Thesis (B.A.)--Haverford College, Dept. of History, 2002. / Includes bibliographical references.

published_or_final_version / Medical Sciences / Master / Master of Medical Sciences

The primary goal of clinical trials is to collect enough scientific evidence for a new intervention. Despite the widespread use of equal randomization in clinical trials, response-adaptive randomization has attracted considerable interest in terms of ethical concerns. In this thesis, delayed response problems and innovative designs for cytostatic agents in oncology clinical trials are studied. There is typically a prerun of equal randomization before the implementation of response-adaptive randomization, while it is often not clear how many subjects are needed in this prephase, and in practice an arbitrary number of patients are allocated in this equal randomization stage. In addition, real-time response-adaptive randomization often requires patient response to be immediately available after the treatment, while clinical response, such as tumor shrinkage, may take a relatively long period of time to exhibit. In the first part of the thesis, a nonparametric fractional model and a parametric optimal allocation scheme are developed to tackle the common problem caused by delayed response. In addition, a two-stage procedure to achieve a balance between power and the number of responders is investigated, which is equipped with a likelihood ratio test before skewing the allocation probability toward a better treatment. The operating characteristics of the two-stage designs are evaluated through extensive simulation studies and an HIV clinical trial is used for illustration. Numerical results show that the proposed method satisfactorily resolves the issues involved in response-adaptive randomization and delayed response. In phase I clinical trials with cytostatic agents, toxicity endpoints, as well as efficacy effects, should be taken into consideration for identifying the optimal biological dose (OBD). In the second part of the thesis, a two-stage Bayesian mixture modeling approach is developed, which first locates the maximum tolerated dose (MTD) through a mixture of parametric and nonparametric models, and then determines the most efficacious dose using Bayesian adaptive randomization among multiple candidate models. In the first stage searching for the MTD, a beta-binomial model in conjunction with a probit model as a mixture modeling approach is studied, and decisions are made based on the model that better fits the toxicity data. The model fitting adequacy is measured by the deviance information criterion and the posterior model probability. In the second stage searching for the OBD, the assumption that efficacy monotonically increases with the dose is abandoned and, instead, all the possibilities that each dose could have the highest efficacy effect are enumerated so that the dose-efficacy curve can be increasing, decreasing, or umbrella-shape. Simulation studies show the advantages of the proposed mixture modeling approach for pinpointing the MTD and OBD, and demonstrate its satisfactory performance with cytostatic agents. / published_or_final_version / Statistics and Actuarial Science / Master / Master of Philosophy

published_or_final_version / Medical Sciences / Master / Master of Medical Sciences

published_or_final_version / Statistics / Master / Master of Social Sciences

Randomized clinical trials provide the best evidence on the effect of treatment studied. There are different types of measures on the treatment effect, depending on the endpoints of the trials. For a given measure, based on the data from clinical trials, various statistical procedures are available for the inference of the treatment effect in terms of this measure. In a cancer clinical trial with a time to an event as the endpoint, hazard ratio is a popular measure for the relative difference between treatment groups. Most current statistical inference procedures for hazard ratio rely on the proportional hazard assumption, which may not be applicable to practice when it does not hold. Nonparametric confidence intervals for the hazard ratio have been proposed based on the asymptotic normality of the kernel estimate for the hazard ratio, but they were found not very satisfactory in the simulation studies. In the first part of this thesis, the empirical likelihood method is used to construct the confidence interval for the time-dependent hazard ratio. The asymptotic distribution of the empirical likelihood ratio is derived and simulation studies are conducted to evaluate the proposed method. It was also argued that the measure of the relative treatment effect based on the hazard ratio may be difficult to understand by clinicians. An alternative measure called probabilistic index was suggested and the C-index was proposed to estimate this index. However, it was pointed out recently that the expected value of the estimate based on the C-index may be far removed from the true index. In the second part of this thesis, assuming a semi-parametric density ratio model, two new estimates based on respectively the conditional likelihood and weighted empirical likelihood are proposed. Associated confidence intervals are also derived based on the bootstrap re-sampling method. The proposed inference procedures are evaluated by Monte-Carlo simulations and applied to the analysis of data from a clinical trial on early breast cancer. After primary analysis including all patients is completed in clinical trials, analysis by subgroups defined based on covariates of patients is often of interest to assess the homogeneity of treatment effects over these subgroups. The treatment-covariate interaction is usually used for this assessment. In the last part of this thesis, a non-parametric measure is used to quantify the interaction between treatments and binary covariates in the presence of censoring. Asymptotic distribution of the interaction estimates are derived and the bootstrap method is applied to construct the confidence intervals. The proposed approaches are also evaluated and compared by Monte-Carlo simulations and applied to a real data set from clinical trial. / Thesis (Ph.D, Mathematics & Statistics) -- Queen's University, 2011-05-21 11:07:52.992

