Optimal, minimax, and Bayesian two-stage designs in two-dose phase II clinical trials

Background: For most cytotoxic and biologic anti-cancer agents, the response rate of the drug is commonly assumed to be non-decreasing with an increasing dose. However, an increasing dose does not always result in an appreciable increase in the response rate. This may especially be true at high doses for a biologic agent. Therefore, in a phase II trial the investigators may be interested in testing the anti-tumor activity of a drug at more than one (often two) doses, instead of only at the maximum tolerated dose (MTD). This way, when the lower dose appears equally effective, this dose can be recommended for further confirmatory testing in a phase III trial under potential long-term toxicity and cost considerations. A common approach to designing such a phase II trial has been to use an independent (e.g., Simon's two-stage) design at each dose ignoring the prior knowledge about the ordering of the response probabilities at the different doses. However, failure to account for this ordering constraint in estimating the response probabilities may result in an inefficient design. In this dissertation, we developed extensions of Simon's optimal and minimax two-stage designs, including both frequentist and Bayesian methods, for two doses that assume ordered response rates between doses. ^ Methods: Optimal and minimax two-stage designs are proposed for phase II clinical trials in settings where the true response rates at two dose levels are ordered. We borrow strength between doses using isotonic regression and control the joint and/or marginal error probabilities. Bayesian two-stage designs are also proposed under a stochastic ordering constraint. ^ Results: Compared to Simon's designs, when controlling the power and type I error at the same levels, the proposed frequentist and Bayesian designs reduce the maximum and expected sample sizes. Most of the proposed designs also increase the probability of early termination when the true response rates are poor. ^ Conclusion: Proposed frequentist and Bayesian designs are superior to Simon's designs in terms of operating characteristics (expected sample size and probability of early termination, when the response rates are poor) Thus, the proposed designs lead to more cost-efficient and ethical trials, and may consequently improve and expedite the drug discovery process. The proposed designs may be extended to designs of multiple group trials and drug combination trials.^

Clinical trials, informed consent, & emergency medicine: A systematic literature review

Background: In the United States, the Food and Drug Administration (FDA) regulates clinical trials. These regulations address good clinical practices as well as human subject protection (FDA, 2012). One of the most important legal and ethical concerns in clinical trials is informed consent. 21 CFR 50 governs human subjects research. Part 50.24 provides an emergency research exception to the informed consent requirement. Research was conducted to determine the appropriateness of this exception, whether the benefit justifies the exception, and its public health significance.^ Methods: A systematic literature review was conducted and articles were identified from peer-reviewed journals.^ Results: There is some variance in opinions regarding the appropriateness of the exception, but the literature reviewed found the study results of these trials justified the waiver.^ Conclusion: The exception to the informed consent requirement is likely appropriate and justified in emergency research when implemented within the specified guidelines.^

Organizational perspectives on the management and evaluation of large -scale multicenter controlled clinical trials: An exploratory study of participation in the beta -blocker heart attack trial

A review of literature related to appointment-keeping served as the basis for the development of an organizational paradigm for the study of appointment-keeping in the Beta-blocker Heart Attack Trial (BHAT). Features of the organizational environment, demographic characteristics of BHAT enrollees, organizational structure and processes and previous organizational performance variables were measured so as to provide exploratory information relating to the appointment-keeping behavior of 3,837 participants enrolled at thirty-two Clinical Centers. Results suggest that the social context of individual behavior is an important consideration for the understanding of patient compliance. In particular, the degree to which previous organizational performance--as measured by obtaining recruitment goals--and the ability to utilize resources had particularly strong bivariate associations with appointment-keeping. Implications for future theory development, research and practical implications were provided as was a suggestion for the development of multidisciplinary research efforts conducted within the context of Centers for the study and application of adherence behaviors. ^

A simulation study of the effects of small center enrollment in multi -center clinical trials

