registry of female veterans maintained at the Department of Veterans Affairs Medical Center , White River Junction , Vermont . These women were mailed one of four question naires , which differed only in how the same information on average risk reduction with mammography was presented . Reminder letters were sent to nonrespondents after 2 weeks , and new copies of the question naire were mailed after 4 weeks . The study was closed in February 1996 . Because of errors in the registry , 26 entries in our sample were not usable ( 10 of the listed persons were deceased , and 16 either were not female veterans or were listed twice ) . Thus , our possible respondent pool totaled 474 , of which 302 ( 64 % ) returned the question naire . The returned surveys sometimes contained unanswered questions . Such blanks may represent information about the respondent 's abilities , or they may be a marker of an unmotivated respondent or one who has become fatigued from the survey burden . To remove those who were unmotivated or fatigued , we required completion of four of the five questions on the last page of the survey for inclusion in the analyses . Only 15 women did not meet this criterion . Thus , our final sample comprised 287 women , for a 61 % completion rate . Unanswered numeracy questions were considered wrong answers . Restricting the sample to women who answered these questions yielded results similar to those seen with the entire sample . Question naire Design Assessment of Numeracy Numeracy was assessed with three questions and was scored as the total number of correct responses . The first question assessed basic familiarity with probability : Imagine that we flip a fair coin 1,000 times . What is your best guess about how many times the coin would come up heads in 1,000 flips ? _ _ _ _ times out of 1,000 . The second question asked respondents to convert a percentage ( 1 % ) to a proportion ( 10 in 1000 ) : In the BIG BUCKS LOTTERY , the chance of winning a $ 10 prize is 1 % . What is your best guess about how many people would win a $ 10 prize if 1000 people each buy a single ticket to BIG BUCKS?____person(s ) out of 1,000 . The third question reversed this task , asking the respondent to convert a proportion ( 1 in 1000 ) to a percentage ( 0.1 % ) : In ACME PUBLISHING SWEEPSAKES , the chance of winning a car is 1 in 1,000 . What percent of tickets to ACME PUBLISHING SWEEPSAKES win a car?____% . Presentation of Risk Reduction Data We r and omly assigned each of the 500 women to receive one of four question naires ; the question naires differed only in how the information on risk reduction was framed ( Figure 1 ) . Risk reduction data were framed as a 33 % relative risk reduction , a 33 % relative risk reduction together with the baseline risk for death from breast cancer in the next 10 years , a 4 in 1000 absolute risk reduction , or a 4 in 1000 absolute risk reduction together with the baseline risk for death from breast cancer in the next 10 years . Figure 1 . Overview of the task presented to a woman completing the survey and our measures of her ability to accurately apply risk reduction information . Assessment of Perceived Benefit To assess their perceived risk for death from breast cancer with and without mammography  ( Figure 1 ) , women were asked to do the following : Imagine 1,000 women exactly like you . Of these women , what is your best guess about how many will die from breast cancer during the next 10 years if they are not screened every year for breast cancer by mammogram _ _ _ _ _ out of 1000 they are screened every year for breast cancer by mammogram _ _ _ _ _ out of 1000 Calculation of Accuracy To see how accurately respondents applied the risk reduction data that they were given , we compared their perceived risk for death from breast cancer with mammography with their perceived risk for death without mammography ( Figure 1 ) . Accuracy was judged by the ability to adjust perceived risk in accordance with the risk reduction data presented . Accuracy was judged solely by the change between perceived risk with mammography and perceived risk without mammography , which we calculated from the responses to these two questions . Thus , women could grossly overestimate their risk while still accurately applying the risk reduction information . In fact , many women did overestimate their perceived risk without mammography , as observed in previous work [ 23 ] . For the two groups presented with the absolute risk reduction , we subtracted each woman 's perceived risk with mammography from her perceived risk without mammography . For example , a woman who perceived her risk to be 100 out of 1000 without mammography and 96 out of 1000 with mammography indicated an absolute risk reduction of 4 out of 1000 . Because the women in these groups were told that mammography decreases the risk for death from breast cancer by 4 in 1000 , only women whose responses indicated an absolute risk reduction of 4 in 1000 were judged to