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• W2895832800 abstract "Assessing batch-to-batch variability in CSF biomarkers is critical to accurately assessing longitudinal AD-related brain changes. Batching CSF samples can reduce measurement variability, but intra-assay coefficients of variation differ among various CSF biomarkers, which could result in significantly different values from the same sample measured at several points in time. For this analysis, statistical models were developed to account for batch differences and transform the CSF values from one batch to another, allowing results to be used in the same analysis. 792 CSF samples were measured in three batches, including 96 overlapping samples measured in batches 1 and 2, 89 in batches 1 and 3, and 607 in only one batch (Figure 1). CSF variables included 12 AD biomarkers and three ratios (Table 1). For each CSF variable, a separate model was developed for transformation from batch 1 to batch 2, 2 to 1, 1 to 3, and 3 to 1. Generalized linear models were applied on the CSF values of the samples measured in both batches. The predictor is the value measured in the batch to be transformed from, and the outcome is the value measured in the batch to be transformed to. Outliers were identified using Pearson residuals, and models were refitted with outliers excluded. Pearson correlation coefficient r ≥ .65 between CSF values from the two batches served as a criteria for the transformation to be valid. Distribution of CSF Samples between Three Measurement Batches. The developed transformation models included generalized linear models with identity link function and gamma, Poisson, or normal distribution of errors (Tables 1 to 4). Correlations r ranged from .66 to .99, except for p-tau between batches 1 and 3, r = .40. Using the models, the CSF values measured in only one batch can be transformed to allow CSF values from different batches to be included in the same analysis. Low correlation for p-tau between batches 1 and 3 was likely due to the difference in the assay methods (INNOTEST vs Xmap). Until fully-automated, highly-reproducible assays are available for all AD biomarkers, such conversion methods will be important in the analysis of longitudinal CSF data." @default.
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• W2895832800 date "2018-07-01" @default.
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• W2895832800 title "P1‐286: TRANSFORMATION OF CSF BIOMARKER VALUES BETWEEN MEASUREMENT BATCHES" @default.
• W2895832800 doi "https://doi.org/10.1016/j.jalz.2018.06.292" @default.
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