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Academia and Clinic The STROBE Statement: Explanation and Elaboration Academia and Clinic universally agreed definitions for participation, response, or likely to be related to health status (for example, the letter follow-up rates, readers need to understand how authors of invitation was not delivered because of an incorrect ad- calculated such proportions (134). dress) will affect the precision of estimates but will proba- Ideally, investigators should give an account of the bly not introduce bias. Conversely, if many individuals opt numbers of individuals considered at each stage of recruit- out of the survey because of illness or perceived good ing study participants, from the choice of a target popula- health, results may underestimate or overestimate the prev- tion to the inclusion of participants’ data in the analysis. alence of ill health in the population. Depending on the type of study, this may include the number of individuals considered to be potentially eligible, 13(c) Consider use of a flow diagram. the number assessed for eligibility, the number found to be eligible, the number included in the study, the number Example examined, the number followed up, and the number in- See the Figure. cluded in the analysis. Information on different sampling units may be required, if sampling of study participants is carried out in 2 or more stages as in the example above Explanation (multistage sampling). In case–control studies, we advise An informative and well-structured flow diagram can that authors describe the flow of participants separately for readily and transparently convey information that might case and control groups (135). Controls can sometimes be otherwise require a lengthy description (142), as in the selected from several sources, including, for example, hos- example above. The diagram may usefully include the pitalized patients and community dwellers. In this case, we main results, such as the number of events for the primary recommend a separate account of the numbers of partici- outcome. While we recommend the use of a flow diagram, pants for each type of control group. Olson and colleagues particularly for complex observational studies, we do not proposed useful reporting guidelines for controls recruited propose a specific format for the diagram. through random-digit dialing and other methods (136). A recent survey of epidemiologic studies published in 14 Descriptive data: 10 general epidemiology, public health, and medical jour- 14(a) Give characteristics of study participants (e.g., demo- nals found that some information regarding participation graphic, clinical, social) and information on exposures and was provided in 47 of 107 case–control studies (59%), 49 potential confounders. of 154 cohort studies (32%), and 51 of 86 cross-sectional studies (59%) (137). Incomplete or absent reporting of Example participation and nonparticipation in epidemiologic stud- See Table 1. ies was also documented in 2 other surveys of the literature (4, 5). Finally, there is evidence that participation in epi- demiologic studies may have declined in recent decades Explanation (137, 138), which underscores the need for transparent Readers need descriptions of study participants and reporting (139). their exposures to judge the generalizability of the findings. Information about potential confounders, including 13(b) Give reasons for nonparticipation at each stage. whether and how they were measured, influences judg- ments about study validity. We advise authors to summa- rize continuous variables for each study group by giving the Example mean and standard deviation, or, when the data have an “The main reasons for nonparticipation were the par- asymmetrical distribution (as is often the case), the median ticipant was too ill or had died before interview (cases and percentile range (for example, 25th and 75th percen- 30%, controls Ͻ 1%), nonresponse (cases 2%, controls tiles). Variables that make up a small number of ordered 21%), refusal (cases 10%, controls 29%), and other rea- categories (such as stages of disease I to IV) should not be sons (refusal by consultant or general practitioner, non- presented as continuous variables; it is preferable to give English speaking, mental impairment) (cases 7%, controls numbers and proportions for each category (see Box 4). In 5%)” (140). studies that compare groups, the descriptive characteristics and numbers should be given by group, as in the example Explanation above. Explaining the reasons why people no longer partici- Inferential measures such as standard errors and con- pated in a study or why they were excluded from statistical fidence intervals should not be used to describe the vari- analyses helps readers judge whether the study population ability of characteristics, and significance tests should be was representative of the target population and whether avoided in descriptive tables. Also, P values are not