Current trends in the application of causal inference methods to pooled longitudinal observational infectious disease studies-A protocol for a methodological systematic review

INTRODUCTION: 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. METHODS AND ANALYSIS: 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)

Current trends in the application of causal inference methods to pooled longitudinal non-randomised data: A protocol for a methodological systematic review

Introduction Causal methods have been adopted and adapted across health disciplines, particularly for the analysis of single studies. However, the sample sizes necessary to best inform decision-making are often not attainable with single studies, making pooled individual-level data analysis invaluable for public health efforts. Researchers commonly implement causal methods prevailing in their home disciplines, and how these are selected, evaluated, implemented and reported may vary widely. To our knowledge, no article has yet evaluated trends in the implementation and reporting of causal methods in studies leveraging individual-level data pooled from several studies. We undertake this review to uncover patterns in the implementation and reporting of causal methods used across disciplines in research focused on health outcomes. We will investigate variations in methods to infer causality used across disciplines, time and geography and identify gaps in reporting of methods to inform the development of reporting standards and the conversation required to effect change. Methods and analysis We will search four databases (EBSCO, Embase, PubMed, Web of Science) using a search strategy developed with librarians from three universities (Heidelberg University, Harvard University, and University of California, San Francisco). The search strategy includes terms such as 'pool∗', 'harmoniz∗', 'cohort∗', 'observational', variations on 'individual-level data'. Four reviewers will independently screen articles using Covidence and extract data from included articles. The extracted data will be analysed descriptively in tables and graphically to reveal the pattern in methods implementation and reporting. This protocol has been registered with PROSPERO (CRD42020143148). Ethics and dissemination No ethical approval was required as only publicly available data were used. The results will be submitted as a manuscript to a peer-reviewed journal, disseminated in conferences if relevant, and published as part of doctoral dissertations in Global Health at the Heidelberg University Hospital

Application of causal inference methods in individual-participant data meta-analyses in medicine: addressing data handling and reporting gaps with new proposed reporting guidelines

Observational data provide invaluable real-world information in medicine, but certain methodological considerations are required to derive causal estimates. In this systematic review, we evaluated the methodology and reporting quality of individual-level patient data meta-analyses (IPD-MAs) conducted with non-randomized exposures, published in 2009, 2014, and 2019 that sought to estimate a causal relationship in medicine. We screened over 16,000 titles and abstracts, reviewed 45 full-text articles out of the 167 deemed potentially eligible, and included 29 into the analysis. Unfortunately, we found that causal methodologies were rarely implemented, and reporting was generally poor across studies. Specifically, only three of the 29 articles used quasi-experimental methods, and no study used G-methods to adjust for time-varying confounding. To address these issues, we propose stronger collaborations between physicians and methodologists to ensure that causal methodologies are properly implemented in IPD-MAs. In addition, we put forward a suggested checklist of reporting guidelines for IPD-MAs that utilize causal methods. This checklist could improve reporting thereby potentially enhancing the quality and trustworthiness of IPD-MAs, which can be considered one of the most valuable sources of evidence for health policy

Pooling Heterogeneous Data for Public Health: A Brief Look at Data Standardization. Notes from a meeting with Data Standards Meeting

Public health researchers are not well-trained regarding the various types or function of data standards. Often, a choice of a data standard for a study is based on the few that one knows of. However, an awareness of tradeoffs is often lacking. We held a meeting with a group of leaders from data standards organization to discuss the past and current efforts to make data standards more friendly to observational data studies as well as their efforts on interoperability. The following is a summary of the presentations given by the data standards. We hope that this assist researchers in making educated decisions when selecting a standard in the future

Dealing with missing data using the Heckman selection model: methods primer for epidemiologists

Missing data is a common problem in epidemiologic studies and is often addressed by omitting incomplete records or adopting multiple imputation. Although these methods can produce unbiased estimates of study associations, their validity becomes problematic when data are missing not at random (MNAR), and the missing data mechanism is nonignorable. This situation typically arises when the presence of missing values depends on characteristics of the measurement or recording process, which is common in surveys and databases with electronic healthcare records. In this article, we discuss the relevance and implementation of Heckman selection models to impute variables that are missing not at random

Systematic Review Reveals Lack of Causal Methodology Applied to Pooled Longitudinal Observational Infectious Disease Studies

