No description available.

Background: There are a plethora of potential statistical designs which can be used to evaluate efficacy of a novel cancer treatment in the phase II clinical trial setting. Unfortunately, there is no consensus as to which design one should prefer, nor even which definition of efficacy should be used and the primary endpoint conclusion can vary depending on which design is chosen. It would be useful if an all-encompassing methodology was possible which could evaluate all the different designs simultaneously and allow investigators an understanding of the trial results under the varying scenarios. Methods: Finite Markov chain imbedding is a method which can be used in the setting of phase II oncology clinical trials but never previously evaluated in this scenario. Simple variations to the transition matrix or end-state probability definitions can be performed which allow for evaluation of multiple designs and endpoints for a single trial. A computer program is written in R which allows for computation of p-values and conditional power, two common statistical measures used for evaluation of trial results. A simulation study is performed on data arising from an actual phase II clinical trial performed recently in which the study conclusion regarding the efficacy of the potential treatment was debatable. Results: Finite Markov chain imbedding is shown to be useful for evaluating phase II oncology clinical trial results. The R code written for evaluating the simulation study is demonstrated to be fast and useful for investigating different trial designs. Further detail regarding the clinical trial results are presented, including the potential prolongation of stable disease of the treatment, which is a potentially useful marker of efficacy for this cytostatic agent. Conclusions: This novel methodology may prove to be an useful investigative technique for the evaluation of phase II oncology clinical trial data. Future studies which have disputable conclusions might become less controversial with the aid of finite Markov chain imbedding and the possible multiple evaluations which is now viable. Better understanding of activity for a given treatment might expedite the drug development process or help distinguish active from inactive treatments

Several cell culture and animal studies demonstrated that citrus limonoids have protective effects against certain types of cancer. These chemopreventive properties of citrus limonoids are attributed to the induction of phase II enzyme, glutathione Stransferase (GST). In the current study, six citrus limonoids and two modified limonoids were utilized for the evaluation of NAD(P)H: quinone reductase (QR) activity and glutathione S-tranferase (GST) activity against 1-chloro-2,4-dinitrobenzene (CDNB) and 4-nitroquinoline 1-oxide (4NQO) in A/J female mice. In liver, limonoids that induced phase II enzyme activity were limonin-7- methoxime (32% CBNB), (270% 4NQO), (65% QR); and deacetylnomilin (180% QR). In stomach, limonin-7-methoxime (51% 4NQO); deacetyl nomilinic acid glucoside (55% 4NQO), nomilin (58% CDNB), (75% 4NQO); isoobacunoic acid (25% CDNB); deacetylnomilin (19% CDNB); limonoid mixture (45% 4NQO), (200% QR). Furthermore, in intestine, nomilin (280% 4NQO); deacetylnomilin (73% 4NQO), (22% QR); and the limonoid mixture (93% 4NQO) increase enzymatic activity. Finally in lung, deacetyl nomilinic acid glucoside (67% CDNB); limonin-7-methoxime (32% QR); and defuran limonin (45% QR) diplayed induction properties. Furthermore, D-glucaric acid (GA), a chemoprotective compound found in fruits and vegetables, was quantified using High Performance Liquid Chromatography (HPLC) grapefruit. Nine widely used grapefruit varieties were analyzed for the levels of D-glucaric acid. Seasonal levels of GA in each of the grapefruit varieties tested were found to be Thompson (58.36-126.8 mg/100ml), Henderson (29.6-49.7 mg/100ml), Rio Red (40.0-58.8mg/100ml), Star Ruby (25.5-46.7 mg/100ml), I-48 (26.6-58.3 mg/100ml), Ruby Red (49.3-63.0 mg/100ml), Ray’s Ruby (58.2-72.1 mg/100ml), Marsh (53.7-65.8 mg/100ml) and Duncan (50.17 mg/100ml). The HPLC method developed for the quantification of D-glucaric acid was found to be simple, fast, and reproducible. Additionally, the labor intensity and cost of sample preparation were greatly reduced.