be accurate . For the two groups presented with the relative risk reduction , we determined the percentage reduction between the perceived risk without mammography and the perceived risk with mammography . For example , a woman with a perceived risk of 100 out of 1000 without mammography and 67 out of 1000 with mammography indicated a 33 % risk reduction . The women in these groups were told that mammography decreases the risk for death from breast cancer by 33 % . To allow for rounding off , women whose responses indicated a relative risk reduction between 30 % and 40 % were judged to be accurate . Statistical Analysis For the assessment of both numeracy and accuracy , the percentage of the sample that had correct answers for each measure was calculated as the number of respondents with correct responses divided by the total number of respondents ( n = 287 ) . Chi-square tests and Kruskal-Wallis tests were used to compare participant characteristics across the four groups . All comparisons were two-sided and were considered statistically significant at a P value of less than 0.05 . We used multiple logistic regression to assess the relation between accuracy in applying the risk reduction information ( dependent variable ) and numeracy ( independent variable ) after adjusting for age , income , education , and the framing of the information provided . Role of the Funding Sponsor This research was funded by the Department of Veterans Affairs Fellowship Program in Ambulatory Care and the Department of Defense 's Breast Cancer Research Program . Neither department had any role in the gathering , analysis , or interpretation of the data or in deciding whether to su bmi t the report for publication . Results Study Sample Overall , the study sample consisted of older female veterans , almost all of whom had at least a high school education . Most reported having annual household incomes of less than $ 25 000 , and fewer than one fourth were currently employed ( Table 1 ) . Most reported having had at least one mammogram , and 9 % had a history of breast cancer . No significant differences were seen in any characteristic across the four groups . Table 1 . Characteristics of the Study Sample * Assessment of Numeracy Almost half of the women ( 46 % ) answered the coin-flip question ( which asked how many times a coin would come up heads in 1000 flips ) incorrectly , raising questions about basic underst and ing of probability . Incorrect answers ranged from 0 to 800 and were largely underestimates ( one third of the sample thought that 1000 flips of a fair coin would result in < 300 heads ) . The most common incorrect answers were 25 , 50 , and 250 . Women also had trouble converting between percentages and probability expressions . The difficulty was greater for the conversion of 1 in 1000 to 0.1 % than for the conversion of 1 % to 10 in 1000 . Although 46 % of 287 respondents were unable to convert 1 % to 10 in 1000 , 80 % were unable to convert 1 in 1000 to 0.1 % . The total number of correct responses to these three simple tasks were as OBJECTIVE To measure the effects of a summary -of- findings ( SoF ) table on user satisfaction , underst and ing , and time spent finding key results in a Cochrane review . STUDY DESIGN AND SETTING We r and omized participants in an evidence -based practice workshop ( r and omized controlled trial [ RCT ] I ) and a Cochrane Collaboration entities meeting ( RCT II ) to receive a Cochrane review with or without an SoF table .  In RCT I , we measured user satisfaction . In RCT II , we measured correct comprehension and time spent finding key results . RESULTS RCT I : Participants with the SoF table ( n=47 ) were more likely to " agree " or " strongly agree " that it was easy to find results for important outcomes than ( n=25 ) participants without the SoF table-68 % vs. 40 % ( P=0.021 ) . RCT II : Participants with the SoF table ( n=18 ) were more likely to correctly answer two questions regarding results than ( n=15 ) those without the SoF table : 93 % vs. 44 % ( P=0.003 ) and 87 % vs. 11 % ( P<0.001 ) . Participants with the SoF table spent an average of 90 seconds to find key information compared with 4 minutes for participants without the SoF table ( P=0.002 ) . CONCLUSION In two small trials , we found that inclusion of an SoF table in a review improved underst and ing and rapid retrieval of key findings compared with review s with no SoF table Context Direct-to-consumer drug advertisements do not provide st and ardized information about the benefits and harms of drug therapies . Contribution These r and omized trials tested whether adding a drug facts box to consumer prescription advertisements improved consumer knowledge and judgment . The facts box showed numbers of outcomes , including adverse events , which might occur with 2 alternative drug therapies . Consumers given advertisements that included the box had more accurate perceptions of drug benefits and side effects than consumers given