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Dissertations / Theses on the topic 'Clinical trials analysis'
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1
Bamias, Christina. "Analysis of clinical trials with rescue medication." Thesis, London School of Hygiene and Tropical Medicine (University of London), 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.249574.
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Todd, Susan Clare. "Methods of analysis for sequential clinical trials." Thesis, University of Reading, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.239477.
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Warne, David W. "Bayesian design and analysis of clinical trials." Thesis, University of Reading, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.303459.
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James, Susan Elizabeth. "The analysis of multiple endpoints in clinical trials." Thesis, University of Leicester, 1993. http://hdl.handle.net/2381/34548.
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Parmar, M. K. B. "Multiplicity in clinical trials and other medical studies." Thesis, University of Oxford, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.377230.
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Hardy, Rebecca Jane. "Meta-analysis techniques in medical research : a statistical perspective." Thesis, London School of Hygiene and Tropical Medicine (University of London), 1995. http://researchonline.lshtm.ac.uk/682268/.
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Meta-analysis is now commonly used in medical research. However there are statistical issues relating to the subject that require investigation and some are considered here, from both a methodological and a practical perspective. Each of the fixed effect and the random effects models for meta-analysis are based on certain assumptions and the validity of these is investigated. A formal test of the homogeneity assumption made in the fixed effect model may be performed. Since the test has low power, simulation was used to investigate the power under various conditions. The random effects model incorporates a between-study component of variance into the model. A likelihood based method was used to obtain a confidence interval for this variance and also to provide an interval for the overall treatment effect which takes into account the fact that the between-study variance is estimated, rather than assuming it to be known. In order to obtain confidence intervals for the treatment effect for both the fixed effect and the random effects models, distributional assumptions of normality are usually made. Such assumptions may be checked using q-q plots of the residuals obtained for each trial in the meta-analysis. In both meta-analysis models it is assumed that the weight allocated to each study is known, when in fact it must be estimated from the data. The effect of estimating the weights on the overall treatment effect estimate, its confidence intervals, the between-study variance estimate and the test statistic for homogeneity, is investigated by both analytic and simulation methods. It is shown how meta-analysis methods may be used to analyse multicentre trials of a paired cluster randomised design. Meta-analysis techniques are found to be preferable to previously published methods specifically developed for the analysis of such designs, which produce biased and potentially misleading results when a large treatment effect is present.
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Burke, Brian A. "Motivational interviewing: A meta-analysis of controlled clinical trials." Diss., The University of Arizona, 2003. http://hdl.handle.net/10150/280346.
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This is a meta-analytic review of controlled clinical trials investigating adaptations of motivational interviewing (AMIs), a promising approach to treating problem behaviors. For each study, descriptive characteristics were coded and individual effect sizes (Cohen's d) were computed. In order to evaluate comparative efficacy, combined effect sizes for AMIs were calculated, separated by comparison group and problem area. To test for sustained efficacy, post-treatment & follow-up effect sizes for AMIs were compared. Additional data were compiled to evaluate the clinical impact of AMIs. Finally, potential moderators were analyzed to test five specific hypotheses related to the effects of AMIs. Thirty clinical trials were included in this review, representing a wide variety of studies. AMIs were equivalent to other active treatments and yielded moderate effects (ranging from .25 to .57) compared to no-treatment or placebo controls for problems involving alcohol, drugs, and diet & exercise. These effects were sustained through an average of 67 weeks of follow-up and for as long as 4 years post-treatment. Based on four studies, there was weak evidence for AMIs in the areas of smoking cessation and HIV-risk behaviors. Overall, AMIs demonstrated considerable clinical impact, with 51% improvement rates, a mean within-group effect size of .82, a 56% reduction in client drinking, and moderate effects on social impact measures (d = .47) such as days of work lost due to substance use. Each of the five specific hypotheses in this meta-analysis was at least partially confirmed. Miller's lab (the founder of motivational interviewing) produced the best outcomes for AMIs, while AMI treatments were most efficacious for severe client samples. AMIs generated the best results when used as preludes to further clinical services rather than as stand-alone treatments. Studies of low methodological quality yielded better outcomes for AMIs than did high quality studies, although the overall picture with regards to quality was unclear. Finally. AMIs showed a significant dose-effect relationship, with higher treatment doses resulting in better study outcomes. Additional analyses provided evidence that the conclusions of this meta-analysis are reasonably immune to the effects of client attrition as well as to publication bias.
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Jadad-Bechara, Alejandro Ricardo. "Meta-analysis of randomised clinical trials in pain relief." Thesis, University of Oxford, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.239290.
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Xu, Shilin. "Contributions to the statistical analysis of multicenter clinical trials." Thesis, University of Reading, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.239146.
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Facey, Karen Maria. "Sequential procedures for clinical trials design, monitoring and analysis." Thesis, University of Reading, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.315536.
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Campbell, Ian. "Statistical analysis of end-points in cancer clinical trials." Thesis, University of Cambridge, 1994. https://www.repository.cam.ac.uk/handle/1810/240684.
