Measurement Error and Regression to the Mean in Matched Samples

Social Forces ◽  
1971 ◽  
Vol 50 (2) ◽  
pp. 206-214 ◽  
Author(s):  
R. P. Althauser ◽  
D. Rubin
Keyword(s):  
Measurement Error ◽  
Regression To The Mean ◽  
The Mean ◽  
Matched Samples

Measurement Error and Regression to the Mean in Matched Samples

Social Forces ◽  
1971 ◽  
Vol 50 (2) ◽  
pp. 206 ◽  
Author(s):  
Robert P. Althauser ◽  
Donald Rubin
Keyword(s):  
Measurement Error ◽  
Regression To The Mean ◽  
The Mean ◽  
Matched Samples

scholarly journals Controlling for baseline telomere length biases estimates of the rate of telomere attrition

2019 ◽  
Vol 6 (10) ◽  
pp. 190937 ◽  
Author(s):  
Melissa Bateson ◽  
Dan T. A. Eisenberg ◽  
Daniel Nettle
Keyword(s):  
Measurement Error ◽  
Telomere Length ◽  
Statistical Models ◽  
Meta Analysis ◽  
Regression To The Mean ◽  
Inference Models ◽  
Telomere Attrition ◽  
The Mean ◽  
Invalid Inference ◽  
Over Time

Longitudinal studies have sought to establish whether environmental exposures such as smoking accelerate the attrition of individuals' telomeres over time. These studies typically control for baseline telomere length (TL) by including it as a covariate in statistical models. However, baseline TL also differs between smokers and non-smokers, and telomere attrition is spuriously linked to baseline TL via measurement error and regression to the mean. Using simulated datasets, we show that controlling for baseline TL overestimates the true effect of smoking on telomere attrition. This bias increases with increasing telomere measurement error and increasing difference in baseline TL between smokers and non-smokers. Using a meta-analysis of longitudinal datasets, we show that as predicted, the estimated difference in telomere attrition between smokers and non-smokers is greater when statistical models control for baseline TL than when they do not, and the size of the discrepancy is positively correlated with measurement error. The bias we describe is not specific to smoking and also applies to other exposures. We conclude that to avoid invalid inference, models of telomere attrition should not control for baseline TL by including it as a covariate. Many claims of accelerated telomere attrition in individuals exposed to adversity need to be re-assessed.

Explaining and Controlling Regression to the Mean in Longitudinal Research Designs

2003 ◽  
Vol 46 (6) ◽  
pp. 1340-1351 ◽  
Author(s):  
Xuyang Zhang ◽  
J. Bruce Tomblin
Keyword(s):  
Measurement Error ◽  
Longitudinal Research ◽  
Formal Analysis ◽  
Sampling Bias ◽  
Regression To The Mean ◽  
Regression Effects ◽  
Regression Effect ◽  
Research Designs ◽  
Research Findings ◽  
The Mean

This tutorial is concerned with examining how regression to the mean influences research findings in longitudinal studies of clinical populations. In such studies participants are often obtained because of performance that deviates systematically from the population mean and are then subsequently studied with respect to change in the trait used for this selection. It is shown that in such research there is a potential for the estimates of change to be erroneous due to the effect of regression to the mean. The source of the regression effect is shown to arise from measurement error and a sampling bias of this measurement error in the process of selecting on extreme scores. It is also shown that regression effects are greater with measures that are less reliable and with samples that are selected with more extreme scores. Furthermore, it is shown that regression effects are particularly prominent when measures of change are based on changes in dichotomous states formed from quantitative, normally distributed traits. In addition to a formal analysis of the regression to the mean, the features of regression to the mean are demonstrated via a simulation.

scholarly journals The proportional recovery rule redux: Arguments for its biological and predictive relevance

2021 ◽  
Author(s):  
Jeff Goldsmith ◽  
Tomoko Kitago ◽  
Angel Garcia de la Garza ◽  
Robinson Kundert ◽  
Andreas Luft ◽  
...  
Keyword(s):  
Measurement Error ◽  
Motor Impairment ◽  
Biological Models ◽  
Regression To The Mean ◽  
Relevant Model ◽  
Biologically Relevant ◽  
Fixed Proportion ◽  
Baseline Values ◽  
The Mean ◽  
Mathematical Coupling

The proportional recovery rule (PRR) posits that most stroke survivors can expect to reverse a fixed proportion of motor impairment. As a statistical model, the PRR explicitly relates change scores to baseline values -- an approach that has the potential to introduce artifacts and flawed conclusions. We describe approaches that can assess associations between baseline and changes from baseline while avoiding artifacts either due to mathematical coupling or regression to the mean due to measurement error. We also describe methods that can compare different biological models of recovery. Across several real datasets, we find evidence for non-artifactual associations between baseline and change, and support for the PRR compared to alternative models. We conclude that the PRR remains a biologically-relevant model of recovery, and also introduce a statistical perspective that can be used to assess future models.