advertisements without the box . Implication A st and ardized table quantifying outcomes that might occur with different therapies improved consumer knowledge of drug benefits and side effects . The Editors Direct-to-consumer advertising surpasses all other efforts to alert the U.S. public about prescription drugs . Industry spent more than $ 5 billion on these ads in 2007 (1)more than twice the total U.S. Food and Drug Administration ( FDA ) budget ( 2 ) . Although the ads are often justified as serving an educational purpose , they generally fail to provide the most fundamental information consumers need to make informed decisions : data on how well the drug works . To address this problem , we developed a drug facts box , a 1-page summary of a drug 's benefit and side effects . The central information is provided in a table that shows the chance of various outcomes for people who do and do not take the drug . In previous studies , we showed that consumers underst and and value this information ( 3 , 4 ) . We performed 2 r and omized , controlled trials in nationally representative sample s of U.S. adults ( the target audience for direct-to-consumer advertising ) to test whether a drug facts box improves consumer knowledge and helps people make better choices . Methods Design We conducted 2 r and omized , controlled trials with the same basic design from October 2006 through April 2007 : the symptom drug box trial and the prevention drug box trial . We conducted a pair of trials because consumers generally face decisions about 2 categories of drugs : those taken to treat current symptoms and those taken to reduce the risk for future events ( prevention ) . In the symptom drug box trial , we used direct-to-consumer ads for drugs to treat heartburn , an outcome ( heartburn relief ) that is common and experienced directly . We chose to include a proton-pump inhibitor ( PPI ) and a histamine-2 (H2)blocker because both drugs are used to treat current symptoms of the same disease and have similar side effect profiles , but PPIs clearly outperform H2-blockers ; we could therefore assess whether the box helped consumers choose the superior drug . In the prevention drug box trial , we used advertisements for a statin and clopidogrel for secondary cardiovascular prevention . These drugs are used to reduce the risk for future events that are important but relatively rare ( heart attack and death ) , so absolute effects can only be small . The goal of this trial was to see whether the box improved the accuracy of consumers ' perceptions about drugs used to reduce cardiovascular risk . Figure 1 shows the ads used in each trial . Figure 1 . Advertisements given to trial participants . Appendix 1 and Appendix 2 provide full-size images of all 8 ads . PPI = proton-pump inhibitor . Supplement . Appendix 1 Supplement . Appendix 2 Committees for the protection of human subjects at Dartmouth Medical School and the University of Massachusetts approved the trials . Trial protocol s were registered with Clinical Trials.gov before recruitment began . Sample Selection Figure 2 summarizes participant flow into the study . Figure 2 . Study flow diagram . R and om-Digit Dialing Protocol The Center for Survey Research , University of Massachusetts ( who conducted both surveys ) , created 2 national , r and om-digit dial sample s , each with about 3000 telephone numbers . Trained interviewers used a st and ard protocol in which they called each telephone number at least 6 times ( different days , weeks , and times ) . Whenever someone answered , the interviewer explained that we are conducting an important study about how people feel about different ways to provide information about prescription drugs in advertising . English-speaking adults age 35 to 70 years in the household were invited to participate ( if > 1 adult was eligible , the computerwhich guided these screening interviewsr and omly selected one ) . R and omization and Mail Survey Protocol Participants were r and omly assigned to the drug box or control group by using a central computerized r and om-number generator ( to ensure allocation concealment ) and were mailed survey material s ( 2 drug ads , the survey , and a stamped return envelope ) plus a prepaid $ 10 incentive with a promise of an additional $ 10 upon survey completion . Participants were not told about the alternate survey form . Nonresponders received reminder postcards after 2 weeks , a replacement question naire after 4 weeks , a telephone call after 6 weeks , and a question naire after 8 weeks . Response Rates We used st and ard Association for Public Opinion Research methods to calculate overall response rates ( 5 ) . The numerator is completed surveys ( 231 symptom drug box trial participants [ 122 in the drug box group and 109 in the control group ] and 219 prevention drug box trial participants [ 111 in the drug box group and 108 in the control group ] ) . The denominator is individuals known to be eligible ( 289 in the