Department of Clincial Research, Basel Institute for Clinical Epidemiology and Biostatistics, University Hospital Basel, University of Basel, Basel, Switzerland; Department of Medicine, Stanford Prevention Research Center, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Department of Clincial Research, Basel Institute for Clinical Epidemiology and Biostatistics, University Hospital Basel, University of Basel, Basel, Switzerland.
J Clin Epidemiol. 2018 Jan;93:94-102. doi: 10.1016/j.jclinepi.2017.09.013. Epub 2017 Sep 21.
Confounding bias is a most pervasive threat to validity of observational epidemiologic research. We assessed whether authors of observational epidemiologic studies consider confounding bias when interpreting the findings.
We randomly selected 120 cohort or case-control studies published in 2011 and 2012 by the general medical, epidemiologic, and specialty journals with the highest impact factors. We used Web of Science to assess citation metrics through January 2017.
Sixty-eight studies (56.7%, 95% confidence interval: 47.8-65.5%) mentioned "confounding" in the Abstract or Discussion sections, another 20 (16.7%; 10.0-23.3%) alluded to it, and there was no mention or allusion at all in 32 studies (26.7%; 18.8-34.6%). Authors often acknowledged that for specific confounders, there was no adjustment (34 studies; 28.3%) or deem it possible or likely that confounding affected their main findings (29 studies; 24.2%). However, only two studies (1.7%; 0-4.0%) specifically used the words "caution" or "cautious" for the interpretation because of confounding-related reasons and eventually only four studies (3.3%; 0.1-6.5%) had limitations related to confounding or any other bias in their Conclusions. Studies mentioning that the findings were possibly or likely affected by confounding were more frequently cited than studies with a statement that findings were unlikely affected (median 6.3 vs. 4.0 citations per year, P = 0.04).
Many observational studies lack satisfactory discussion of confounding bias. Even when confounding bias is mentioned, authors are typically confident that it is rather irrelevant to their findings and they rarely call for cautious interpretation. More careful acknowledgment of possible impact of confounding is not associated with lower citation impact.
混杂偏倚是观察性流行病学研究有效性最普遍的威胁。我们评估了观察性流行病学研究的作者在解释研究结果时是否考虑混杂偏倚。
我们随机选择了 2011 年和 2012 年发表在普通医学、流行病学和专业期刊上具有最高影响因素的 120 项队列或病例对照研究。我们使用 Web of Science 通过 2017 年 1 月评估了引文指标。
68 项研究(56.7%,95%置信区间:47.8-65.5%)在摘要或讨论部分提到了“混杂”,另有 20 项研究(16.7%,10.0-23.3%)暗示了这一点,而 32 项研究(26.7%,18.8-34.6%)根本没有提到或暗示。作者经常承认,对于特定的混杂因素,没有进行调整(34 项研究;28.3%),或者认为混杂可能影响了他们的主要发现(29 项研究;24.2%)。然而,只有两项研究(1.7%,0-4.0%)出于与混杂相关的原因,专门使用了“谨慎”或“小心”等词来解释,最终只有四项研究(3.3%,0.1-6.5%)在结论中提到了与混杂或任何其他偏倚相关的局限性。提到研究结果可能受到混杂影响的研究比那些表明研究结果不太可能受混杂影响的研究更常被引用(中位数每年 6.3 次与 4.0 次引用,P=0.04)。
许多观察性研究对混杂偏倚缺乏令人满意的讨论。即使提到混杂偏倚,作者通常也很自信,认为混杂对他们的发现影响不大,他们很少呼吁谨慎解释。更仔细地承认混杂可能产生的影响与较低的引用影响无关。