Mines in the End Zone: Are there Downsides to Team Performance?

2020 ◽  
Vol 23 ◽  
Author(s):  
Troy V. Mumford ◽  
M. Travis Maynard
Keyword(s):  
Team Performance ◽  
Role Overload ◽  
Future Research ◽  
Negative Consequences ◽  
Regression To The Mean ◽  
Occupational Psychology ◽  
Team Success ◽  
The Mean ◽  
Overconfidence Bias

Abstract Research on teams in organizations tends to focus on understanding the causes of team performance with a focus on how to enjoy the benefits of team success and avoid the negative consequences of team failure. This paper instead asks the question, ‘what are some of the negative consequences of team success?’ A review of the literature on teams is augmented with research from cognitive science, sociology, occupational psychology, and psychology to explore the potential negative long-term consequences of teamwork success. The general topics of groupthink, overconfidence bias, regression to the mean, role overload, and strategy calcification are reviewed while discussing the implications for future research streams and practical team management.

scholarly journals Are the Effects of Homeopathy Attributable to a Statistical Artefact? A Reanalysis of an Observational Study

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Rainer Lüdtke ◽  
Stefan N. Willich ◽  
Thomas Ostermann
Keyword(s):  
Quality Of Life ◽  
Cohort Studies ◽  
Summary Score ◽  
Regression To The Mean ◽  
Sf 36 ◽  
The Mean ◽  
Component Scores ◽  
True Population ◽  
Respective Standard Deviation

Background. Cohort studies have reported that patients improve considerably after individualised homeopathic treatment. However, these results may be biased by regression to the mean (RTM).Objective. To evaluate whether the observed changes in previous cohort studies are due to RTM and to estimate RTM adjusted effects.Methods. SF-36 quality-of-life (QoL) data from a German cohort of 2827 chronically diseased adults treated by a homeopath were reanalysed by Mee and Chua’s modifiedt-test.Results. RTM adjusted effects, standardized by the respective standard deviation at baseline, were 0.12 (95% CI: 0.06–0.19,P<0.001) in the mental and 0.25 (0.22–0.28,P<0.001) in the physical summary score. Small-to-moderate effects were confirmed for the most individual diagnoses in physical, but not in mental component scores. Under the assumption that the true population mean equals the mean of all actually diseased patients, RTM adjusted effects were confirmed for both scores in most diagnoses.Conclusions. Changes in QoL after treatment by a homeopath are small but cannot be explained by RTM alone. As all analyses made conservative assumptions, true RTM adjusted effects are probably larger than presented.

Empirical methods for assessing meaningful neuropsychological change following epilepsy surgery

1996 ◽  
Vol 2 (6) ◽  
pp. 556-564 ◽  
Author(s):  
Stephen M. Sawrie ◽  
Gordon J. Chelune ◽  
Richard I. Naugle ◽  
Hans O. Lüders
Keyword(s):  
Epilepsy Surgery ◽  
Intractable Epilepsy ◽  
Practice Effects ◽  
Cutoff Score ◽  
Regression To The Mean ◽  
Neuropsychological Battery ◽  
Reliable Change ◽  
Medical Interventions ◽  
The Mean ◽  
Test Retest Reliability

AbstractTraditional methods for assessing the neurocognitive effects of epilepsy surgery are confounded by practice effects, test-retest reliability issues, and regression to the mean. This study employs 2 methods for assessing individual change that allow direct comparison of changes across both individuals and test measures. Fifty-one medically intractable epilepsy patients completed a comprehensive neuropsychological battery twice, approximately 8 months apart, prior to any invasive monitoring or surgical intervention. First, a Reliable Change (RC) index score was computed for each test score to take into account the reliability of that measure, and a cutoff score was empirically derived to establish the limits of statistically reliable change. These indices were subsequently adjusted for expected practice effects. The second approach used a regression technique to establish “change norms” along a common metric that models both expected practice effects and regression to the mean. The RC index scores provide the clinician with a statistical means of determining whether a patient's retest performance is “significantly” changed from baseline. The regression norms for change allow the clinician to evaluate the magnitude of a given patient's change on 1 or more variables along a common metric that takes into account the reliability and stability of each test measure. Case data illustrate how these methods provide an empirically grounded means for evaluating neurocognitive outcomes following medical interventions such as epilepsy surgery. (JINS, 1996, 2, 556–564.)

Regression to the mean

Nutrition ◽  
2000 ◽  
Vol 16 (1) ◽  
pp. 81-82 ◽  
Author(s):  
Garrett Fitzmaurice
Keyword(s):  
Regression To The Mean ◽  
The Mean
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