Collaborative, pooled and harmonized study designs for epidemiologic research: challenges and opportunities

Approaches to protocol standardization and data harmonization in the ECHO-wide cohort study

The United States (U.S.) National Institutes of Health-funded Environmental influences on Child Health Outcomes (ECHO)-wide Cohort was established to conduct high impact, transdisciplinary science to improve child health and development. The cohort is a collaborative research design in which both extant and new data are contributed by over 57,000 children across 69 cohorts. In this review article, we focus on two key challenging issues in the ECHO-wide Cohort: data collection standardization and data harmonization. Data standardization using a Common Data Model and derived analytical variables based on a team science approach should facilitate timely analyses and reduce errors due to data misuse. However, given the complexity of collaborative research designs, such as the ECHO-wide Cohort, dedicated time is needed for harmonization and derivation of analytic variables. These activities need to be done methodically and with transparency to enhance research reproducibility. IMPACT: Many collaborative research studies require data harmonization either prior to analyses or in the analyses of compiled data. The Environmental influences on Child Health Outcomes (ECHO) Cohort pools extant data with new data collection from over 57,000 children in 69 cohorts to conduct high-impact, transdisciplinary science to improve child health and development, and to provide a national database and biorepository for use by the scientific community at-large. We describe the tools, systems, and approaches we employed to facilitate harmonized data for impactful analyses of child health outcomes.

Opportunities for Examining Child Health Impacts of Early-Life Nutrition in the ECHO Program: Maternal and Child Dietary Intake Data from Pregnancy to Adolescence

Background

Longitudinal measures of diet spanning pregnancy through adolescence are needed from a large, diverse sample to advance research on the effect of early-life nutrition on child health. The Environmental influences on Child Health Outcomes (ECHO) Program, which includes 69 cohorts, >33,000 pregnancies, and >31,000 children in its first 7-y cycle, provides such data, now publicly available.

Objectives

This study aimed to describe dietary intake data available in the ECHO Program as of 31 August, 2022 (end of year 6 of Cycle 1) from pregnancy through adolescence, including estimated sample sizes, and to highlight the potential for future analyses of nutrition and child health.

Methods

We identified and categorized ECHO Program dietary intake data, by assessment method, participant (pregnant person or child), and life stage of data collection. We calculated the number of maternal-child dyads with dietary data and the number of participants with repeated measures. We identified diet-related variables derived from raw dietary intake data and nutrient biomarkers measured from biospecimens.

Results

Overall, 66 cohorts (26,941 pregnancies, 27,103 children, including 22,712 dyads) across 34 US states/territories provided dietary intake data. Dietary intake assessments included 24-h recalls (1548 pregnancies and 1457 children), food frequency questionnaires (4902 and 4117), dietary screeners (8816 and 23,626), and dietary supplement use questionnaires (24,798 and 26,513). Repeated measures were available for ∼70%, ∼30%, and ∼15% of participants with 24-h recalls, food frequency questionnaires, and dietary screeners, respectively. The available diet-related variables describe nutrient and food intake, diet patterns, and breastfeeding practices. Overall, 17% of participants with dietary intake data had measured nutrient biomarkers.

Conclusions

ECHO cohorts have collected longitudinal dietary intake data spanning pregnancy through adolescence from a geographically, socioeconomically, and ethnically diverse US sample. As data collection continues in Cycle 2, these data present an opportunity to advance the field of nutrition and child health.

Considerations for pooling real-world data as a comparator cohort to a single arm trial: a simulation study on assessment of heterogeneity

BACKGROUND

Novel precision medicine therapeutics target increasingly granular, genomically-defined populations. Rare sub-groups make it challenging to study within a clinical trial or single real-world data (RWD) source; therefore, pooling from disparate sources of RWD may be required for feasibility. Heterogeneity assessment for pooled data is particularly complex when contrasting a pooled real-world comparator cohort (rwCC) with a single-arm clinical trial (SAT), because the individual comparisons are not independent as all compare a rwCC to the same SAT. Our objective was to develop a methodological framework for pooling RWD focused on the rwCC use case, and simulate novel approaches of heterogeneity assessment, especially for small datasets.

METHODS

We present a framework with the following steps: pre-specification, assessment of dataset eligibility, and outcome analyses (including assessment of outcome heterogeneity). We then simulated heterogeneity assessments for a binary response outcome in a SAT compared to two rwCCs, using standard methods for meta-analysis, and an Adjusted Cochran's Q test, and directly comparing the individual participant data (IPD) from the rwCCs.

RESULTS

We found identical power to detect a true difference for the adjusted Cochran's Q test and the IPD method, with both approaches superior to a standard Cochran's Q test. When assessing the impact of heterogeneity in the null scenario of no difference between the SAT and rwCCs, a lack of statistical power led to Type 1 error inflation. Similarly, in the alternative scenario of a true difference between SAT and rwCCs, we found substantial Type 2 error, with underpowered heterogeneity testing leading to underestimation of the treatment effect.

CONCLUSIONS

We developed a methodological framework for pooling RWD sources in the context of designing a rwCC for a SAT. When testing for heterogeneity during this process, the adjusted Cochran's Q test matches the statistical power of IPD heterogeneity testing. Limitations of quantitative heterogeneity testing in protecting against Type 1 or Type 2 error indicate these tests are best used descriptively, and after careful selection of datasets based on clinical/data considerations. We hope these findings will facilitate the rigorous pooling of RWD to unlock insights to benefit oncology patients.

Mortality Burden and Life-Years Lost Across the Age Spectrum for Adults Living with CKD

Key Points

Limited data exist to inform younger persons with CKD how their risk for mortality compares with equivalently aged individuals without CKD. Compared with the general population without CKD, the age-stratified risk for mortality was highest in younger individuals with CKD. From a lifetime perspective, the estimated reduction of lifespan secondary to CKD was greatest at younger ages.

