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.

Reconciling the biomedical data commons and the GDPR: three lessons from the EUCAN ELSI collaboratory.

The coming-into-force of the EU General Data Protection Regulation (GDPR) is a watershed moment in the legal recognition of enforceable rights to informational self-determination. The rapid evolution of legal requirements applicable to data use, however, has the potential to outstrip the capabilities of networks of biomedical data users to respond to the shifting norms. It can also delegitimate established institutional bodies that are responsible for assessing and authorising the downstream use of data, including research ethics committees and institutional data custodians. These burdens are especially pronounced for clinical and research networks that are of transnational scale, because the legal compliance burden for outbound international data transfers from the EEA is especially high. Legislatures, courts, and regulators in the EU should therefore implement the following three legal changes. First, the responsibilities of particular actors in a data sharing network should be delimited through the contractual allocation of responsibilities between collaborators. Second, the use of data through secure data processing environments should not trigger the international transfer provisions of the GDPR. Third, the use of federated data analysis methodologies that do not provide analysis nodes or downstream users access to identifiable personal data as part of the outputs of those analyses should not be considered circumstances of joint controllership, nor lead to the users of non-identifiable data to be considered controllers or processors. These small clarifications of, or modifications to, the GDPR would facilitate the exchange of biomedical data amongst clinicians and researchers.

How to plan and manage an individual participant data meta-analysis. An illustrative toolkit.

Individual participant data meta-analyses (IPD-MAs) have several benefits over standard aggregate data meta-analyses, including the consideration of additional participants, follow-up time, and the joint consideration of study- and participant-level heterogeneity for improved diagnostic and prognostic model development and evaluation. However, IPD-MAs are resource-intensive and require careful budgeting of time from data contributing groups, a dedicated management team, diversity of expertise, clearly documented data sharing and authorship agreements, and consistent and clear communication. We present a toolkit to facilitate the implementation and management of IPD-MAs, from study recruitment to retrospective harmonization. The toolkit was developed and refined over our work on multiple multinational IPD-MA projects over the last 13 years. The toolkit's budget and email templates, agreements, project management spreadsheets, and standard operating procedures are meant to facilitate routine IPD-MA tasks to expedite implementing and managing future IPD-MA projects.

Application of Causal Inference Methods to Pooled Longitudinal Non- Randomized Studies: A Methodological Systematic Review.

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) 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.

FAIR, ethical, and coordinated data sharing for COVID-19 response: a scoping review and cross-sectional survey of COVID-19 data sharing platforms and registries.

Data sharing is central to the rapid translation of research into advances in clinical medicine and public health practice. In the context of COVID-19, there has been a rush to share data marked by an explosion of population-specific and discipline-specific resources for collecting, curating, and disseminating participant-level data. We conducted a scoping review and cross-sectional survey to identify and describe COVID-19-related platforms and registries that harmonise and share participant-level clinical, omics (eg, genomic and metabolomic data), imaging data, and metadata. We assess how these initiatives map to the best practices for the ethical and equitable management of data and the findable, accessible, interoperable, and reusable (FAIR) principles for data resources. We review gaps and redundancies in COVID-19 data-sharing efforts and provide recommendations to build on existing synergies that align with frameworks for effective and equitable data reuse. We identified 44 COVID-19-related registries and 20 platforms from the scoping review. Data-sharing resources were concentrated in high-income countries and siloed by comorbidity, body system, and data type. Resources for harmonising and sharing clinical data were less likely to implement FAIR principles than those sharing omics or imaging data. Our findings are that more data sharing does not equate to better data sharing, and the semantic and technical interoperability of platforms and registries harmonising and sharing COVID-19-related participant-level data needs to improve to facilitate the global collaboration required to address the COVID-19 crisis.

Overlapping research efforts in a global pandemic: a rapid systematic review of COVID-19-related individual participant data meta-analyses.

