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Related Concept Videos

Stratified Sampling Method01:16

Stratified Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
Cluster Sampling Method01:20

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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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...
Convenience Sampling Method00:55

Convenience Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population.
Convenience sampling is a non-random method of sample selection; this method selects individuals that are easily accessible and may result in biased data. For example, a marketing...
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
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 14, 2026

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

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Outcome vector dependent sampling with longitudinal continuous response data: stratified sampling based on summary

Jonathan S Schildcrout1, Shawn P Garbett, Patrick J Heagerty

  • 1Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN, USA. jonathan.schildcrout@vanderbilt.edu

Biometrics
|February 16, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces outcome-dependent sampling for longitudinal data analysis. This method efficiently collects exposure data in targeted substudies, improving analysis of factors influencing change over time.

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An R-Based Landscape Validation of a Competing Risk Model
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An R-Based Landscape Validation of a Competing Risk Model

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Last Updated: May 14, 2026

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

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Published on: January 8, 2020

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Area of Science:

  • Biostatistics
  • Longitudinal Data Analysis
  • Epidemiology

Background:

  • Traditional longitudinal analysis focuses on explanatory factors affecting mean levels or rates of change.
  • Challenges arise when planning substudies to collect additional exposure data on limited subjects from existing longitudinal data.

Purpose of the Study:

  • To introduce valid design and analysis methods for outcome-dependent sampling of longitudinal data.
  • To develop methods for scenarios where all outcome data exist, but targeted exposure data collection is needed for a substudy.
  • To link longitudinal regression model goals with desirable sampling designs.

Main Methods:

  • Proposed a stratified sampling approach based on summaries of individual longitudinal trajectories.
  • Detailed an ascertainment-corrected maximum likelihood estimation for biased samples.
  • Evaluated the efficiency of outcome-based sampling versus simple random sampling.

Main Results:

  • The efficiency of outcome-based sampling is highly dependent on the chosen outcome summary statistic.
  • Demonstrated a clear link between longitudinal regression model objectives and optimal sampling designs.
  • Applied methods to the Childhood Asthma Management Program data, examining lung function profiles based on IL-13 cytokine genotype.

Conclusions:

  • Outcome-dependent sampling offers a valid and efficient approach for longitudinal studies requiring targeted exposure data collection.
  • The choice of summary statistic is critical for maximizing the efficiency of such designs.
  • This methodology enhances the analysis of genetic factors influencing longitudinal health outcomes, as exemplified by asthma research.