Generating correlated discrete ordinal data using R and SAS IML

Ibrahim, Noor Akma; Suliadi, Suliadi

doi:10.1016/j.cmpb.2011.06.003

Cited by 11 publications

(12 citation statements)

References 13 publications

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“…This was done according to the convex combination algorithm introduced by Lee (1997) and implemented in by Ibrahim and Suliadi (2011). The algorithm originally considered the coefficient of uncertainty U, Goodman and Kruskal's τ, and Goodman and Kruskal's γ‐coefficient as association measures between pairs of categorical variables.…”

Section: Simulationsmentioning

confidence: 99%

“…To study the behavior of the type I error rate (α), we simulated multilevel dependent categorical variables with fixed marginal probability distribution and kappa coefficient between pairs of variables. This was done according to the convex combination algorithm introduced by Lee (1997) and implemented in R by Ibrahim and Suliadi (2011). The algorithm originally considered the coefficient of uncertainty U, Goodman and Kruskal's τ , and Goodman and Kruskal's γ -coefficient as association measures between pairs of categorical variables.…”

Section: Simulationsmentioning

confidence: 99%

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Comparing dependent kappa coefficients obtained on multilevel data

Vanbelle

2017

Biometrical J

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Reliability and agreement are two notions of paramount importance in medical and behavioral sciences. They provide information about the quality of the measurements. When the scale is categorical, reliability and agreement can be quantified through different kappa coefficients. The present paper provides two simple alternatives to more advanced modeling techniques, which are not always adequate in case of a very limited number of subjects, when comparing several dependent kappa coefficients obtained on multilevel data. This situation frequently arises in medical sciences, where multilevel data are common. Dependent kappa coefficients can result from the assessment of the same individuals at various occasions or when each member of a group is compared to an expert, for example. The method is based on simple matrix calculations and is available in the R package “multiagree”. Moreover, the statistical properties of the proposed method are studied using simulations. Although this paper focuses on kappa coefficients, the method easily extends to other statistical measures.

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Section: Simulationsmentioning

confidence: 99%

Section: Simulationsmentioning

confidence: 99%

Comparing dependent kappa coefficients obtained on multilevel data

Vanbelle

2017

Biometrical J

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“…The effect analysis includes two main effects, assuming group effect and time effect, as well as their interaction. The simulated data with five repeated observations for all three distributions are generated by Monte Carlo method 36 ; 0.3 is set to be the correlation coefficient, autoregression-1 is chosen to be the correlation component; in particular, 5 categories are established for longitudinal ordinal data; simulation is set to be 5000 times and assume two groups with 30 cases in each one equally. The significance levels are set to be 0.10 for the interaction and 0.05 for main effects, respectively.…”

Section: Simulationmentioning

confidence: 99%

A novel rank-based non-parametric method for longitudinal ordinal data

Zhuang

Guan

Qiu

et al. 2017

Stat Methods Med Res

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Longitudinal ordinal data are common in biomedical research. Although various methods for the analysis of such data have been proposed in the past few decades, they are limited in several ways. For instance, the constraints on parameters in the proportional odds model may result in convergence problems; the rank-based aligned rank transform method imposes constraints on other parameters and the distributional assumptions with parametric model. We propose a novel rank-based non-parametric method that models the profile rather than the distribution of the data to make an effective statistical inference without the constraint conditions. We construct the test statistic of the interaction first, and then construct the test statistics of the main effects separately with or without the interaction, while "adjusted coefficient" for the case of ties is derived. A simulation study is conducted for comparison between rank-based non-parametric and rank-transformed analysis of variance. The results show that type I errors of the two methods are both maintained closer to the priori level, but the statistical power of rank-based non-parametric is greater than that of rank-transformed analysis of variance, suggesting higher efficiency of the former. We then apply rank-based non-parametric to two real studies on acne and osteoporosis, and the results also illustrate the effectiveness of rank-based non-parametric, particularly when the distribution is skewed.

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“…Correlated ordinal responses were generated with the SAS macro developed by Ibrahim and Suliadi (2011) and based on Lee's algorithm (Lee, 1997). The basic measurement model utilized in this study includes as covariates a binary group effect (X = 0 or 1), an assessment time (T ) and an…”

Section: Longitudinal Ordinal Data-generating Modelmentioning

confidence: 99%

A Simulation Study Comparing Multiple Imputation Methods for Incomplete Longitudinal Ordinal Data

Donneau

Mauer

Molenberghs

et al. 2014

Communications in Statistics - Simulation and Computation

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Generating correlated discrete ordinal data using R and SAS IML

Cited by 11 publications

References 13 publications

Comparing dependent kappa coefficients obtained on multilevel data

Comparing dependent kappa coefficients obtained on multilevel data

A novel rank-based non-parametric method for longitudinal ordinal data

A Simulation Study Comparing Multiple Imputation Methods for Incomplete Longitudinal Ordinal Data

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