Background

Younger individuals living with CKD face a lifetime at risk for complications and mortality. Limited data exist to inform individual patients with CKD across the lifespan how their risk for mortality compares with equivalently aged individuals without CKD, particularly at younger ages. The objective of this study was to provide age-specific contexts to the risk of mortality associated with a diagnosis of CKD.

Methods

We created a pooled study cohort using participants with CKD enrolled in the Chronic Renal Insufficiency Cohort along with participants aged 21–75 years included in the 1999–2008 National Health and Nutrition Examination Survey surveys. Age-stratified mortality rates, along with unadjusted and adjusted hazard ratios (HRs) for mortality, were generated to compare differences between those with and without CKD. The mean life-years lost (LYL) relating to CKD was calculated using Centers for Disease Control and Prevention life tables.

Results

A total of 16,725 participants were included. Mortality rates were higher in those with CKD at all ages. The adjusted age-stratified HR for mortality in those with CKD versus without was highest in the 21–35 years strata (HR [95% confidence interval (CI)], 4.9 [2.8 to 8.6])) and lowest in the 65–75 years strata (HR [95% CI], 2.0 [1.7 to 2.3]). The mean LYL secondary to CKD was inversely related with increasing age.

Conclusions

Compared with age-matched peers without CKD, the age-stratified risk for mortality and LYL associated with a diagnosis of CKD is highest in younger individuals. Further research is needed to elucidate the societal and personal costs of premature mortality associated with CKD in young adults.

Life course of retrospective harmonization initiatives: key elements to consider

Optimizing research on the developmental origins of health and disease (DOHaD) involves implementing initiatives maximizing the use of the available cohort study data; achieving sufficient statistical power to support subgroup analysis; and using participant data presenting adequate follow-up and exposure heterogeneity. It also involves being able to undertake comparison, cross-validation, or replication across data sets. To answer these requirements, cohort study data need to be findable, accessible, interoperable, and reusable (FAIR), and more particularly, it often needs to be harmonized. Harmonization is required to achieve or improve comparability of the putatively equivalent measures collected by different studies on different individuals. Although the characteristics of the research initiatives generating and using harmonized data vary extensively, all are confronted by similar issues. Having to collate, understand, process, host, and co-analyze data from individual cohort studies is particularly challenging. The scientific success and timely management of projects can be facilitated by an ensemble of factors. The current document provides an overview of the 'life course' of research projects requiring harmonization of existing data and highlights key elements to be considered from the inception to the end of the project.

John J, Kono N, Henderson VW, Mack WJ. Generalizability of cognitive results from clinical trial participants to an older adult population: Addressing external validity

Introduction

Study inclusion criteria and recruitment practices limit the generalizability of randomized-controlled trial (RCT) results. Statistical modeling could enhance generalizability of outcomes. To illustrate this, the cognition-depression relationship was assessed with and without adjustment relative to the target population of older women.

Methods

Randomized participants from four RCTs and non-randomized participants from two cohorts were included in this study. Prediction models estimated probability of being randomized into trials from target populations. These probabilities were used for inverse odds weighting relative to target populations. Weighted linear regression was used to assess the depression-cognition relationship.

Results

There was no depression-cognition relationship in the combined randomized sample. After applying weights relative to a representative cohort, negative relationships were observed. After applying weights relative to a non-representative cohort, bias of estimates increased.

Discussion

Quantitative approaches to transportability using representative samples may explain the absence of a-priori established relationships in RCTs.

Development of a novel machine learning-based weighted modeling approach to incorporate Salmonella enterica heterogeneity on a genetic scale in a dose-response modeling framework

Estimating microbial dose-response is an important aspect of a food safety risk assessment. In recent years, there has been considerable interest to advance these models with potential incorporation of gene expression data. The aim of this study was to develop a novel machine learning model that considers the weights of expression of Salmonella genes that could be associated with illness, given exposure, in hosts. Here, an elastic net-based weighted Poisson regression method was proposed to identify Salmonella enterica genes that could be significantly associated with the illness response, irrespective of serovar. The best-fit elastic net model was obtained by 10-fold cross-validation. The best-fit elastic net model identified 33 gene expression-dose interaction terms that added to the predictability of the model. Of these, nine genes associated with Salmonella metabolism and virulence were found to be significant by the best-fit Poisson regression model (p < 0.05). This method could improve or redefine dose-response relationships for illness from relative proportions of significant genes from a microbial genetic dataset, which would help in refining endpoint and risk estimations.

A harmonized analysis of five Canadian pregnancy cohort studies: exploring the characteristics and pregnancy outcomes associated with prenatal alcohol exposure

BACKGROUND

As a teratogen, alcohol exposure during pregnancy can impact fetal development and result in adverse birth outcomes. Despite the clinical and social importance of prenatal alcohol use, limited routinely collected information or epidemiological data exists in Canada. The aim of this study was to pool data from multiple Canadian cohort studies to identify sociodemographic characteristics before and during pregnancy that were associated with alcohol consumption during pregnancy and to assess the impact of different patterns of alcohol use on birth outcomes.

METHODS

We harmonized information collected (e.g., pregnant women's alcohol intake, infants' gestational age and birth weight) from five Canadian pregnancy cohort studies to consolidate a large sample (n = 11,448). Risk factors for any alcohol use during pregnancy, including any alcohol use prior to pregnancy recognition, and binge drinking, were estimated using binomial regressions including fixed effects of pregnancy cohort membership and multiple maternal risk factors. Impacts of alcohol use during pregnancy on birth outcomes (preterm birth and low birth weight for gestational) were also estimated using binomial regression models.

RESULTS

In analyses adjusting for multiple risk factors, women's alcohol use during pregnancy, both any use and any binge drinking, was associated with drinking prior to pregnancy, smoking during pregnancy, and white ethnicity. Higher income level was associated with any drinking during pregnancy. Neither drinking during pregnancy nor binge drinking during pregnancy was significantly associated with preterm delivery or low birth weight for gestational age in our sample.

