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Related Concept Videos

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...
Assessment of the Cardiovascular System I: Subjective Data01:23

Assessment of the Cardiovascular System I: Subjective Data

A thorough health history and physical assessment are essential for identifying cardiovascular disease (CVD) symptoms and distinguishing them from other health issues.
Initial Enquiry
Ask the patient about their primary concern and thoroughly explore all reported symptoms.
Medical History
Investigate past illnesses affecting the cardiovascular system, such as angina, anemia, rheumatic fever, congenital heart disease, stroke, thrombophlebitis, dysrhythmias, varicosities
Inquire about symptoms...
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.
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time until a...
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...
Censoring Survival Data01:09

Censoring Survival Data

Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different reasons...

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Related Experiment Video

Updated: May 17, 2026

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease
08:51

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease

Published on: September 20, 2024

Missing Data Essentials Part 1: Detecting and Evaluating Patterns of Missingness in Longitudinal Cardiovascular

Quin E Denfeld1, Shirin O Hiatt2, Nathan Dieckmann3

  • 1Oregon Health & Science University School of Nursing, Portland, OR, USA; Oregon Health & Science University Knight Cardiovascular Institute Portland, OR, USA.

European Journal of Cardiovascular Nursing
|May 16, 2026
PubMed
Summary

Missing data in health research can reduce study power and bias results. This paper explains types and causes of missing data and offers design strategies to minimize it.

Related Experiment Videos

Last Updated: May 17, 2026

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease
08:51

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease

Published on: September 20, 2024

Area of Science:

  • Cardiovascular Nursing
  • Allied Health Research
  • Health Sciences

Background:

  • Missing data is prevalent in cardiovascular nursing and allied health research, particularly in longitudinal studies.
  • Consequences include reduced sample size, statistical power, precision, and potential for biased findings.

Purpose of the Study:

  • To elucidate common types and mechanisms of missing data.
  • To present design and methodological strategies for minimizing missing data in research.
  • To provide practical examples for implementation.

Main Methods:

  • Discussion of missing data types: item nonresponse, item-level missingness, wave nonresponse, and structural missingness.
  • Explanation of missingness mechanisms: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR).
  • Inclusion of worked examples to demonstrate design and methodological considerations.

Main Results:

  • Identified key challenges posed by missing data in health research.
  • Outlined effective design strategies such as minimizing survey items and using reminders.
  • Illustrated the practical application of methods for handling missing data.

Conclusions:

  • Understanding missing data types and mechanisms is crucial for researchers.
  • Proactive design strategies can significantly mitigate the impact of missing data.
  • Methodological awareness improves the validity and reliability of research findings.