Aspects of the analysis of variance for classifactory data

This thesis is directed toward data analysis and analysis of variance for data in a classificatory structure. Different approaches to looking at data by different authors are examined and some of their results extended;An analysis of variance of data is a partial description of the data. The fitted model is an approximating description of the data. P. D. Finch has worked on the quantification of the quality of a description, in particular, the description of a strong ordering by an ordered dichotomy. His ideas have been extended to ordered polychotomous numerical data and data in some basic ANOVA type structures;D. R. Cox looked at one-way, two-way and three-way classifications of data and obtained the expected mean squares under random permutation. These expected mean squares are expressed simply in terms of the quantities (SIGMA) which are defined as certain combinations of the variance components (sigma). The (SIGMA) are easily derived whether or not there is unit-treatment additivity. The derivations of the (SIGMA) are extended, in this thesis, to the general n-way classification of data;Many authors have written about the mixed model. The controversy with respect to the proper error term when testing for the random factor in the mixed model is examined from several viewpoints;References;Cox, D. R. 1958. The interpretation of the effects of non-additivity in the Latin square. Biometrika 45:69-73;Finch, P. D. 1979. Description and analogy in the practice of Statistics and Probability; Biometrics 3:1-21

Diagnosis and treatment trends in mucopolysaccharidosis I: findings from the MPS I Registry

Our objective was to assess how the diagnosis and treatment of mucopolysaccharidosis I (MPS I) have changed over time. We used data from 891 patients in the MPS I Registry, an international observational database, to analyze ages at symptom onset, diagnosis, treatment initiation, and treatment allocation (hematopoietic stem cell transplantation, enzyme replacement therapy with laronidase, both, or neither) over time for all disease phenotypes (Hurler, Hurler–Scheie, and Scheie syndromes). The interval between diagnosis and treatment has become shorter since laronidase became available in 2003 (gap during 2006–2009: Hurler—0.2 year, Hurler–Scheie—0.5 year, Scheie—1.4 years). However, the age at diagnosis has not decreased for any MPS I phenotype over time, and the interval between symptom onset and treatment initiation remains substantial for both Hurler–Scheie and Scheie patients (gap during 2006–2009, 2.42 and 6.71 years, respectively). Among transplanted patients, an increasing proportion received hematopoietic stem cells from cord blood (34 out of 64 patients by 2009) and was also treated with laronidase (42 out of 45 patients by 2009). Conclusions: Despite the availability of laronidase since 2003, the diagnosis of MPS I is still substantially delayed for patients with Hurler–Scheie and Scheie phenotypes, which can lead to a sub-optimal treatment outcome. Increased awareness of MPS I signs and symptoms by primary care providers and pediatric subspecialists is crucial to initiate early treatment and to improve the quality of life of MPS I patients

Aspects of the analysis of variance for classifactory data

This thesis is directed toward data analysis and analysis of variance for data in a classificatory structure. Different approaches to looking at data by different authors are examined and some of their results extended;An analysis of variance of data is a partial description of the data. The fitted model is an approximating description of the data. P. D. Finch has worked on the quantification of the quality of a description, in particular, the description of a strong ordering by an ordered dichotomy. His ideas have been extended to ordered polychotomous numerical data and data in some basic ANOVA type structures;D. R. Cox looked at one-way, two-way and three-way classifications of data and obtained the expected mean squares under random permutation. These expected mean squares are expressed simply in terms of the quantities (SIGMA) which are defined as certain combinations of the variance components (sigma). The (SIGMA) are easily derived whether or not there is unit-treatment additivity. The derivations of the (SIGMA) are extended, in this thesis, to the general n-way classification of data;Many authors have written about the mixed model. The controversy with respect to the proper error term when testing for the random factor in the mixed model is examined from several viewpoints;References;Cox, D. R. 1958. The interpretation of the effects of non-additivity in the Latin square. Biometrika 45:69-73;Finch, P. D. 1979. Description and analogy in the practice of Statistics and Probability; Biometrics 3:1-21.</p

Phase 1 Study to Evaluate the Effect of the Investigational Anticancer Agent Sapanisertib on the QTc Interval in Patients With Advanced Solid Tumors

The aim of this phase 1 study was to determine the effects of sapanisertib on the heart rate–corrected QT (QTc) interval in patients with advanced solid tumors. Adult patients with advanced solid tumors were enrolled to receive a single sapanisertib 40‐mg dose. Blood samples for pharmacokinetic analysis were collected and electrocardiogram readings were recorded at baseline and up to 48 hours after dosing. Patients could continue to receive sapanisertib 30 mg once weekly in 28‐day cycles for up to 12 months. The primary objective was to characterize the effect of a single dose of sapanisertib (40 mg) on the QT interval. Secondary objectives were to evaluate safety, tolerability, and pharmacokinetics. Following a single sapanisertib 40‐mg dose in 44 patients, the maximum least squares mean (upper bound of 1‐sided 95% confidence interval) changes from time‐matched baseline were 7.1 milliseconds (11.4 milliseconds) for individual rate‐corrected QT interval at 24 hours after dosing, and 1.8 milliseconds (5.6 milliseconds) for Fridericia‐corrected QTc at 1 hour post‐dose. There was no sapanisertib plasma concentration‐dependent increase in the change from time‐matched baseline individual rate‐corrected QTc interval or Fridericia‐corrected QTc. The most common adverse events following sapanisertib 30 mg once‐weekly dosing were nausea (80%), fatigue (61%), vomiting (57%), and decreased appetite (45%). A single sapanisertib 40 mg dose did not produce clinically relevant effects on QTc interval in patients with advanced solid tumors. The safety profile of sapanisertib 30 mg once weekly was favorable, and no new safety signals were observed (NCT02197572, clinicaltrials.gov)

Effects of rifampin, itraconazole and esomeprazole on the pharmacokinetics of alisertib, an investigational aurora a kinase inhibitor in patients with advanced malignancies

Aim Two studies investigated the effect of gastric acid reducing agents and strong inducers/inhibitors of CYP3A4 on the pharmacokinetics of alisertib, an investigational Aurora A kinase inhibitor, in patients with advanced malignancies. Methods In Study 1, patients received single doses of alisertib (50 mg) in the presence and absence of either esomeprazole (40 mg once daily [QD]) or rifampin (600 mg QD). In Study 2, patients received single doses of alisertib (30 mg) in the presence and absence of itraconazole (200 mg QD). Blood samples for alisertib and 2 major metabolites were collected up to 72 h (Study 1) and 96 h (Study 2) postdose. Area under the curve from time zero extrapolated to infinity (AUC ) and maximum concentrations (C ) were calculated and compared using analysis of variance to estimate least squares (LS) mean ratios and 90% confidence intervals (CIs). Results The LS mean ratios (90% CIs) for alisertib AUC and C in the presence compared to the absence of esomeprazole were 1.28 (1.07, 1.53) and 1.14 (0.97, 1.35), respectively. The LS mean ratios (90% CIs) for alisertib AUC and C in the presence compared to the absence of rifampin were 0.53 (0.41, 0.70) and 1.03 (0.84, 1.26), respectively. The LS mean ratios (90% CIs) for alisertib AUC and C in the presence compared to the absence of itraconazole were 1.39 (0.99, 1.95) and 0.98 (0.82, 1.19), respectively. Conclusions The use of gastric acid reducing agents, strong CYP3A inhibitors or strong metabolic enzyme inducers should be avoided in patients receiving alisertib