BACKGROUND: Individual participant data meta-analyses (IPD-MAs), which involve harmonising and analysing participant-level data from related studies, provide several advantages over aggregate data meta-analyses, which pool study-level findings. IPD-MAs are especially important for building and evaluating diagnostic and prognostic models, making them an important tool for informing the research and public health responses to COVID-19. METHODS: We conducted a rapid systematic review of protocols and publications from planned, ongoing, or completed COVID-19-related IPD-MAs to identify areas of overlap and maximise data request and harmonisation efforts. We searched four databases using a combination of text and MeSH terms. Two independent reviewers determined eligibility at the title-abstract and full-text stages. Data were extracted by one reviewer into a pretested data extraction form and subsequently reviewed by a second reviewer. Data were analysed using a narrative synthesis approach. A formal risk of bias assessment was not conducted. RESULTS: We identified 31 COVID-19-related IPD-MAs, including five living IPD-MAs and ten IPD-MAs that limited their inference to published data (e.g., case reports). We found overlap in study designs, populations, exposures, and outcomes of interest. For example, 26 IPD-MAs included RCTs; 17 IPD-MAs were limited to hospitalised patients. Sixteen IPD-MAs focused on evaluating medical treatments, including six IPD-MAs for antivirals, four on antibodies, and two that evaluated convalescent plasma. CONCLUSIONS: Collaboration across related IPD-MAs can leverage limited resources and expertise by expediting the creation of cross-study participant-level data datasets, which can, in turn, fast-track evidence synthesis for the improved diagnosis and treatment of COVID-19. TRIAL REGISTRATION: 10.17605/OSF.IO/93GF2.

"How about me giving blood for the COVID vaccine and not being able to get vaccinated?" A cognitive interview study on understanding of and agreement with broad consent for future use of data and samples in Colombia and Nicaragua.

Broad consent for future use, wherein researchers ask participants for permission to share participant-level data and samples collected within the study for purposes loosely related to the study objectives, is central to enabling ethical data and sample reuse. Ensuring that participants understand broad consent-related language is key to maintaining trust in the study and public health research. We conducted 52 cognitive interviews to explore cohort research participants' and their parents' understanding of the broad consent-related language in the University of California at Berkeley template informed consent (IC) form for biomedical research. Participants and their parents were recruited from long-standing infectious disease cohort studies in Nicaragua and Colombia and interviewed during the COVID-19 pandemic. We conducted semi-structured interviews to assess participants' agreement with the key concepts in the IC after clarifying them through the cognitive interview. Participants did not understand abstract concepts, including collecting and reusing genetic data. Participants wanted to learn about incidental findings, future users and uses. Trust in the research team and the belief that sharing could lead to new vaccines or treatments were critical to participant support for data and sample sharing. Participants highlighted the importance of data and sample sharing for COVID-19 response and equitable access to vaccines and treatments developed through sharing. Our findings on participants' understanding of broad consent and preferences for data and sample sharing can help inform researchers and ethics review committees working to enable ethical and equitable data and sample sharing.

Adjusting for misclassification of an exposure in an individual participant data meta-analysis.

A common problem in the analysis of multiple data sources, including individual participant data meta-analysis (IPD-MA), is the misclassification of binary variables. Misclassification may lead to biased estimators of model parameters, even when the misclassification is entirely random. We aimed to develop statistical methods that facilitate unbiased estimation of adjusted and unadjusted exposure-outcome associations and between-study heterogeneity in IPD-MA, where the extent and nature of exposure misclassification may vary across studies. We present Bayesian methods that allow misclassification of binary exposure variables to depend on study- and participant-level characteristics. In an example of the differential diagnosis of dengue using two variables, where the gold standard measurement for the exposure variable was unavailable for some studies which only measured a surrogate prone to misclassification, our methods yielded more accurate estimates than analyses naive with regard to misclassification or based on gold standard measurements alone. In a simulation study, the evaluated misclassification model yielded valid estimates of the exposure-outcome association, and was more accurate than analyses restricted to gold standard measurements. Our proposed framework can appropriately account for the presence of binary exposure misclassification in IPD-MA. It requires that some studies supply IPD for the surrogate and gold standard exposure, and allows misclassification to follow a random effects distribution across studies conditional on observed covariates (and outcome). The proposed methods are most beneficial when few large studies that measured the gold standard are available, and when misclassification is frequent.

