Abstract

Objective

Informative presence (IP) is the phenomenon whereby the presence or absence of patient data is potentially informative with respect to their health condition, with informative observation (IO) being the longitudinal equivalent. These phenomena predominantly exist within routinely collected healthcare data, in which data collection is driven by the clinical requirements of patients and clinicians. The extent to which IP and IO are considered when using such data to develop clinical prediction models (CPMs) is unknown, as is the existing methodology aiming at handling these issues. This review aims to synthesize such existing methodology, thereby helping identify an agenda for future methodological work.

Materials and Methods

A systematic literature search was conducted by 2 independent reviewers using prespecified keywords.

Results

Thirty-six articles were included. We categorized the methods presented within as derived predictors (including some representation of the measurement process as a predictor in the model), modeling under IP, and latent structures. Including missing indicators or summary measures as predictors is the most commonly presented approach amongst the included studies (24 of 36 articles).

Discussion

This is the first review to collate the literature in this area under a prediction framework. A considerable body relevant of literature exists, and we present ways in which the described methods could be developed further. Guidance is required for specifying the conditions under which each method should be used to enable applied prediction modelers to use these methods.

Conclusions

A growing recognition of IP and IO exists within the literature, and methodology is increasingly becoming available to leverage these phenomena for prediction purposes. IP and IO should be approached differently in a prediction context than when the primary goal is explanation. The work included in this review has demonstrated theoretical and empirical benefits of incorporating IP and IO, and therefore we recommend that applied health researchers consider incorporating these methods in their work.

中文翻译：

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常规健康数据中的信息存在和观察：临床风险预测方法论的回顾

摘要

目的

信息存在（IP）是一种现象，通过这种观察，患者数据的存在与否可能对他们的健康状况提供有益的信息，而对信息的观察（IO）就是纵向等效。这些现象主要存在于常规收集的医疗数据中，其中数据收集是由患者和临床医生的临床需求驱动的。使用此类数据开发临床预测模型（CPM）时考虑IP和IO的程度是未知的，旨在解决这些问题的现有方法也是未知的。这篇综述旨在综合这种现有方法论，从而帮助确定未来方法论工作的议程。

材料和方法

由2名独立审阅者使用预定的关键词进行了系统的文献检索。

结果

包括三十六篇文章。我们将其中介绍的方法归类为派生的预测变量（包括模型中作为预测变量的测量过程的某种表示形式），IP下的建模和潜在结构。在所包括的研究中（36篇文章中的24篇），最常出现的方法是将缺失的指标或汇总指标用作预测指标。

讨论区

这是在预测框架下整理该领域文献的第一篇综述。存在大量相关文献，我们提出了可以进一步发展所描述方法的方法。需要指导来指定应在哪种条件下使用每种方法，以使应用的预测建模者可以使用这些方法。

结论

文献中对IP和IO的认识日益提高，并且越来越多地使用方法来利用这些现象进行预测。与主要目标是解释时相比，在预测上下文中应对IP和IO的方法应有所不同。这篇综述中的工作已经证明了将IP和IO纳入的理论和经验优势，因此我们建议应用健康研究人员考虑将这些方法纳入其工作。