Selection in reported epidemiological risks: an empirical assessment.
Selection in reported epidemiological risks: an empirical assessment.
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报告的流行病学风险中的选择：经验评估。
DOI：
10.1371/journal.pmed.0040079
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发表时间：
2007-03
期刊：
影响因子：
15.8
通讯作者：
Ioannidis JP
中科院分区：
文献类型：
--
作者：
Kavvoura FK;Liberopoulos G;Ioannidis JP
流行病学研究可能受到选择性报告的影响，但其经验证据有限。我们经验性地评估了在最近文章的大样本中选择显著结果和大效应量的程度。我们评估了389篇流行病学研究的文章，这些文章在各自的摘要中报告了至少一个连续风险因素的相对风险，这些风险因素是基于中位数、五分位数、四分位数或五分位数分类的。我们检查了摘要中报告统计上显著和不显著结果的比例和相关性，以及所呈现的相对风险的大小（一致≥1.00）是否因风险因素使用的对比类型而不同。342篇（87.9%）的文章在摘要中报告了≥1个具有统计学意义的相对风险，而只有169篇（43.4%）的文章在摘要中报告了≥1个具有统计学意义的相对风险。报告统计上显著的结果在结构化摘要中更为常见，而在美国研究和癌症结果中不太常见。在随机选择的50篇文章中，有9篇（四分位数范围5-16）的相对风险有统计学意义，6篇（四分位数范围3-16）的相对风险无统计学意义（p = 0.25）。矛盾的是，最小的相对风险是基于极端五分位数的对比；平均而言，相对危险度分别是极端四分位数、极端四分位数和中位数上下的1.41倍、1.42倍和1.36倍（p < 0.001）。已发表的流行病学调查几乎普遍强调了风险因素与结果之间的显著关联。对于持续的风险因素，研究人员选择性地在相对风险本身较低的更极端的群体之间进行对比。对报告流行病学研究的已发表文章的评估发现，它们几乎都强调了风险因素与结果之间的显著关联。医学和科学研究人员使用统计测试来试图弄清楚他们的观察结果——例如，看到两组人之间某些特征的差异——是否仅仅是偶然的结果。统计测试不能确定这一点，而只能给出观察结果偶然出现的概率。当研究人员有许多不同的假设，并对同一组数据进行许多统计测试时，他们可能会得出结论，认为存在真正的差异，而实际上没有差异。与此同时，人们早就知道，科学和医学研究人员倾向于挑选研究结果，然后在他们的论文中报告。更有趣、更令人印象深刻或统计上更有意义的发现更有可能被发表。这被称为“发表偏倚”或“选择性报道偏倚”。因此，有些人担心已发表的科学文献可能包含许多假阳性发现，即不真实的发现，而仅仅是数据偶然变化的结果。这将严重影响已发表的科学文献的准确性，并倾向于高估正在研究的关系的强度和方向。选择性报告偏倚已经在随机试验领域进行了详细的研究（研究参与者被随机分配接受一种干预措施，例如，一种新药，而不是另一种干预措施或“比较物”，以了解新干预措施的益处或安全性）。这些研究表明，很多试验的发现从未发表过，统计上显著的发现比不显著的发现更有可能被纳入已发表的论文。然而，许多医学研究都没有使用随机试验方法，要么是因为这种方法对回答手头的问题没有用，要么是不道德的。流行病学研究通常关注的是风险因素与疾病发展之间的联系，这类研究通常使用观察而不是实验来发现联系。这里的研究人员担心，在流行病学研究中，选择性报告偏差可能和在随机试验研究中一样是一个问题，并希望专门研究这个问题。在这项调查中，检索了PubMed（一个生物医学研究数据库），以提取2004年1月至2005年10月间发表的流行病学研究。研究人员想专门研究报告持续风险因素的影响及其对健康或疾病结果的影响的研究（持续风险因素是像年龄或血液中的葡萄糖浓度这样的东西，是一个数字，可以在滑动刻度上有任何值）。发现了389项原始研究，研究人员从这些论文的摘要和全文中提取了报告的相对风险以及对它们的统计测试结果。（相对风险是指一组相对于另一组获得某种结果（比如疾病）的几率。）研究人员发现，近90%的这些研究在摘要中报告了一个或多个具有统计学意义的风险，但只有43%的研究报告了一个或多个不具有统计学意义的风险。当研究人员查看其中50项研究的全文中报告的所有发现时，他们发现论文总体上报告的统计上显著的风险多于不显著的风险。最后，在这里研究的论文中，统计分析的方式似乎对更极端的发现产生了偏见：对于显示相对风险较小的数据集，论文更有可能报告数据的极端子集之间的比较，从而报告更大的相对风险。这些发现表明，流行病学研究人员有一种倾向，即在他们的研究论文中突出统计上显著的发现，而避免突出不显著的发现。这种行为可能是一个问题，因为这些重大发现中的许多可能在未来被证明是“假阳性”。目前，研究人员可以通过注册来描述正在进行的临床试验，并列出他们计划对这些试验进行分析的结果。这些注册将在一定程度上解决这里描述的一些问题，但仅用于临床试验研究。流行病学研究的登记还不存在，因此，重要的是研究人员和读者要意识到流行病学研究中选择性报告的问题，并对此保持谨慎。请通过本摘要的在线版本http://dx.doi.org/10.1371/journal.pmed.0040079访问这些网站。关于发表偏倚的维基百科条目（注：维基百科是一个任何人都可以编辑的互联网百科全书）国际医学期刊编辑委员会给出了向其成员期刊提交手稿的指导方针，并包括对正在进行的研究的注册和发表负面研究的义务的评论ClinicalTrials.gov和ISRCTN注册是正在进行的临床试验的两个注册
Epidemiological studies may be subject to selective reporting, but empirical evidence thereof is limited. We empirically evaluated the extent of selection of significant results and large effect sizes in a large sample of recent articles. We evaluated 389 articles of epidemiological studies that reported, in their respective abstracts, at least one relative risk for a continuous risk factor in contrasts based on median, tertile, quartile, or quintile categorizations. We examined the proportion and correlates of reporting statistically significant and nonsignificant results in the abstract and whether the magnitude of the relative risks presented (coined to be consistently ≥1.00) differs depending on the type of contrast used for the risk factor. In 342 articles (87.9%), ≥1 statistically significant relative risk was reported in the abstract, while only 169 articles (43.4%) reported ≥1 statistically nonsignificant relative risk in the