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Related Experiment Video

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Improving likelihood-based inference in control rate regression.

Annamaria Guolo1

  • 1University of Padova, Via Cesare Battisti 241/243, I-35121, Padova, Italy.

Statistics in Medicine
|October 5, 2017
PubMed
Summary

This study improves control rate regression for meta-analysis by addressing small sample sizes and measurement errors. Higher-order asymptotics, specifically Skovgaard

Keywords:
control ratehigher-order asymptoticslikelihood inferencemeasurement errormeta-analysis

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Area of Science:

  • Biostatistics
  • Meta-analysis
  • Statistical modeling

Background:

  • Control rate regression is vital for meta-analysis, accounting for heterogeneity by incorporating control group risk.
  • Accurate inference requires correcting for measurement error in both treated and control groups.
  • Small sample sizes can lead to misleading results in control rate regression analysis.

Purpose of the Study:

  • To investigate the impact of small sample sizes on control rate regression inference.
  • To propose a more accurate statistical approach for control rate regression under challenging conditions.
  • To enhance the reliability of inferential conclusions in meta-analysis.

Main Methods:

  • Utilizing higher-order asymptotics to improve upon first-order likelihood approximations.
  • Deriving and applying Skovgaard's statistic to enhance the accuracy of the signed profile log-likelihood ratio statistic.
  • Conducting simulation experiments and analyzing real-world data to validate the proposed methods.

Main Results:

  • First-order likelihood procedures are inaccurate with increasing heterogeneity and correlated measurement errors.
  • Skovgaard's statistic significantly improves the accuracy of inference compared to standard likelihood methods.
  • The proposed approach offers substantial gains in accuracy with no significant computational increase.

Conclusions:

  • Higher-order asymptotics, particularly Skovgaard's statistic, provide a more robust solution for control rate regression.
  • The method effectively addresses limitations posed by small sample sizes and measurement errors in meta-analysis.
  • The study provides practical tools (R code) for implementing improved inference in control rate regression.