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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...
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...
Sampling Plans01:23

Sampling Plans

Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
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...
Observational Studies01:11

Observational Studies

Observational studies are a type of analytical study where researchers observe events without any interventions. In other words, the researcher does not influence the response variable or the experiment's outcome.
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 example of...
Data Collection by Observations01:08

Data Collection by Observations

Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
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...

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Related Experiment Video

Updated: Jun 3, 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

Outcome-dependent sampling from existing cohorts with longitudinal binary response data: study planning and analysis.

Jonathan S Schildcrout1, Patrick J Heagerty

  • 1Department of Biostatistics, Vanderbilt University School of Medicine, 1161 21st Avenue South, S-2323 Medical Center North, Nashville, Tennessee 37232, USA. jonathan.schildcrout@vanderbilt.edu

Biometrics
|April 5, 2011
PubMed
Summary
This summary is machine-generated.

Efficiently collect new scientific data from existing cohorts using outcome-dependent sampling. This method aids in estimating covariate coefficients for time-varying and time-invariant factors, even with limited resources.

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Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Related Experiment Videos

Last Updated: Jun 3, 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

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Area of Science:

  • Biostatistics
  • Epidemiology
  • Genetics

Background:

  • Longitudinal studies often require additional data collection for novel research questions.
  • Resource limitations frequently necessitate selective subject sampling for detailed assessments.

Purpose of the Study:

  • To propose novel longitudinal outcome-dependent sampling schemes.
  • To develop efficient statistical analyses for estimating covariate effects with limited exposure data.

Main Methods:

  • Longitudinal outcome-dependent sampling schemes.
  • Design-corrected conditional maximum likelihood analysis.
  • Study planning phase for feasibility assessment.

Main Results:

  • Highly efficient estimation of time-varying and time-invariant covariate coefficients.
  • Proactive examination of substudy feasibility using existing cohort data.

Conclusions:

  • Proposed methods enable efficient data collection and analysis under resource constraints.
  • The approach is applicable to genetic association studies, such as the interleukin-10 cytokine polymorphism and adolescent asthma.