Background: There are a plethora of potential statistical designs which can be used to evaluate efficacy of a novel cancer treatment in the phase II clinical trial setting. Unfortunately, there is no consensus as to which design one should prefer, nor even which definition of efficacy should be used and the primary endpoint conclusion can vary depending on which design is chosen. It would be useful if an all-encompassing methodology was possible which could evaluate all the different designs simultaneously and allow investigators an understanding of the trial results under the varying scenarios. Methods: Finite Markov chain imbedding is a method which can be used in the setting of phase II oncology clinical trials but never previously evaluated in this scenario. Simple variations to the transition matrix or end-state probability definitions can be performed which allow for evaluation of multiple designs and endpoints for a single trial. A computer program is written in R which allows for computation of p-values and conditional power, two common statistical measures used for evaluation of trial results. A simulation study is performed on data arising from an actual phase II clinical trial performed recently in which the study conclusion regarding the efficacy of the potential treatment was debatable. Results: Finite Markov chain imbedding is shown to be useful for evaluating phase II oncology clinical trial results. The R code written for evaluating the simulation study is demonstrated to be fast and useful for investigating different trial designs. Further detail regarding the clinical trial results are presented, including the potential prolongation of stable disease of the treatment, which is a potentially useful marker of efficacy for this cytostatic agent. Conclusions: This novel methodology may prove to be an useful investigative technique for the evaluation of phase II oncology clinical trial data. Future studies which have disputable conclusions might become less controversial with the aid of finite Markov chain imbedding and the possible multiple evaluations which is now viable. Better understanding of activity for a given treatment might expedite the drug development process or help distinguish active from inactive treatments

Idarubicin, a new analogue of daunorubicin, was administered intravenously at a dose of 15 mg/m2 to 31 patients with previously treated patients with unfavorable non-Hodgkin's lymphoma. Clinical characteristics included median age 69 years, performance status 1, and prior chemotherapeutic regimens 1. Twenty of the patients were relapsing after prior therapy and 11 were refractory; 29 had received prior anthracycline or anthracenedione. Responses were observed in 43% of patient (3 CR and 10 PR) with a median duration of 10 + months (2-29+ months). Idarubicin was well tolerated with non-hematologic toxicities (nausea/vomiting, mucositis, and anorexia) seen in <50% of patients. Median hematologic values during the first cycle for this dosage included WBC 1300/mm3 platelets 129,000/mm3, and hemoglobin 10.9 mg/dl. With dose escalation, hematologic toxicity was dose-limiting. Symptomatic cardiac toxicity was observed in one patient who had received maximum dose doxorubicin and radiotherapy. Median values for the cardiac ejection fraction during the full course of therapy for the entire group of patients were 0.62 (initial) and 0.60 (final). Idarubicin in intravenous form is an active drug in previously treated patients with unfavorable non-Hodgkin's lymphoma. Further studies employing idarubicin in non-Hodgkin's lymphoma should be considered. Cardiac function should be followed in trials utilizing anthracycline-type chemotherapeutic agents.

In March 2003, many communities in Mississippi fell under National Pollutant Discharge Elimination System (NPDES) regulations and were required to develop Stormwater Pollution Prevention Plans (SWPPPs). This study surveyed those in charge of SWPPPs in Mississippi’s regulated communities to determine the knowledge, efforts, and associated expenses, of complying with Stormwater Phase II regulations as well as what attempts regulated communities made to include urban forestry in their SWPPPs. While results indicated that all respondents were compliant with Stormwater Phase II regulations, regulated communities can improve efforts in several areas to best mitigate stormwater runoff pollution (e.g., public education and urban forestry). Findings will be useful when presented to current and, soon to be, regulated communities in an educational and outreach effort to increase their knowledge levels, reduce incurred costs, increase the effectiveness of their SWPPP, and enhance their ability to utilize urban and community forests as a stormwater mitigation tool.

No description available.

