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Systematic Error: Methodological and Sampling Errors01:15

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In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
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Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
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Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
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Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
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TMBocelot: an omnibus statistical control model optimizing the TMB thresholds with systematic measurement errors.

Xin Lai1, Shaoliang Wang1, Xuanping Zhang1

  • 1School of Computer Science and Technology, Faculty of Electronics and Information Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, China.

Frontiers in Immunology
|February 4, 2025
PubMed
Summary

Accurate tumor mutation burden (TMB) assessment is crucial for immunotherapy. A new framework, TMBocelot, corrects for measurement errors, improving TMB threshold determination for better patient stratification.

Keywords:
Bayesian frameworkimmunotherapy endpointspairwise error controlpositive-threshold optimizationtumor mutation burden

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Area of Science:

  • Oncology
  • Bioinformatics
  • Statistical modeling

Background:

  • Tumor mutation burden (TMB) is a key immunotherapy biomarker for patient stratification.
  • Measurement errors in TMB assessment can lead to inaccurate clinical decisions.
  • Reliable TMB thresholds are essential for effective immunotherapy implementation.

Purpose of the Study:

  • To propose a universal framework, TMBocelot, for accurate TMB threshold determination.
  • To address and correct for pairwise measurement errors in clinical TMB data.
  • To enhance the reliability of TMB-based immunotherapy decision-making.

Main Methods:

  • Developed TMBocelot, a novel framework incorporating measurement error correction.
  • Utilized a Bayesian approach with the stationarity principle of Markov chains.
  • Implemented enhanced error control using moderately informative priors.

Main Results:

  • TMBocelot demonstrated superior accuracy and consistency compared to conventional methods.
  • The framework effectively stabilized the determination of hierarchical TMB thresholds.
  • Simulations and retrospective analysis of 438 patients validated TMBocelot's performance.

Conclusions:

  • TMBocelot provides precise and reliable delineation of TMB-positive thresholds.
  • The framework facilitates improved patient stratification for immunotherapy.
  • Accurate TMB assessment using TMBocelot supports optimized clinical decision-making.