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Related Concept Videos

Percentile01:18

Percentile

A percentile indicates the relative standing of a data value when data are sorted into numerical order from smallest to largest. It represents the percentages of data values that are less than or equal to the pth percentile. For example, 15% of data values are less than or equal to the 15th percentile. Low percentiles always correspond to lower data values. High percentiles always correspond to higher data values.Percentiles divide ordered data into hundredths. To score in the...
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
Quartile01:15

Quartile

Quartiles are numbers that separate the data into quarters. Quartiles may or may not be part of the data. To find the quartiles, first, find the median or second quartile. The first quartile, Q1, is the middle value of the lower half of the data, and the third quartile, Q3, is the middle value, or median, of the upper half of the data. To get the idea, consider the same data set:
1; 1; 2; 2; 4; 6; 6.8; 7.2; 8; 8.3; 9; 10; 10; 11.5
The median or second quartile is seven. The lower half of the...
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

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.
Random Error01:04

Random Error

Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...

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Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Quantile Regression With Measurement Error.

Ying Wei1, Raymond J Carroll

  • 1Assistant Professor, Department of Biostatistics, Columbia University, 722 West 168th St., New York, NY 10032.

Journal of the American Statistical Association
|March 23, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for accurate linear quantile estimation, even with measurement errors in covariates. The approach corrects bias by using joint estimating equations, improving reliability in statistical analysis.

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Area of Science:

  • Statistics
  • Biostatistics
  • Epidemiology

Background:

  • Covariate measurement error can introduce significant bias in regression quantile estimation.
  • Existing methods may not adequately address this bias, leading to unreliable results.

Purpose of the Study:

  • To develop a new method for consistent linear quantile estimation in the presence of covariate measurement error.
  • To correct for bias induced by measurement errors in covariates.

Main Methods:

  • Propose a novel method using joint estimating equations for all quantile levels.
  • Employ an iterative EM-type algorithm for solving the joint estimation equations.
  • Compare the proposed method with the standard regression calibration approach via simulation.

Main Results:

  • The proposed method demonstrates consistent linear quantile estimation.
  • Simulation studies show improved performance compared to regression calibration.
  • The methodology is successfully applied to longitudinal growth data with measurement error.

Conclusions:

  • The novel method effectively corrects bias from covariate measurement error in linear quantile estimation.
  • This approach offers a more reliable alternative to standard methods for handling measurement error.
  • The technique is applicable to real-world longitudinal data analysis.