Informative presence and observation in routine health data: A review of methodology for clinical risk prediction. (9th November 2020)

• Record Type: Journal Article
• Authors: Sisk, Rose; Lin, Lijing; Sperrin, Matthew; Barrett, Jessica K; Tom, Brian; Diaz-Ordaz, Karla; Peek, Niels; Martin, Glen P
• Is Part Of: Journal of the American Medical Informatics Association. Volume 28:Number 1(2021)
• Page Start: 155
• Page End: 166
• Publication Date: 2020-11-09
• Subjects: clinical prediction model -- electronic health records -- informative observation -- informative presence; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocaa242
• Languages: English
• ISSNs: 1067-5027

Abstract

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.