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Related Concept Videos

Longitudinal Research02:20

Longitudinal Research

Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
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.
Cancer Survival Analysis01:21

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
Longitudinal Studies01:26

Longitudinal Studies

Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
Actuarial Approach01:20

Actuarial Approach

The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...

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Related Experiment Video

Updated: May 24, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Standardizing Late-Effects Data Capture in Childhood Cancer Survivorship: A FHIR-Based Follow-Up Questionnaire.

Roberta Gazzarata1,2, Monica Muraca3, Andrea Beccaria3

  • 1HL7 Europe, Brussels, Belgium.

Studies in Health Technology and Informatics
|May 23, 2026
PubMed
Summary

Childhood cancer survivors need standardized follow-up care for long-term effects. This study created a computable, interoperable electronic questionnaire using HL7 FHIR to improve data collection and reuse across Europe.

Keywords:
Childhood cancer survivorsHL7 FHIRLate effectsLong-term follow-up (LTFU)Questionnaire standardizationSurvivorship Passport (SurPass)

Related Experiment Videos

Last Updated: May 24, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Area of Science:

  • Health Informatics
  • Oncology
  • Public Health

Background:

  • Childhood cancer survivors face long-term health issues requiring lifelong monitoring.
  • Current follow-up data collection is inconsistent, hindering data sharing and research.
  • The PanCareSurPass project addresses these challenges in survivorship care.

Purpose of the Study:

  • To standardize the Survivorship Passport (SurPass) follow-up questionnaire.
  • To implement this questionnaire as a computable artifact using HL7 FHIR standards.
  • To facilitate interoperable data capture for clinical care and research.

Main Methods:

  • Developed a survivorship-oriented extension based on CTCAE (Common Terminology Criteria for Adverse Events).
  • Modeled the questionnaire using HL7 FHIR Questionnaire and QuestionnaireResponse resources.
  • Integrated the computable questionnaire into the PanCareSurPass FHIR Implementation Guide.

Main Results:

  • Created a standardized, computable follow-up questionnaire for childhood cancer survivors.
  • The questionnaire supports longitudinal documentation of late effects in routine care.
  • The implementation aligns with the European Health Data Space for data reuse.

Conclusions:

  • Established a reusable foundation for interoperable late-effects data capture.
  • Enhanced continuity of care for childhood cancer survivors.
  • Enabled observational studies and registries across Europe through standardized data.