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 ◽  
...  

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.

2019 ◽  
Vol 6 (10) ◽  
pp. 190937 ◽  
Author(s):  
Melissa Bateson ◽  
Dan T. A. Eisenberg ◽  
Daniel Nettle

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.


2003 ◽  
Vol 46 (6) ◽  
pp. 1340-1351 ◽  
Author(s):  
Xuyang Zhang ◽  
J. Bruce Tomblin

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.


2020 ◽  
Author(s):  
Maria Inês Schmidt ◽  
Paula Bracco ◽  
Scheine Canhada ◽  
Joanna MN Guimarães ◽  
Sandhi Maria Barreto ◽  
...  

<b>Objective </b> <p>Glycemic regression is common in real world settings, but the contribution of regression to the mean (RTM) has been little investigated. We aimed to estimate glycemic regression before and after adjusting for RTM in a free-living cohort of adults with newly ascertained diabetes and intermediate hyperglycemia (IH). </p> <p><b>Research Design and Methods</b></p> <p>ELSA-Brasil is a cohort study of 15,105 adults screened between 2008-2010 with standardized OGTT and HbA1c, repeated after 3.84 (0.42) years. After excluding those receiving medical treatment for diabetes, we calculated partial or complete regression before and after adjusting baseline values for RTM. </p> <p><b>Results</b></p> <p>Regarding newly ascertained diabetes, partial or complete regression was seen in 49.4% (95%CI 45.2 – 53.7); after adjustment for RTM, in 20.2% (95%CI 12.1 – 28.3). Regarding IH, regression to normal levels was seen in 39.5% (95%CI 37.9 – 41.3) or in 23.7% (95%CI 22.6% – 24.3%) depending on the WHO or the ADA definition, respectively; after adjustment, corresponding frequencies were 26.1% (95%CI 22.4 – 28.1) and 19.4% (95%CI 18.4 – 20.5). Adjustment for RTM reduced the number of cases detected at screening: 526 to 94 cases of diabetes; 3118 to 1986 cases of WHO-defined IH; and 6182 to 5711 cases of AD-defined IH. Weight loss ≥2.6% was associated with greater regression from diabetes (RR=1.52 95%CI 1.26-1.84) and IH (RR=1.30 95%CI 1.17-1.45). </p> <p><b>Conclusions</b></p> <p>In this quasi-real-world setting, regression from diabetes at ~4 years was common, less so for IH. Regression was frequently explained by RTM, but, in part, also related to improved weight loss and homeostasis over the follow-up. </p>


2020 ◽  
Author(s):  
Maria Inês Schmidt ◽  
Paula Bracco ◽  
Scheine Canhada ◽  
Joanna MN Guimarães ◽  
Sandhi Maria Barreto ◽  
...  

<b>Objective </b> <p>Glycemic regression is common in real world settings, but the contribution of regression to the mean (RTM) has been little investigated. We aimed to estimate glycemic regression before and after adjusting for RTM in a free-living cohort of adults with newly ascertained diabetes and intermediate hyperglycemia (IH). </p> <p><b>Research Design and Methods</b></p> <p>ELSA-Brasil is a cohort study of 15,105 adults screened between 2008-2010 with standardized OGTT and HbA1c, repeated after 3.84 (0.42) years. After excluding those receiving medical treatment for diabetes, we calculated partial or complete regression before and after adjusting baseline values for RTM. </p> <p><b>Results</b></p> <p>Regarding newly ascertained diabetes, partial or complete regression was seen in 49.4% (95%CI 45.2 – 53.7); after adjustment for RTM, in 20.2% (95%CI 12.1 – 28.3). Regarding IH, regression to normal levels was seen in 39.5% (95%CI 37.9 – 41.3) or in 23.7% (95%CI 22.6% – 24.3%) depending on the WHO or the ADA definition, respectively; after adjustment, corresponding frequencies were 26.1% (95%CI 22.4 – 28.1) and 19.4% (95%CI 18.4 – 20.5). Adjustment for RTM reduced the number of cases detected at screening: 526 to 94 cases of diabetes; 3118 to 1986 cases of WHO-defined IH; and 6182 to 5711 cases of AD-defined IH. Weight loss ≥2.6% was associated with greater regression from diabetes (RR=1.52 95%CI 1.26-1.84) and IH (RR=1.30 95%CI 1.17-1.45). </p> <p><b>Conclusions</b></p> <p>In this quasi-real-world setting, regression from diabetes at ~4 years was common, less so for IH. Regression was frequently explained by RTM, but, in part, also related to improved weight loss and homeostasis over the follow-up. </p>


