Comparison of two treatments with skewed ordinal responses

Lü, Tao; Poon, Wai‐Yin; Cheung, Siu Hung

doi:10.1002/sim.6626

Cited by 5 publications

(25 citation statements)

References 18 publications

(38 reference statements)

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“…A final remark regarding the LNM relates to the estimation of thresholds. As explained by Lu , extensive simulation studies have indicated that the test result is not affected by the choice of treatment for the initial step in estimating the thresholds when the sample is large. For both examples discussed in this paper, we select each of the treatments for use in the initial step to provide estimated thresholds.…”

Section: Discussionmentioning

confidence: 99%

Pairwise comparisons with ordered categorical data

Lin

Cheung

Poon

et al. 2013

Statistics in Medicine

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Clinical trials frequently involve pairwise comparisons of different treatments to evaluate their relative efficacy. In this study, we examine methods for conducting pairwise tests of treatments with ordered categorical responses. A modified version of the Wilcoxon-Mann-Whitney test based on a logistic regression model assuming proportional odds is a popular choice for comparing two treatments. This paper discusses the extension of this test to pairwise comparisons involving more than two treatments. However, when the proportional odds assumption is not valid, the Wilcoxon-Mann-Whitney-type test procedure cannot control the overall type I error rate at the prespecified level of significance. We therefore propose a better strategy in which a latent normal model is employed. We presented a simulated comparative study of power and the overall type I error rate to illustrate the superiority of the latent normal model. Examples are also given for illustrative purposes.

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Section: Discussionmentioning

confidence: 99%

Pairwise comparisons with ordered categorical data

Lin

Cheung

Poon

et al. 2013

Statistics in Medicine

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“…6,11,12 However, a recent study by Lu et al. 13 has reported that although the performance of the latent normal model is satisfactory in most situations, it also suffers from a lack of control of the type I error rate in certain extreme cases when treatment efficacies are compared, specifically, when the degrees of skewness of the treatments differ greatly.…”

Section: Latent Weibull Modelmentioning

confidence: 99%

“…To provide a better model to cope with the problem of the inflated type I error rate for a wider spectrum of data environments, Lu et al. 13,14 suggested the latent Weibull model. The Weibull distribution is a member of the log-location-scale distribution family, and its corresponding distribution in the location-scale family is the extreme value distribution.…”

Section: Latent Weibull Modelmentioning

confidence: 99%

“…23 Hence, the estimation procedures can easily be obtained by generalizing the estimation procedures that were previously derived from a model using the corresponding location-scale distribution. 13 Unlike the normal distribution, which is symmetric, the Weibull distribution has a very flexible distribution that can be applied to model data that are highly skewed. With the latent Weibull model, the distribution function of Z i is with ωi=(ηi,βi)', i = 1, 2.…”

Section: Latent Weibull Modelmentioning

confidence: 99%

“…Lu et al. 13,14 recently found that the latent Weibull model is a superb candidate to compare treatments with highly skewed ordinal outcomes. Testing procedures have been developed with very competitive test power even when the data are not skewed.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Response-adaptive treatment allocation for clinical studies with ordinal responses

Lü

Chung

Poon

et al. 2019

Stat Methods Med Res

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Ordinal responses are common in clinical studies. Although the proportional odds model is a popular option for analyzing ordered-categorical data, it cannot control the type I error rate when the proportional odds assumption fails to hold. The latent Weibull model was recently shown to be a superior candidate for modeling ordinal data, with remarkably better performance than the latent normal model when the data are highly skewed. In clinical trials with ordinal responses, a balanced design is common, with equal sample allocation for each treatment. However, a more ethical approach is to adopt a response-adaptive allocation scheme in which more patients receive the better treatment. In this paper, we propose the use of the doubly adaptive biased coin design to generate treatment allocations that benefit the trial participants. The proposed treatment allocation scheme not only allows more patients to receive the better treatment, it also maintains compatible test power for the comparison of treatment efficiencies. A clinical example is used to illustrate the proposed procedure.

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Comparison of treatments with ordinal responses in trials with sequential monitoring and response‐adaptive randomization

Zhong

et al. 2022

Statistics in Medicine

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In clinical trials, comparisons of treatments with ordinal responses are frequently conducted using the proportional odds model. However, the use of this model necessitates the adoption of the proportional odds assumption, which may not be appropriate. In particular, when responses are skewed, the use of the proportional odds model may result in a markedly inflated type I error rate. The latent Weibull distribution has recently been proposed to remedy this problem, and it has been demonstrated to be superior to the proportional odds model, especially when response-adaptive randomization is incorporated. However, there are several drawbacks associated with the latent Weibull model and the previously suggested response-adaptive treatment randomization scheme. In this paper, we propose the modified latent Weibull model to address these issues.Based on the modified latent Weibull model, the original response-adaptive design was also revised. In addition, the group sequential monitoring mechanism was included to enable interim analyses to be performed to determine, during a trial, whether a specific treatment is significantly more effective than another. If so, this will enable the trial to be terminated at a much earlier stage than a trial based on a fixed sample size. We performed a simulation study that clearly demonstrated the merits of our proposed framework. Furthermore, we redesigned a clinical study to further illustrate the advantages of our response-adaptive approach.

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Comparison of two treatments with skewed ordinal responses

Cited by 5 publications

References 18 publications

Pairwise comparisons with ordered categorical data

Pairwise comparisons with ordered categorical data

Response-adaptive treatment allocation for clinical studies with ordinal responses

Comparison of treatments with ordinal responses in trials with sequential monitoring and response‐adaptive randomization

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