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Related Concept Videos

Bias01:22

Bias

Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
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...
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...
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:
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.

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

Published on: January 8, 2020

Correction of bias from non-random missing longitudinal data using auxiliary information.

Cuiling Wang1, Charles B Hall

  • 1Albert Einstein College of Medicine, 1300 Morris Park Ave, Bronx, NY 10461, USA. cuiling.wang@einstein.yu.edu

Statistics in Medicine
|December 24, 2009
PubMed
Summary

Auxiliary variables can test missing at random (MAR) assumptions in longitudinal studies. Joint modeling and multiple imputation (MI) methods were compared, with joint modeling being most efficient but sensitive to mis-specification.

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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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Published on: September 17, 2019

Area of Science:

  • Biostatistics
  • Longitudinal Data Analysis
  • Missing Data Methods

Background:

  • Missing data are prevalent in longitudinal studies, impacting analysis validity.
  • Missing at random (MAR) is a common assumption but untestable without auxiliary information.
  • Auxiliary variables correlated with outcomes can help assess and address MAR violations.

Purpose of the Study:

  • To evaluate methods for utilizing auxiliary information to test MAR assumptions.
  • To compare joint modeling and multiple imputation (MI) for handling missing data with auxiliary variables.
  • To assess bias and efficiency of these methods under different model specifications.

Main Methods:

  • Developed and simulated likelihood-based joint modeling of outcome and auxiliary variable.
  • Implemented and simulated multiple imputation (MI) using auxiliary information.
  • Compared performance of joint modeling and MI through simulation studies.

Main Results:

  • Joint modeling is consistent and most efficient when correctly specified but vulnerable to mis-specification.
  • Multiple imputation (MI) is robust to imputation model mis-specification if key variables are included.
  • MI is generally less efficient than correctly specified joint modeling.

Conclusions:

  • Auxiliary variables offer a pathway to test MAR assumptions in longitudinal data.
  • Both joint modeling and MI can reduce bias from MAR violations but require careful model specification.
  • The choice between joint modeling and MI depends on data characteristics and potential for model mis-specification.