Profile monitoring is a well-known approach used in statistical process control where the quality of the product or process is characterized by a profile or a relationship between a response variable and one or more explanatory variables. Profile monitoring is conducted over two phases, labeled as Phase I and Phase II. In Phase I profile monitoring, regression methods are used to model each profile and to detect the possible presence of out-of-control profiles in the historical data set (HDS). The out-of-control profiles can be detected by using the statis-tic. However, previous methods of calculating the statistic are based on using all the data in the HDS including the data from the out-of-control process. Consequently, the ability of using this method can be distorted if the HDS contains data from the out-of-control process. This work provides a new profile monitoring methodology for Phase I analysis. The proposed method, referred to as the cluster-based profile monitoring method, incorporates a cluster analysis phase before calculating the statistic. Before introducing our proposed cluster-based method in profile monitoring, this cluster-based method is demonstrated to work efficiently in robust regression, referred to as cluster-based bounded influence regression or CBI. It will be demonstrated that the CBI method provides a robust, efficient and high breakdown regression parameter estimator. The CBI method first represents the data space via a special set of points, referred to as anchor points. Then a collection of single-point-added ordinary least squares regression estimators forms the basis of a metric used in defining the similarity between any two observations. Cluster analysis then yields a main cluster containing at least half the observations, with the remaining observations comprising one or more minor clusters. An initial regression estimator arises from the main cluster, with a group-additive DFFITS argument used to carefully activate the minor clusters through a bounded influence regression frame work. CBI achieves a 50% breakdown point, is regression equivariant, scale and affine equivariant and distributionally is asymptotically normal. Case studies and Monte Carlo results demonstrate the performance advantage of CBI over other popular robust regression procedures regarding coefficient stabil-ity, scale estimation and standard errors. The cluster-based method in Phase I profile monitoring first replaces the data from each sampled unit with an estimated profile, using some appropriate regression method. The estimated parameters for the parametric profiles are obtained from parametric models while the estimated parameters for the nonparametric profiles are obtained from the p-spline model. The cluster phase clusters the profiles based on their estimated parameters and this yields an initial main cluster which contains at least half the profiles. The initial estimated parameters for the population average (PA) profile are obtained by fitting a mixed model (parametric or nonparametric) to those profiles in the main cluster. Profiles that are not contained in the initial main cluster are iteratively added to the main cluster provided their statistics are "small" and the mixed model (parametric or nonparametric) is used to update the estimated parameters for the PA profile. Those profiles contained in the final main cluster are considered as resulting from the in-control process while those not included are considered as resulting from an out-of-control process. This cluster-based method has been applied to monitor both parametric and nonparametric profiles. A simulated example, a Monte Carlo study and an application to a real data set demonstrates the detail of the algorithm and the performance advantage of this proposed method over a non-cluster-based method is demonstrated with respect to more accurate estimates of the PA parameters and improved classification performance criteria. When the profiles can be represented by vectors, the profile monitoring process is equivalent to the detection of multivariate outliers. For this reason, we also compared our proposed method to a popular method used to identify outliers when dealing with a multivariate response. Our study demonstrated that when the out-of-control process corresponds to a sustained shift, the cluster-based method using the successive difference estimator is clearly the superior method, among those methods we considered, based on all performance criteria. In addition, the influence of accurate Phase I estimates on the performance of Phase II control charts is presented to show the further advantage of the proposed method. A simple example and Monte Carlo results show that more accurate estimates from Phase I would provide more efficient Phase II control charts. / Ph. D.

