scholarly journals Avoiding misclassification bias with the traditional Charnley classification: Rationale for a fourth Charnley class BB

2006 ◽  
Vol 24 (9) ◽  
pp. 1803-1808 ◽  
Author(s):  
C. Röder ◽  
L.P. Staub ◽  
P. Eichler ◽  
M. Widmer ◽  
D. Dietrich ◽  
...  
Keyword(s):  
Misclassification Bias ◽  
Charnley Class

scholarly journals Habitual Physical Activity Behavior of Patients After Primary Total Hip Arthroplasty

2008 ◽  
Vol 88 (9) ◽  
pp. 1039-1048 ◽  
Author(s):  
Robert Wagenmakers ◽  
Martin Stevens ◽  
Wiebren Zijlstra ◽  
Monique L Jacobs ◽  
Inge van den Akker-Scheek ◽  
...  
Keyword(s):  
Physical Activity ◽  
Total Hip Arthroplasty ◽  
Odds Ratio ◽  
Physical Activity Behavior ◽  
Moderate Intensity ◽  
Physically Active ◽  
Normative Population ◽  
Activity Behavior ◽  
Charnley Class ◽  
Health Enhancing Physical Activity

Background and Purpose Despite recognized health benefits of physical activity, little is known about the habitual physical activity behavior of patients after total hip arthroplasty (THA). The purpose of this study was to analyze this behavior and the fulfillment of guidelines for health-enhancing physical activity of these patients compared with a normative population. Subjects and Methods The participants were 273 patients who had undergone a primary THA (minimum of 1 year postoperatively). Comparisons were made between this group and 273 age- and sex-matched individuals from a normative population. Comparisons also were made between participants with THA under 65 years of age and those 65 years of age and older and among participants with THA in different Charnley classes. Level of physical activity was assessed with the Short QUestionnaire to ASsess Health-enhancing physical activity (SQUASH). Results No significant differences in total amount of physical activity or time spent in different categories of physical activity were found between the THA group and the normative group. Participants with THA spent significantly more minutes in activities of moderate intensity compared with the normative group. Participants with THA who were under 65 years of age were significantly more active than older participants with THA. Charnley class had significant effects on time spent at work, time spent in moderate-intensity activities, and total amount of activity, with the least activity performed by participants in Charnley class C. The guidelines were met by 51.2% of the participants with THA and 48.8% of the normative population. Female participants met the guidelines less frequently than male participants in both the combined groups (odds ratio=0.50, 95% confidence interval=0.35–0.72, P<.001) and the THA group (odds ratio=0.48, 95% confidence interval=0.28–0.80, P=.001). Discussion and Conclusion The results suggest that patients after THA are at least as physically active as a normative population. Nevertheless, a large percentage of these patients do not meet the guidelines; therefore, they need to be stimulated to become more physically active.

Correction of misclassification bias induced by the residential mobility in studies examining the link between socioeconomic environment and cancer incidence

2015 ◽  
Vol 39 (2) ◽  
pp. 256-264 ◽  
Author(s):  
Josephine Bryere ◽  
Carole Pornet ◽  
Olivier Dejardin ◽  
Ludivine Launay ◽  
Lydia Guittet ◽  
...  
Keyword(s):  
Residential Mobility ◽  
Cancer Incidence ◽  
Socioeconomic Environment ◽  
Misclassification Bias

A new generic method to improve machine learning applications in official statistics

2021 ◽  
pp. 1-16
Author(s):  
Kevin Kloos
Keyword(s):  
Machine Learning ◽  
Mean Squared Error ◽  
Statistical Properties ◽  
Machine Learning Algorithms ◽  
Official Statistics ◽  
Academic Literature ◽  
Misclassification Bias ◽  
Squared Error ◽  
Machine Learning Applications ◽  
Applications Of Machine Learning

The use of machine learning algorithms at national statistical institutes has increased significantly over the past few years. Applications range from new imputation schemes to new statistical output based entirely on machine learning. The results are promising, but recent studies have shown that the use of machine learning in official statistics always introduces a bias, known as misclassification bias. Misclassification bias does not occur in traditional applications of machine learning and therefore it has received little attention in the academic literature. In earlier work, we have collected existing methods that are able to correct misclassification bias. We have compared their statistical properties, including bias, variance and mean squared error. In this paper, we present a new generic method to correct misclassification bias for time series and we derive its statistical properties. Moreover, we show numerically that it has a lower mean squared error than the existing alternatives in a wide variety of settings. We believe that our new method may improve machine learning applications in official statistics and we aspire that our work will stimulate further methodological research in this area.

scholarly journals Misclassification Bias in Estimates of Bereavement Effects

1997 ◽  
Vol 145 (11) ◽  
pp. 995-1002 ◽  
Author(s):  
S. Korenman ◽  
N. Goldman ◽  
H. Fu
Keyword(s):  
Misclassification Bias

scholarly journals Accounting for misclassification bias of binary outcomes due to underscreening: a sensitivity analysis

2017 ◽  
Vol 17 (1) ◽  
Author(s):  
Nanhua Zhang ◽  
Si Cheng ◽  
Lilliam Ambroggio ◽  
Todd A. Florin ◽  
Maurizio Macaluso
Keyword(s):  
Sensitivity Analysis ◽  
Binary Outcomes ◽  
Misclassification Bias
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