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The major end-points arising from cancer clinical trials are reviewed. These are: tumour response, treatment morbidity, survival with related data, and quality of life. A survey of tumour response data from 81 published clinical trials found the most common statistical test in use to be a Chi squared test of the total response rate, but a total of 21 different statistical methods were used. The various statistical tests available are reviewed, including the Mann-Whitney test and the Chi squared test for trend which make use of all the categories of response and their intrinsic order. The assumptions underlying the tests are described. Theoretical considerations support the Mann-Whitney test as the optimum choice for the analysis of tumour response data. Methods for comparing alternative statistical tests are summarised, and a new method is described which uses a number of typical sets of data to estimate the relative efficiency of two statistical tests by the median value of the square of the ratio of the z-values. Using this technique, and data from the 81 trials, the Mann-Whitney test is found to be around 40% more efficient than the Chi squared test of the total response rate (this increased efficiency is equivalent to increasing the recruitment to the trial by 40%).This practical result is confirmed by mathematical modelling of tumour response using the power relation of the Mann-Whitney test for ordered categorical data, which is derived. Clinical data is found to fit best a shift model which assumes homogeneity of treatment effect across the different grades of response. On the basis of this model, the Mann-Whitney test is found to be 30% to 110% more efficient than a Chi squared test of the total response rate. The similarities of acute morbidity data to tumour response data lead to similar general conclusions on the optimum method of statistical analysis. In a survey of 36 published clinical trials, the most common method of statistical analysis was again a Chi squared test of a dichotomy (such as no morbidity versus morbidity of any grade). Analysis of data from these trials shows the Mann-Whitney test to be more efficient by around 30%.A survey of 81 papers reporting tumour response in clinical trials found that few of them used methods of estimation of the difference between the treatments, or derived confidence intervals of the size of such a difference. Methods of estimation and calculation of confidence intervals were found even less often in a survey of methods of presentation of morbidity results. The possible reasons for this are discussed. It is concluded that the current methods of analysis of tumour response data and many sets of acute treatment morbidity data are not optimum, and a change should be made from the Chi squared test to the Mann-Whitney test. Such a change could be equivalent to an increase in recruitment into many cancer clinical trials of around 40%.
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Kilgore, Meredith L. "Effects of trial design on participation and costs in clinical trials : with an examination of cost analysis methods and data sources /." Santa Monica, Calif. : Pardee RAND Graduate School, 2004. http://www.rand.org/publications/RGSD/RGSD179.
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Higgins, Julian P. T. "Exploiting information in random effects meta-analysis." Thesis, University of Reading, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.387704.
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Blatchford, Patrick Judson. "Monitoring bivariate endpoints in group sequential clinical trials /." Connect to full text via ProQuest. Limited to UCD Anschutz Medical Campus, 2007.
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Thesis (Ph.D. in Biostatistics) -- University of Colorado Denver, 2007.Typescript. Includes bibliographical references (leaves 104-106). Free to UCD affiliates. Online version available via ProQuest Digital Dissertations;
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Chan, Hung-kin Clive. "A multi-dimensional survey and critical analysis of clinical trial regulations in Hong Kong and a comparison of the status of clinical trial regulations in some Asian countries/Regions." Click to view the E-thesis via HKUTO, 2004. http://sunzi.lib.hku.hk/hkuto/record/B31971465.
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Rivero-Arias, Oliverio. "Economic issues in cost-effectiveness analysis alongside multinational clinical trials." Thesis, University of Oxford, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.543005.
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Vovoras, Dimitrios. "Statistical analysis and modeling: cancer, clinical trials, environment and epidemiology." Scholar Commons, 2011. http://scholarcommons.usf.edu/etd/3397.
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The current thesis is structured in four parts. Vector smoothing methods are used to study environmental data, in particular records of extreme precipitation, the models utilized belong to the vector generalized additive class. In the statistical analysis of observational studies the identification and adjustment for prognostic factors is an important component of the analysis; employing flexible statistical methods to identify and characterize the effect of potential prognostic factors in a clinical trial, namely "generalized additive models", presents an alternative to the traditional linear statistical model. The classes of models for which the methodology gives generalized additive extensions include grouped survival data from the Surveillance, Epidemiology, and End Results tumors of the brain and the central nervous system database; we are employing piecewise linear functions of the covariates to characterize the survival experienced by the population. Finally, both descriptive and analytical methods are utilized to study incidence rates and tumor sizes associated with the disease.
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Frison, Lars Johan. "Analysis of repeated measures in clinical trials using summary statistics." Thesis, London School of Hygiene and Tropical Medicine (University of London), 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.388955.
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Cro, S. "Relevant accessible sensitivity analysis for clinical trials with missing data." Thesis, London School of Hygiene and Tropical Medicine (University of London), 2017. http://researchonline.lshtm.ac.uk/3817571/.
