Abstract:
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Mondern measurement and collection methods generate a large amount of data that become increasingly imstrumental for public health and medicine. Missing data are ubiquitous in these new types of data, and there is often a strong need to adjust inference for nonignorable data incompleteness. However, unlike in traditional studies, nonignorable missingness in these data poses significant new analytic challenges and calls for more general, flexible, and robust methods that are applicable in these studies to quantify and improve the reliability, validity and usability of the collected data. To tackle the issue, we develop principled and parsimonious statistical index measures and an R package that are scalable to these new types of data to quantify the reliability and validity of empirical findings. We illustrate the use of the method and the R package in dataset collected using Ecological Momentary Assessment (EMA) method.
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