Le cancer du sujet âgé est un réel problème de santé publique. L’incidence du cancer augmentant avec l’âge couplée au vieillissement général de la population font que plus de la moitié des tumeurs diagnostiquées aujourd’hui le sont chez des patients de plus de 65 ans. Cependant, cette population hétérogène a longtemps été exclue des essais cliniques et le manque de données prospectives rend difficile la prise en charge de ces patients. Plusieurs publications soulignent l’importance et la complexité de réaliser des essais cliniques dans cette population. Les schémas classiques ne prenant pas en compte l’hétérogénéité, les essais de phase II spécifiques aux sujets âgés sont rares et généralement stratifiés en sous-groupes définis selon un critère gériatrique ce qui augmente le nombre de patients à inclure et donc diminue la faisabilité. L’objectif de cette thèse est de présenter, comparer et développer des schémas de phase II adaptatifs stratifiés permettant de prendre en compte l’hétérogénéité de la population âgée. L’utilisation de ce type d’approche permet de réduire le nombre de patients à inclure tout en maintenant la puissance statistique et en contrôlant le risque d’erreur de type I. Ce qui implique une diminution du coût et de la durée de l’étude et donc une augmentation de la faisabilité. Afin d’améliorer l’efficacité de la recherche clinique en oncogériatrie, il est donc primordial d’utiliser des schémas adaptatifs stratifiés prenant en compte l’hétérogénéité de la population et permettant d’identifier un sous-groupe d’intérêt susceptible de pouvoir bénéficier (ou non) de la nouvelle thérapeutique. / Elderly cancer is a real public health problem. With the overall aging population and the increased incidence of cancer, more than half of all tumors diagnosed today are in patients aged 65 years or older. However, this heterogeneous population has long been excluded from clinical trials and the lack from prospective data makes it difficult managing these patients. Many publications highlight the importance and the complexity of conducting clinical trials in this population. As classical phase II designs do not take into account the heterogeneity, elderly specific phase II clinical trials are very uncommon and generally conducted in specific subgroups defined by geriatric criteria which increases the number of patients to be included and thus reduces the feasibility. The objective of this thesis is to present, compare and develop stratified adaptive designs that address the heterogeneity of the elderly population. The use of this methodology can minimize the number of patients to be included while maintaining statistical power and controlling the type I error risk. This implies a reduction in the cost and duration of the study and thus increases the feasibility. In order to improve the efficiency of clinical research in geriatric oncology, it is essential to use stratified adaptive designs that take into account the heterogeneity of the population and make it possible to identify a subgroup of interest that might benefit (or not) from the new therapeutic.

