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Related Concept Videos

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:
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...
Two-Way ANOVA01:17

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The two-way ANOVA is an extension of the one-way ANOVA. It is a statistical test performed on three or more samples categorized by two factors - a row factor and a column factor. Ronald Fischer mentioned it in 1925 in his book 'Statistical Methods for Researchers.'
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Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures from...
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...

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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

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Published on: July 3, 2020

Bias in 2-part mixed models for longitudinal semicontinuous data.

Li Su1, Brian D M Tom, Vernon T Farewell

  • 1Medical Research Council, Biostatistics Unit, Robinson Way, Cambridge CB2 0SR, UK. li.su@mrc-bsu.cam.ac.uk

Biostatistics (Oxford, England)
|January 13, 2009
PubMed
Summary

Misspecifying random effects as independent in two-part mixed models can bias results for correlated data. This bias is significant in biomedical research, especially with artificial zeros from detection limits.

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Area of Science:

  • Biostatistics
  • Biomedical Research
  • Longitudinal Data Analysis

Background:

  • Semicontinuous data (mixture of zeros and positive values) are common in biomedical research.
  • Two-part mixed models are suitable for longitudinal semicontinuous data.
  • Independence assumption for random effects is often used for convenience but may be incorrect.

Purpose of the Study:

  • To investigate bias in regression coefficients when random effects are correlated but misspecified as independent in two-part mixed models.
  • To evaluate the performance of misspecified models using Monte Carlo simulations.
  • To explore potential bias introduced by artificial zeros due to left censoring.

Main Methods:

  • Derivation and investigation of asymptotic bias in misspecified two-part mixed models.
  • Monte Carlo simulations to assess model performance.
  • Application of different two-part mixed models to psoriatic arthritis data.

Main Results:

  • Misspecification of correlated random effects as independent induces bias in regression coefficients.
  • Bias can be exacerbated by artificial zeros from left censoring.
  • Analysis of psoriatic arthritis data revealed practical issues in variance component estimation.

Conclusions:

  • The independence assumption in two-part mixed models can lead to biased results when random effects are correlated.
  • Careful model specification is crucial for accurate analysis of longitudinal semicontinuous biomedical data.
  • Further research is needed on handling artificial zeros and variance estimation in these models.