CONCLUSIONS

Pooling data across pregnancy cohort studies allowed us to create a large sample of Canadian women and investigate the risk factors for alcohol consumption during pregnancy. We suggest that future pregnancy and birth cohorts should always include questions related to the frequency and amount of alcohol consumed before and during pregnancy that are prospectively harmonized to support data reusability and collaborative research.

Data quality considerations for evaluating COVID-19 treatments using real world data: learnings from the National COVID Cohort Collaborative (N3C)

BACKGROUND

Multi-institution electronic health records (EHR) are a rich source of real world data (RWD) for generating real world evidence (RWE) regarding the utilization, benefits and harms of medical interventions. They provide access to clinical data from large pooled patient populations in addition to laboratory measurements unavailable in insurance claims-based data. However, secondary use of these data for research requires specialized knowledge and careful evaluation of data quality and completeness. We discuss data quality assessments undertaken during the conduct of prep-to-research, focusing on the investigation of treatment safety and effectiveness.

METHODS

Using the National COVID Cohort Collaborative (N3C) enclave, we defined a patient population using criteria typical in non-interventional inpatient drug effectiveness studies. We present the challenges encountered when constructing this dataset, beginning with an examination of data quality across data partners. We then discuss the methods and best practices used to operationalize several important study elements: exposure to treatment, baseline health comorbidities, and key outcomes of interest.

RESULTS

We share our experiences and lessons learned when working with heterogeneous EHR data from over 65 healthcare institutions and 4 common data models. We discuss six key areas of data variability and quality. (1) The specific EHR data elements captured from a site can vary depending on source data model and practice. (2) Data missingness remains a significant issue. (3) Drug exposures can be recorded at different levels and may not contain route of administration or dosage information. (4) Reconstruction of continuous drug exposure intervals may not always be possible. (5) EHR discontinuity is a major concern for capturing history of prior treatment and comorbidities. Lastly, (6) access to EHR data alone limits the potential outcomes which can be used in studies.

CONCLUSIONS

The creation of large scale centralized multi-site EHR databases such as N3C enables a wide range of research aimed at better understanding treatments and health impacts of many conditions including COVID-19. As with all observational research, it is important that research teams engage with appropriate domain experts to understand the data in order to define research questions that are both clinically important and feasible to address using these real world data.

Cohort profile: the ECHO prenatal and early childhood pathways to health consortium (ECHO-PATHWAYS)

PURPOSE

Exposures early in life, beginning in utero, have long-term impacts on mental and physical health. The ECHO prenatal and early childhood pathways to health consortium (ECHO-PATHWAYS) was established to examine the independent and combined impact of pregnancy and childhood chemical exposures and psychosocial stressors on child neurodevelopment and airway health, as well as the placental mechanisms underlying these associations.

PARTICIPANTS

The ECHO-PATHWAYS consortium harmonises extant data from 2684 mother-child dyads in three pregnancy cohort studies (CANDLE [Conditions Affecting Neurocognitive Development and Learning in Early Childhood], TIDES [The Infant Development and Environment Study] and GAPPS [Global Alliance to Prevent Prematurity and Stillbirth]) and collects prospective data under a unified protocol. Study participants are socioeconomically diverse and include a large proportion of Black families (38% Black and 51% White), often under-represented in research. Children are currently 5-15 years old. New data collection includes multimodal assessments of primary outcomes (airway health and neurodevelopment) and exposures (air pollution, phthalates and psychosocial stress) as well as rich covariate characterisation. ECHO-PATHWAYS is compiling extant and new biospecimens in a central biorepository and generating the largest placental transcriptomics data set to date (N=1083).

FINDINGS TO DATE

Early analyses demonstrate adverse associations of prenatal exposure to air pollution, phthalates and maternal stress with early childhood airway outcomes and neurodevelopment. Placental transcriptomics work suggests that phthalate exposure alters placental gene expression, pointing to mechanistic pathways for the developmental toxicity of phthalates. We also observe associations between prenatal maternal stress and placental corticotropin releasing hormone, a marker of hormonal activation during pregnancy relevant for child health. Other publications describe novel methods for examining exposure mixtures and the development of a national spatiotemporal model of ambient outdoor air pollution.

FUTURE PLANS

The first wave of data from the unified protocol (child age 8-9) is nearly complete. Future work will leverage these data to examine the combined impact of early life social and chemical exposures on middle childhood health outcomes and underlying placental mechanisms.

A guide for researchers seeking training in retrospective data harmonization for population neuroscience studies of Alzheimer's disease and related dementias

Due to needs surrounding rigor and reproducibility, subgroup specific disease knowledge, and questions of external validity, data harmonization is an essential tool in population neuroscience of Alzheimer's disease and related dementias (ADRD). Systematic harmonization of data elements is necessary to pool information from heterogeneous samples, and such pooling allows more expansive evaluations of health disparities, more precise effect estimates, and more opportunities to discover effective prevention or treatment strategies. The key goal of this Tutorial in Population Neuroimaging Curriculum, Instruction, and Pedagogy article is to guide researchers in creating a customized population neuroscience of ADRD harmonization training plan to fit their needs or those of their mentees. We provide brief guidance for retrospective data harmonization of multiple data types in this area, including: (1) clinical and demographic, (2) neuropsychological, and (3) neuroimaging data. Core competencies and skills are reviewed, and resources are provided to fill gaps in training as well as data needs. We close with an example study in which harmonization is a critical tool. While several aspects of this tutorial focus specifically on ADRD, the concepts and resources are likely to benefit population neuroscientists working in a range of research areas.