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The statistical analysis of longitudinal randomised controlled trials is frequently complicated by the occurrence of protocol deviations which result in incomplete datasets for analysis. However analysis is approached, an unverifiable assumption about the distribution of the unobserved post-deviation data must be made. In such circumstances it is consequently important to assess the robustness of the primary analysis of the trial to different credible assumptions about the distribution of the missing data. Reference based multiple imputation procedures have been proposed for contextually relevant sensitivity analysis of longitudinal trials. Differences between the mean and variance of observed and missing data are specified with qualitative reference to trial arms and multiple imputation is used for estimation and inference. The primary analysis model is retained in the sensitivity analysis to assess the impact of alternative sampling behaviour on the original planned analysis. Rubin's rules are used to combine the treatment effect and variance estimates across imputed datasets, however it is unclear precisely what an appropriate measure of variance is in this setting and how Rubin's variance formula relates to this. We begin by defining a lower bound for variance estimation in the reference based settings as the variance estimate we would obtain were we able to observe the deviation data under the postulated post-deviation data assumption. We show Rubin's variance estimate always exceeds this and moreover it approximately preserves the loss of information in the primary analysis. We also explore Rubin's variance estimate in the δ-adjusted sensitivity analysis setting and show that Rubin's variance formula preserves the loss of information in this context. Alongside, we develop a new Stata command “mimix" for implementation of reference based sensitivity analyses. We illustrate the relevance and accessibility of the proposed methods of sensitivity analysis using data from a chronic asthma trial and a study of peer review.
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Rojas, Cordova Alba Claudia. "Resource Allocation Decision-Making in Sequential Adaptive Clinical Trials." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/86348.
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Adaptive clinical trials for new drugs or treatment options promise substantial benefits to both the pharmaceutical industry and the patients, but complicate resource allocation decisions. In this dissertation, we focus on sequential adaptive clinical trials with binary response, which allow for early termination of drug testing for benefit or futility at interim analysis points. The option to stop the trial early enables the trial sponsor to mitigate investment risks on ineffective drugs, and to shorten the development time line of effective drugs, hence reducing expenditures and expediting patient access to these new therapies. In this setting, decision makers need to determine a testing schedule, or the number of patients to recruit at each interim analysis point, and stopping criteria that inform their decision to continue or stop the trial, considering performance measures that include drug misclassification risk, time-to-market, and expected profit. In the first manuscript, we model current practices of sequential adaptive trials, so as to quantify the magnitude of drug misclassification risk. Towards this end, we build a simulation model to realistically represent the current decision-making process, including the utilization of the triangular test, a widely implemented sequential methodology. We find that current practices lead to a high risk of incorrectly terminating the development of an effective drug, thus, to unrecoverable expenses for the sponsor, and unfulfilled patient needs. In the second manuscript, we study the sequential resource allocation decision, in terms of a testing schedule and stopping criteria, so as to quantify the impact of interim analyses on the aforementioned performance measures. Towards this end, we build a stochastic dynamic programming model, integrated with a Bayesian learning framework for updating the drug’s estimated efficacy. The resource allocation decision is characterized by endogenous uncertainty, and a trade-off between the incentive to establish that the drug is effective early on (exploitation), due to a time-decreasing market revenue, and the benefit from collecting some information on the drug’s efficacy prior to committing a large budget (exploration). We derive important structural properties of an optimal resource allocation strategy and perform a numerical study based on realistic data, and show that sequential adaptive trials with interim analyses substantially outperform traditional trials. Finally, the third manuscript integrates the first two models, and studies the benefits of an optimal resource allocation decision over current practices. Our findings indicate that our optimal testing schedules outperform different types of fixed testing schedules under both perfect and imperfect information.Ph. D.
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Richert, Laura. "Trial design and analysis of endpoints in HIV vaccine trials." Thesis, Bordeaux 2, 2013. http://www.theses.fr/2013BOR22048/document.
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Des données complexes sont fréquentes dans les essais cliniques récents et nécessitent des méthodes statistiques adaptées. La recherche vaccinale du VIH est un exemple d’un domaine avec des données complexes et une absence de critères de jugement validés dans les essais précoces. Cette thèse d’Université concerne des recherches méthodologiques sur la conception et les aspects statistiques des essais cliniques vaccinaux du VIH, en particulier sur les critères de jugement d’immunogénicité et les schémas d’essai de phase I-II. A l’aide des données cytokiniques multiplex, nous illustrons les aspects méthodologiques spécifiques à une technique de mesure. Nous proposons ensuite des définitions de critères de jugement et des méthodes statistiques adéquates pour l'analyse des données d'immunogénicité multidimensionnelles. En particulier, nous montrons l’intérêt des scores multivariés non-paramétriques, permettant de résumer l’information à travers différents marqueurs d’immunogénicité et de faire des comparaisons inter- et intra-groupe. Dans l’objectif de contribuer à la conception méthodologique des nouveaux essais vaccinaux, nous présentons la construction d’un schéma d’essai optimisé pour le développement clinique précoce. En imbriquant les phases I et II d’évaluation clinique, ce schéma permet d’accélerer le développement de plusieurs stratégies vaccinales en parallèle. L’intégration d’une règle d’arrêt est proposée dans des perspectives fréquentistes et Bayesiennes. Les méthodes mises en avant dans cette thèse sont transposables à d’autres domaines d’application avec des données complexes, telle que les données d’imagerie ou les essais d’autres immunothérapiesComplex data are frequently recored in recent clinical trials and require the use of appropriate statistical methods. HIV vaccine research is an example of a domaine with complex data and a lack of validated endpoints for early-stage clinical trials. This thesis concerns methodological research with regards to the design and analysis aspects of HIV vaccine trials, in particular the definition of immunogenicity endpoints and phase I-II trial designs. Using cytokine multiplex data, we illustrate the methodological aspects specific to a given assay technique. We then propose endpoint definitions and statistical methods appropriate for the analysis of multidimensional immunogenicity data. We show in particular the value of non-parametric multivariate scores, which allow for summarizing information across different immunogenicity markers and for making statistical comparisons between and within groups. In the aim of contributing to the design of new vaccine trials, we present the construction of an optimized early-stage HIV vaccine design. Combining phase I and II assessments, the proposed design allows for accelerating the clinical development of several vaccine strategies in parallel. The integration of a stopping rule is proposed from both a frequentist and a Bayesian perspective. The methods advocated in this thesis are transposable to other research domains with complex data, such as imaging data or trials of other immune therapies
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Karlsson, Kristin E. "Benefits of Pharmacometric Model-Based Design and Analysis of Clinical Trials." Doctoral thesis, Uppsala universitet, Institutionen för farmaceutisk biovetenskap, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-133104.