Multi-center clinical trials are very common in the development of new drugs and devices. One concern in such trials, is the effect of individual investigational sites enrolling small numbers of patients on the overall result. Can the presence of small centers cause an ineffective treatment to appear effective when treatment-by-center interaction is not statistically significant?^ In this research, simulations are used to study the effect that centers enrolling few patients may have on the analysis of clinical trial data. A multi-center clinical trial with 20 sites is simulated to investigate the effect of a new treatment in comparison to a placebo treatment. Twelve of these 20 investigational sites are considered small, each enrolling less than four patients per treatment group. Three clinical trials are simulated with sample sizes of 100, 170 and 300. The simulated data is generated with various characteristics, one in which treatment should be considered effective and another where treatment is not effective. Qualitative interactions are also produced within the small sites to further investigate the effect of small centers under various conditions.^ Standard analysis of variance methods and the "sometimes-pool" testing procedure are applied to the simulated data. One model investigates treatment and center effect and treatment-by-center interaction. Another model investigates treatment effect alone. These analyses are used to determine the power to detect treatment-by-center interactions, and the probability of type I error.^ We find it is difficult to detect treatment-by-center interactions when only a few investigational sites enrolling a limited number of patients participate in the interaction. However, we find no increased risk of type I error in these situations. In a pooled analysis, when the treatment is not effective, the probability of finding a significant treatment effect in the absence of significant treatment-by-center interaction is well within standard limits of type I error. ^

Oat Variety Trials, 2010 and 2011

Oat is the major spring-sown, small grain crop in Iowa. Spring-sown small grains can be used for grain and straw production, as a companion crop to establish hay and pastures, or as a source of early-season forage as hay or haylage. Because small grains generally mature before the end of July, a forage legume, cover crop, or green manure crop can follow oats, or animal manure can be spread on the field in which oats were grown.

On-farm Cooperator Trials 2011: Effect of Extended-duration Row Covers on Muskmelon and Winter Squash on Bacterial Wilt and Yield

Susceptible cucurbit crops are difficult to grow in Iowa because of bacterial wilt, caused by Erwinia tracheiphila. Striped and spotted cucumber beetles transmit bacterial wilt. Other insect pests such as squash vine borer and squash bugs may also have an economic impact on yield, particularly in squash.

Ship manoeuvrability: full scale trials of colombian navy riverine support patrol vessel

Methodology and results of full scale maneuvering trials for Riverine Support Patrol Vessel “RSPV”, built by COTECMAR for the Colombian Navy are presented. !is ship is equipped with a “Pump – Jet” propulsion system and the hull corresponds to a wide-hull with a high Beam – Draft ratio (B/T=9.5). Tests were based on the results of simulation of turning diameters obtained from TRIBON M3© design software, applying techniques of Design of Experiments “DOE”, to rationalize the number of runs in di"erent conditions of water depth, ship speed, and rudder angle. Results validate the excellent performance of this class of ship and show that turning diameter and other maneuvering characteristics improve with decreasing water depth.

ACGT: advancing clinico-genomic trials on cancer - four years of experience.

The challenges regarding seamless integration of distributed, heterogeneous and multilevel data arising in the context of contemporary, post-genomic clinical trials cannot be effectively addressed with current methodologies. An urgent need exists to access data in a uniform manner, to share information among different clinical and research centers, and to store data in secure repositories assuring the privacy of patients. Advancing Clinico-Genomic Trials (ACGT) was a European Commission funded Integrated Project that aimed at providing tools and methods to enhance the efficiency of clinical trials in the -omics era. The project, now completed after four years of work, involved the development of both a set of methodological approaches as well as tools and services and its testing in the context of real-world clinico-genomic scenarios. This paper describes the main experiences using the ACGT platform and its tools within one such scenario and highlights the very promising results obtained.

Identifying the semantics of eligibility criteria of clinical trials based on relevant medical ontologies

An important objective of the INTEGRATE project1 is to build tools that support the efficient execution of post-genomic multi-centric clinical trials in breast cancer, which includes the automatic assessment of the eligibility of patients for available trials. The population suited to be enrolled in a trial is described by a set of free-text eligibility criteria that are both syntactically and semantically complex. At the same time, the assessment of the eligibility of a patient for a trial requires the (machineprocessable) understanding of the semantics of the eligibility criteria in order to further evaluate if the patient data available for example in the hospital EHR satisfies these criteria. This paper presents an analysis of the semantics of the clinical trial eligibility criteria based on relevant medical ontologies in the clinical research domain: SNOMED-CT, LOINC, MedDRA. We detect subsets of these widely-adopted ontologies that characterize the semantics of the eligibility criteria of trials in various clinical domains and compare these sets. Next, we evaluate the occurrence frequency of the concepts in the concrete case of breast cancer (which is our first application domain) in order to provide meaningful priorities for the task of binding/mapping these ontology concepts to the actual patient data. We further assess the effort required to extend our approach to new domains in terms of additional semantic mappings that need to be developed.