abstract. Reporting of statistically significant results was more common with structured abstracts, and was less common in US-based studies and in cancer outcomes. Among 50 randomly selected articles in which the full text was examined, a median of nine (interquartile range 5–16) statistically significant and six (interquartile range 3–16) statistically nonsignificant relative risks were presented (p = 0.25). Paradoxically, the smallest presented relative risks were based on the contrasts of extreme quintiles; on average, the relative risk magnitude was 1.41-, 1.42-, and 1.36-fold larger in contrasts of extreme quartiles, extreme tertiles, and above-versus-below median values, respectively (p < 0.001). Published epidemiological investigations almost universally highlight significant associations between risk factors and outcomes. For continuous risk factors, investigators selectively present contrasts between more extreme groups, when relative risks are inherently lower. An evaluation of published articles reporting epidemiological studies found that they almost universally highlight significant associations between risk factors and outcomes. Medical and scientific researchers use statistical tests to try to work out whether their observations—for example, seeing a difference in some characteristic between two groups of people—might have occurred as a result of chance alone. Statistical tests cannot determine this for sure, rather they can only give a probability that the observations would have arisen by chance. When researchers have many different hypotheses, and carry out many statistical tests on the same set of data, they run the risk of concluding that there are real differences where in fact there are none. At the same time, it has long been known that scientific and medical researchers tend to pick out the findings on which to report in their papers. Findings that are more interesting, impressive, or statistically significant are more likely to be published. This is termed “publication bias” or “selective reporting bias.” Therefore, some people are concerned that the published scientific literature might contain many false-positive findings, i.e., findings that are not true but are simply the result of chance variation in the data. This would have a serious impact on the accuracy of the published scientific literature and would tend to overestimate the strength and direction of relationships being studied. Selective reporting bias has already been studied in detail in the area of randomized trials (studies where participants are randomly allocated to receive an intervention, e.g., a new drug, versus an alternative intervention or “comparator,” in order to understand the benefits or safety of the new intervention). These studies have shown that very many of the findings of trials are never published, and that statistically significant findings are more likely to be included in published papers than nonsignificant findings. However, much medical research is carried out that does not use randomized trial methods, either because that method is not useful to answer the question at hand or is unethical. Epidemiological research is often concerned with looking at links between risk factors and the development of disease, and this type of research would generally use observation rather than experiment to uncover connections. The researchers here were concerned that selective reporting bias