symptom drug box trial and 290 in the prevention drug box trial ) plus an estimate of eligible individuals in households that could not be screened . In the symptom drug box trial , this number184was calculated by applying the 44.4 % eligibility fraction observed among screened households to the 414 unscreened households . The overall response rates were 49 % in the symptom drug box trial ( 231/[289 + 184 ] ) and 46 % in the prevention drug box trial ( 219/[290 + 182 ] ) . Completion rates were calculated by using completed surveys as the numerator and participants as the denominator . For the symptom drug box trial , the completion rate was 89 % ( 231/258 ) ; the drug box group had a higher completion rate than the control group ( 95 % [ 122/129 ] vs. 84 % [ 109/129 ] ; P= 0.010 ) . In the prevention drug box trial , the completion rate was 84 % ( 219/260 ) and did not differ between groups . Study Material s We modified actual drug ads by disguising the name of the drug and manufacturer to avoid preconceived notions that participants might hold . Appendix 1 and Appendix 2 include all material s. Ad Image Page All participants received the same ad image page ( the colorful front page ) . Participants in the symptom drug box trial received ads for an H2-blocker ( Amcid ) and a PPI ( Maxtor ) . In the prevention drug box trial , the ads were for a statin ( Concor ) and clopidogrel ( Pridclo ) . Second Page In both trials , the second page differed according to r and omization . For control participants , the second page was the actual ad 's brief summary : the drug label excerpt that the FDA requires in drug ads . The drug box groups received drug facts boxes . These boxes ( Figure 3 shows the Pridclo [ clopidogrel ] box ) include a descriptive section with basic drug information ( such as indications and other things to consider doing ) . The central element is a summary table of efficacy and side effects presented as the chance of various outcomes for people who do or do not take the drug . We completed the boxes by using data from individual trials because that is how the FDA approaches drug approval ; they do not use meta- analysis to judge efficacy . Figure 3 . Pridclo ( clopidogrel ) drug facts box . FDA = U.S. Food and Drug Administration . To find efficacy data for the boxes in the symptom drug box trial , we used the Cochrane Collaboration review ( 6 ) on short-term treatment of gastroesophageal reflux diseaselike symptoms to identify all placebo-controlled trials of PPIs and H2-blockers for empirical therapy . For each drug , we chose the largest of the trials with same outcome : sustained relief , which is no heartburn episodes for 3 consecutive days . To facilitate comparisons , we st and ardized the base rate of sustained relief by averaging the findings in the placebo groups of the 2 studies ( 23 % in the H2-blocker study [ 7 ] vs. 15 % in the PPI study [ 8 ] , yielding an average base rate of 19 % ) . We calculated the relative risk ( drug vs. placebo ) for sustained relief in the H2-blocker ( relative risk , 1.87 ) and PPI ( relative risk , 4.67 ) trials by using the observed findings . To get st and ardized rates , we multiplied the averaged placebo rate by each relative risk . To frame the outcome as the proportion of patients still having heartburn , we used the complement of these calculated rates . Side effect data for the drug boxes were abstract ed from the corresponding brief summaries ( given to control participants ) to ensure that both groups had access to similar information . Although many side effects are listed throughout the brief summary , the drug box includes only the most important and largest relative to placebo or other comparator ( organized into life-threatening and symptom categories ) to focus the reader 's attention and minimize cognitive burden . To find efficacy data for the boxes in the prevention drug box trial , we began with the currently approved label for clopidogrel ( 9 ) . The label cites a single trial supporting the use of clopidogrel for heart attack prevention : CAPRIE ( Clopidogrel vs. Aspirin in Patients at Risk of Ischemic Events ) ( 10 ) . To find a similar study for the statin ( in terms of sample size , high-risk population , and outcome of heart attack ) , we review ed a meta- analysis of cholesterol-lowering treatment ( 11 ) . We chose the Medical Research Council Heart Protection Study of simvastatin ( 12 ) . To facilitate comparisons , we st and ardized the base rates of heart attack and all-cause mortality by taking the average risk in the comparator groups of the 2 studies . To get the st and ardized risk in both drug groups , we multiplied the st and ardized
BACKGROUND Despite limited evidence , it is often asserted that natural frequencies ( for example , 2 in 1000 ) are the best way to communicate absolute risks .
OBJECTIVE To compare comprehension of treatment benefit and harm when absolute risks are presented as natural frequencies , percents , or both .