Heterogeneity of Zika virus exposure and outcome ascertainment across cohorts of pregnant women, their infants and their children: a metadata survey.

OBJECTIVES: To support the Zika virus (ZIKV) Individual Participant Data (IPD) Consortium's efforts to harmonise and analyse IPD from ZIKV-related prospective cohort studies and surveillance-based studies of pregnant women and their infants and children; we developed and disseminated a metadata survey among ZIKV-IPD Meta-Analysis (MA) study participants to identify and provide a comprehensive overview of study-level heterogeneity in exposure, outcome and covariate ascertainment and definitions. SETTING: Cohort and surveillance studies that measured ZIKV infection during pregnancy or at birth and measured fetal, infant, or child outcomes were identified through a systematic search and consultations with ZIKV researchers and Ministries of Health from 20 countries or territories. PARTICIPANTS: Fifty-four cohort or active surveillance studies shared deidentified data for the IPD-MA and completed the metadata survey, representing 33 061 women (11 020 with ZIKV) and 18 281 children. PRIMARY AND SECONDARY OUTCOME MEASURES: Study-level heterogeneity in exposure, outcome and covariate ascertainment and definitions. RESULTS: Median study sample size was 268 (IQR=100, 698). Inclusion criteria, follow-up procedures and exposure and outcome ascertainment were highly heterogenous, differing meaningfully across regions and multisite studies. Enrolment duration and follow-up for children after birth varied before and after the declaration of the Public Health Emergency of International Concern (PHEIC) and according to the type of funding received. CONCLUSION: This work highlights the logistic and statistical challenges that must be addressed to account for the multiple sources of within-study and between-study heterogeneity when conducting IPD-MAs of data collected in the research response to emergent pathogens like ZIKV.

Clinical prediction models for mortality in patients with covid-19: external validation and individual participant data meta-analysis.

OBJECTIVE: To externally validate various prognostic models and scoring rules for predicting short term mortality in patients admitted to hospital for covid-19. DESIGN: Two stage individual participant data meta-analysis. SETTING: Secondary and tertiary care. PARTICIPANTS: 46 914 patients across 18 countries, admitted to a hospital with polymerase chain reaction confirmed covid-19 from November 2019 to April 2021. DATA SOURCES: Multiple (clustered) cohorts in Brazil, Belgium, China, Czech Republic, Egypt, France, Iran, Israel, Italy, Mexico, Netherlands, Portugal, Russia, Saudi Arabia, Spain, Sweden, United Kingdom, and United States previously identified by a living systematic review of covid-19 prediction models published in The BMJ, and through PROSPERO, reference checking, and expert knowledge. MODEL SELECTION AND ELIGIBILITY CRITERIA: Prognostic models identified by the living systematic review and through contacting experts. A priori models were excluded that had a high risk of bias in the participant domain of PROBAST (prediction model study risk of bias assessment tool) or for which the applicability was deemed poor. METHODS: Eight prognostic models with diverse predictors were identified and validated. A two stage individual participant data meta-analysis was performed of the estimated model concordance (C) statistic, calibration slope, calibration-in-the-large, and observed to expected ratio (O:E) across the included clusters. MAIN OUTCOME MEASURES: 30 day mortality or in-hospital mortality. RESULTS: Datasets included 27 clusters from 18 different countries and contained data on 46 914patients. The pooled estimates ranged from 0.67 to 0.80 (C statistic), 0.22 to 1.22 (calibration slope), and 0.18 to 2.59 (O:E ratio) and were prone to substantial between study heterogeneity. The 4C Mortality Score by Knight et al (pooled C statistic 0.80, 95% confidence interval 0.75 to 0.84, 95% prediction interval 0.72 to 0.86) and clinical model by Wang et al (0.77, 0.73 to 0.80, 0.63 to 0.87) had the highest discriminative ability. On average, 29% fewer deaths were observed than predicted by the 4C Mortality Score (pooled O:E 0.71, 95% confidence interval 0.45 to 1.11, 95% prediction interval 0.21 to 2.39), 35% fewer than predicted by the Wang clinical model (0.65, 0.52 to 0.82, 0.23 to 1.89), and 4% fewer than predicted by Xie et al's model (0.96, 0.59 to 1.55, 0.21 to 4.28). CONCLUSION: The prognostic value of the included models varied greatly between the data sources. Although the Knight 4C Mortality Score and Wang clinical model appeared most promising, recalibration (intercept and slope updates) is needed before implementation in routine care.

