Related Experiment Video
Updated: Jan 24, 2026

Hemodynamic Precision in the Neonatal Intensive Care Unit using Targeted Neonatal Echocardiography
Published on: January 27, 2023
Sequential co-enrolment in randomised trials in neonatal intensive care medicine
Whitney Yoder1, Floris Groenendaal2, Wes Onland3
1Departmentof Clinical, Neuro and Developmental Psychology, Faculty of Behavioural andMovement Sciences, Free University, Amtersdam, the Netherlands.
Insights
Recruiting patients for clinical trials can be challenging, especially in neonatal intensive care units. This study addresses sequential co-enrolment in multiple trials to estimate treatment effects accurately.
Area of Science:
- Medical research methodology
- Clinical trial design
- Biostatistics
Background:
- Patient recruitment for clinical trials is often limited in specialized settings like neonatal intensive care units.
- Sequential co-enrolment into multiple trials presents challenges, including the risk of contaminated results.
- Infants may have prior trial enrollment as fetuses, complicating new trial participation.
Purpose of the Study:
- To define requirements for estimating different treatment effects (estimands) in sequential clinical trials.
- To analyze how accounting for prior trial participation status and treatment impacts estimand interpretation.
- To provide guidance for researchers when co-enrolment is unavoidable.
Main Methods:
- Consideration of a scenario involving two sequential randomized clinical trials.
- Description of various estimands based on how prior trial information is incorporated.
- Analysis of how differences in available prior trial data affect interpretation and generalizability of results.
Main Results:
- Estimands vary in their consideration of previous trial participation and treatment status.
- Analytical results can differ in interpretation and generalizability due to prior trial data availability, unless treatment interactions are absent.
- Co-enrolment necessitates careful data collection on prior trial status.
Conclusions:
- Researchers must collect data on co-enrolment and prior treatment status when sequential trials are involved.
- Trial analysis plans should be adapted to mitigate risks associated with co-enrolment.
- Accurate estimation of meaningful treatment effects requires addressing potential biases from concurrent or prior trial participation.
Abstract:
In many medical research settings, such as the neonatal intensive care unit, the number of patients who are eligible for a randomised clinical trial is relatively small and recruiting a sufficient number of patients into trials is often difficult. Furthermore, some infants may have already been enrolled into a trial as a fetus. Sequential co-enrolment of patients into more than one trial may offer a solution, yet runs the risk of contaminated results. We consider the situation of two sequential trials and describe requirements for different possible treatments effects ('estimands') to be estimated in such situations. These estimands differ regarding the extent to which participation status and treatment status in the previous trial is accounted for. Because of differences in available information about previous trials, analyses may result in estimated effects which differ in terms of interpretation and generalisability, except when in the absence of an interaction between the studied treatments. If co-enrolment cannot be ruled out, researchers should collect information about co-enrolment and treatment status in a previous or concurrent trial and mitigate the trial analysis plan in order to estimate meaningful effects.
More Related Videos
Related Concept Videos
Clinical Trials
There are four phases in a clinical trial. A phase one...
Clinical Trials: Overview
Trial and Error and Algorithm
Sound Intensity
Sound Intensity Level
The human ear can perceive an extensive range of sound intensity, necessitating the use of the logarithmic scale to define a physical quantity—the intensity level. It is a ratio of two intensities and...
Intensity Of Electromagnetic Waves

