Background The inter- and intra-subject variations of scintigraphy, which are used to identify colonic transit disturbances in irritable bowel syndrome (IBS), are unclear. The relationship between colonic transit and bowel functions is incompletely understood. To assess inter- and intra-subject variations of scintigraphic colonic transit measurements in 86 IBS patients and 17 healthy subjects and to quantify the relationship between colonic transit and bowel symptoms in 147 IBS patients and 46 healthy subjects. Methods Data from participants with multiple colonic transit measurements were analysed. Primary end points were colonic filling at 6 h (CF6h) and geometric center (GC) at 24 and 48 h for colonic transit. Bowel functions were assessed by daily stool diaries. Key Results Inter- and intra-subject variations were greater for small intestinal than colonic transit. Overall, inter- and intra-subject variations were relatively narrow for colonic transit (both GC24h and GC48h, with lower COV at 48 h); there was little intra-subject variation in health and IBS-constipation over a period of ≤3 weeks and over 2.0 years (median, range 0.1, 11.0 years). Significant intra-individual differences in GC24h were observed only in IBS-D patients. Colonic transit was significantly associated with stool form (accounting for 19-27% of the variance), frequency (19%), and ease of stool passage (12%). Conclusions & Inferences Despite inter-subject variation in scintigraphic colonic transit results, the intra-subject measurements are reproducible over time in healthy volunteers and patients with IBS; significant changes in colonic transit at 24 h were observed only in IBS-D. Colonic transit is associated with stool form, frequency and ease of passage.

Snoring is a highly prevalent disorder affecting 20-40% of adult population. Snoring is also a major indicative of obstructive sleep apnea (OSA). Despite the magnitude of effort, the acoustical properties of snoring in relation to physiological states are not yet known. This thesis explores statistical properties of snoring sounds and their association with OSA. First, an unsupervised technique was developed to automatically extract the snoring sound segments from the lengthy recordings of respiratory sounds. This technique was tested over 5665 snoring sound segments of 30 participants and the detection accuracy of 98.6% was obtained. Second, the relationship between anthropometric parameters of snorers with different degrees of obstruction and their snoring sounds’ statistical characteristics was investigated. Snoring sounds are non-Gaussian in nature; thus second order statistical methods such as power spectral analysis would be inadequate to extract information from snoring sounds. Therefore, higher order statistical features, in addition to the second order ones, were extracted. Third, the variability of snoring sound segments within and between 57 snorers with and without OSA was investigated. It was found that the sound characteristics of non-apneic (when there is no apneic event), hypopneic (when there is hypopnea), and post-apneic (after apnea) snoring events were significantly different. Then, this variability of snoring sounds was used as a signature to discriminate the non-OSA snorers from OSA snorers. The accuracy was found to be 96.4%. Finally, it was observed that some snorers formed distinct clusters of snoring sounds in a multidimensional feature space. Hence, using Polysomnography (PSG) information, the dependency of snoring sounds on body position, sleep stage, and blood oxygen level was investigated. It was found that all the three variables affected snoring sounds. However, body position was found to have the highest effect on the characteristics of snoring sounds. In conclusion, snoring sounds analysis offers valuable information on the upper airway physiological state and pathology. Thus, snoring sound analysis may further find its use in determining the exact state and location of obstruction.

Snoring is a highly prevalent disorder affecting 20-40% of adult population. Snoring is also a major indicative of obstructive sleep apnea (OSA). Despite the magnitude of effort, the acoustical properties of snoring in relation to physiological states are not yet known. This thesis explores statistical properties of snoring sounds and their association with OSA. First, an unsupervised technique was developed to automatically extract the snoring sound segments from the lengthy recordings of respiratory sounds. This technique was tested over 5665 snoring sound segments of 30 participants and the detection accuracy of 98.6% was obtained. Second, the relationship between anthropometric parameters of snorers with different degrees of obstruction and their snoring sounds’ statistical characteristics was investigated. Snoring sounds are non-Gaussian in nature; thus second order statistical methods such as power spectral analysis would be inadequate to extract information from snoring sounds. Therefore, higher order statistical features, in addition to the second order ones, were extracted. Third, the variability of snoring sound segments within and between 57 snorers with and without OSA was investigated. It was found that the sound characteristics of non-apneic (when there is no apneic event), hypopneic (when there is hypopnea), and post-apneic (after apnea) snoring events were significantly different. Then, this variability of snoring sounds was used as a signature to discriminate the non-OSA snorers from OSA snorers. The accuracy was found to be 96.4%. Finally, it was observed that some snorers formed distinct clusters of snoring sounds in a multidimensional feature space. Hence, using Polysomnography (PSG) information, the dependency of snoring sounds on body position, sleep stage, and blood oxygen level was investigated. It was found that all the three variables affected snoring sounds. However, body position was found to have the highest effect on the characteristics of snoring sounds. In conclusion, snoring sounds analysis offers valuable information on the upper airway physiological state and pathology. Thus, snoring sound analysis may further find its use in determining the exact state and location of obstruction.

Bioequivalence determines if two drugs are alike. The three kinds of bioequivalence are Average, Population, and Individual Bioequivalence. These Bioequivalence criteria can be evaluated using aggregate and disaggregate methods. Considerable work assessing bioequivalence in a frequentist method exists, but the advantages of Bayesian methods for Bioequivalence have been recently explored. Variance parameters are essential to any of theses existing Bayesian Bioequivalence metrics. Usually, the prior distributions for model parameters use either informative priors or vague priors. The Bioequivalence inference may be sensitive to the prior distribution on the variances. Recently, there have been questions about the routine use of inverse gamma priors for variance parameters. In this paper we examine the effect that changing the prior distribution of the variance parameters has on Bayesian models for assessing Bioequivalence and the carry-over effect. We explore our method with some real data sets from the FDA.