Neurodevelopment in Normocephalic Children Exposed to Zika Virus in Utero with No Observable Defects at Birth: A Systematic Review with Meta-Analysis.

Zika virus (ZIKV) infection during pregnancy is a cause of pregnancy loss and multiple clinical and neurological anomalies in children. This systematic review aimed to assess the effect of ZIKV exposure in utero on the long-term neurodevelopment of normocephalic children born to women with ZIKV infection in pregnancy. This review was conducted according to the PRISMA guidelines for systematic reviews and meta-analyses. We performed a random effects meta-analysis to estimate the cross-study prevalence of neurodevelopmental delays in children using the Bayley Scales for Infant and Toddler Development (BSID-III). The risk of bias was assessed using Cochrane's Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. Full-text reviews were performed for 566 articles, and data were extracted from 22 articles corresponding to 20 studies. Nine articles including data from 476 children found 6.5% (95% CI: 4.1-9.3) of infants and children to have any type of non-language cognitive delay; 29.7% (95% CI: 21.7-38.2) to have language delay; and 11.5% (95% CI: 4.8-20.1) to have any type of motor delay. The pooled estimates had a high level of heterogeneity; thus, results should be interpreted with caution. Larger prospective studies that include a non-exposed control group are needed to confirm whether ZIKV exposure in utero is associated with adverse child neurodevelopmental outcomes.

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.

Do laws promoting gender equity and freedom from violence benefit the most vulnerable? A multilevel analysis of women's and adolescent girls' experiences in 15 low- and-middle-income countries.

In this analysis, we assess whether laws that promote gender equity and freedom from violence are associated with a lower risk of prior-year physical and sexual intimate partner violence (IPV) among adolescent girls and adult women (AGW) and whether these laws protect more and less vulnerable AGW equally. We included all 15 countries that administered the Domestic Violence Module in a Demographic and Health Survey since 2015. The primary exposure was a validated, country-level index of laws on violence against women and girls (LoVI). A multilevel approach was used to model five forms of violence (prior-year partner physical, sexual, physical or sexual violence and prior-year non-partner physical violence or sexual violence) among ever-partnered, non-widowed adolescent girls 13-19 years (n = 6691) and women 20-49 years (n = 119 343). Across countries, partner physical violence ranged from 0% to 33% and sexual violence from 0% to 23%. Laws on marital rape, child marriage and sexual harassment were negatively associated with prior-year physical and sexual IPV for women and girls. Comprehensive domestic violence legislation was unrelated to girls' experiences of prior-year physical or prior-year sexual IPV. No interaction was observed between LoVI component laws and a score meant to capture adolescent vulnerability. Three of the four LoVI component laws had consistent, negative associations with partner violence for girls and women, but negative associations were stronger for women than girls. Thus, while laws promoting gender equity and freedom from violence are generally protective, they may be more so for women than adolescent girls. Future research should explore the impact of gender equitable laws on women's and adolescent girls' experiences of violence, and countries may consider more comprehensive legal protections against violence for adolescent girls.