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Quantitative pharmacokinetic-pharmacodynamic and disease progression models are the core of the science of pharmacometrics which has been identified as one of the strategies that can make drug development more effective. To adequately develop and utilize these models one needs to carefully consider the nature of the data, choice of appropriate estimation methods, model evaluation strategies, and, most importantly, the intended use of the model. The general aim of this thesis was to investigate how the use of pharmacometric models can improve the design and analysis of clinical trials within drug development. The development of pharmacometric models for clinical assessment scales in stroke and graded severity events, in this thesis, show the benefit of describing data as close to its true nature as possible, as it increases the predictive abilities and allows for mechanistic interpretations of the models. Performance of three estimation methods implemented in the mixed-effects modeling software NONMEM; 1) Laplace, 2) SAEM, and 3) Importance sampling, applied when modeling repeated time-to-event data, was investigated. The two latter methods are to be preferred if less than approximately half of the individuals experience events. In addition, predictive performance of two validation procedures, internal and external validation, was explored, with internal validation being preferred in most cases. Model-based analysis was compared to conventional methods by the use of clinical trial simulations and the power to detect a drug effect was improved with a pharmacometric design and analysis. Throughout this thesis several examples have shown the possibility of significantly reducing sample sizes in clinical trials with a pharmacometric model-based analysis. This approach will reduce time and costs spent in the development of new drug therapies, but foremost reduce the number of healthy volunteers and patients exposed to experimental drugs.
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McHugh, Gillian Stephanie. "Efficient analysis of ordinal data from clinical trials in head injury." Thesis, University of Edinburgh, 2012. http://hdl.handle.net/1842/6479.
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Many promising Phase II trials have been carried out in head injury however to date there has been no successful translation of the positive results from these explanatory trials into improved patient outcomes in Phase III trials. Many reasons have been hypothesised for this failure. Outcomes in head injury trials are usually measured using the five point Glasgow Outcome Scale. Traditionally the ordinality of this scale is disregarded and it is dichotomised into two groups, favourable and unfavourable outcome. This thesis explores whether suboptimal statistical analysis techniques, including the dichotomisation of outcomes could have contributed to the reasons why Phase III trials have been unsuccessful. Based on eleven completed head injury studies, simulation modelling is used to compare outcome as assessed by the conventional dichotomy with both modelling that takes into account the ordered nature of the outcome (proportional odds modelling) and modelling which individualises a patient’s risk of a good or poor outcome ( the ‘sliding dichotomy’). The results of this modelling show that both analyses which use the full outcome scale and those which individualise risk show great efficiency gains (as measured by reduction in required sample sizes) over the conventional analysis of the binary outcome. These results are consistent both when the simulated treatment effects followed a proportional odds model and when they did not. Consistent results were also observed when targeting or restricting improvement to groups of subjects based on clinical characteristics or prognosis. Although proportional odds modelling shows consistently greater sample size reductions the choice of whether to use proportional odds modelling or the sliding dichotomy depends on the question of interest.
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Solomon, P. J. "Some problems in the statistical analysis of large scale clinical trials." Thesis, Imperial College London, 1985. http://hdl.handle.net/10044/1/37860.
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Magirr, Dominic. "Design, conduct and analysis of multi-arm multi-stage clinical trials." Thesis, Lancaster University, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.664454.
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In this thesis, contributions are made towards improving the design, conduct and analysis of multi-arm multi-stage clinical trials. First, we generalize the Dunnett (1955) test to derive efficacy and futility boundaries for a multi-arm multi-stage clinical trial. We show the boundaries control the familywise error rate in the strong sense. The method is applicable for any number of treatment arms, number of stages and number of patients per treatment per stage. It can be used for a wide variety of boundary types or rules derived from a-spending functions. Additionally, we show how sample size can be computed under a least favourable configuration power requirement and derive formulae for expected sample sizes. Next, we describe a general method for finding a confidence region for a vector of K unknown parameters that is compatible with the decisions of a two-stage closed testing procedure in an adaptive experiment. The closed test procedure is characterized by the fact that rejection or nonrejection of a null hypothesis may depend on the decisions for other hypotheses and the compatible confidence region will, in general, have a complex, nonrectangular shape. We find the smallest Cartesian product of simultaneous confidence intervals containing the region and provide computational shortcuts for calculating the lower bounds for parameters corresponding to the rejected null hypotheses. We illustrate the methodology with a detailed example of an adaptive Phase II/III clinical trial. Finally, using the combination test principle and the conditional error principle, we develop flexible sequential designs for multi-arm clinical trials with early stopping for efficacy and futility. Such designs have the flexibility to cope with a large range of exigencies that may occur in practice. They also have the advantage that test decisions are based on sufficient statistics if the trial proceeds as originally planned.
