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Related Concept Videos

Data Reporting and Recording01:24

Data Reporting and Recording

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Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...
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Dimensional analysis simplifies complex physical problems and guides experimental investigations, but it does not provide complete solutions. It identifies the dimensionless groups that influence a phenomenon, but experimental data is needed to establish the specific relationships and validate theoretical predictions.
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We present an optimized tandem mass tag (TMT) labeling protocol that includes detailed information for each of the following steps: protein extraction, quantification, precipitation, digestion, labeling, submission to a proteomics facility, and data...
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This protocol presents an approach for whole transcriptome analysis from zebrafish embryos, larvae, or sorted cells. We include isolation of RNA, pathway analysis of RNASeq data, and qRT-PCR-based validation of gene expression...
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One challenge of analyzing synchronized time-series experiments is that the experiments often differ in the length of recovery from synchrony and the cell-cycle period. Thus, the measurements from different experiments cannot be analyzed in aggregate or readily compared. Here, we describe a method for aligning experiments to allow for phase-specific...
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Related Experiment Video

Updated: Jan 20, 2026

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01:24

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Two-sample test for correlated data under outcome-dependent sampling with an application to self-reported weight loss

Yi Cai1, Jing Huang2, Jing Ning3

  • 1AT&T Services, Inc, Plano, Texas.

Statistics in Medicine
|September 7, 2019
PubMed
Summary

This study introduces a new score test for clustered data, offering accurate statistical analysis without assumptions on data structure. This method effectively compares two groups, handling mean and variance differences simultaneously.

Keywords:
U-statisticscorrelated dataoutcome-dependent samplingpseudolikelihoodtwo-sample test

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Area of Science:

  • Statistics
  • Biostatistics
  • Data Analysis

Background:

  • Standard two-sample tests (t-test, Wilcoxon rank sum) yield inaccurate Type I errors with longitudinal or clustered data.
  • Existing alternatives for clustered data often impose restrictive assumptions on correlation structure or cluster size.

Purpose of the Study:

  • To propose a novel score test for two-sample comparisons in clustered data.
  • To develop a method that does not require prior knowledge of the correlation structure or assume missingness at random.
  • To create a test capable of detecting simultaneous differences in both mean and variance between groups.

Main Methods:

  • Utilized a novel pseudolikelihood approach for correlated data.
  • Derived a score test statistic using projection theory.
  • Employed empirical estimation for the covariance matrix of the test statistic.
  • Conducted simulation studies for performance evaluation and comparison with existing methods.

Main Results:

  • The proposed score test demonstrated robust performance in simulation studies.
  • It effectively captured differences in both mean and variance between groups.
  • The test avoids common pitfalls associated with standard methods on clustered data.

Conclusions:

  • The novel score test provides a flexible and reliable alternative for analyzing clustered data.
  • It offers a powerful tool for comparing two groups, especially when correlation structures are unknown or complex.
  • The method's applicability is illustrated with real-world weight loss data analysis.