Analysis of Rank Measures of Association for Ordinal Data from Longitudinal Studies

Carr, Gregory J.; Hafner, Kerry B.; Koch, Gary G.

doi:10.1080/01621459.1989.10478840

Cited by 31 publications

(16 citation statements)

References 22 publications

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“…Thus, with a moderately large sample size, the distributions of the estimators are asymptotically normal, and consistent estimators for their variances are available. The discussion in the Appendix is based on asymptotic properties of U-statistics found in Davis and Quade [13], Hoeffding [14], Quade [15], Sen and Singer [16], and Serfling [17], as well as an example using these properties from Carr et al [18].…”

Section: Statistical Methodologymentioning

confidence: 99%

A repeated measures concordance correlation coefficient

King

Chinchilli

Carrasco

2007

Statistics in Medicine

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The concordance correlation coefficient is commonly used to assess agreement between two raters or two methods of measuring a response when the data are measured on a continuous scale. However, the situation may arise in which repeated measurements are taken for each rater or method, e.g. longitudinal studies in clinical trials or bioassay data with subsamples. This paper proposes a coefficient for measuring agreement between two raters or two methods of measuring a response in the presence of repeated measurements. We illustrate the methodology with examples comparing (1) 1-hr versus 2-hr blood draws for measuring cortisol in an asthma clinical trial and (2) two measurements of percentage body fat, from skinfold calipers and dual energy X-ray absorptiometry.

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Section: Statistical Methodologymentioning

confidence: 99%

A repeated measures concordance correlation coefficient

King

Chinchilli

Carrasco

2007

Statistics in Medicine

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“…Second, the Goodman-Kruskal Gamma index between the predictions and the SMD consensus scores was computed and defined as the performance criterion. This index tests the strength of association of cross-tabulated data when both variables are measured at the ordinal level [26]. At each iteration, the feature elimination conducting to the highest improvement of the Goodman-Kruskal Gamma index was performed.…”

Section: Methodsmentioning

confidence: 99%

Finger Tapping Clinimetric Score Prediction in Parkinson's Disease Using Low-Cost Accelerometers

Stamatakis

Ambroise

Crémers

et al. 2013

Computational Intelligence and Neuroscience

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The motor clinical hallmarks of Parkinson's disease (PD) are usually quantified by physicians using validated clinimetric scales such as the Unified Parkinson's Disease Rating Scale (MDS-UPDRS). However, clinical ratings are prone to subjectivity and inter-rater variability. The PD medical community is therefore looking for a simple, inexpensive, and objective rating method. As a first step towards this goal, a triaxial accelerometer-based system was used in a sample of 36 PD patients and 10 age-matched controls as they performed the MDS-UPDRS finger tapping (FT) task. First, raw signals were epoched to isolate the successive single FT movements. Next, eighteen FT task movement features were extracted, depicting MDS-UPDRS features and accelerometer specific features. An ordinal logistic regression model and a greedy backward algorithm were used to identify the most relevant features in the prediction of MDS-UPDRS FT scores, given by 3 specialists in movement disorders (SMDs). The Goodman-Kruskal Gamma index obtained (0.961), depicting the predictive performance of the model, is similar to those obtained between the individual scores given by the SMD (0.870 to 0.970). The automatic prediction of MDS-UPDRS scores using the proposed system may be valuable in clinical trials designed to evaluate and modify motor disability in PD patients.

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“…In their method, it is possible to assume not only a symmetric distribution such as normal or logistic but also an asymmetric distribution such as double exponential or generalized logistic. Further, in the case where the variable which indicates treatment group is ordinal or dichotomous, Carr et al (1989) investigated rank correlation between the treatment and the response variables at each time point. In the marginal approach, the frequency table of the ordered categorical response at each observation time point is constructed in the early stage of the analysis.…”

Section: Methods For Analysing the Loc Datamentioning

confidence: 99%

Performances of Randomization Test for Comparing Longitudinal Ordered Categorical Outcome

Uragari

Goto

1993

Behaviormetrika

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In this paper, we consider methods to analyse longitudinal ordered categorical data. Several methods for analysing such data have been proposed, but they are often limited in their ranges of application.We propose to treat the ordered categorical data as if they are continu ous ones by scoring ordered categories, and to apply randomization test to them. In addition, we recommend joint use of time-continuous type method (Zerbe, 1979) and time-discrete type method (Raz, 1989) when both treatment main effect and treatment-by-time interaction are to he tested by the randomization test. By a simulation study, we evaluated performances of our recommending approach for the tests of treatment main effect and treatment-by-time interac tion based on longitudinal ordered categorical data. We defined effects of these two factors on an underlying continuous scale in the simulation study. As a result, it was found that the performances of these two tests are satisfactory with respect to type I error rate and power. However, in the case where extremely large (or small) score was given to the maximum (or the minimum) category, the powers of these two tests were slightly low.

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Analysis of Rank Measures of Association for Ordinal Data from Longitudinal Studies

Cited by 31 publications

References 22 publications

A repeated measures concordance correlation coefficient

A repeated measures concordance correlation coefficient

Finger Tapping Clinimetric Score Prediction in Parkinson's Disease Using Low-Cost Accelerometers

Performances of Randomization Test for Comparing Longitudinal Ordered Categorical Outcome

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