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Purpose: Passively generated cell-phone location ("mobility") data originally intended for commercial use has become frequently used in epidemiologic research, notably during the COVID-19 pandemic to study the impact of physical-distancing recommendations on aggregate population behavior (e.g., average daily mobility). Given the opaque nature of how individuals are selected into these datasets, researchers have cautioned that their use may give rise to selection bias, yet little guidance exists for assessing this potential threat to validity in mobility-data research. Through an example analysis of cell-phone-derived mobility data, we present a set of conditions to guide the assessment of selection bias in measures comparing aggregate mobility patterns over time and between groups.Methods: We specifically consider bias in measures comparing group-level mobility in the same group (difference, ratio, percent difference) and between groups (difference in differences, ratio of ratios, ratio of percent differences). We illustrate no-bias conditions in these measures through an example comparing block-group-level mobility between income groups in United States metro areas before (January 1st-March 10, 2020) and after (March 11th-April 19th, 2020) the day COVID-19 was declared a pandemic.Results: Within-group contrasts describing mobility over time, especially for the higher-income decile, were expected to be most resistant to bias during the example study period.Conclusions: The presented conditions can be used to assess the susceptibility to selection bias of group-level measures comparing mobility. Importantly, they can be used even without knowledge of the degree of bias in each group at each time point. We further highlight links between no-bias principles originating in epidemiology and economics, showing that certain assumptions (e.g., parallel trends) can apply to biases beyond their original application. [ABSTRACT FROM AUTHOR]

Purpose: To examine the extent to which commonly ordered laboratory values obtained from large health care databases are representative of the distribution of laboratory values from the general population as reflected in the National Health and Nutrition Examination Survey.Methods: Means of test values from commercial insurance laboratory data and National Health and Nutrition Examination Survey data were compared. Inverse probability of selection weighting was used to account for possible selection bias and to create comparability between the two data sources.Results: The average values of most of the laboratory results from routine care were very close to their population means as estimated from NHANES. Tests that were more selectively ordered tended to differ. The inverse probability of selection weighting approach generally had a small effect on the estimated means but did improve estimation of some of the more selected tests.Conclusions: Commonly ordered laboratory tests appear to be representative of values from the underlying population. This suggests that trends and other patterns in biomarker levels in the population may be reasonably studied using data collected during the routine delivery of medical care. [ABSTRACT FROM AUTHOR]

Purpose: Selection bias is a form of systematic error that can be severe in compromised study designs such as case-control studies with inappropriate selection mechanisms or follow-up studies that suffer from extensive attrition. External adjustment for selection bias is commonly undertaken when such bias is suspected, but the methods used can be overly simplistic, if not unrealistic, and fail to allow for simultaneous adjustment of associations of the exposure and covariates with the outcome, when of interest. Internal adjustment for selection bias via inverse probability weighting allows bias parameters to vary with the levels of covariates but has only been formalized for longitudinal studies with covariate data on patients up until loss to follow-up.Methods: We demonstrate the use of inverse probability weighting and externally obtained bias parameters to perform internal adjustment of selection bias in studies lacking covariate data on unobserved participants.Results: The "true" or selection-adjusted odds ratio for the association between exposure and outcome was successfully obtained by analyzing only data on those in the selected stratum (i.e., responders) weighted by the inverse probability of their being selected as function of their observed covariate data.Conclusions: This internal adjustment technique using user-supplied bias parameters and inverse probability weighting for selection bias can be applied to any type of observational study. [ABSTRACT FROM AUTHOR]