Better together: Advancing life course research through multi-cohort analytic approaches

Longitudinal cohorts can provide timely and cost-efficient evidence about the best points of health service and preventive interventions over the life course. Working systematically across cohorts has the potential to further exploit these valuable data assets, such as by improving the precision of estimates, enhancing (or appropriately reducing) confidence in the replicability of findings, and investigating interrelated questions within a broader theoretical model. In this conceptual review, we explore the opportunities and challenges presented by multi-cohort approaches in life course research. Specifically, we: 1) describe key motivations for multi-cohort work and the analytic approaches that are commonly used in each case; 2) flag some of the scientific and pragmatic challenges that arise when adopting these approaches; and 3) outline emerging directions for multi-cohort work in life course research. Harnessing their potential while thoughtfully considering limitations of multi-cohort approaches can contribute to the robust and granular evidence base needed to promote health and wellbeing over the life span.

Life events and treatment prognosis for depression: A systematic review and individual patient data meta-analysis

OBJECTIVE

To investigate associations between major life events and prognosis independent of treatment type: (1) after adjusting for clinical prognostic factors and socio-demographics; (2) amongst patients with depressive episodes at least six-months long; and (3) patients with a first life-time depressive episode.

METHODS

Six RCTs of adults seeking treatment for depression in primary care met eligibility criteria, individual patient data (IPD) were collated from all six (n = 2858). Participants were randomized to any treatment and completed the same baseline assessment of life events, demographics and clinical prognostic factors. Two-stage random effects meta-analyses were conducted.

RESULTS

Reporting any major life events was associated with poorer prognosis regardless of treatment type. Controlling for baseline clinical factors, socio-demographics and social support resulted in minimal residual evidence of associations between life events and treatment prognosis. However, removing factors that might mediate the relationships between life events and outcomes reporting: arguments/disputes, problem debt, violent crime, losing one's job, and three or more life events were associated with considerably worse prognoses (percentage difference in 3-4 months depressive symptoms compared to no reported life events =30.3%(95%CI: 18.4-43.3)).

CONCLUSIONS

Assessing for clinical prognostic factors, social support, and socio-demographics is likely to be more informative for prognosis than assessing self-reported recent major life events. However, clinicians might find it useful to ask about such events, and if they are still affecting the patient, consider interventions to tackle problems related to those events (e.g. employment support, mediation, or debt advice). Further investigations of the efficacy of such interventions will be important.

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.

Developing real-world evidence from real-world data: Transforming raw data into analytical datasets

Development of evidence-based practice requires practice-based evidence, which can be acquired through analysis of real-world data from electronic health records (EHRs). The EHR contains volumes of information about patients-physical measurements, diagnoses, exposures, and markers of health behavior-that can be used to create algorithms for risk stratification or to gain insight into associations between exposures, interventions, and outcomes. But to transform real-world data into reliable real-world evidence, one must not only choose the correct analytical methods but also have an understanding of the quality, detail, provenance, and organization of the underlying source data and address the differences in these characteristics across sites when conducting analyses that span institutions. This manuscript explores the idiosyncrasies inherent in the capture, formatting, and standardization of EHR data and discusses the clinical domain and informatics competencies required to transform the raw clinical, real-world data into high-quality, fit-for-purpose analytical data sets used to generate real-world evidence.

Association of the VACS Index With Hospitalization Among People With HIV in the NA-ACCORD

BACKGROUND

People with HIV (PWH) have a higher hospitalization rate than the general population. The Veterans Aging Cohort Study (VACS) Index at study entry well predicts hospitalization in PWH, but it is unknown if the time-updated parameter improves hospitalization prediction. We assessed the association of parameterizations of the VACS Index 2.0 with the 5-year risk of hospitalization.

SETTING

PWH ≥30 years old with at least 12 months of antiretroviral therapy (ART) use and contributing hospitalization data from 2000 to 2016 in North American AIDS Cohort Collaboration on Research and Design (NA-ACCORD) were included. Three parameterizations of the VACS Index 2.0 were assessed and categorized by quartile: (1) "baseline" measurement at study entry; (2) time-updated measurements; and (3) cumulative scores calculated using the trapezoidal rule.

METHODS

Discrete-time proportional hazard models estimated the crude and adjusted associations (and 95% confidence intervals [CIs]) of the VACS Index parameterizations and all-cause hospitalizations. The Akaike information criterion (AIC) assessed the model fit with each of the VACS Index parameters.

RESULTS

Among 7289 patients, 1537 were hospitalized. Time-updated VACS Index fitted hospitalization best with a more distinct dose-response relationship [score <43: reference; score 43-55: aHR = 1.93 (95% CI: 1.66 to 2.23); score 55-68: aHR = 3.63 (95% CI: 3.12 to 4.23); score ≥68: aHR = 9.98 (95% CI: 8.52 to 11.69)] than study entry and cumulative VACS Index after adjusting for known risk factors.

CONCLUSIONS

Time-updated VACS Index 2.0 had the strongest association with hospitalization and best fit to the data. Health care providers should consider using it when assessing hospitalization risk among PWH.

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.

Cognitive Recovery After Stroke: A Meta-analysis and Metaregression of Intervention and Cohort Studies

BACKGROUND

Cognition affects poststroke recovery, but meta-analyses of cognition have not yet provided a comparison of observational and intervention evidence.

OBJECTIVE

To describe the trajectory of poststroke cognition and the factors that moderate it across intervention and observational cohorts.

METHODS

Six databases were searched up to January 2020. Studies describing quantitative changes in cognition in adults poststroke were included. Interventions were classified into pharmacological, therapist-led, nonroutine/alternative, and usual care. Summary estimates were compared via hierarchical mixed-effects models. Age, recovery stage, stroke etiology, cognitive domain targeted in studies, and intervention types were investigated as moderators of cognition. Recovery stage and intervention were further analyzed in a multiplicative metaregression model.