The purpose of this research was to evaluate a self-instructional package (Fazzio & Martin, 2007) to train parents of children with autism to conduct discrete-trials teaching (DTT). In Study 1, I investigated the effectiveness of a self-instructional manual and a self-instructional video for teaching five parents of children with autism to correctly apply DTT to teach three tasks to a confederate who role-played a child with autism. For three of the parents I also evaluated their ability to apply DTT to their children with autism. Following an average of 4.76 hours of training, the package produced a strong effect with three parents and a weak effect with two parents. In Study 2, I investigated the effectiveness of the self-instructional manual combined with role-playing and feedback, plus the self-instructional video, for teaching an additional five parents of children with autism to apply DTT to a confederate and to their children. Following an average of 4.68 hours of training, all five parents demonstrated large, clinically significant gains in their performance of DTT, both with a confederate as well as with their own child, with a minimal investment of one-on-one instructor time. The treatment procedures in both experiments were very well received by the parent participants. These results suggest that the training package in Experiment 2 has considerable potential as an effective, efficient and acceptable method of training parents of children with autism to apply DTT.

Response adaptive designs are developed for ethical considerations, which sequentially modify the treatment allocations based on the accumulating information in the trial so more patients receive the potentially better treatment. Yi and Wang (2009) proposed a variance-penalized criterion to evaluate the performance of a response adaptive design based on the expected number of patients assigned to the better treatment and the power of statistical test. We use the variance-penalized criterion to examine different response adaptive randomization procedures for normally distributed responses. We propose a new target allocation proportion which increases the chance that more patients receive the better treatment. Simulation results indicate that our proposed design has the advantage of assigning more patients to the potentially better treatment with minimum loss in statistical power, and our design performs better than the designs in the literature based on the variance-penalized criterion.

Clinical laboratory safety data consist of a wide range of biochemical and haematological variables which are collected to monitor the safety of a new treatment during a clinical trial. Although the data are multivariate, testing for abnormal measurements is usually done for only one variable at a time. A Monte Carlo simulation study is described, which compares 16 methods, some of which are new, for detecting multivariate outliers with a view to finding patients with an unusual set of laboratory measurements at a follow-up assessment. Multivariate normal and bootstrap simulations are used to create data sets of various dimensions. Both symmetrical and asymmetrical contamination are considered in this study. The results indicate that in addition to the routine univariate methods, it is desirable to run a battery of multivariable methods on laboratory safety data in an attempt to highlight possible outliers. Mahalanobis distance is a well-known criterion which is included in the study. Appropriate critical values when testing for a single multivariate outlier using Mahalanobis Distance are derived in this thesis, and the jack-knifed Mahalanobis distance is also discussed. Finally, the presence of missing data in laboratory safety data sets is the motivation behind a study which compares eight multiple imputation methods. The multiple imputation study is described, and the performance of two outlier detection methods in the presence of three different proportions of missing data are discussed. Measures are introduced for assessing the accuracy of the missing data results, depending on which method of analysis is used.

The purpose of this research was to evaluate a self-instructional package (Fazzio & Martin, 2007) to train parents of children with autism to conduct discrete-trials teaching (DTT). In Study 1, I investigated the effectiveness of a self-instructional manual and a self-instructional video for teaching five parents of children with autism to correctly apply DTT to teach three tasks to a confederate who role-played a child with autism. For three of the parents I also evaluated their ability to apply DTT to their children with autism. Following an average of 4.76 hours of training, the package produced a strong effect with three parents and a weak effect with two parents. In Study 2, I investigated the effectiveness of the self-instructional manual combined with role-playing and feedback, plus the self-instructional video, for teaching an additional five parents of children with autism to apply DTT to a confederate and to their children. Following an average of 4.68 hours of training, all five parents demonstrated large, clinically significant gains in their performance of DTT, both with a confederate as well as with their own child, with a minimal investment of one-on-one instructor time. The treatment procedures in both experiments were very well received by the parent participants. These results suggest that the training package in Experiment 2 has considerable potential as an effective, efficient and acceptable method of training parents of children with autism to apply DTT.