Mapping between measurement scales in meta-analysis, with application to measures of body mass index in children
arxiv(2024)
摘要
Quantitative evidence synthesis methods aim to combine data from multiple
medical trials to infer relative effects of different interventions. A
challenge arises when trials report continuous outcomes on different
measurement scales. To include all evidence in one coherent analysis, we
require methods to `map' the outcomes onto a single scale. This is particularly
challenging when trials report aggregate rather than individual data. We are
motivated by a meta-analysis of interventions to prevent obesity in children.
Trials report aggregate measurements of body mass index (BMI) either expressed
as raw values or standardised for age and sex. We develop three methods for
mapping between aggregate BMI data using known relationships between individual
measurements on different scales. The first is an analytical method based on
the mathematical definitions of z-scores and percentiles. The other two
approaches involve sampling individual participant data on which to perform the
conversions. One method is a straightforward sampling routine, while the other
involves optimization with respect to the reported outcomes. In contrast to the
analytical approach, these methods also have wider applicability for mapping
between any pair of measurement scales with known or estimable individual-level
relationships. We verify and contrast our methods using trials from our data
set which report outcomes on multiple scales. We find that all methods recreate
mean values with reasonable accuracy, but for standard deviations, optimization
outperforms the other methods. However, the optimization method is more likely
to underestimate standard deviations and is vulnerable to non-convergence.
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