RESULTS

A total of 43 intervention trials and 79 observation cohorts involving 28 222 stroke participants were included. Heterogeneity was significant (τ² = 0.09; CI = 0.01-0.21, P < .001) with no evidence of publication bias. Cognitive recovery was greater in intervention trials (g = 0.47; CI = 0.37-0.58) than observational cohorts (g = 0.28; CI = 0.20-0.36) across all moderators analyzed. Nonroutine/alternative and pharmacological trials achieved the best overall results (g = 0.57, CI = 0.42-0.73, and g = 0.52, CI = 0.30-0.74, respectively), followed by therapist-led (g = 0.46; CI = 0.17-0.74), and usual care (g = 0.28; CI = 0.11-0.45) interventions. Medium recovery effects (ie, g ≥ 0.5) were observed in examining first-ever stroke, executive function, visuo-perceptual, consciousness, and psychomotor skills, 61 to 180 days poststroke, in participants aged 65 to 70 years.

CONCLUSION

Cognitive recovery is possible using different controlled interventions in all recovery stages, with smaller benefits ≥2 years poststroke. Longer-term studies are needed to determine the role of nonroutine/alternative therapies and the association between cognitive recovery and performance in everyday activities.

Combining Effect Estimates Across Cohorts and Sufficient Adjustment Sets for Collaborative Research: A Simulation Study

BACKGROUND

Collaborative research often combines findings across multiple, independent studies via meta-analysis. Ideally, all study estimates that contribute to the meta-analysis will be equally unbiased. Many meta-analyses require all studies to measure the same covariates. We explored whether differing minimally sufficient sets of confounders identified by a directed acyclic graph (DAG) ensures comparability of individual study estimates. Our analysis applied four statistical estimators to multiple minimally sufficient adjustment sets identified in a single DAG.

METHODS

We compared estimates obtained via linear, log-binomial, and logistic regression and inverse probability weighting, and data were simulated based on a previously published DAG.

RESULTS

Our results show that linear, log-binomial, and inverse probability weighting estimators generally provide the same estimate of effect for different estimands that are equally sufficient to adjust confounding bias, with modest differences in random error. In contrast, logistic regression often performed poorly, with notable differences in effect estimates obtained from unique minimally sufficient adjustment sets, and larger standard errors than other estimators.

CONCLUSIONS

Our findings do not support the reliance of collaborative research on logistic regression results for meta-analyses. Use of DAGs to identify potentially differing minimally sufficient adjustment sets can allow meta-analyses without requiring the exact same covariates.

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).

Establishing the value of genomics in medicine: the IGNITE Pragmatic Trials Network

PURPOSE

A critical gap in the adoption of genomic medicine into medical practice is the need for the rigorous evaluation of the utility of genomic medicine interventions.

METHODS

The Implementing Genomics in Practice Pragmatic Trials Network (IGNITE PTN) was formed in 2018 to measure the clinical utility and cost-effectiveness of genomic medicine interventions, to assess approaches for real-world application of genomic medicine in diverse clinical settings, and to produce generalizable knowledge on clinical trials using genomic interventions. Five clinical sites and a coordinating center evaluated trial proposals and developed working groups to enable their implementation.

RESULTS

Two pragmatic clinical trials (PCTs) have been initiated, one evaluating genetic risk APOL1 variants in African Americans in the management of their hypertension, and the other to evaluate the use of pharmacogenetic testing for medications to manage acute and chronic pain as well as depression.

CONCLUSION

IGNITE PTN is a network that carries out PCTs in genomic medicine; it is focused on diversity and inclusion of underrepresented minority trial participants; it uses electronic health records and clinical decision support to deliver the interventions. IGNITE PTN will develop the evidence to support (or oppose) the adoption of genomic medicine interventions by patients, providers, and payers.

Occupational Exposure to Extremely Low-Frequency Magnetic Fields and Risk of Amyotrophic Lateral Sclerosis: Results of a Feasibility Study for a Pooled Analysis of Original Data

Previous meta-analyses have suggested an increased risk of amyotrophic lateral sclerosis (ALS) associated with occupational exposure to extremely low-frequency magnetic fields (ELF-MF). However, results should be interpreted with caution since studies were methodologically heterogeneous. Here, we assessed the feasibility of a pooling study to harmonize and re-analyze available original data. A systematic literature search was conducted. Published epidemiological studies were identified in PubMed and EMF-Portal from literature databases' inception dates until January 2019. The characteristics of all studies were described, including exposure metrics, exposure categories, and confounders. A survey among the principal investigators (PI) was carried out to assess their willingness to provide their original data. The statistical power of a pooling study was evaluated. We identified 15 articles published between 1997 and 2019. Studies differed in terms of outcome, study population, exposure assessment, and exposure metrics. Most studies assessed ELF-MF as average magnetic flux density per working day; however, exposure categories varied widely. The pattern of adjustment for confounders was heterogeneous between studies, with age, sex, and socioeconomic status being most frequent. Eight PI expressed their willingness to provide original data. A relative risk of ≥1.14 for ALS and occupational exposure to ELF-MF can be detected with a power of more than 80% in a pooled study. The pooling of original data is recommended and could contribute to a better understanding of ELF-MF in the etiology of ALS based on a large database and reduced heterogeneity due to a standardized analysis protocol with harmonized exposure metrics and exposure categories. Bioelectromagnetics. © 2021 Bioelectromagnetics Society.

Target validity: Bringing treatment of external validity in line with internal validity

Purpose of Review

"Target bias" is the difference between an estimate of association from a study sample and the causal effect in the target population of interest. It is the sum of internal and external bias. Given the extensive literature on internal validity, here, we review threats and methods to improve external validity.

Recent findings

External bias may arise when the distribution of modifiers of the effect of treatment differs between the study sample and the target population. Methods including those based on modeling the outcome, modeling sample membership, and doubly robust methods are available, assuming data on the target population is available.

Summary

The relevance of information for making policy decisions is dependent on both the actions that were studied and the sample in which they were evaluated. Combining methods for addressing internal and external validity can improve the policy relevance of study results.

