SAVE THE DATE - JUNE 21, 2021
The SPER Advanced Methods Workshop immediately precedes the SPER annual meeting. The workshop provides researchers and students with an introduction to advanced epidemiologic methods.
Causal inference in pediatric and perinatal epidemiologic research: from questions to methods
Causal inference is challenging with real-world data. In many pediatric and perinatal epidemiologic studies, the first challenge is articulating a meaningful causal question. In this workshop, we will briefly introduce attendees to the process (and challenges) of articulating a causal question and how this leads to a choice of statistical analysis. We focus on settings where the question is about causal effects of interventions on a treatment or exposure that varies over time and review some statistical methods that may recover such an effect in longitudinal studies with time-varying confounding. We illustrate an application of these ideas to data from Project Viva, a longitudinal pre-birth cohort study.
Thank you for participating in the 2020 Advanced Methods Workshop! The workshop was held on November 10, 2020.
Did you miss the workshop? You can register to view the recording for $25. Email Sue Bevan (email@example.com) if you are interested.
Semi-competing risks: Accounting for death as a competing risk in public health research when the the outcome of interest is non-terminal.
Sebastien Haneuse, PhD, is Associate Professor of Biostatistics at the Harvard T.H. Chan School of Public Health. Read more
Harrison Reeder is a PhD student in the Biostatistics Department at the Harvard T.H. Chan School of Public Health. Read more
The workshop will provide an overview of semi-competing risks data analysis. Briefly, semi-competing risks corresponds to the setting where primary interest lies in some non-terminal event, the occurrence of which is subject to a terminal event. Although not as well-known as standard competing risks, semi-competing risks arise in any study of any event that is not mortality but where the force of mortality is strong. Examples include: Alzheimer’s disease in the elderly; quality of end-of-life care among patients with a terminal cancer diagnosis; graft-versus-host disease among bone marrow transplant recipients; and, developmental outcomes among infants admitted to a NICU. Semi-competing risks also arise in some settings where the terminal event is not mortality. In studies of preeclampsia, for example, “delivery” is a competing risk but not vice-versa. In this workshop, we will cover basic concepts of semi-competing risks, various modeling strategies, methods for prediction, and software. In addition we will apply and illustrate the methods to a study of preeclampsia using data from the Beth Israel Deaconess Medical Center, in Boston, MA, specifically with the goals of quantifying risk factor associations and the joint prediction of preeclampsia and delivery.