A blank check or a global public good? A qualitative study of how ethics review committee members in Colombia weigh the risks and benefits of broad consent for data and sample sharing during a pandemic.

Broad consent for future use facilitates the reuse of participant-level data and samples, which can conserve limited resources by confirming research findings and facilitate the development and evaluation of public health and clinical advances. Ethics review committees (ERCs) have to balance different stakeholder concerns when evaluating the risks and benefits associated with broad consent for future use. In this qualitative study, we evaluated ERC members' concerns about different aspects of broad consent, including appropriate governance, community engagement, evaluation of risks and benefits, and communication of broad consent for future use in Colombia, which does not currently have national guidance related to broad consent for future use. We conducted semi-structured, in-depth interviews with 24 ERC members from nine Colombian ERCs. We used thematic analysis to explore ERC members' concerns related to broad consent for future use. Most ERC members expressed concern about the idea of not specifying the purposes for which data would be used and by whom and suggested that pre-specifying governance procedures and structure would address some of their concerns about broad consent. ERC members emphasized the need for engaging communities and ensuring research participants understood broad consent for future use-related language in informed consent forms. Researchers and research institutions are under increasing pressure to share public health-related data. ERC members play a central role in balancing the priorities of different stakeholders and maintaining their community's trust in public health research. Further work is needed on guidelines for developing language around broad consent, evaluating community preferences related to data sharing, and developing standards for describing governance for data or sample sharing in the research protocol to address ERC members' concerns around broad consent for future use.

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.

Measurement error in meta-analysis (MEMA)-A Bayesian framework for continuous outcome data subject to non-differential measurement error.

Ideally, a meta-analysis will summarize data from several unbiased studies. Here we look into the less than ideal situation in which contributing studies may be compromised by non-differential measurement error in the exposure variable. Specifically, we consider a meta-analysis for the association between a continuous outcome variable and one or more continuous exposure variables, where the associations may be quantified as regression coefficients of a linear regression model. A flexible Bayesian framework is developed which allows one to obtain appropriate point and interval estimates with varying degrees of prior knowledge about the magnitude of the measurement error. We also demonstrate how, if individual-participant data (IPD) are available, the Bayesian meta-analysis model can adjust for multiple participant-level covariates, these being measured with or without measurement error.

Guidance for ensuring fair and ethical broad consent for future use. A scoping review protocol.

Introduction: Broad consent for future use is the reuse of data and/or samples collected by a study by researchers who may not be affiliated with the original study team for purposes that may differ from the objectives of the original study. Sharing participant-level data and samples collected from research participants facilitates reuse and transparency and can accelerate drug or vaccine development, research findings, and translation. Data reuse and synthesis help prevent unnecessary research, thereby respecting research participants time and efforts and building their trust in the research process. Despite these myriad benefits, data and sample sharing represent a significant investment of time for the team that collected the data or samples, and may present additional risks for research participants, including that of re-identifiability and incidental findings, or for the source community. This scoping review will summarize existing guidance on broad consent for future use and highlight evidence gaps related to the ethical, equitable implementation of broad consent for future use. Methods and analysis: We will apply the Arskey and O'Malley scoping review methodology and best practice as outlined in the Joanna Briggs scoping review guidelines.  The research questions have been identified through a literature review and consultation with subject-matter experts. The systematic search will be conducted in three databases using a tailored search strategy. We will search the reference lists of included articles or related systematic reviews for additional citations. The title-abstract and full text screening and charting the data will be conducted independently by two reviewers. Discrepancies will be resolved by a third reviewer. Results will be summarized in narrative form. Ethics and dissemination: This scoping review summarizes findings from existing publications and grey literature rather than primary data and, as such, does not require ethics review. Findings will be disseminated through an open access publication and webinar.

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).