an bias was possibly introduced. For example, in a cross-sec- appropriate criterion for selecting which confounders to tional health survey, nonparticipation due to reasons un- adjust for in analysis; even small differences in a con- www.annals.org 16 October 2007 Annals of Internal Medicine Volume 147 • Number 8 W-179 Academia and Clinic The STROBE Statement: Explanation and Elaboration Figure. Example of a flow diagram. From reference 141. Table 1. Characteristics of the Study Base at Enrollment, Castellana G (Italy), 1985–1986* Characteristic HCV Negative HCV Positive Unknown (513 ؍ n) (511 ؍ n) (1458 ؍ n) Sex, n (%) Male 936 (64) 296 (58) 197 (39) Female 522 (36) 215 (42) 306 (61) Mean age at enrollment (SD), y 45.7 (10.5) 52.0 (9.7) 52.5 (9.8) Daily alcohol intake, n (%) None 250 (17) 129 (25) 119 (24) Moderate† 853 (59) 272 (53) 293 (58) Excessive‡ 355 (24) 110 (22) 91 (18) * Adapted from reference 143. HCV ϭ hepatitis C virus. † Men, Ͻ60 g ethanol/d; women, Ͻ30 g ethanol/d. ‡ Men, Ͼ60 g ethanol/d; women Ͼ30 g ethanol/d. W-180 16 October 2007 Annals of Internal Medicine Volume 147 • Number 8 www.annals.org The STROBE Statement: Explanation and Elaboration Academia and Clinic (as is done in cohorts) can be achieved by exploring the Table 2. Symptom End Points Used in Survival Analysis* source population of the cases: if the control group is large enough and represents the source population, exposed and End Point Cough, Shortness Sleeplessness, n (%) of Breath, n (%) unexposed controls can be compared for potential con- n (%) founders (121, 147). Symptom resolved 201 (79) 138 (54) 171 (67) Censored 27 (10) 21 (8) 24 (9) 14(b) Indicate the number of participants with missing data Never symptomatic 0 46 (18) 11 (4) Data missing 28 (11) 51 (20) 50 (20) for each variable of interest. Total 256 (100) 256 (100) 256 (100) Example * Adapted from reference 141. See Table 2. founder that has a strong effect on the outcome can be Explanation important (144, 145). As missing data may bias or affect generalizability of In cohort studies, it may be useful to document how results, authors should tell readers the amounts of missing an exposure relates to other characteristics and potential data for exposures, potential confounders, and other im- confounders. Authors could present this information in a portant characteristics of patients (see item 12c and Box 6). table with columns for participants in 2 or more exposure In a cohort study, authors should report the extent of loss categories, which permits to judge the differences in con- to follow-up (with reasons), because incomplete follow-up founders between these categories. may bias findings (see items 12d and 13) (148). We advise In case–control studies, potential confounders cannot authors to use their tables and figures to enumerate be judged by comparing cases and controls. Control per- amounts of missing data. sons represent the source population and will usually be different from the cases in many respects. For example, in 14(c) Cohort study: Summarize follow-up time—e.g., average a study of oral contraceptives and myocardial infarction, a and total amount. sample of young women with infarction more often had risk factors for that disease, such as high serum cholesterol, smoking, and a positive family history, than the control Example group (146). This does not influence the assessment of the “During the 4366 person-years of follow-up (median effect of oral contraceptives, as long as the prescription of 5.4, maximum 8.3 years), 265 subjects were diagnosed as oral contraceptives was not guided by the presence of these having dementia, including 202 with Alzheimer’s disease” risk factors—for example, because the risk factors were (149). only established after the event (see Box 5). In case–con- trol studies, the equivalent of comparing exposed and non- Explanation exposed controls for the presence of potential confounders Readers need to know the duration and extent of fol- low-up for the available outcome data. Authors can present a summary of the average follow-up with either the mean Table 3. Rates of HIV-1 Seroconversion by Selected or median follow-up time or both. The mean allows a Sociodemographic Variables, 1990–1993* reader to calculate the total number of person-years by multiplying it with the number of study participants. Au- Variable Person-Years Seroconverted, Rate/1000 thors also may present minimum and maximum times or n Person-Years (95% CI) percentiles of the distribution to show readers the spread of Calendar year follow-up times. They may report total person-years of fol- 1990 2197.5 18 8.2 (4.4–12.0) low-up or some indication of the proportion of potential 1991 3210.7 22 6.9 (4.0–9.7) data that was captured (148). All such information may be 1992 3162.6 18 5.7 (3.1–8.3) 1993 2912.9 26 8.9 (5.5–12.4) presented separately for participants in 2 or more exposure 1994 1104.5 5 4.5 (0.6–8.5) categories.
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