Related Experiment Video

Updated: Aug 6, 2026

Assessment of Maternal Vascular Remodeling During Pregnancy in the Mouse Uterus
06:55

Assessment of Maternal Vascular Remodeling During Pregnancy in the Mouse Uterus

Published on: December 5, 2015

Revisiting Severe Maternal Morbidity Recurrence: Selection Bias and the Implications for Risk Estimation Across

Rashida S Smith-Webb1, Martha M Werler, Samantha E Parker

  • 1Department of Epidemiology, Boston University School of Public Health, Boston, MA 02118, USA.

Epidemiology (Cambridge, Mass.)
|July 21, 2026
PubMed
Abstract

Keywords:
competing eventsinverse probability of continuation weightslongitudinal studieslost to follow-upselection biassevere maternal morbidity

Related Concept Videos

Regression Toward the Mean01:52

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.