The concept of collecting Adverse Events (AEs) arose with the advent of the Thalidomide incident. Prior to this the development and marketing of drugs was not regulated in any way. It was the teterogenic effects which raised people's awareness of the damage prescription drugs could cause. This thesis will begin by describing the background to the foundation of the Committee for the Safety of Medicines (CSM) and how AEs are collected today. This thesis will investigate survival analysis, discriminant analysis and logistic regression to identify prognostic indicators. These indicators will be developed to build, assess and compare predictor models produced to see if the factors identified are similar amongst the methodologies used and if so are the background assumptions valid in this case. ROC analysis will be used to classify the prognostic indices produced by a valid cut-off point, in many medical applications the emphasis is on creating the index - the cut-off points are chosen by clinical judgement. Here ROC analysis is used to give a statistical background to the decision. In addition neural networks will be investigated and compared to the other models. Two sets of data are explored within the thesis, firstly data from a Phase III clinical trial used to assess the efficacy and safety of a new drug used to repress the advance of Alzheimer's disease where AEs are collected routinely and secondly data from a drug monitoring system used by the Department of Rheumatology at the Haywood Hospital to identify patients likely to require a change in their medication based on their blood results.

No description available.

Simple randomisation is the easiest method for allocating participants to treatment groups in clinical trials. In the long run it balances all features of participants across the groups but may not be suitable for small to medium sized trials. If important prognostic factors are identified at the design stage then stratified randomisation or minimisation can help to balance these features. Aim: To examine the relative benefits of different randomisation algorithms and determine guidelines for which randomisation design is advisable for a given trial. For a trial of known size with a specified number of important prognostic factors, and levels within these, it will be possible to identify the most appropriate randomisation technique for that trial. Methods: A review of methods of randomisation was first conducted followed by a survey of trialists into the current use of randomisation methods in clinical trials. Using simulations the following comparisons were made; simple randomisation compared with minimisation, whether to stratify or minimise by centre and predictability versus balance when using minimisation. The recommendations resulting from the simulations were used to design a prototype generic randomisation program. Results: The review and the survey both highlighted the probability of imbalance using simple randomisation. Minimisation was seen to be superior in producing balanced groups but the method was criticised for being more complex and unpredictable. The simulations showed that several factors influence imbalance including size of trial, the number of prognostic factors and the number of categories within these. Optimal algorithms for maintaining balance while reducing predictability were presented for varying trial parameters. Conclusions: Minimisation is a suitable method of randomisation for most clinical trials. Several strategies can be employed to address the conflicting issues of predictability and imbalance without resorting to complex mathematical algorithms.

Randomization schemes for two-treatment clinical trials are studied. Theoretical expressions for the power are derived under both complete randomization and Efron’s biased coin design for normal and binary responses. The better the scheme is at balancing the numbers of patients across treatments, the higher the power is. Efron’s biased coin design is more powerful than complete randomization. Normal approximations to the powers are obtained. The power of the adjustable biased coin design is also investigated by simulation. Covariate-adaptive randomization schemes are analysed when either global or marginal balance across cells is sought. By considering a fixed-effects linear model for normal treatment responses with several covariates, an analysis of covariance t test is carried out. Its power is simulated for global and marginal balance, both in the absence and in the presence of interactions between the covariates. Global balancing covariate-adaptive schemes are more efficient when there are interactions between the covariates. Restricted randomization schemes for more than two treatments are then considered. Their asymptotic properties are provided. An adjustable biased coin design is introduced for which assignments are based on the imbalance across treatments. The finitesample properties of the imbalance under these randomization schemes are studied by simulation. Assuming normal treatment responses, the power of the test for treatment differences is also obtained and is highest for the new design. Imbalance properties of complete randomization and centre-stratified permuted block randomization for several treatments are investigated. It is assumed that the patient recruitment process follows a Poisson-gamma model. When the number of centres is large, the imbalance for both schemes is approximately multivariate normal. The power of a test for treatment differences is simulated for normal responses. The loss of power can be compensated for by a small increase in sample size.

No description available.

Thesis (M. Med. Sc.)--University of Hong Kong, 2006.

Thesis (M.S.)--Brigham Young University. Dept. of Computer Science, 2005. / Includes bibliographical references (p. 49-50).

Thesis (M. Med. Sc.)--University of Hong Kong, 2001. / Includes bibliographical references (leaves 59-62).

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