may be oversampled in one cohort (such as women receiving no prenatal care who would only be present in the birth cohort), should any special considerations be made for the sampling probability in order to construct appropriate weights?

2. How can data imputation be used effectively, particularly for prenatal exposure?

a. What threshold level of imputation of prenatal exposure data is acceptable?

b. What should the proposed trigger for the more expensive comprehensive sampling look like—should this be a random sampling, event-based trigger, or a validation subset or some combination?

c. How should the sample be recalibrated in the future to account for attrition?

KEY POINTS OF THE DISCUSSION

Steven Cohen (Agency for Healthcare Research and Quality) moderated this session, and the panelists were Graham Kalton (Westat, Inc.), Colm O’Muircheartaigh (Harris School of Public Policy and NORC at the University of Chicago), and Richard Valliant (Joint Program in Survey Methodology at the University of Maryland and University of Michigan, Ann Arbor). The panel discussion below is organized by the topics covered during the session, which included the desirability of a unified design, allocation, weighting, missing data, and the special population sample.

Unified Design

In responding to the questions above on combining data from different cohorts and imputation, Kalton, O’Muircheartaigh, and Valliant agreed that a unified design with a clearly defined population of inference, as proposed by Kalton, has many advantages over an approach with separate prenatal and birth cohorts. In the unified design outlined by Kalton, the population of inference is defined as all births in a specified enrollment period. In that approach, a sample of women who would give birth in the enrollment period would be selected from a list of prenatal care providers that includes hospitals and birthing centers. Women who do not receive prenatal care, or receive it from a provider that is not on the list, would be sampled at the hospital or birthing center (at the birth). The integrated design has significant analytic advantages over a design with separate prenatal and birth cohorts, as well as leading to important simplifications in weighting, point estimation, and variance estimation. Some of the details and complications of implementing a unified design are described below.



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