Browsing by Subject "Epidemiologic methods"

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    Incremental Propensity Score Effects for Time-Fixed Exposures
    (Wolters Kluwer, 2021) Naimi, Ashley I.; Rudolph, Jacqueline E.; Kennedy, Edward H.; Cartus, Abigail; Kirkpatrick, Sharon I.; Haas, David M.; Simhan, Hyagriv; Bodnar, Lisa M.; Obstetrics and Gynecology, School of Medicine
    When causal inference is of primary interest, a range of target parameters can be chosen to define the causal effect of interest, such as average treatment effects (ATEs). However, ATEs may not always align with the research question at hand. Furthermore, the assumptions needed to interpret estimates as ATEs, such as exchangeability, consistency, and positivity, are often not met. Here, we present the incremental propensity score (incremental PS) approach to quantify the effect of shifting each person’s exposure propensity by some pre-determined amount. Compared to the ATE, incremental PS may better reflect the impact of certain policy interventions, and do not require that positivity hold. Using the Nulliparous Pregnancy Outcomes Study: monitoring mothers-to-be (nuMoM2b), we quantified the relation between total vegetable intake and the risk of preeclampsia, and compared it to average treatment effect estimates. The ATE estimates suggested a reduction of between two and three preeclampsia cases per 100 pregnancies for consuming at least 1/2 a cup of vegetables per 1,000 kcal. However, positivity violations obfuscate the interpretation of these results. In contrast, shifting each woman’s exposure propensity by odds ratios ranging from 0.20 to 5.0 yielded no difference in the risk of preeclampsia. Our analyses show the utility of the incremental propensity score effects in addressing public health questions with fewer assumptions.
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    Using PhenX Measures to Identify Opportunities for Cross-Study Analysis
    (Wiley, 2012) Pan, Huaqin; Tryka, Kimberly A.; Vreeman, Daniel J.; Huggins, Wayne; Phillips, Michael J.; Mehta, Jayashri P.; Phillips, Jacqueline H.; McDonald, Clement J.; Junkins, Heather A.; Ramos, Erin M.; Hamilton, Carol M.; Medicine, School of Medicine
    The PhenX Toolkit provides researchers with recommended, well-established, low-burden measures suitable for human subject research. The database of Genotypes and Phenotypes (dbGaP) is the data repository for a variety of studies funded by the National Institutes of Health, including genome-wide association studies. The dbGaP requires that investigators provide a data dictionary of study variables as part of the data submission process. Thus, dbGaP is a unique resource that can help investigators identify studies that share the same or similar variables. As a proof of concept, variables from 16 studies deposited in dbGaP were mapped to PhenX measures. Soon, investigators will be able to search dbGaP using PhenX variable identifiers and find comparable and related variables in these 16 studies. To enhance effective data exchange, PhenX measures, protocols, and variables were modeled in Logical Observation Identifiers Names and Codes (LOINC® ). PhenX domains and measures are also represented in the Cancer Data Standards Registry and Repository (caDSR). Associating PhenX measures with existing standards (LOINC® and caDSR) and mapping to dbGaP study variables extends the utility of these measures by revealing new opportunities for cross-study analysis.