Tsung-Shan Tsou

Overcoming Patient-related Barriers to Cancer Pain Management for Home Care Patients: A Pilot Study

The purpose of this pilot study was to explore the effectiveness of a pain education program to overcome patient-related barriers in managing cancer pain for Taiwanese home care patients with cancer. The pain education program was developed based on previous studies of Taiwanese patient-related barriers to cancer pain management. The Barriers Questionnaire–Taiwan form, the Brief Pain Inventory, the Medication Adherence Questionnaire, and a demographic questionnaire were used for data collection. The sample consisted of 18 patients in the experimental group and 19 patients in the control group. Descriptive statistics, t tests, and paired t tests were used to analyze the data. Results of this study revealed that patients who received the pain educational program had significantly greater reduction in Barriers Questionnaire–Taiwan form scores and more improvement in medication adherence compared with patients who did not participate in the program. When compared to pretest scores, patients scores after receiving the pain education intervention showed significant improvement on the Barriers Questionnaire–Taiwan form, medication adherence, pain intensity, and pain interference. The results of this study support the effectiveness of the pain education program on overcoming the barriers to cancer pain management for Taiwanese home care patients with cancer.

Changes in levels of hope after diagnostic disclosure among Taiwanese patients with cancer.

The purposes of this study were to explore the extent to which the practice of disclosing cancer diagnoses to patients is used in Taiwan, to examine the relation between cancer diagnosis disclosure and levels of hope, and to investigate the relation between length of time since diagnosis and levels of hope. The participants in this study were 124 Taiwanese oncology inpatients and outpatients. Of these patients, 79% were informed of their cancer diagnosis. The informed patients reported significantly higher levels of hope than those who were not informed. Finally, patient levels of hope decreased as the time between cancer diagnosis and disclosure increased. The implications of this study are discussed in terms of cancer disclosure practice and enhancement of levels of hope for Taiwanese patients with cancer.

Inferences of variance function – a parametric robust way

Abstract Tsou (2003a) proposed a parametric procedure for making robust inference for mean regression parameters in the context of generalized linear models. This robust procedure is extended to model variance heterogeneity. The normal working model is adjusted to become asymptotically robust for inference about regression parameters of the variance function for practically all continuous response variables. The connection between the novel robust variance regression model and the estimating equations approach is also provided.

Comparing Two Population Means and Variances: A Parametric Robust Way

Abstract This article introduces a parametric robust way of comparing two population means and two population variances. With large samples the comparison of two means, under model misspecification, is lesser a problem, for, the validity of inference is protected by the central limit theorem. However, the assumption of normality is generally required, so that the inference for the ratio of two variances can be carried out by the familiar F statistic. A parametric robust approach that is insensitive to the distributional assumption will be proposed here. More specifically, it will be demonstrated that the normal likelihood function can be adjusted for asymptotically valid inferences for all underlying distributions with finite fourth moments. The normal likelihood function, on the other hand, is itself robust for the comparison of two means so that no adjustment is needed.

A Robust Score Test for Testing Several Coefficients of Variation with Unknown Underlying Distributions

A parametric robust test is proposed for comparing several coefficients of variation. This test is derived by properly correcting the normal likelihood function according to the technique suggested by Royall and Tsou. The proposed test statistic is asymptotically valid for general random variables, as long as their underlying distributions have finite fourth moments. Simulation studies and real data analyses are provided to demonstrate the effectiveness of the novel robust procedure.

Parametric robust inferences for correlated ordinal data

The aim of this article is to provide asymptotically valid likelihood inferences about regression parameters for correlated ordinal response variables. The legitimacy of this novel approach requires no knowledge of the underlying joint distributions so long as their second moments exist. The efficacy of the proposed parametric approach is demonstrated via simulations and the analyses of two real data sets.

Parametric robust inference about regression parameters for the correlation coefficient

This article establishes a robust likelihood function about regression parameters for the correlation coefficients modeled in a generalized linear model fashion. The validity of the proposed likelihood requires no knowledge of the true underlying distributions, so long as they have finite fourth moments. The efficacy of the robust methodology is shown via simulations. The asymptotic variance of the maximum-likelihood estimate and the empirical error probabilities of the resultant robust likelihood ratio test are specifically exhibited.

Parametric Robust Regression Analysis of Contaminated Data

Summary In reality it is more often the case when part of data have been drawn from some distribution other than that we believe they have been drawn from. Traditional nonparametric or parametric statistics are insufficient for such contaminated data. Tsou [Tsou, T.-S. (2003). Parametric robust inferences for regression parameters under generalized linear models. Submitted] proposed parametric robust regression techniques that provide asymptotically valid inference for regression parameters so long as the true distributions that generate the data have finite second moments. It will be demonstrated that the novel robust regression tools fit well for the regression analysis of data generated from unknown and distinct families of distributions.

A data mining method to predict transcriptional regulatory sites based on differentially expressed genes in human genome

Very large-scale gene expression analysis, i.e., UniGene and dbEST, are provided to find those genes with significantly differential expression in specific tissues. The differentially expressed genes in a specific tissue are potentially regulated concurrently by a combination of transcription factors. This study attempts to mine putative binding sites on how combinations of the known regulatory sites homologs and over-represented repetitive elements are distributed in the promoter regions of considered groups of differentially expressed genes. We propose a data mining approach to statistically discover the significantly tissue-specific combinations of known site homologs and over-represented repetitive sequences, which are distributed in the promoter regions of differential gene groups. The association rules mined would facilitate to predict putative regulatory elements and identify genes potentially co-regulated by the putative regulatory elements.

Performing Legitimate Parametric Regression Analysis without Knowing the True Underlying Random Mechanisms

Real data are rarely normally distributed. Nonetheless, regression analysis is routinely done under the assumption of normality. Such a practice generally results in invalid statistical inferences once normality is false. This article shows how one could carry out corrected normal regression and gamma regression analysis, which provides asymptotically valid inferences without the knowledge of the true underlying distributions. No additional programming is necessary in order to implement the proposed novel regression method. Outputs provided by existing statistical software suffice.