Related Experiment Video
Updated: May 30, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
Accounting for Twins and Other Multiple Births in Perinatal Studies of Live Births Conducted Using Healthcare
Jeremy P Brown1, Jennifer J Yland1, Paige L Williams2
1From the Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA.
The analysis of perinatal studies is complicated by twins and other multiple births even when multiples are not the exposure, outcome, or a confounder of interest. In analyses of infant outcomes restricted to live births, common approaches to handling multiples include restriction to singletons, counting outcomes at the pregnancy level (i.e., by counting if at least one twin experienced a binary outcome), or infant-level analysis including all infants and accounting for clustering of outcomes, such as by using generalized estimating equations or mixed effects models. Several healthcare administration databases only support restriction to singletons or pregnancy-level approaches. For example, in MarketScan insurance claims data, diagnoses in twins are often assigned to a single infant identifier, thereby preventing ascertainment of infant-level outcomes among multiples. Different approaches correspond to different questions, produce different estimands, and often rely on different assumptions. We demonstrate the differences that can arise from these different approaches using Monte Carlo simulations, algebraic formulas, and an applied example.
The analysis of perinatal studies is complicated by twins and other multiple births even when multiples are not the exposure, outcome, or a confounder of interest. In analyses of infant outcomes restricted to live births, common approaches to handling multiples include restriction to singletons, counting outcomes at the pregnancy level (i.e., by counting if at least one twin experienced a binary outcome), or infant-level analysis including all infants and accounting for clustering of outcomes, such as by using generalized estimating equations or mixed effects models. Several healthcare administration databases only support restriction to singletons or pregnancy-level approaches. For example, in MarketScan insurance claims data, diagnoses in twins are often assigned to a single infant identifier, thereby preventing ascertainment of infant-level outcomes among multiples. Different approaches correspond to different questions, produce different estimands, and often rely on different assumptions. We demonstrate the differences that can arise from these different approaches using Monte Carlo simulations, algebraic formulas, and an applied example.
More Related Videos
Related Concept Videos
Case Studies
Data Collection by Observations
An astronomer viewing the motion and brightness of stars in the sky and recording the data is an example of observational data collection. A botanist recording...
Observational Studies
There are three types of observational studies – Prospective, retrospective, and cross-sectional.
Prospective Study
Prospective studies, also known as longitudinal or cohort studies, are carried out by collecting future data from groups sharing similar characteristics. One...
Regression Toward the Mean
Health Information Technology and Healthcare Information System
Health Information Technology, commonly called HIT, integrates advanced information systems and technology in healthcare settings. Its primary functions include:
Data Reporting and Recording

