概括性偏差的风险是可以概括的吗? 一项超流行病学研究
Lauren von Klinggraeff1, Chris D Pfledderer2, Sarah Burkart3
1Augusta University, Augusta University.
Research square
|March 11, 2024
概括
概括性偏差风险 (RGBs) 膨胀了初步卫生干预研究结果. 发现这些偏见在更大规模的试验中降低了各种健康行为的有效性,而不仅仅是肥胖.
科学领域:
- 健康行为干预研究研究健康行为干预研究
- 基于证据的实践实践.
- 研究方法研究方法论.
背景情况:
- 初步研究可能会高估干预的有效性,导致过早地进入大规模试验.
- 概括性偏差的风险 (RGBs) 是在早期研究中膨胀有效性估计的特征.
- 虽然已建立用于肥胖干预措施,但RGBs对其他健康领域的概括性尚不清楚.
研究的目的:
- 调查RGB是否存在,并影响超越肥胖症的健康行为干预措施的有效性估计.
主要方法:
- 对健康行为干预措施 (烟草,酒精,人际暴力,性病) 的系统审查,包括初步和更大的试验阶段.
- 提取健康结果并为RGBs的存在编码.
- 进行元回归分析,以评估RGBs对研究阶段之间的标准化平均差异 (ΔSMD) 变化的影响.
主要成果:
- 确定了69个研究对,其中包括47个 (156个效果). 在所有被研究的行为中都存在RGB.
- 在初步研究中,对RGB的干预表明,在更大的试验中,ΔSMD=-0.38的有效性平均下降.
- 没有RGBs的研究显示, ΔSMD=-0.24.的有效性平均下降较小.
结论:
- RGBs似乎与各种健康干预研究领域的膨胀效应估计有关.
- 跨健康行为干预领域的共同点可能有助于在初步研究中引入RGB.
- 研究结果表明,RGBs并不仅限于特定的健康行为,而是更广泛的方法关注.
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