scholarly journals What not to do in medical statistics

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.

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>


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.


Author(s):  
Janet L. Peacock ◽  
Phil J. Peacock

A good understanding of medical statistics is essential to evaluate medical research and to choose appropriate ways of implementing findings in clinical practice. The Oxford Handbook of Medical Statistics, second edition, has been written to provide doctors and medical students with a comprehensive yet concise account of this often difficult subject. Described by readers as a ‘statistical Bible’, this new edition maintains the accessibility and thoroughness of the original, and includes comprehensive updates including new sections on transitional medicine, cluster designs, and modern statistical packages. The handbook promotes understanding and interpretation of statistical methods across a wide range of topics, from study design and sample size considerations, through t and chi-squared tests, to complex multifactorial analyses, all using examples from published research. References and further reading are included, to allow deeper understanding on specific topics. Featuring a new chapter on how to use this book in different medical contexts, the Oxford Handbook of Medical Statistics helps readers to conduct their own research and critically appraise others' work.


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.


2021 ◽  
Vol 126 (1) ◽  
Author(s):  
Adam Taube

During more than five decades, the author has kept a critical eye on how statistical methods are (mis-)used in medical research. Some areas are presented where serious statistical mistakes are prevalent. Two investigations with erroneous conclusions are described in detail. Situations where outside authorities have tried to mute medical researchers are also commented upon. The authors own efforts to improve the use of statistical methods and the current situation with easily accessible statistical program packages are described. Finally, the importance of continued ‘statistical cleansing’ is stressed.


Author(s):  
Srijita Pal ◽  
Somnath Bharadwaj ◽  
Abhik Ghosh ◽  
Samir Choudhuri

Abstract We apply the Tapered Gridded Estimator (TGE) for estimating the cosmological 21-cm power spectrum from 150 MHz GMRT observations which corresponds to the neutral hydrogen (HI) at redshift z = 8.28. Here TGE is used to measure the Multi-frequency Angular Power Spectrum (MAPS) Cℓ(Δν) first, from which we estimate the 21-cm power spectrum P(k⊥, k∥). The data here are much too small for a detection, and the aim is to demonstrate the capabilities of the estimator. We find that the estimated power spectrum is consistent with the expected foreground and noise behaviour. This demonstrates that this estimator correctly estimates the noise bias and subtracts this out to yield an unbiased estimate of the power spectrum. More than $47\%$ of the frequency channels had to be discarded from the data owing to radio-frequency interference, however the estimated power spectrum does not show any artifacts due to missing channels. Finally, we show that it is possible to suppress the foreground contribution by tapering the sky response at large angular separations from the phase center. We combine the k modes within a rectangular region in the ‘EoR window’ to obtain the spherically binned averaged dimensionless power spectra Δ2(k) along with the statistical error σ associated with the measured Δ2(k). The lowest k-bin yields Δ2(k) = (61.47)2 K2 at k = 1.59 Mpc−1, with σ = (27.40)2 K2. We obtain a 2 σ upper limit of (72.66)2 K2 on the mean squared HI 21-cm brightness temperature fluctuations at k = 1.59 Mpc−1.


2020 ◽  
Vol 23 ◽  
Author(s):  
Troy V. Mumford ◽  
M. Travis Maynard

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.


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