Meta-analysis of multi-arm trials has been used increasingly in recent years, the aims of which are to combine evidence from all possible similar studies and draw inferences about the effectiveness of multiple compared-treatments. Antiplatelet therapy is a pharmacologic therapy which aims to inhibit platelet activation and aggregation in the setting of arterial thrombosis. Throughout the thesis we use binary data from antiplatelet therapy to apply the model and sensitivity analysis. The normal approximation model using empirical logistic transform has been employed to compare different treatments in multi-arm trials, allowing studies of both direct and indirect comparisons. The issue of direct-indirect comparison is studied in detail, borrowing the strength from the indirect comparisons and making inferences about appropriately chosen parameters. Additionally, a hierarchical structure of the model addresses the problem of heterogeneity among different studies. However the model requires a large sample size of each individual study. When the sample size is small, an exact logistic regression model is introduced. Both unconditional and conditional maximum likelihood approaches are performed to make inferences for the logistic regression model. We use Gaussian-Hermite quadrature to approximate the integral involved in the likelihood functions. Both approaches have been examined to different cases in the simulation study. Studies with statistically significant results (positive results) are potentially more likely to be submitted or selected more rapidly than studies with non-significant results (negative results). This leads to false-positive results or an incorrect, usually over-optimistic, conclusion, a problem known as selection bias in the meta-analysis. A funnel plot is a graphical tool which is used to detect selection bias in this research. We apply the idea of a sensitivity analysis by defining a selection model to the available data of a meta-analysis, by allowing different amounts of selection bias in the model and investigate how sensitive the main interest parameter is when compared to the estimates of the standard model. We also examine the sensitivity analysis by the simulation study.

A current concern in medical research is low productivity In the pharmaceutical industry. Failure rates of Phase III clinical trials are high, and this is very costly in terms of using resources and money. Our aim in this thesis is to incorporate prior information in clinical trial design and develop better assessments of the chances of successful clinical trials, so that trial sponsors can improve their success rates. Assurance calculations, which take into account uncertainty about how effective the treatment actually is, provide a more reliable assessment of the probability of a successful trial outcome comparing with power calculations. We develop assurance methods to accommodate survival outcome measures, assuming both parametric and nonparametric models. We also develop prior elicitation procedures for each survival model so that the assurance calculations can be performed more easily and reliably. Prior elicitation is not an easy task, and we may be uncertain about what distribution 'best' represents an expert's beliefs. We demonstrated that robustness of the assurance to different choices of prior distribution can be assessed by treating the elicitation process as a Bayesian inference problem, using a nonparametric Bayesian approach to quantify uncertainty in the expert's density function of the true treatment effect. In this thesis, we also consider a decision-making problem for a single-arm open label Phase 11 trial for the PhD sponsor Roche. Based on the Bayesian decision- theoretic approach and assurance calculations, a model is developed for the trial sponsor in finding the optimal trial strategies according to their beliefs about the true treatment effect.

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In recent years, there has been a growing interest within the medical research community in the application of adaptive designs in drug development. One potential application of adaptive designs is for bioequivalence trials, as they provide a means for managing the uncertainty around the anticipated variance to be used to power the study. If the anticipated variance is less than the true variance, the study may be under-powered resulting in the inability to demonstrate bioequivalence. Adaptive designs which allow one to both stop for success or futility at the interim or re-estimate the sample size if the study continues are explored in this thesis.

In practice, patient response is often measured some time after treatment commences. If data are analysed group sequentially, there will be subjects in the pipeline at each interim analysis who have started treatment but have yet to respond. If the stopping rule is satisfied, data will continue to accrue as these pipeline subjects respond. Standard designs stipulate that the overrun data be excluded from any analysis. However, their inclusion may be mandatory if trial results are to be included in a filing for regulatory approval. Methods have been proposed to provide a more complete treatment of the pipeline data, for example Whitehead (1992) and Faldum & Hommel(2007), although several issues remain unresolved. The work presented in this thesis provides a complete framework for dealing systematically with delayed responses in a group sequential setting. We formulate designs providing a proper treatment of the pipeline data which can be planned ahead of time. Optimal versions are used to assess the benefits for early stopping of group sequential analysis when there is a delay in response. Our new tests still deliver substantial benefits when the delay in response is small. While these fall as the delay increases, incorporating data on a highly correlated short-term endpoint is found to be effective at recouping many of these losses. P-values and confidence intervals for on termination of our delayed response tests are derived. We also extend our methodology to formulate user-friendly error spending tests for delayed responses which can deal with unpredictable sequences of information. Survival data are a special type of delayed response, where the length of delay is random and of primary interest. Deriving optimal survival tests, we conclude that tests minimising expected sample size for “standard” data are also highly efficient survival trials, achieving a rapid expected time to a conclusion.