2007 ◽  
Vol 7 (3) ◽  
pp. 327-338 ◽  
Author(s):  
Neal Alexander

There have been major efforts to improve the application of statistical methods in medical research, although some errors and misconceptions persist. In this paper I will review some of the topics which most often cause problems: a) comparison of two methods of clinical measurement; b) comparison of baseline values between arms of a randomized trial; c) absence of evidence as opposed to evidence of absence; and d) regression to the mean. I will also revisit a statistical error in one of my own publications. I review some causes of the continuing misuse of statistics, and make some suggestions for modifying the education of statistical and non-statistical medical researchers in order to alleviate this.


Social Forces ◽  
1971 ◽  
Vol 50 (2) ◽  
pp. 206-214 ◽  
Author(s):  
R. P. Althauser ◽  
D. Rubin

2020 ◽  
Vol 4 (10) ◽  
Author(s):  
Kelsey M Cochrane ◽  
Brock A Williams ◽  
Jordie A J Fischer ◽  
Kaitlyn L I Samson ◽  
Lulu X Pei ◽  
...  

ABSTRACT Background Regression to the mean (RTM) is a statistical phenomenon where second measurements are more likely to be closer to the mean. This is particularly observed in those with baseline values further from the mean. Anemic individuals (hemoglobin &lt;120 g/L) are often recruited when evaluating iron supplementation programs, as they are more likely to elicit a greater hemoglobin response; however, they are also at greater risk for RTM as their baseline values are lower than the overall population mean. Objective The aim was to calculate and apply RTM to a previously conducted iron supplementation trial of women in Cambodia at increasingly severe baseline anemia cutoffs (hemoglobin &lt;120 g/L, &lt;115 g/L, and &lt;110 g/L). Methods Women received either 60 mg/d iron (n = 191) or placebo (n = 185) for 12 wk. Hemoglobin was measured at baseline and at 12 wk (endline), and change in hemoglobin was calculated in each group for each cutoff. RTM was calculated in the placebo group at each cutoff and applied to the change observed at each cutoff in the iron group to obtain the RTM-free effect. Results In the placebo group, mean change in hemoglobin increased as cutoffs became more extreme (0.9 g/L to 1.9 g/L in those with baseline hemoglobin &lt;120 g/L and &lt;110 g/L, respectively). RTM estimates similarly increased: 1.0 g/L (&lt;120 g/L), 1.3 g/L (&lt;115 g/L), and 1.8 g/L (&lt;110g/L). When applying RTM to the iron group, we found that ∼10% of the “treatment effect” could be attributable to RTM at each cutoff. However, iron supplementation was still effective in increasing hemoglobin, with an increased effect in those with lower baseline values, as proven by the RTM-free effect at each cutoff: 8.7 g/L (&lt;120 g/L), 10.9 g/L (&lt;115 g/L), and 13.6g/L (&lt;110 g/L). Conclusions RTM may have accounted for ∼10% of the observed change in hemoglobin following iron supplementation; however, appropriate use of a placebo group in the statistical analyses of the trial controls for this potential RTM effect.


Social Forces ◽  
1971 ◽  
Vol 50 (2) ◽  
pp. 206 ◽  
Author(s):  
Robert P. Althauser ◽  
Donald Rubin

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