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Mason, Tracey. "Application of survival methods for the analysis of adverse event data." Thesis, Keele University, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.267646.
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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.
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Hoeppner, Bettina Birgit. "Longitudinal methods for randomized clinical trials in health behavior change research /." View online ; access limited to URI, 2007. http://0-digitalcommons.uri.edu.helin.uri.edu/dissertations/AAI3276985.
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Öhrn, Carl Fredrik. "Group sequential and adaptive methods : topics with applications for clinical trials." Thesis, University of Bath, 2011. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.538283.
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This thesis deals with sequential and adaptive methods for clinical trials, and how such methods can be used to achieve efficient clinical trial designs. The efficiency gains that can be achieved through non-adaptive group sequential methods are well established, while the newer adaptive methods seek to combine the best of the classical group sequential framework with an approach that gives increased flexibility. Our results show that the adaptive methods can provide some additional efficiency, as well as increased possibilities to respond to new internal and external information. Care is however needed when applying adaptive methods. While sub-optimal rules for adaptation can lead to inefficiencies, the logistical challenges can also be considerable. Efficient non-adaptive group sequential designs are often easier to implement in practice, and have for the cases we have considered been quite competitive in terms of efficiency. The four problems that are presented in this thesis are very relevant to how clinical trials are run in practice. The solutions that we present are either new approaches to problems that have not previously been solved, or methods that are more efficient than the ones currently available in the literature. Several challenging optimisation problems are solved through numerical computations. The optimal designs that are achieved can be used to benchmark new methods proposed in this thesis as well as methods available in the statistical literature. The problem that is solved in Chapter 5 can be viewed as a natural extension to the other problems. It brings together methods that we have used to the design of individual trials, to solve the more complex problem of designing a sequence of trials that are the core part of a clinical development program. The expected utility that is maximised is motivated by how the development of new medicines works in practice.
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Beacon, Heather J. "Statistical analysis of self-assessed quality of life in cancer clinical trials." Thesis, London School of Hygiene and Tropical Medicine (University of London), 1996. http://researchonline.lshtm.ac.uk/682265/.
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The assessment of quality of life as a primary outcome in cancer clinical trials is now almost universal. Such data are necessarily longitudinal and multidimensional, and are often severely unbalanced by missing values or early patient death. However, to date, their reporting in the applied literature has generally used simple descriptive summaries that ignore many of these complexities. Not only can these be misleading, but they generally do not allow firm conclusions to be drawn about a major endpoint. The aim of this thesis is to assess the practical application of recent developments in statistical methodology for the analysis of quality of life data collected using self assessment questionnaires within cancer clinical trials. Its emphasis is on the use of relatively simple and flexible tools that will allow more reliable and powerful inferences to be drawn from the data than is done at present. The principal statistical tools considered are random coefficient and marginal models. It is shown that these can be successfully used for the analysis of continuous, binary and ordinal responses. In particular, they offer a simple approach to the analysis of repeated multivariate outcomes and can be very easily extended to model the complex patterns of response that are often seen in following cancer treatment. In relation to the problem of censored quality of life as a result of patient death, analyses that attempt to combine the survival and quality of life endpoints in a single variable are contrasted with those that consider the two endpoints as a multivariate problem. It is shown how this latter model can provide a summary of the quality of life response conditional on patient survival that with further work should have great application to such quality of life data. Finally, the problem of intermittent missing data is reviewed. The implications of missing data for some of the analyses presented in the thesis are assessed, and two models that attempt to determine the nature of intermittent missing data are developed. It is concluded that the problem of non-ignorable intermittent missing data presents a very challenging area of further research.
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Sirois, Jean-Karl. "Statistical Analysis of Treatment Compliance for Clinical Trials using Electronic Compliance Monitoring." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/32235.
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Compliance, the extent to which patients follow a medication regimen, has been recognized as one of the most serious problems facing medical practice today. Recent developments in assessing compliance include electronic compliance monitors (ECM), devices that record the date and time of the release of medication from its original container. This allows utilizing ECM compliance data in statistical analyses related to clinical trials.
This thesis proposes ways of dealing with the time-varying nature of compliance. We examine the compliance behaviour from real ECM data through statistical analysis of compliance rate, followed by a time-to-event analysis with respect to first noncompliance event. Then, using discrete event simulation and proportional hazards models we compare analyses using a fixed treatment covariate and time-varying compliance covariate based on pharmacokinetic principles in estimating treatment effect. We observe a reduction of up to 40% in EMSE in favour of the latter model for treatment effect estimation.