Racial and geographic variation in effects of maternal education and neighborhood-level measures of socioeconomic status on gestational age at birth: Findings from the ECHO cohorts

Preterm birth occurs at excessively high and disparate rates in the United States. In 2016, the National Institutes of Health (NIH) launched the Environmental influences on Child Health Outcomes (ECHO) program to investigate the influence of early life exposures on child health. Extant data from the ECHO cohorts provides the opportunity to examine racial and geographic variation in effects of individual- and neighborhood-level markers of socioeconomic status (SES) on gestational age at birth. The objective of this study was to examine the association between individual-level (maternal education) and neighborhood-level markers of SES and gestational age at birth, stratifying by maternal race/ethnicity, and whether any such associations are modified by US geographic region. Twenty-six ECHO cohorts representing 25,526 mother-infant pairs contributed to this disseminated meta-analysis that investigated the effect of maternal prenatal level of education (high school diploma, GED, or less; some college, associate's degree, vocational or technical training [reference category]; bachelor's degree, graduate school, or professional degree) and neighborhood-level markers of SES (census tract [CT] urbanicity, percentage of black population in CT, percentage of population below the federal poverty level in CT) on gestational age at birth (categorized as preterm, early term, full term [the reference category], late, and post term) according to maternal race/ethnicity and US region. Multinomial logistic regression was used to estimate odds ratios (OR) and 95% confidence intervals (CIs). Cohort-specific results were meta-analyzed using a random effects model. For women overall, a bachelor's degree or above, compared with some college, was associated with a significantly decreased odds of preterm birth (aOR 0.72; 95% CI: 0.61-0.86), whereas a high school education or less was associated with an increased odds of early term birth (aOR 1.10, 95% CI: 1.00-1.21). When stratifying by maternal race/ethnicity, there were no significant associations between maternal education and gestational age at birth among women of racial/ethnic groups other than non-Hispanic white. Among non-Hispanic white women, a bachelor's degree or above was likewise associated with a significantly decreased odds of preterm birth (aOR 0.74 (95% CI: 0.58, 0.94) as well as a decreased odds of early term birth (aOR 0.84 (95% CI: 0.74, 0.95). The association between maternal education and gestational age at birth varied according to US region, with higher levels of maternal education associated with a significantly decreased odds of preterm birth in the Midwest and South but not in the Northeast and West. Non-Hispanic white women residing in rural compared to urban CTs had an increased odds of preterm birth; the ability to detect associations between neighborhood-level measures of SES and gestational age for other race/ethnic groups was limited due to small sample sizes within select strata. Interventions that promote higher educational attainment among women of reproductive age could contribute to a reduction in preterm birth, particularly in the US South and Midwest. Further individual-level analyses engaging a diverse set of cohorts are needed to disentangle the complex interrelationships among maternal education, neighborhood-level factors, exposures across the life course, and gestational age at birth outcomes by maternal race/ethnicity and US geography.

Biological and environmental predictors of heterogeneity in neurocognitive ageing: Evidence from Betula and other longitudinal studies

Individual differences in cognitive performance increase with advancing age, reflecting marked cognitive changes in some individuals along with little or no change in others. Genetic and lifestyle factors are assumed to influence cognitive performance in ageing by affecting the magnitude and extent of age-related brain changes (i.e., brain maintenance or atrophy), as well as the ability to recruit compensatory processes. The purpose of this review is to present findings from the Betula study and other longitudinal studies, with a focus on clarifying the role of key biological and environmental factors assumed to underlie individual differences in brain and cognitive ageing. We discuss the vital importance of sampling, analytic methods, consideration of non-ignorable dropout, and related issues for valid conclusions on factors that influence healthy neurocognitive ageing.

A framework for assessing the impact of chemical exposures on neurodevelopment in ECHO: Opportunities and challenges

The Environmental influences on Child Health Outcomes (ECHO) Program is a research initiative funded by the National Institutes of Health that capitalizes on existing cohort studies to investigate the impact of early life environmental factors on child health and development from infancy through adolescence. In the initial stage of the program, extant data from 70 existing cohort studies are being uploaded to a database that will be publicly available to researchers. This new database will represent an unprecedented opportunity for researchers to combine data across existing cohorts to address associations between prenatal chemical exposures and child neurodevelopment. Data elements collected by ECHO cohorts were determined via a series of surveys administered by the ECHO Data Analysis Center. The most common chemical classes quantified in multiple cohorts include organophosphate pesticides, polychlorinated biphenyls, polybrominated diphenyl ethers, environmental phenols (including bisphenol A), phthalates, and metals. For each of these chemicals, at least four ECHO cohorts also collected behavioral data during infancy/early childhood using the Child Behavior Checklist. For these chemicals and this neurodevelopmental assessment (as an example), existing data from multiple ECHO cohorts could be pooled to address research questions requiring larger sample sizes than previously available. In addition to summarizing the data that will be available, the article also describes some of the challenges inherent in combining existing data across cohorts, as well as the gaps that could be filled by the additional data collection in the ECHO Program going forward.

An Assessment of Environmental Health Measures in the Deepwater Horizon Research Consortia

Research consortia play a key role in our understanding of how environmental exposures influence health and wellbeing, especially in the case of catastrophic events such as the Deepwater Horizon oil spill. A common challenge that prevents the optimal use of these data is the difficulty of harmonizing data regarding the environmental exposures and health effects across the studies within and among consortia. A review of the measures used by members of the Deepwater Horizon Research Consortia highlights the challenges associated with balancing timely implementation of a study to support disaster relief with optimizing the long-term value of the data. The inclusion of common, standard measures at the study design phase and a priori discussions regarding harmonization of study-specific measures among consortia members are key to overcoming this challenge. As more resources become available to support the use of standard measures, researchers now have the tools needed to rapidly coordinate their studies without compromising research focus or timely completion of the original study goals.

