Optimal multiwave validation of secondary use data with outcome and exposure misclassification

Abstract

Observational databases provide unprecedented opportunities for secondary use in biomedical research. However, these data can be error‐prone and must be validated before use. It is usually unrealistic to validate the whole database because of resource constraints. A cost‐effective alternative is a two‐phase design that validates a subset of records enriched for information about a particular research question. We consider odds ratio estimation under differential outcome and exposure misclassification and propose optimal designs that minimize the variance of the maximum likelihood estimator. Our adaptive grid search algorithm can locate the optimal design in a computationally feasible manner. Because the optimal design relies on unknown parameters, we introduce a multiwave strategy to approximate the optimal design. We demonstrate the proposed design's efficiency gains through simulations and two large observational studies.

Study design features increase replicability in cross-sectional and longitudinal brain-wide association studies

“…Because standardized ESs are directly related to study efficiency, design considerations that optimize study efficiency will increase ESs and study replicability. Two-phase, extreme group, and outcome-dependent sampling designs can inform what subjects should be selected for imaging from a larger sample in order to increase the efficiency and ESs of brain-behavior associations [42][43][44][45][46][47][48] . When there are multiple covariates of interest, multivariate optimal sampling designs can be used to increase ESs 49 .…”

“…Because standardized ESs are dependent on study design, careful design choices can simultaneously increase standardized ESs and study replicability. Two-phase, extreme group, and outcome-dependent sampling designs can inform which subjects should be selected for imaging from a larger sample in order to increase the efficiency and standardized ESs of brainbehavior associations [28][29][30][31][32][33][34] . For example, given the high degree of accessibility of cognitive and behavioral testing (e.g., to be performed virtually or electronically), individuals scoring at the extremes on a testing scale/battery ("phase I") could be prioritized for subsequent brain scanning ("phase II").…”

Lessons learned from over a decade of data audits in international observational HIV cohorts in Latin America and East Africa

“…Still, one-hundredpercent source document verification for large datasets is both time-and cost-prohibitive [18]. Fortunately, the following can provide improved approaches to data quality assessment: (i) riskbased auditing [19][20], which allows for customizing audits to prioritize monitoring of high-risk activities, study processes, site components, or data points; (ii) new audit designs that strategically target the most informative records for validation [e.g., [21][22][23][24][25]; and (iii) statistical methods for addressing errors that incorporate both audit and original data into analyses, which can recover unbiased and statistically efficient estimates [e.g., [26][27][28][29].…”