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• W4307979293 abstract "Abstract Although superficially similar to data from clinical research, data extracted from electronic health records (EHRs) may require fundamentally different approaches to analysis and model building. Some outcome and predictor variables may not be well-defined at the start of the study. Selecting specific definitions requires exploratory data analysis. Specifying the rules for computing a new variable inevitably leads to exploratory analyses. Achieving replicability, i.e., a high probability that a similar future study will reach the same conclusions, requires special approaches. We recommend a study design strategy based on stratified sample splitting for studies using EHRs. The split-sample design ensures meeting the goal of replicability. Stratified sampling of EHRs increases generalizability by allowing heterogeneity between subgroups to be tested appropriately with good statistical power. Building a model from EHR data to predict uptake of hepatic cancer screening illustrates the recommended approach." @default.
• W4307979293 created "2022-11-07" @default.
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• W4307979293 date "2022-11-02" @default.
• W4307979293 modified "2023-10-18" @default.
• W4307979293 title "Stratified Split Sampling of Electronic Health Records: Turning Early Science into Replicable Research" @default.
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• W4307979293 doi "https://doi.org/10.21203/rs.3.rs-2176798/v1" @default.
• W4307979293 hasPublicationYear "2022" @default.
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