Construction of confidence sets with application to classification and some other problems

The construction of a confidence set can be applied in many problems. In this study, we are focusing on comparison and classification problems. For comparison problem, we can construct a confidence set for equivalence test and upper confidence bounds on several samples by three methods: using the theorem from Liu et al. (2009), F statistic and Studentized range statistic. For classification problem, we would like to classify a new case into its true class, based on some measurements. Five classification methods have been studied. They are logistic regression, classification tree, Bayesian method, support vector machine and the new confidence set method. The new method constructs a confidence set for the true class for a new case by inverting the acceptance sets. The advantage of this method is that the probability of correct classification is not less than 1􀀀. The methods are illustrated specifically with the well-known Iris data, seeds data and applied to a data set for classifying patients as normal, having fibrosis or having cirrhosis based on some measurements on blood samples. The total misclassification error and sensitivity (true positive rate) are used for comparing the methods

Binary response analysis using logistic regression in dentistry

Multivariate analysis with binary response is extensively utilized in dental research due to 3 variations in dichotomous outcomes. One of the analysis for binary response variable is binary 4 logistic regression, which explores the associated factors and predicts the response probability 5 of the binary variable. This article aims to explain the statistical concepts of binary logistic 6 regression analysis applicable to the field of dental research, including model fitting, goodness 7 of fit test, and model validation. Moreover, interpretation of the model and logistic regression 8 are also discussed with relevant examples. Practical guidance is also provided for dentists and 9 dental researchers to enhance their basic understanding of binary logistic regression analysis

Influence of past advanced behavior guidance experience on parental acceptance for autistic individuals in the dental setting

Abstract Background Autism is a lifelong neurodevelopmental disorder that poses challenges during dental treatment. Advanced behavior guidance techniques (BGTs) have been used to provide dental care for autistic people who have specific characteristics and complex dental treatment. This study was conducted to evaluate parental acceptance and analyze parents’ opinions of advanced BGTs during dental treatment in autistic people. Methods This cross-sectional study was conducted on 141 parents of autistic people from the Mahidol Dental Hospital and the Autism online community. Informed consent was obtained before enrolling participants in the study. All parents were asked to rate their acceptance after watching VDO clips: passive restraint by device (PRBD), oral sedation (OS), and general anesthesia (GA) to evaluate parental acceptance of advanced BGTs through an online questionnaire survey. The online questionnaire included a visual analog scale (VAS) and open-ended questions to collect their opinions on each advanced BGT. Participants were categorized into two subgroups as follows: 81 in the “Experience group” and 60 in the “No experience group” according to their autistic people’ advanced BGT experience. Friedman’s two-way analysis of variance and the Mann–Whitney U test were used for statistical analyses. Open-ended questions were analyzed using quantitative content analysis. Results PRBD was ranked the highest, followed by GA and OS. Parents in the “Experience group” rated significantly higher acceptance of their BGT experience than parents in the “No experience group” in all the three advanced BGTs. Conclusions All advanced BGTs were particularly accepted in this study. Previous experience of advanced BGTs had an influence on parental acceptance. Parents commented on their opinions toward each advanced BGT with a variety of perspectives. Trial registration: The protocol was approved by the ethical committee of the Faculty of Dentistry/Faculty of Pharmacy, Mahidol University (COA.No.MU-DT/PY-IRB 2021/022.1702) and was registered with Thai Clinical Trials Registry (TCTR20220521001)

Confidence sets for statistical classification

Classification has applications in a wide range of fields including medicine, engineering, computer science and social sciences among others. In statistical terms, classification is inference about the unknown parameters, i.e. the true classes of future objects. Hence various standard statistical approaches can be used, such as point estimators, confidence sets and decision theoretic approaches. For example, a classifier that classifies a future object as belonging to only one of several known classes is a point estimator. The purpose of this paper is to propose a confidence-set-based classifier that classifies a future object into a single class only when there is enough evidence to warrant this, and into several classes otherwise. By allowing classification of an object into possibly more than one class, this classifier guarantees a pre-specified proportion of correct classification among all future objects. An example is provided to illustrate the method, and a simulation study is included to highlight the desirable feature of the method

Two-dimensional facial measurements for anterior tooth selection in complete denture treatment

Purpose: Anterior tooth selection is one of the most challenging parts in determining tooth dimensions and critical to the aesthetic aspect of the complete denture treatment. However, the methods for anterior tooth size selection using facial measurements are still controversial. This study aimed to investigate the relationship between dental measurements and facial measurements, and to establish the anterior tooth size prediction equation using facial dimensions in the Thai population for the complete denture treatment. Materials &amp; methods: One hundred and twenty-five Thai participants (53 men and 72 women) aged 18–35 years old with Angle class I occlusion, did not currently undergo orthodontic treatment, had normal alignment on the maxillary anterior teeth, no attrition, abrasion, proximal restoration or prosthesis were investigated. One frontal facial photograph and one dental photograph of each participant were made using an image analyzing program (ImageJ version 1.53b) to measure the six horizontal facial distances, five vertical facial distances and three dental distances as 2D facial and dental measurements. Pearson correlation and multiple linear regression analysis were performed. Results: The difference of facial and dental measurements between men and women were statistically significant (P < .001). Interpupillary width, interlateral canthal width, intercommissural width and bizygomatic width were correlated to dental measurements in both sexes. Intermedial canthal width and lip thickness were correlated to dental measurements in women. Face length and lateral canthus to lower border of face were correlated to dental measurements in men. Prediction equations of each dental measurement were established using only horizontal facial dimension and using both horizontal and vertical facial dimensions. Conclusions: Facial and dental dimensions are sex-dependent. Facial measurements can be applied in a regression equation to predict dental measurements. Adding vertical dimensions of facial measurements to the prediction equations of anterior tooth size selection results in a higher R squared to 0.444. This finding can be used as a tool for anterior tooth size selection in the complete denture treatment

Assessment of the confidence interval in the multivariable normal tissue complication probability model for predicting radiation-induced liver disease in primary liver cancer

We developed a confidence interval-(CI) assessing model in multivariable normal tissue complication probability (NTCP) modeling for predicting radiation-induced liver disease (RILD) in primary liver cancer patients using clinical and dosimetric data. Both the mean NTCP and difference in the mean NTCP (Delta NTCP) between two treatment plans of different radiotherapy modalities were further evaluated and their CIs were assessed. Clinical data were retrospectively reviewed in 322 patients with hepatocellular carcinoma (n = 215) and intrahepatic cholangiocarcinoma (n = 107) treated with photon therapy. Dose-volume histograms of normal liver were reduced to mean liver dose (MLD) based on the fraction size-adjusted equivalent uniform dose. The most predictive variables were used to build the model based on multivariable logistic regression analysis with bootstrapping. Internal validation was performed using the cross-validation leave-one-out method. Both the mean NTCP and the mean Delta NTCP with 95% CIs were calculated from computationally generated multivariate random sets of NTCP model parameters using variance-covariance matrix information. RILD occurred in 108/322 patients (33.5%). The NTCP model with three clinical and one dosimetric parameter (tumor type, Child-Pugh class, hepatitis infection status and MLD) was most predictive, with an area under the receiver operative characteristics curve (AUC) of 0.79 (95% CI 0.74-0.84). In eight clinical subgroups based on the three clinical parameters, both the mean NTCP and the mean Delta NTCP with 95% CIs were able to be estimated computationally. The multivariable NTCP model with the assessment of 95% CIs has potential to improve the reliability of the NTCP model-based approach to select the appropriate radiotherapy modality for each patient