A machine learning approach to identify clinical trials involving nanodrugs and nanodevices from ClinicalTrials.gov

BACKGROUND: Clinical Trials (CTs) are essential for bridging the gap between experimental research on new drugs and their clinical application. Just like CTs for traditional drugs and biologics have helped accelerate the translation of biomedical findings into medical practice, CTs for nanodrugs and nanodevices could advance novel nanomaterials as agents for diagnosis and therapy. Although there is publicly available information about nanomedicine-related CTs, the online archiving of this information is carried out without adhering to criteria that discriminate between studies involving nanomaterials or nanotechnology-based processes (nano), and CTs that do not involve nanotechnology (non-nano). Finding out whether nanodrugs and nanodevices were involved in a study from CT summaries alone is a challenging task. At the time of writing, CTs archived in the well-known online registry ClinicalTrials.gov are not easily told apart as to whether they are nano or non-nano CTs-even when performed by domain experts, due to the lack of both a common definition for nanotechnology and of standards for reporting nanomedical experiments and results. METHODS: We propose a supervised learning approach for classifying CT summaries from ClinicalTrials.gov according to whether they fall into the nano or the non-nano categories. Our method involves several stages: i) extraction and manual annotation of CTs as nano vs. non-nano, ii) pre-processing and automatic classification, and iii) performance evaluation using several state-of-the-art classifiers under different transformations of the original dataset. RESULTS AND CONCLUSIONS: The performance of the best automated classifier closely matches that of experts (AUC over 0.95), suggesting that it is feasible to automatically detect the presence of nanotechnology products in CT summaries with a high degree of accuracy. This can significantly speed up the process of finding whether reports on ClinicalTrials.gov might be relevant to a particular nanoparticle or nanodevice, which is essential to discover any precedents for nanotoxicity events or advantages for targeted drug therapy.

Supporting patient screening to identify suitable clinical trials.

To support the efficient execution of post-genomic multi-centric clinical trials in breast cancer we propose a solution that streamlines the assessment of the eligibility of patients for available trials. The assessment of the eligibility of a patient for a trial requires evaluating whether each eligibility criterion is satisfied and is often a time consuming and manual task. The main focus in the literature has been on proposing different methods for modelling and formalizing the eligibility criteria. However the current adoption of these approaches in clinical care is limited. Less effort has been dedicated to the automatic matching of criteria to the patient data managed in clinical care. We address both aspects and propose a scalable, efficient and pragmatic patient screening solution enabling automatic evaluation of eligibility of patients for a relevant set of trials. This covers the flexible formalization of criteria and of other relevant trial metadata and the efficient management of these representations.

Are pharmacological randomised controlled clinical trials relevant to real-life asthma populations? : A protocol for an UNLOCK study from the IPCRG

Funding The IPCRG provided funding for this research project as an UNLOCK Group study for which the funding was obtained through an unrestricted grant by Novartis AG, Basel, Switzerland. Novartis has no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. This study will include data from the Optimum Patient Care Research Database and is undertaken in collaboration with Optimum Patient Care and the Respiratory Effectiveness Group.

Detection of cortical activation during averaged single trials of a cognitive task using functional magnetic resonance imaging

Functional neuroimaging studies in human subjects using positron emission tomography or functional magnetic resonance imaging (fMRI) are typically conducted by collecting data over extended time periods that contain many similar trials of a task. Here methods for acquiring fMRI data from single trials of a cognitive task are reported. In experiment one, whole brain fMRI was used to reliably detect single-trial responses in a prefrontal region within single subjects. In experiment two, higher temporal sampling of a more limited spatial field was used to measure temporal offsets between regions. Activation maps produced solely from the single-trial data were comparable to those produced from blocked runs. These findings suggest that single-trial paradigms will be able to exploit the high temporal resolution of fMRI. Such paradigms will provide experimental flexibility and time-resolved data for individual brain regions on a trial-by-trial basis.