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Lai, Xin. "Extensions on long-term survivor model with random effects /." access full-text access abstract and table of contents, 2009. http://libweb.cityu.edu.hk/cgi-bin/ezdb/thesis.pl?phd-ms-b3008233xf.pdf.
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Thesis (Ph.D.)--City University of Hong Kong, 2009."Submitted to Department of Management Sciences in partial fulfillment of the requirement for the degree of Doctor of Philosophy." Includes bibliographical references (leaves 118-126)
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Savaluny, Elly. "Analysis of ordered categorical data : partial proportional odds and stratified models." Thesis, University of Reading, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.326978.
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Lou, Yiyue. "Principal stratification : applications and extensions in clinical trials with intermediate variables." Diss., University of Iowa, 2017. https://ir.uiowa.edu/etd/5961.
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Randomized clinical trials (RCTs) are considered to be the "gold standard" in order to demonstrate a causal relationship between a treatment and an outcome because complete randomization ensures that the only difference between the two units being compared is the treatment. The intention-to-treat (ITT) comparison has long been regarded as the preferred analytic approach for RCTs. However, if there exists an “intermediate” variable between the treatment and outcome, and the analysis conditions on this intermediate, randomization will break down, and the ITT approach does not account properly for the intermediate. In this dissertation, we explore the principal stratification approach for dealing with intermediate variables, illustrate its applications in two different clinical trial settings, and extend the existing analytic approaches with respect to specific challenges in these settings.
The first part of our work focuses on clinical endpoint bioequivalence (BE) studies with noncompliance and missing data. In clinical endpoint BE studies, the primary analysis for assessing equivalence between a generic and an innovator product is usually based on the observed per-protocol (PP) population (usually completers and compliers). The FDA Missing Data Working Group recently recommended using “causal estimands of primary interest.” This PP analysis, however, is not generally causal because the observed PP is post-treatment, and conditioning on it may introduce selection bias. To date, no causal estimand has been proposed for equivalence assessment. We propose co-primary causal estimands to test equivalence by applying the principal stratification approach. We discuss and verify by simulation the causal assumptions under which the current PP estimator is unbiased for the primary principal stratum causal estimand – the "Survivor Average Causal Effect" (SACE). We also propose tipping point sensitivity analysis methods to assess the robustness of the current PP estimator from the SACE estimand when these causal assumptions are not met. Data from a clinical endpoint BE study is used to illustrate the proposed co-primary causal estimands and sensitivity analysis methods. Our work introduces a causal framework for equivalence assessment in clinical endpoint BE studies with noncompliance and missing data.
The second part of this dissertation targets the use of principal stratification analysis approaches in a pragmatic randomized clinical trial -- the Patient Activation after DXA Result Notification (PAADRN) study. PAADRN is a multi-center, pragmatic randomized clinical trial that was designed to improve bone health. Participants were randomly assigned to either intervention group with usual care augmented by a tailored patient-activation Dual-energy X-ray absorptiometry (DXA) results letter accompanied by an educational brochure, or control group with usual care only. The primary analyses followed the standard ITT principle, which provided a valid estimate for the intervention assignment. However, findings might underestimate the effect of intervention because PAADRN might not have an effect if the patient did not read, remember and act on the letter. We apply principal stratification to evaluate the effectiveness of PAADRN for subgroups, defined by patient's recall of having received a DXA result letter, which is an intermediate outcome that's post-treatment. We perform simulation studies to compare the principal score weighting methods with the instrumental variable (IV) methods. We examine principal strata causal effects on three outcome measures regarding pharmacological treatment and bone health behaviors. Finally, we conduct sensitivity analyses to assess the effect of potential violations of relevant causal assumptions. Our work is an important addition to the primary findings based on ITT. It provides a profound understanding of why the PAADRN intervention does (or does not) work for patients with different letter recall statuses, and sheds light on the improvement of the intervention.
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Sooriyarachchi, Marina Roshini. "The use of interim inspections for making decisions and testing assumptions in clinical trials." Thesis, University of Southampton, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.239435.
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Morrey, Gilbert Heneage. "Statistical considerations in the design and analysis of cross-over trials." Thesis, De Montfort University, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.292354.
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Hetzer, Joel D. Johnston Dennis A. "Statistical considerations in the analysis of multivariate Phase II testing." Waco, Tex. : Baylor University, 2008. http://hdl.handle.net/2104/5277.
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Burke, Danielle Lisa. "Use of Bayesian methods for the design, analysis and synthesis of clinical trials." Thesis, University of Birmingham, 2015. http://etheses.bham.ac.uk//id/eprint/5931/.
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This thesis explores Bayesian methods for the statistical design, analysis and synthesis of clinical trials, and compares these with a frequentist approach in several settings to determine key differences, advantages and limitations. A review of randomised trials indicates that Bayesian methods are rarely applied, but useful for making probability statements and incorporating prior evidence, especially in trials with small sample sizes. These advantages are illustrated in a trial in congenital lower urinary tract obstruction, which has few events but elicited prior distributions about treatment effect. Bayesian methods are then developed for meta-analysis of phase II trials and multiple outcomes, with an emphasis on informing phase Ill trial decisions. A Bayesian random-effects logistic regression is advocated for meta-analysis of a binary outcome to account for all parameter uncertainty, and to derive prediction intervals for the treatment effect in a new phase III trial. Bayesian multivariate meta-analysis methods arc then encouraged to make joint inferences across multiple outcomes and incorporate prior distributions for missing correlations. However, a simulation study identifies that external evidence or clinical guidance is needed to ensure appropriate prior distributions for between-study variances and correlations to avoid misleading results. Researchers should thus consider Bayesian methods for clinical trials, but recognise potential difficulties when adopting the approach in practice.