Introduction/Background: Chronic widespread pain (CWP) affects around 11% of the population and while aetiology is well documented it has been difficult to translate this into effective management strategies. Patients in clinical trials are known to be different from the patient populations that they represent and treatment preference is one area where they may differ. Treatment preference may also influence outcome, particularly when participants cannot be blinded to treatment allocation. Aims: To assess whether patient treatment preference has an influence on 1. Recruitment, 2. Outcome, and 3. Adherence In a clinical trial of interventions for CWP. Methods: In the MUSICIAN trial, a 2x2 factorial trial of exercise and telephone cognitive behavioural therapy (T-CBT) for CWP, treatment preferences were recorded when eligibility was assessed using a population postal survey. Eligible individuals who did and did not go on to enter the trial were compared to address aim 1. Trial participants were followed up after 6 months of treatment and outcomes were compared according to whether they received their preferred treatment to address aim 2 and T-CBT and exercise logs were used to assess adherence to examine aim 3. Results: Eligible individuals were more likely to be randomised into the MUSICIAN trial if they expressed a treatment preference in the screening questionnaire (Relative Risk 1.46, 95% confidence interval 1.19-1.79). Treatment preferences were also associated with prognostic factors (anxiety and fear of movement). At follow-up participants were more likely to achieve a good outcome (global assessment of change) if they had received their preferred treatment (Relative Risk 2.50, 95% confidence interval 1.54-4.03)and this may be due to those individuals being more likely to adhere to treatment programmes. Conclusions: Wherever possible participant treatment preferences should be recorded prior to randomisation in clinical trials. Additional benefit may be gained in clinical practice by tailoring treatment to patients’ preferences.

Du fait de leurs propriétés immunostimulantes, les entérotoxines de Staphylococcus aureus (SEs) sont aussi considérées comme des outils thérapeutiques anticancéreux potentiels. Cependant, leurs implications dans de nombreuses pathologies humaines limitent leurs utilisations. Récemment, un opéron dénommé enterotoxin gene cluster (egc) codant pour cinq entérotoxines (SEG, SEI, SElM, SElN et SElO) supposées être de moindre virulence pour l’organisme, a été mis en évidence. En 2004, des patients atteints de carcinome broncho-pulmonaire ont été traités par l’administration d’un surnageant de culture d’une souche de S. aureus, contenant l’opéron egc. Ce traitement a permis d’allonger la durée de survie, et n’a eu aucun effet secondaire. Dans ce cadre, l’objectif de cette thèse a été d’étudier l’activité anti-tumorale des toxines de l’egc. Nos travaux ont mis en évidence l’activité tumoricide de ces toxines, induite par l’activation du système immunitaire. Cette toxicité est médiée par la sécrétion de nombreux médiateurs solubles comme le TNF-α et le NO. Nous avons confirmé le caractère pro-inflammatoire de type Th1 des toxines de l’egc. Nos travaux ont également montré qu’hormis SEI, les toxines de l’egc induisent des sécrétions de cytokines, chimiokines, protéases matricielles (MMPs) et facteurs de croissances nettement inférieures à celle induites par le reste des SEs. Ces résultats pourraient expliquer la faible toxicité associée aux toxines de l’egc. Enfin, nous avons montré que SElO possèdent une toxicité intrinsèque vis-à-vis des lignées tumorales. Cette étude plaide en faveur de l'intérêt des toxines de l’egc dans le développement de nouvelles approches en thérapie anti-tumorale / The use of classical superantigens (e.g. SEA, SEB and SEC) for treatment of cancer has resulted in a low response rates due to serious toxicity in humans. However, in a recent clinical study, remarkable results in treating lung cancer were obtained using superantigens encoded by the enterotoxin gene cluster (egc) without causing any significant toxicity. The current study was performed to investigate how egc superantigens (i.e. SEG, SEI, SElM, SElN and SElO) have tumoricidal activity with low toxicity. Indeed, we first demonstrated that tumoricidal activity of egc-SEs is mediated by immune cell activation, in particular, by secretion of soluble mediators such as nitric oxide and TNF-α. Thus, the proteomic analysis of the PBMC supernatants, showed that SEs-egc enhance the expression of pro-inflammatory cytokines, chimokines and many other biomarkers. Interestingly, levels were significantly higher in supernatants of SEA-stimulated PBMC than those with egc superantigens suggesting that staphylococcal superantigens differs in their inflammatory proprerties. Our results suggest that the relative lower pro-inflammatory activity of egc toxins may explain the low toxicity of these toxins observed during the clinical trial. Finally, we showed that SElO have a direct cytostatic activity against tumor cells. These findings suggest that egc-SEs seems to be good candidates for the development of new drugs in cancer therapy