The combination of ecological and case-control data

Ecological studies, in which data are available at the level of the group, rather than at the level of the individual, are susceptible to a range of biases due to their inability to characterize within-group variability in exposures and confounders. In order to overcome these biases, we propose a hybrid design in which ecological data are supplemented with a sample of individual-level case-control data. We develop the likelihood for this design and illustrate its benefits via simulation, both in bias reduction when compared to an ecological study, and in efficiency gains relative to a conventional case-control study. An interesting special case of the proposed design is the situation where ecological data are supplemented with case-only data. The design is illustrated using a dataset of county-specific lung cancer mortality rates in the state of Ohio from 1988

osDesign: An R Package for the Analysis, Evaluation, and Design of Two-Phase and Case-Control Studies

The two-phase design has recently received attention in the statistical literature as an extension to the traditional case-control study for settings where a predictor of interest is rare or subject to missclassification. Despite a thorough methodological treatment and the potential for substantial efficiency gains, the two-phase design has not been widely adopted. This may be due, in part, to a lack of general-purpose, readily-available software. The osDesign package for R provides a suite of functions for analyzing data from a two-phase and/or case-control design, as well as evaluating operating characteristics, including bias, efficiency and power. The evaluation is simulation-based, permitting flexible application of the package to a broad range of scientific settings. Using lung cancer mortality data from Ohio, the package is illustrated with a detailed case-study in which two statistical goals are considered: (i) the evaluation of small-sample operating characteristics for two-phase and case-control designs and (ii) the planning and design of a future two-phase study

Estimating weighted quantile treatment effects with missing outcome data by double sampling

Causal weighted quantile treatment effects (WQTE) are a useful compliment to standard causal contrasts that focus on the mean when interest lies at the tails of the counterfactual distribution. To-date, however, methods for estimation and inference regarding causal WQTEs have assumed complete data on all relevant factors. Missing or incomplete data, however, is a widespread challenge in practical settings, particularly when the data are not collected for research purposes such as electronic health records and disease registries. Furthermore, in such settings may be particularly susceptible to the outcome data being missing-not-at-random (MNAR). In this paper, we consider the use of double-sampling, through which the otherwise missing data is ascertained on a sub-sample of study units, as a strategy to mitigate bias due to MNAR data in the estimation of causal WQTEs. With the additional data in-hand, we present identifying conditions that do not require assumptions regarding missingness in the original data. We then propose a novel inverse-probability weighted estimator and derive its' asymptotic properties, both pointwise at specific quantiles and uniform across a range of quantiles in (0,1), when the propensity score and double-sampling probabilities are estimated. For practical inference, we develop a bootstrap method that can be used for both pointwise and uniform inference. A simulation study is conducted to examine the finite sample performance of the proposed estimators

A Prospective Assessment of Racial/Ethnic Differences in Future Mammography Behavior among Women Who had Early Mammography

29% of women aged 30-39 report having had a mammogram though sensitivity and specificity are low. We investigate racial/ethnic differences in future mammography behavior among women who had a baseline screening mammogram prior to age 40