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Engel, Mark E. "Steroids for tuberculous pleurisy : a systematic review and meta-analysis of clinical trials." Master's thesis, University of Cape Town, 2004. http://hdl.handle.net/11427/9384.
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Includes bibliographical references.Corticosteroids used in addition to anti-tuberculous therapy have been reported to benefit people with tuberculous pleurisy. However, research findings are not consistent, raising doubt as to whether such treatment is worthwhile. Concem also exists regarding the potential adverse effects of steroids, especially in HIV positive patients. An earlier Cochrane review summarized the existing evidence on the effects of steroids in people with tuberculous pleurisy. This review updates the evidence and, for the first time sheds light on the effects of steroids in patients with tuberculous pleurisy co-infected with HIV.
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Jackson, Richard. "Some statistical methods for the analysis of survival data in cancer clinical trials." Thesis, University of Liverpool, 2015. http://livrepository.liverpool.ac.uk/2020619/.
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Randomised Clinical Trials (RCT) are one of the most powerful tools of medical re- search and provide the basis for changing clinical practice. In oncology, the RCT is of particular importance in searching for new therapies and treatment approaches for patients diagnosed with cancer. Many of these trials have overall survival as a primary endpoint and are often designed with marginal e�ects being of clinical interest. As a result trials are typically large and are expensive in both time and money. Given the substantial cost involved in running clinical trials, it is an ethical imper- ative that statisticians endeavour to make the most e�cient use of any data obtained. A number of methods are explored in this thesis for the analysis of survival data from clinical trials with this e�ciency in mind. Statistical methods of analysis which take account of extreme values of covariates are proposed as well as a method for the analysis of survival data where the assumption of proportionality cannot be assumed. Beyond this, Bayesian theory applied to oncology studies is explored with examples of Bayesian survival models used in a study of pancreatic cancer. Also using a Bayesian approach, methodology for the design and analysis of trial data is proposed whereby trial data are supplemented by the information taken from previous trials. Arguments are made towards unequal allocation ratios for future trials with informative prior distributions.
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Claggett, Brian Lee. "Statistical Methods for Clinical Trials with Multiple Outcomes, HIV Surveillance, and Nonparametric Meta-Analysis." Thesis, Harvard University, 2012. http://dissertations.umi.com/gsas.harvard:10440.
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Central to the goals of public health are obtaining and interpreting timely and relevant information for the benefit of humanity. In this dissertation, we propose methods to monitor and assess the spread HIV in a more rapid manner, as well as to improve decisions regarding patient treatment options. In Chapter 1, we propose a method, extending the previously proposed dual-testing algorithm and augmented cross-sectional design, for estimating the HIV incidence rate in a particular community. Compared to existing methods, our proposed estimator allows for shorter follow-up time and does not require estimation of the mean window period, a crucial, but often unknown, parameter. The estimator performs well in a wide range of simulation settings. We discuss when this estimator would be expected to perform well and offer design considerations for the implementation of such a study. Chapters 2 and 3 are concerned with obtaining a more complete understanding of the impact of treatment in randomized clinical trials in which multiple patient outcomes are recorded. Chapter 2 provides an illustration of methods that may be used to address concerns of both risk-benefit analysis and personalized medicine simultaneously, with a goal of successfully identifying patients who will be ideal candidates for future treatment. Riskbenefit analysis is intended to address the multivariate nature of patient outcomes, while “personalized medicine” is concerned with patient heterogeneity, both of which complicate the determination of a treatment’s usefulness. A third complicating factor is the duration of treatment use. Chapter 3 features proposed methods for assessing the impact of treatment as a function of time, as well as methods for summarizing the impact of treatment across a range of follow-up times. Chapter 4 addresses the issue of meta-analysis, a commonly used tool for combining information for multiple independent studies, primarily for the purpose of answering a clinical question not suitably addressed by any one single study. This approach has proven highly useful and attractive in recent years, but often relies on parametric assumptions that cannot be verified. We propose a non-parametric approach to meta-analysis, valid in a wider range of scenarios, minimizing concerns over compromised validity.
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Gillen, Daniel L. "The use of weighted logrank statistics in group sequential testing and non-proportional hazards /." Thesis, Connect to this title online; UW restricted, 2003. http://hdl.handle.net/1773/9557.
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Li, Qing. "Interim monitoring efficacy, safety and futility in phase III clinical trials." Thesis, Birmingham, Ala. : University of Alabama at Birmingham, 2008. https://www.mhsl.uab.edu/dt/2008p/li.pdf.
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Barden, Jodie. "Meta-analysis to investigate methodological issues in clinical trials using pain as an exemplar." Thesis, University of Oxford, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.418560.
