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Full-Text Articles in Physical Sciences and Mathematics
Marginal Structural Models: An Application To Incarceration And Marriage During Young Adulthood, Valerio Bacak, Edward Kennedy
Marginal Structural Models: An Application To Incarceration And Marriage During Young Adulthood, Valerio Bacak, Edward Kennedy
Edward H. Kennedy
Advanced methods for panel data analysis are commonly used in research on family life and relationships, but the fundamental issue of simultaneous time-dependent confounding and mediation has received little attention. In this article the authors introduce inverse-probability-weighted estimation of marginal structural models, an approach to causal analysis that (unlike conventional regression modeling) appropriately adjusts for confounding variables on the causal pathway linking the treatment with the outcome. They discuss the need for marginal structural models in social science research and describe their estimation in detail. Substantively, the authors contribute to the ongoing debate on the effects of incarceration on marriage …
Instrumental Variable Analyses: Exploiting Natural Randomness To Understand Causal Mechanisms, Theodore Iwashyna, Edward Kennedy
Instrumental Variable Analyses: Exploiting Natural Randomness To Understand Causal Mechanisms, Theodore Iwashyna, Edward Kennedy
Edward H. Kennedy
Instrumental variable analysis is a technique commonly used in the social sciences to provide evidence that a treatment causes an outcome, as contrasted with evidence that a treatment is merely associated with differences in an outcome. To extract such strong evidence from observational data, instrumental variable analysis exploits situations where some degree of randomness affects how patients are selected for a treatment. An instrumental variable is a characteristic of the world that leads some people to be more likely to get the specific treatment we want to study but does not otherwise change thosepatients’ outcomes. This seminar explains, in nonmathematical …
Improved Cardiovascular Risk Prediction Using Nonparametric Regression And Electronic Health Record Data, Edward Kennedy, Wyndy Wiitala, Rodney Hayward, Jeremy Sussman
Improved Cardiovascular Risk Prediction Using Nonparametric Regression And Electronic Health Record Data, Edward Kennedy, Wyndy Wiitala, Rodney Hayward, Jeremy Sussman
Edward H. Kennedy
Use of the electronic health record (EHR) is expected to increase rapidly in the near future, yet little research exists on whether analyzing internal EHR data using flexible, adaptive statistical methods could improve clinical risk prediction. Extensive implementation of EHR in the Veterans Health Administration provides an opportunity for exploration. Our objective was to compare the performance of various approaches for predicting risk of cerebrovascular and cardiovascular (CCV) death, using traditional risk predictors versus more comprehensive EHR data. Regression methods outperformed the Framingham risk score, even with the same predictors (AUC increased from 71% to 73% and calibration also improved). …