Objectives Among ID studies seeking to make causal inferences and pooling individual-level longitudinal data from multiple infectious disease cohorts, we sought to assess what methods are being used, how those methods are being reported, and whether these factors have changed over time. Study Design and Setting Systematic review of longitudinal observational infectious disease studies pooling individual-level patient data from 2+ studies published in English in 2009, 2014, or 2019. This systematic review protocol is registered with PROSPERO (CRD42020204104). Results Our search yielded 1,462 unique articles. Of these, 16 were included in the final review. Our analysis showed a lack of causal inference methods and of clear reporting on methods and the required assumptions. Conclusion There are many approaches to causal inference which may help facilitate accurate inference in the presence of unmeasured and time-varying confounding. In observational ID studies leveraging pooled, longitudinal IPD, the absence of these causal inference methods and gaps in the reporting of key methodological considerations suggests there is ample opportunity to enhance the rigor and reporting of research in this field. Interdisciplinary collaborations between substantive and methodological experts would strengthen future work

Linguistic and Cultural Acceptability of a Spanish Translation of the Ohio State University Traumatic Brain Injury Identification Method Among Community-Dwelling Spanish-Dominant Older Adults.

ObjectiveOur objective was to (1) evaluate the linguistic and cultural acceptability of a Spanish translation of the Ohio State University traumatic brain injury identification method (OSU TBI-ID) and (2) to assess the usability and acceptability of a tablet-based version of this instrument in a cohort of Spanish-dominant older adults.SettingUniversity clinical research center and local community center.ParticipantsCommunity-dwelling Spanish-dominant adults age 50 years or older without dementia residing in the Bay Area of California (N=22).DesignCross-sectional cohort study.Main outcome measuresQualitative assessment of linguistic or cultural acceptability of a Spanish translation of the OSU TBI-ID as well as usability or acceptability of a tablet-based self-administered version of this instrument.ResultsThe Spanish translation had high linguistic and cultural acceptability and was further optimized based on participant feedback. Cognitive interviews to review survey wording revealed high levels of homogeneity in the clinical definitions and synonyms given by participants-for example, results for the clinical term "Quedó Inconsciente/Pérdida (temporal) de la conciencia" (To be unconscious/[Temporary] loss of consciousness) used in the survey included "perder el conocimiento" (loss of consciousness), "knockeado" (knocked out), "No es que esté dormida, porque está inconsciente, pero su corazón está todavía palpitando" (it's not that they're sleeping, because they're unconscious, but their heart is still palpitating). The tablet interface had low observer-based usability, revealing that participants with <13 years of education (n=6) had more difficulty using the tablet which could be improved with minor changes to the coding of the application and minimal in-person technology support. Acceptability of the tool was low among all but 1 participant.ConclusionThis linguistically optimized Spanish translation of the OSU TBI-ID is recommended for use as a semistructured interview among Spanish-dominant older adults. Although the tablet-based instrument may be used by interviewers as an efficient electronic case report form among older adults, further research is needed, particularly among older adults with varying levels of education, to validate this instrument as a self-administered survey

Application of causal inference methods in individual-participant data meta-analyses in medicine : addressing data handling and reporting gaps with new proposed reporting guidelines

Observational data provide invaluable real-world information in medicine, but certain methodological considerations are required to derive causal estimates. In this systematic review, we evaluated the methodology and reporting quality of individual-level patient data meta-analyses (IPD-MAs) conducted with non-randomized exposures, published in 2009, 2014, and 2019 that sought to estimate a causal relationship in medicine. We screened over 16,000 titles and abstracts, reviewed 45 full-text articles out of the 167 deemed potentially eligible, and included 29 into the analysis. Unfortunately, we found that causal methodologies were rarely implemented, and reporting was generally poor across studies. Specifically, only three of the 29 articles used quasi-experimental methods, and no study used G-methods to adjust for time-varying confounding. To address these issues, we propose stronger collaborations between physicians and methodologists to ensure that causal methodologies are properly implemented in IPD-MAs. In addition, we put forward a suggested checklist of reporting guidelines for IPD-MAs that utilize causal methods. This checklist could improve reporting thereby potentially enhancing the quality and trustworthiness of IPD-MAs, which can be considered one of the most valuable sources of evidence for health policy