Establishing apomorphine treatment in Thailand: understanding the challenges and opportunities of Parkinson's disease management in developing countries

INTRODUCTION

The increasing global burden of Parkinson's disease (PD) poses a particular challenge for developing countries, such as Thailand, when delivering care to a geographically diverse populace with limited resources, often compounded by a lack of expertise in the use of certain PD medications, such as device-aided therapies (DAT).

AREAS COVERED

A panel of local, regional, and international PD experts convened to review the unmet needs of PD in Thailand and share insights into effective delivery of DAT, focusing on experience with apomorphine infusion. Despite its proven efficacy and safety, implementation of apomorphine infusion as a new option was not straightforward. This has prompted a range of health-care professional and patient-focused initiatives, led by the Chulalongkorn Center of Excellence for Parkinson's Disease and Related Disorders in Bangkok, to help establish a more coordinated approach to PD management throughout the country and ensure patients have access to suitable treatments.

EXPERT OPINION

Overcoming the challenges of education, proficiency, resource capacity and standard of care for PD patients in developing countries requires a coordinated effort both nationally and beyond. The best practices identified in Thailand following the introduction of apomorphine infusion might be helpful for other countries when implementing similar programs.

Preexisting coronary heart disease and susceptibility to long-term effects of traffic-related air pollution: A matched cohort analysis

BACKGROUND

Individuals with coronary heart disease are considered susceptible to traffic-related air pollution exposure. Yet, cohort-based evidence on whether preexisting coronary heart disease modifies the association of traffic-related air pollution with health outcomes is lacking.

AIM

Using data of four Israeli cohorts, we compared associations of traffic-related air pollution with mortality and cancer between coronary heart disease patients and matched controls from the general population.

METHODS

Subjects hospitalized with acute coronary syndrome from two patient cohorts (inception years: 1992-1993 and 2006-2014) were age- and sex-matched to coronary heart disease-free participants of two cycles of the Israeli National Health and Nutrition Surveys (inception years: 1999-2001 and 2005-2006). Ambient concentrations of nitrogen oxides at the residential place served as a proxy for traffic-related air pollution exposure across all cohorts, based on a high-resolution national land use regression model (50 m). Data on all-cause mortality (last update: 2018) and cancer incidence (last update: 2016) were retrieved from national registries. Cox-derived stratum-specific hazard ratios with 95% confidence intervals were calculated, adjusted for harmonized covariates across cohorts, including age, sex, ethnicity, neighborhood socioeconomic status, smoking, diabetes, hypertension, prior stroke and prior malignancy (the latter only in the mortality analysis). Effect-modification was examined by testing nitrogen oxides-by-coronary heart disease interaction term in the entire matched cohort.

RESULTS

The cohort (mean (standard deviation) age 61.5 (14) years; 44% women) included 2393 matched pairs, among them 2040 were cancer-free at baseline. During a median (25th-75th percentiles) follow-up of 13 (10-19) and 11 (7-17) years, 1458 deaths and 536 new cancer cases were identified, respectively. In multivariable-adjusted models, a 10-parts per billion nitrogen oxides increment was positively associated with all-cause mortality among coronary heart disease patients (hazard ratio = 1.13, 95% confidence interval 1.05-1.22), but not among controls (hazard ratio = 1.00, 0.93-1.08) (pinteraction = 0.003). A similar pattern was seen for all-cancer incidence (hazard ratioCHD = 1.19 (1.03-1.37), hazard ratioCHD-Free = 0.93 (0.84-1.04) (pinteraction = 0.01)). Associations were robust to multiple sensitivity analyses.

CONCLUSIONS

Coronary heart disease patients might be at increased risk for traffic-related air pollution-associated mortality and cancer, irrespective of their age and sex. Patients and clinicians should be more aware of the adverse health effects on coronary heart disease patients of chronic exposure to vehicle emissions.

Understanding childhood obesity in the US: the NIH environmental influences on child health outcomes (ECHO) program

BACKGROUND

Few resources exist for prospective, longitudinal analysis of the relationships between early life environment and later obesity in large diverse samples of children in the United States (US). In 2016, the National Institutes of Health launched the Environmental influences on Child Health Outcomes (ECHO) program to investigate influences of environmental exposures on child health and development. We describe demographics and overweight and obesity prevalence in ECHO, and ECHO's potential as a resource for understanding how early life environmental factors affect obesity risk.

METHODS

In this cross-sectional study of 70 extant US and Puerto Rico cohorts, 2003-2017, we examined age, race/ethnicity, and sex in children with body mass index (BMI) data, including 28,507 full-term post-birth to <2 years and 38,332 aged 2-18 years. Main outcomes included high BMI for age <2 years, and at 2-18 years overweight (BMI 85th to <95th percentile), obesity (BMI ≥ 95th percentile), and severe obesity (BMI ≥ 120% of 95th percentile).

RESULTS

The study population had diverse race/ethnicity and maternal demographics. Each outcome was more common with increasing age and varied with race/ethnicity. High BMI prevalence (95% CI) was 4.7% (3.5, 6.0) <1 year, and 10.6% (7.4, 13.7) for 1 to <2 years; overweight prevalence increased from 13.9% (12.4, 15.9) at 2-3 years to 19.9% (11.7, 28.2) at 12 to <18 years. ECHO has the statistical power to detect relative risks for 'high' BMI ranging from 1.2 to 2.2 for a wide range of exposure prevalences (1-50%) within each age group.

CONCLUSIONS

ECHO is a powerful resource for understanding influences of chemical, biological, social, natural, and built environments on onset and trajectories of obesity in US children. The large sample size of ECHO cohorts adopting a standardized protocol for new data collection of varied exposures along with longitudinal assessments will allow refined analyses to identify drivers of childhood obesity.

