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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...
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...
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the Guinness...
Random Sampling Method01:09

Random 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. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
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.
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...

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Related Experiment Video

Updated: Jul 17, 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

Guided multiple imputation of missing data: using a subsample to strengthen the missing-at-random assumption.

Gary Fraser1, Ru Yan

  • 1Loma Linda University, Loma Linda, California, USA. gfraser@llu.edu

Epidemiology (Cambridge, Mass.)
|January 30, 2007
PubMed
Summary

This study introduces a method to improve multiple imputation for missing data. By collecting additional data from a sample of subjects, the approach enhances the reliability of results, especially when data are not missing at random.

Related Experiment Videos

Last Updated: Jul 17, 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:

  • Epidemiology
  • Biostatistics

Background:

  • Missing data pose challenges in statistical analysis, particularly in epidemiology.
  • The assumption of data missing at random (MAR) is often difficult to verify.
  • Traditional methods may fail when the MAR assumption is violated.

Purpose of the Study:

  • To present a practical application of multiple imputation that enhances the plausibility of the MAR assumption.
  • To evaluate the performance of this enhanced imputation method under various missing data scenarios.
  • To provide guidance on sample size and response rates for effective imputation in epidemiologic studies.

Main Methods:

  • A novel multiple imputation procedure involving re-contacting a random subsample of subjects with incomplete data.
  • Adjusting the imputation model to incorporate newly collected data.
  • Simulation studies using data missing not at random (MNAR) and analysis of a real-world dataset with missing data.

Main Results:

  • The proposed method successfully restored original beta coefficients in simulations with MNAR data, outperforming other methods.
  • Different imputation approaches yielded moderately different results on a real dataset.
  • Simulations indicated that imputing approximately 10% of initially missing data can yield unbiased results.

Conclusions:

  • The described multiple imputation technique offers a robust solution for handling missing data, even when the MAR assumption is questionable.
  • High response rates from the re-contacted subsample are crucial for the method's success.
  • Planning data collection for this purpose early in the research process is recommended for optimal outcomes.