La phase II d’un essai clinique représente une étape importante de l’évaluation d’une thérapeutique. Il s’agit d’une étape de sélection ayant pour objectif d’identifier les traitements efficaces, qui seront évalués de manière comparative en phase III, et ceux qui, jugés inefficaces seront abandonnés. Le choix du critère de jugement et la règle de décision sont les éléments essentiels de cette étape de l’essai clinique. En cancérologie, le critère de jugement est principalement de nature binaire (réponse au traitement). Cependant, depuis quelques années, les essais de phase II considèrent également les délais d’événement censurés ou non (délai de survie sans progression). Dans le cadre de ce travail de thèse, nous nous sommes intéressés à ces deux types de critères dans le cas de plans d’expérience à deux étapes comportant une possibilité d’arrêt précoce pour futilité. Les conditions réelles des phases II voient souvent la réalisation pratique de l'essai s'écarter du protocole défini a priori. Les cas les plus fréquents sont l'impossibilité d'évaluer le patient (liée par exemple à un événement intercurrent) ou la réalisation d’un suivi trop court. L’objectif de ce travail de thèse était d'évaluer les répercussions entrainées par ces modifications et de proposer des solutions alternatives. Ce travail comporte deux parties principales. Dans la première partie, nous traitons d’un critère de jugement binaire avec patients non évaluables ; dans la seconde d’un critère censuré avec un suivi plus court. Lorsque le critère de jugement est binaire, le plan d’expérience dit « de Simon » est le plus souvent utilisé. Mais ce plan d’expérience nécessite que la réponse de tous les sujets inclus soit connue au temps d’intérêt clinique choisi. En conséquence, comment devons-nous analyser les résultats de l’essai lorsque certains patients sont non évaluables et quelles sont les conséquences sur les caractéristiques opérationnelles de ce plan ? Plusieurs stratégies ont été envisagées (exclusion, remplacement ou considération des patients non évaluables comme des échecs), l’étude de simulation que nous avons réalisée dans le cadre de ce travail montre qu’aucune de ces stratégies ne permet de respecter les risques de première et de seconde espèce fixés a priori. Pour pallier à ces défaillances, nous avons proposé une stratégie dite de « sauvetage » qui repose sur une reconstruction du plan de Simon à partir des probabilités de réponse sous les hypothèses nulle et alternative sachant que le patient est évaluable. Les résultats de nos études de simulations montrent que cette stratégie permet de minimiser les déviations aux risques de première et de seconde espèce alors qu’il y a moins d’information que planifiée dans l’essai. Depuis la dernière décennie, de nombreux schémas considérant les délais d’événement ont été développés. En pratique, l’approche naïve estime la survie sans progression par un taux brut à un temps fixé et utilise un schéma de Simon pour établir une règle de décision. Case et Morgan ont proposé en 2003 un plan d’expérience comparant les taux de survie sans progression estimés à un temps choisi. Considérant l’ensemble du suivi, Kwak et al. (2013) ont proposé d’utiliser la statistique dite du one-sample log-rank pour comparer la courbe de survie sans progression observée à une courbe théorique. Ce dernier schéma permet d’intégrer le maximum d’information disponible et ainsi d’inclure moins de patients pour tester les mêmes hypothèses. Ce plan d’expérience nécessite cependant un suivi de tous les patients de leur inclusion jusqu’à la fin de l’essai. Cette hypothèse semble peu réaliste ; en conséquence, nous avons proposé une nouvelle stratégie basée sur une modification du schéma de Kwak pour un suivi réduit. (...) / Phase II clinical trials are a key stage in drug development. This screening stage goal is to identify the active drugs which deserve further confirmatory studies in phase III trials from the inactive ones whose development will be stopped. The choice of the endpoint and the decision rules are major elements in phase II trials. In oncology, the endpoint is usually binary (response to treatment). For few years, phase II trials have considered time-to-event data as primary endpoint e.g. progression-free survival. In this work, we studied two-stage designs with futility stop with binary or time-to-event endpoints. In real life, phase II trials deviate from the protocol when patient evaluation is no longer feasible (because of intercurrent event) or when the follow-up is too short. The goal of this work is to evaluate the consequences of these deviations on the planned design and to propose some alternative ways to analyze or plan the trials. This work has two main parts. The first one deals with binary endpoints when some unevaluable patients occur and the second one studies time-to-event endpoints with a reduced follow-up. With binary endpoints, the Simon’s plan is the most often used design in oncology. In Simon’s plans, response at a clinical time point should be available for all the included patients. Therefore, should be analyzed the trial when some patients are unevaluable? What are the consequences of these unevaluable patients on the operating characteristics of the design? Several strategies have been studied (exclusion, replacement, use unevaluable patients as treatment failures), our simulations show that none of these strategies preserve type I and type II error rates planned in the protocol. So, a « rescue » strategy has been proposed by computing the stopping boundaries from the conditional probability of responding for an evaluable patient under null and alternative hypotheses. Although there is less information than required by the protocol, simulations show that the “rescue” strategy minimizes the deviations of type I and type II error rates. For the last decades, several time-to-event designs have been developed. The naive approach established a Simon’s plan with progression-free survival rates estimated by crude rates at clinical time-point. In 2005, Case and Morgan proposed a design comparing progression-free survival rates calculated by Nelson and Aalen estimates. Failure times were incorporated in the estimates but the comparison was defined at a pre-specified time point. Considering the whole follow-up, Kwak et al. proposed to use one-sample log-rank test to compare an observed survival curve to a theoretical survival curve. The maximum amount of information is integrated into this design. Therefore, the design reduces the sample size. In the Kwak’s design, patients must be followed from their inclusions to the end of the trial (no loss to follow-up). In some cases (good prognosis, long duration trial), this hypothesis seems unrealistic and a modification of the Kwak’s design integrating reduced follow-up has been proposed. This restriction has been compared to the original design of Kwak in several censoring scenario. The two new methods have been illustrated using some phase II clinical trials planned in the Institut Curie. They demonstrate the interest of these methods in real life.