Sero- prevalence studies often have a problem of missing data. Few studies report the proportion of missing data and even fewer describe the methods used to adjust the results for missing data. The objective of this review was to determine the analytical methods used for analysis in HIV surveys with missing data. We searched for population, demographic and cross-sectional surveys of HIV published from January 2000 to April 2018 in Pub Med/Medline, Web of Science core collection, Latin American and Caribbean Sciences Literature, Africa-Wide Information and Scopus, and by reviewing references of included articles. All potential abstracts were imported into Covidence and abstracts screened by two independent reviewers using pre-specified criteria. Disagreements were resolved through discussion. A piloted data extraction tool was used to extract data and assess the risk of bias of the eligible studies. Data were analysed through a quantitative approach; variables were presented and summarised using figures and tables. A total of 3426 citations where identified, 194 duplicates removed, 3232 screened and 69 full articles were obtained. Twenty-four studies were included. The response rate for an HIV test of the included studies ranged from 32 to 96% with the major reason for the missing data being refusal to consent for an HIV test. Complete case analysis was the primary method of analysis used, multiple imputations 11(46%) was the most advanced method used, followed by the Heckman’s selection model 9(38%). Single Imputation and Instrumental variables method were used in only two studies each, with 13(54%) other different methods used in several studies. Forty-two percent of the studies applied more than two methods in the analysis, with a maximum of 4 methods per study. Only 6(25%) studies conducted a sensitivity analysis, while 11(46%) studies had a significant change of estimates after adjusting for missing data. Missing data in survey studies is still a problem in disease estimation. Our review outlined a number of methods that can be used to adjust for missing data on HIV studies; however, more information and awareness are needed to allow informed choices on which method to be applied for the estimates to be more reliable and representative.

中文翻译：

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根据缺失数据的人口和横断面调查估计艾滋病毒/艾滋病流行率的分析方法：系统评价。

血清流行率研究经常存在数据缺失的问题。很少有研究报告缺失数据的比例，甚至很少有研究描述用于调整缺失数据结果的方法。本次审查的目的是确定用于分析缺失数据的艾滋病毒调查的分析方法。我们检索了 2000 年 1 月至 2018 年 4 月在 Pub Med/Medline、Web of Science 核心合集、拉丁美洲和加勒比科学文献、非洲范围信息和 Scopus 上发表的艾滋病毒人口、人口统计和横断面调查，并回顾了参考文献包含的文章。所有潜在的摘要均导入 Covidence 中，并由两名独立审稿人使用预先指定的标准筛选摘要。分歧通过讨论解决。使用试点数据提取工具来提取数据并评估合格研究的偏倚风险。通过定量方法分析数据；使用图表来呈现和总结变量。共识别引用 3426 条，删除重复 194 条，筛选 3232 条，获得完整文章 69 篇。纳入了二十四项研究。纳入研究的 HIV 检测的应答率为 32% 至 96%，缺失数据的主要原因是拒绝同意进行 HIV 检测。完整案例分析是主要使用的分析方法，多重插补11（46％）是使用的最先进的方法，其次是赫克曼选择模型9（38％）。单次插补法和工具变量法各仅在两项研究中使用，另有 13 种（54%）其他研究在多项研究中使用。42% 的研究在分析中应用了两种以上的方法，每个研究最多使用 4 种方法。只有 6 项（25%）研究进行了敏感性分析，而 11 项（46%）研究在调整缺失数据后估计值发生了显着变化。调查研究中的数据缺失仍然是疾病估计的一个问题。我们的审查概述了一些可用于调整艾滋病毒研究缺失数据的方法；然而，需要更多的信息和认识，以便能够在知情的情况下选择采用哪种方法，以使估计值更加可靠和具有代表性。