Abstract
Harmonization of data for pooled analysis relies on the principle of inferential equivalence between variables from different sources. Ideally, this is achieved using models of the direct relationship with gold standard criterion measures, but the necessary validation study data are often unavailable. This study examines an alternative method of network harmonization using indirect models. Starting methods were self-report or accelerometry, from which we derived indirect models of relationships with doubly labelled water (DLW)-based physical activity energy expenditure (PAEE) using sets of two bridge equations via one of three intermediate measures. Coefficients and performance of indirect models were compared to corresponding direct models (linear regression of DLW-based PAEE on starting methods). Indirect model beta coefficients were attenuated compared to direct model betas (10%–63%), narrowing the range of PAEE values; attenuation was greater when bridge equations were weak. Directly and indirectly harmonized models had similar error variance but most indirectly derived values were biased at group-level. Correlations with DLW-based PAEE were identical after harmonization using continuous linear but not categorical models. Wrist acceleration harmonized to DLW-based PAEE via combined accelerometry and heart rate sensing had the lowest error variance (24.5%) and non-significant mean bias 0.9 (95%CI: −1.6; 3.4) kJ·day−1·kg−1. Associations between PAEE and BMI were similar for directly and indirectly harmonized values, but most fell outside the confidence interval of the criterion PAEE-to-BMI association. Indirect models can be used for harmonization. Performance depends on the measurement properties of original data, variance explained by available bridge equations, and similarity of population characteristics.
Highlights
Harmonization of data for pooled analysis relies on the principle of inferential equivalence between variables from different sources
By utilizing a combination of published bridge equations and existing datasets, this study examines such an approach by comparing the inferential equivalence of data harmonized to a gold standard format via both direct and indirect models
To demonstrate utility, we examined the associations between all physical activity energy expenditure (PAEE) estimates and body mass index (BMI) using multivariable linear regression adjusted for age and sex in a subset of 1695 participants in the Fenland Study
Summary
Harmonization of data for pooled analysis relies on the principle of inferential equivalence between variables from different sources. An alternative approach to harmonization is to restrict analyses to only those studies which have assessed and expressed the exposure and outcome in the desired way This maintains the detail of the contributing data, but—as highlighted by Aune, Norat, Leitzmann, Tonstad, and Vatten (2015)—greatly reduces the proportion of the available data that can be included in evidence synthesis. This leads to bias if the studies that are included with optimal data have specific characteristics Another approach to harmonization is to use validation studies which report the statistical (e.g., regression) models of relationships between values from the less precise methods and the latent true level of exposure, as assessed by a construct-specific gold-standard criterion method. Unauthenticated | Downloaded 11/02/21 04:24 PM UTC and settings may be studied with unsatisfactorily harmonized data or excluded from analyses altogether
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