The Changing Science of HIV Epidemiology in the United States

In 1984, a large prospective study of the natural history of human immunodeficiency virus (HIV) infection, the Multicenter AIDS Cohort Study (MACS), was established; 10 years later, the Women's Interagency HIV Study (WIHS) was launched. Motivated by the merger and redesign of these long-standing HIV cohort studies in 2019, we review ways in which HIV epidemiology in the United States has transformed over the lives of these studies and how this evolution has influenced planning for enrollment and follow-up. We highlight changes that have occurred in the 3 major domains that are central to epidemiologic science: changes to key populations at highest risk for HIV, refinements in measurement and shifts in the outcomes of interest, and a new era in the tools and approaches that epidemiologists use to synthesize evidence from measurements made on populations. By embracing foundational principles with modern methods, the epidemiologic approach of analyzing the causes and distributions of diseases in contemporaneous populations will continue to advance HIV science over the next decade.

Mind the gap: observation windows to define periods of event ascertainment as a quality control method for longitudinal electronic health record data

PURPOSE

Use of electronic health records (EHRs) in health research may lead to the false assumption of complete event ascertainment. We estimated "observation windows" (OWs), defined as periods within which the assumption of complete ascertainment of events is more likely to hold, as a quality control approach to reducing the likelihood of this false assumption. We demonstrated the impact of OWs on estimating the rates of type II diabetes mellitus (diabetes) from HIV clinical cohorts.

METHODS

Data contributed by 16 HIV clinical cohorts to the NA-ACCORD were used to identify and evaluate OWs for an operationalized definition of diabetes occurrence as a case study. Procedures included (1) gathering cohort-level data; (2) visualizing and summarizing gaps in observations; (3) systematically establishing start and stop dates during which the assumption of complete ascertainment of diabetes events was reasonable; and (4) visualizing the diabetes OWs relative to the cohort open and close dates to identify immortal person-time. We estimated diabetes occurrence event rates and 95% confidence intervals in the most recent decade that data were available (January 1, 2007, to December 31, 2016).

RESULTS

The number of diabetes events decreased by 17% with the use of the diabetes OWs; immortal person-time was removed decreasing total person-years by 23%. Consequently, the diabetes rate increased from 1.23 (95% confidence interval [1.20, 1.25]) per 100 person-years to 1.32 [1.29, 1.35] per 100 person-years with the use of diabetes OWs.

CONCLUSIONS

As the use of EHR-curated data for event-driven health research continues to expand, OWs have utility as a quality control approach to complete event ascertainment, helping to improve accuracy of estimates by removing immortal person-time when ascertainment is incomplete.

Integrating Clinical and Epidemiologic Data on Allergic Diseases Across Birth Cohorts: A Harmonization Study in the Mechanisms of the Development of Allergy Project

The numbers of international collaborations among birth cohort studies designed to better understand asthma and allergies have increased in the last several years. However, differences in definitions and methods preclude direct pooling of original data on individual participants. As part of the Mechanisms of the Development of Allergy (MeDALL) Project, we harmonized data from 14 birth cohort studies (each with 3-20 follow-up periods) carried out in 9 European countries during 1990-1998 or 2003-2009. The harmonization process followed 6 steps: 1) organization of the harmonization panel; 2) identification of variables relevant to MeDALL objectives (candidate variables); 3) proposal of a definition for each candidate variable (reference definition); 4) assessment of the compatibility of each cohort variable with its reference definition (inferential equivalence) and classification of this inferential equivalence as complete, partial, or impossible; 5) convocation of a workshop to agree on the reference definitions and classifications of inferential equivalence; and 6) preparation and delivery of data through a knowledge management portal. We agreed on 137 reference definitions. The inferential equivalence of 3,551 cohort variables to their corresponding reference definitions was classified as complete, partial, and impossible for 70%, 15%, and 15% of the variables, respectively. A harmonized database was delivered to MeDALL investigators. In asthma and allergy birth cohorts, the harmonization of data for pooled analyses is feasible, and high inferential comparability may be achieved. The MeDALL harmonization approach can be used in other collaborative projects.

Measurement Error and Misclassification in Electronic Medical Records: Methods to Mitigate Bias

PURPOSE OF REVIEW

We sought to: 1) examine common sources of measurement error in research using data from electronic medical records (EMR), 2) discuss methods to assess the extent and type of measurement error, and 3) describe recent developments in methods to address this source of bias.

RECENT FINDINGS

We identified eight sources of measurement error frequently encountered in EMR studies, the most prominent being that EMR data usually reflect only the health services and medications delivered within the specific health facility/system contributing to the EMR data. Methods for assessing measurement error in EMR data usually require gold standard or validation data, which may be possible using data linkage. Recent methodological developments to address the impact of measurement error in EMR analyses were particularly rich in the multiple imputation literature.

SUMMARY

Presently, sources of measurement error impacting EMR studies are still being elucidated, as are methods for assessing and addressing them. Given the magnitude of measurement error that has been reported, investigators are urged to carefully evaluate and rigorously address this potential source of bias in studies based in EMR data.

An Environmental influences on Child Health Outcomes viewpoint of data analysis centers for collaborative study designs

PURPOSE OF REVIEW

A highly complex collaborative study design that pools and extends existing studies, such as the Environmental influences on Child Health Outcomes (ECHO) Program, requires a Data Analysis Center (DAC) with resources and expertise to create a secure environment for housing and analyzing the shared data, harmonize and structure the shared data for different purposes, and apply appropriate and innovative designs and analytic methods. The DAC, in partnership with cohort investigators, must ensure that results from ECHO-wide cohort analyses are appropriately interpreted and reproducible.

RECENT FINDINGS

Understanding the cohorts contributing to ECHO is critical for developing a collaborative environment and the methods to best analyze the data without bias. We further describe the development of the ECHO-wide cohort Metadata Catalog, the architecture of the ECHO-wide cohort data platform, and analytical approaches to facilitate early productivity.

SUMMARY

The ECHO DAC has established a secure environment for the transfer and storage of ECHO cohort data and information, and initiated processes to promote productive collaborations. Understanding the ECHO DAC responsibilities and assets will help to overcome communication and trust challenges encountered in the initiation of this complex ECHO-wide cohort collaborative research study.