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Joseph, Royes. "Statistical analysis of longitudinal randomized clinical trials with missing data : a comparison of approaches." Thesis, Keele University, 2015. http://eprints.keele.ac.uk/3242/.
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Objectives: Missing data represent a source of bias in randomized clinical trials (RCTs). This thesis focuses on pragmatic RCTs with missing continuous outcome data and evaluates the use and appropriateness of current methods of analysis. Methods: This thesis consists of three parts. First, a systematic review examined practices relating to missing data in published RCTs. Second, a simulation study compared the performance of various methods for handling missing data in a number of plausible trial scenarios. Finally, an empirical evaluation of two pragmatic RCTs investigated the use of a reminder process to inform whether missingness is likely to be non-ignorable. Results: The majority of 91 trials in the systematic review adopted a form of single imputation, such as last observation carried forward (LOCF) for dealing with missing data. Mixed-effects model for repeated measures (MMRM) and/or multiple imputation (MI) were limited to eight trials. Sensitivity analyses were infrequently and inappropriately used, and insufficiently reported. In the simulation study, LOCF yielded biased estimates of treatment effect in most scenarios, irrespective of missing data mechanisms. All methods, except LOCF, yielded unbiased estimates for scenarios of equal dropout rate and same direction of dropout in both treatment groups. MMRM and MI were more robust to bias than complete-case and LOCF-based analyses. In the empirical study, the evaluation using reminder responses indicated the possibility of biased MMRM estimation in one trial and unbiased MMRM estimation in the other. Conclusion: CCA and LOCF-based analysis should be disregarded in favour of methods such as MMRM and MI-based analysis. The proposed reminder approach can be used to assess the robustness of the missing at random (MAR) assumption by checking expected consistency in MAR-based estimates. If the results deviate, then analyses incorporating a range of plausible missing not at random assumptions are advisable, at least as sensitivity tests for the evaluation of treatment effect.
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Barthel, Friederike Maria-Sophie. "Issues in the design and analysis of clinical trials with time-to-event outcomes." Thesis, University College London (University of London), 2006. http://discovery.ucl.ac.uk/1445310/.
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Designers of clinical trials today face a number of different challenges. In a number of disease areas several treatments become available at any one time with only a limited number of patients available for investigation purposes. Furthermore, in disease areas such as HIV and cancer the pressure is high to find effective treatments quickly. Due to the recent advances in the human genome project more and more interaction effects between a treatment under study and the genetic make-up of a patient may be successfully analysed. This thesis aims to evaluate existing tools for the design and analysis of clinical trials with a time-to-event outcome and provide extensions in areas where existing tools do not perform satisfactorily. Particular emphasis is placed on sample size calculations for multi-arm and multi-stage trials and other complex mechanisms such as loss to follow-up and patient withdrawal from allocated treatment. Furthermore, advances are made in the area of treatment-covariate interactions, particularly in terms of analysis tools for such interactions.
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Zhang, Lin Tubbs Jack Dale. "Semiparametric AUC regression for testing treatment effect in clinical trial." Waco, Tex. : Baylor University, 2008. http://hdl.handle.net/2104/5237.
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Marange, Chioneso Show. "Maximization of power in randomized clinical trials using the minimization treatment allocation technique." Thesis, University of Fort Hare, 2010. http://hdl.handle.net/10353/399.
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Generally the primary goal of randomized clinical trials (RCT) is to make comparisons among two or more treatments hence clinical investigators require the most appropriate treatment allocation procedure to yield reliable results regardless of whether the ultimate data suggest a clinically important difference between the treatments being studied. Although recommended by many researchers, the utilization of minimization has been seldom reported in randomized trials mainly because of the controversy surrounding the statistical efficiency in detecting treatment effect and its complexity in implementation. Methods: A SAS simulation code was designed for allocating patients into two different treatment groups. Categorical prognostic factors were used together with multi-level response variables and demonstration of how simulation of data can help to determine the power of the minimization technique was carried out using ordinal logistic regression models. Results: Several scenarios were simulated in this study. Within the selected scenarios, increasing the sample size significantly increased the power of detecting the treatment effect. This was contrary to the case when the probability of allocation was decreased. Power did not change when the probability of allocation given that the treatment groups are balanced was increased. The probability of allocation { } k P was seen to be the only one with a significant effect on treatment balance. Conclusion: Maximum power can be achieved with a sample of size 300 although a small sample of size 200 can be adequate to attain at least 80% power. In order to have maximum power, the probability of allocation should be fixed at 0.75 and set to 0.5 if the treatment groups are equally balanced.
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曾偉賢 and Wai-yin Tsang. "Analysis of data from a double-blind, placebo-controlled randomised clinical trial for the treatment of stroke." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1993. http://hub.hku.hk/bib/B31977509.
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Tsang, Wai-yin. "Analysis of data from a double-blind, placebo-controlled randomised clinical trial for the treatment of stroke." Hong Kong : University of Hong Kong, 1993. http://sunzi.lib.hku.hk/hkuto/record.jsp?B13787007.
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Odani, Motoi. "A Bayesian meta-analytic approach for safety signal detection in randomized clinical trials." 京都大学 (Kyoto University), 2017. http://hdl.handle.net/2433/225514.
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