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On Generalizable Inference and Prediction for Biased Samples

Creative Commons 'BY' version 4.0 license

Most statistical methods assume that samples are representative of a target population of interest, but this assumption is commonly violated in biomedical applications with human volunteers. Study participants self-selection into a sample can cause it to be unrepresentative which in turn leads to sampling bias. When analyzing data from a biased sample, the sampling scheme must be accounted for to obtain inference and predictions that generalize to the target population. In this dissertation, we discuss an approach for addressing sampling bias by estimating sampling weights using an auxiliary data set. We then apply estimated sampling weights in the field of causal inference. We assess the impact on bias and variance of estimated causal effects when sampling weights are included or omitted when estimating propensity scores and propensity-adjusted causal effects. Lastly, we quantify the impact of sampling bias on estimates of the prediction error for a target population and compare estimation methods.

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