might be just as much of a problem in epidemiological research as in randomized trials research, and wanted to study this specifically. In this investigation, searches were carried out of PubMed, a database of biomedical research studies, to extract epidemiological studies that were published between January 2004 and October 2005. The researchers wanted to specifically look at studies reporting the effect of continuous risk factors and their effect on health or disease outcomes (a continuous risk factor is something like age or glucose concentration in the blood, is a number, and can have any value on a sliding scale). Three hundred and eighty-nine original research studies were found, and the researchers pulled out from the abstracts and full text of these papers the relative risks that were reported along with the results of statistical tests for them. (Relative risk is the chance of getting an outcome, say disease, in one group as compared to another group.) The researchers found that nearly 90% of these studies had one or more statistically significant risks reported in the abstract, but only 43% reported one or more risks that were not statistically significant. When looking at all of the findings reported anywhere in the full text for 50 of these studies, the researchers saw that papers overall reported more statistically significant risks than nonsignificant risks. Finally, it seemed that in the set of papers studied here, the way in which statistical analyses were done produced a bias towards more extreme findings: for datasets showing small relative risks, papers were more likely to report a comparison between extreme subsets of the data so as to report larger relative risks. These findings suggest that there is a tendency among epidemiology researchers to highlight statistically significant findings and to avoid highlighting nonsignificant findings in their research papers. This behavior may be a problem, because many of these significant findings could in future turn out to be “false positives.” At present, registers exist for researchers to describe ongoing clinical trials, and to set out the outcomes that they plan to analyze for those trials. These registers will go some way towards addressing some of the problems described here, but only for clinical trials research. Registers do not yet exist for epidemiological studies, and therefore it is important that researchers and readers are aware of and cautious about the problem of selective reporting in epidemiological research. Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0040079. Wikipedia entry on publication bias (note: Wikipedia is an internet encyclopedia that anyone can edit) The International Committee of Medical Journal Editors gives guidelines for submitting manuscripts to its member journals, and includes comments about registration of ongoing studies and the obligation to publish negative studies ClinicalTrials.gov and the ISRCTN register are two registries of ongoing clinical trials
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影响因子：
15.8
作者：
Pan Z;Trikalinos TA;Kavvoura FK;Lau J;Ioannidis JP
通讯作者：
Ioannidis JP
影响因子：
9.3
作者：
Bossuyt, PM;Reitsma, JB;de Vet, HCW
通讯作者：
de Vet, HCW
影响因子：
105.7
作者：
Gigerenzer, G;Edwards, A
通讯作者：
Edwards, A
影响因子：
168.9
作者：
Ioannidis, JPA;Trikalinos, TA;Contopoulos-Ioannidis, DG
通讯作者：
Contopoulos-Ioannidis, DG
影响因子：
4.8
作者：
Hernández, AV;Steyerberg, EW;Maas, AIR
通讯作者：
Maas, AIR