Heidelberg Institute of Global Health, Heidelberg Medical School, Heidelberg University, Heidelberg, Germany.
Department of Epidemiology and Biostatistics, University of California, San Francisco, California, United States of America.
PLoS One. 2021 Apr 29;16(4):e0250778. doi: 10.1371/journal.pone.0250778. eCollection 2021.
Pooling (or combining) and analysing observational, longitudinal data at the individual level facilitates inference through increased sample sizes, allowing for joint estimation of study- and individual-level exposure variables, and better enabling the assessment of rare exposures and diseases. Empirical studies leveraging such methods when randomization is unethical or impractical have grown in the health sciences in recent years. The adoption of so-called "causal" methods to account for both/either measured and/or unmeasured confounders is an important addition to the methodological toolkit for understanding the distribution, progression, and consequences of infectious diseases (IDs) and interventions on IDs. In the face of the Covid-19 pandemic and in the absence of systematic randomization of exposures or interventions, the value of these methods is even more apparent. Yet to our knowledge, no studies have assessed how causal methods involving pooling individual-level, observational, longitudinal data are being applied in ID-related research. In this systematic review, we assess how these methods are used and reported in ID-related research over the last 10 years. Findings will facilitate evaluation of trends of causal methods for ID research and lead to concrete recommendations for how to apply these methods where gaps in methodological rigor are identified.
We will apply MeSH and text terms to identify relevant studies from EBSCO (Academic Search Complete, Business Source Premier, CINAHL, EconLit with Full Text, PsychINFO), EMBASE, PubMed, and Web of Science. Eligible studies are those that apply causal methods to account for confounding when assessing the effects of an intervention or exposure on an ID-related outcome using pooled, individual-level data from 2 or more longitudinal, observational studies. Titles, abstracts, and full-text articles, will be independently screened by two reviewers using Covidence software. Discrepancies will be resolved by a third reviewer. This systematic review protocol has been registered with PROSPERO (CRD42020204104).
在个体水平上对观察性、纵向数据进行汇集（或合并）和分析，可通过增加样本量来促进推断，从而联合估计研究和个体水平的暴露变量，并更好地评估罕见暴露和疾病。近年来，在健康科学领域，当随机化不道德或不切实际时，越来越多的实证研究利用这些方法。采用所谓的“因果”方法来解释已测量和/或未测量的混杂因素，是理解传染病（IDs）及其干预措施的分布、进展和后果的方法学工具包的一个重要补充。在面对 COVID-19 大流行且没有对暴露或干预措施进行系统随机化的情况下，这些方法的价值更加明显。然而，据我们所知，尚无研究评估涉及汇集个体水平、观察性、纵向数据的因果方法在与 ID 相关的研究中是如何应用的。在本系统评价中，我们评估了这些方法在过去 10 年中在与 ID 相关的研究中是如何使用和报告的。研究结果将有助于评估用于 ID 研究的因果方法的趋势，并针对在确定方法严谨性方面存在差距的情况下如何应用这些方法提出具体建议。
我们将使用 MeSH 和文本术语从 EBSCO（学术搜索综合版、商业资源全文版、CINAHL、经济文献全文版、心理信息数据库）、EMBASE、PubMed 和 Web of Science 中确定相关研究。合格的研究是指那些在使用来自 2 个或更多纵向观察性研究的汇集个体水平数据评估干预或暴露对与 ID 相关的结局的影响时，应用因果方法来解释混杂因素的研究。两名评审员将使用 Covidence 软件独立筛选标题、摘要和全文文章。如有分歧，将由第三名评审员解决。本系统评价方案已在 PROSPERO（CRD42020204104）中注册。
