1,720,972 research outputs found
Bayesian uncertainty-directed dose finding designs
No full textWe introduce Bayesian uncertainty-directed (BUD) designs for phase I–II dose finding trials. This class of designs assigns patients to candidate dose levels with the aim of maximizing explicit information metrics at completion of the trial, while avoiding the treatment of patients with toxic or ineffective dose levels during the trial. Explicit information metrics provide, at completion of the clinical study, accuracy measures of the final selection of optimal or nearly optimal dose levels. The BUD approach utilizes the decision theoretic framework and builds on utility functions that rank candidate dose levels. The utility of a dose combines the probabilities of toxicity events and the probability of a positive response to treatment. We discuss the application of BUD designs in two distinct settings; dose finding studies for single agents and precision medicine studies with biomarker measurements that allow dose optimization at the individual level. The approach proposed and the simulation scenarios used in the evaluation of BUD designs are motivated by a stereotactic body radiation therapy study in lung cancer at our institution
Some contributions to phase I and II clinical trials: incorporating patient characteristics and potential time trends into designs and analysis
Full text linkThis work summarizes the two years of research that I have conducted at Dana-Farber Cancer Institute(DFCI)/Harvard T.H. Chan School of Public Health, in Boston (MA, USA), where I collaborated with Lorenzo Trippa (Associate Professor at Harvard University and Dana Farber Cancer Institute) and Steffen Ventz (Assistant Professor at University of Rhode Island).
The thesis is divided in two main parts. The first part represents the main contribute of my research and on which I spent a dominant portion of my PhD period. In this part, called "Bayesian Uncertainty-Directed Dose Finding Designs", we introduce Bayesian uncertainty directed (BUD) designs for dose finding trials. This class of designs assigns patients to candidate dose levels with the aims of maximizing explicit information metrics at completion of the trial, while also avoiding the treatment of patients with toxic or ineffective dose levels during the trial. Explicit information metrics provide, at completion of the clinical trial, accuracy measures of the final selection of optimal or nearly optimal dose levels. The BUD approach utilizes the decision theoretic framework, and builds on utility functions that rank candidate dose levels. The utility of a dose combines the probabilities of toxicity events and the probability of a positive response to treatment. We discuss the application of BUD designs in three distinct settings; (i) dose finding studies for single agents, (ii) dose optimization for combination therapies of multiple agents, and (iii) precision medicine studies with biomarker measurements that allow dose optimization at the individual level.
The proposed approach and the simulation scenarios used in evaluation of BUD designs are motivated by a Stereotactic Body Radiation Therapy (SBRT) study in lung cancer at Dana Farber Cancer Institute.
The second part of the thesis, called "Inference in Adaptive Trials under Time Trends in the Patient Population", is a smaller project that we started only a few months ago, and thus many questions about the topic have not been investigated yet. The project addresses the problem of changes in the patient population over time during a clinical trial.
Standard analysis methods in clinical trials implicitly assume that the patient characteristics do not change over time, and the treatment effect remains constant during the study period. Since trials run for many years, this hypothesis may not hold and time trends in the patient population can constitute a potential source of bias in both estimation and testing of
the treatment effects. This is especially important for trials using adaptive randomization, where the randomization probabilities change as a function of the outcome observed during the trial.
Consider a randomized two-arm trial of total sample size N with a binary endpoint. The response probability for the first N/2 patients is 0.2 for the control arm and 0.5 for the experimental arm. Due to changes in patient population, the response probabilities changes to 0.4 and 0.7 for the remaining patients in the two arms respectively. With balanced randomization (BR), where patients are allocated to the arms with equal probabilities, the expectation of the estimated overall response probabilities are 0.3 and 0.6 for the two arms, and the difference is 0.3, which is constant before and
after the change. However, if response adaptive randomization is employed and the randomization probability changes to 2:1 for experimental vs control for the last N/2 patients, the expectation of the estimated overall response probabilities are now (0.2N/4 + 0.4N/6)/(N/4 + N/6) = 0.28 and (0.5N/4 + 0.7N/3)/(N/4 + N/3) = 0.61 for the control and experimental arms with a difference of 0.33, which is inflated by 10%.
In this work, we propose a procedure which reduces the bias of treatment effect estimates and preserves the frequentist operating characteristics. We account for time trends by using Generalized Additive Models (GAMs) to estimate the treatment effect. We then use a parametric bootstrap to obtain valid inferences for treatment effects. The testing procedure can be implemented for any adaptive design and any estimator of the treatment effect.
We apply our procedure to some well-known Response Adaptive Randomization (RAR) designs to evaluate the performance of the proposed method. For each design, we assess the estimation and testing capabilities of the method by simulating different time trends in both standard multi-arm clinical trials and platform trials
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Toxicity Adaptive Lists Design: A Practical Design for Phase I Drug Combination Trials in Oncology
No full textPURPOSE We introduce a novel algorithmic approach to design phase I trials for oncology drug combinations. METHODS Our proposed Toxicity Adaptive Lists Design (TALE) is straightforward to implement, requiring the prespecification of a small number of parameters that define rules governing dose escalation, de-escalation, or reassessment of previously explored dose levels. These rules effectively regulate dose exploration and control the number of toxicities. A key feature of TALE is the possibility of simultaneous assignment of multiple-dose combinations that are deemed safe by previously accrued data. RESULTS A numerical study shows that TALE shares comparable operative characteristics, in terms of identification of the maximum tolerated dose (MTD), to alternative approaches such as the Bayesian optimal interval design, the COPULA, the product of independent beta probabilities escalation, and the continual reassessment method for partial ordering designs while reducing the risk of overdosing patients. CONCLUSION The proposed TALE design provides a favorable balance between maintaining patient safety and accurately identifying the MTD. To facilitate the use of TALE, we provide a user-friendly R Shiny application and an R package for computing relevant operating characteristics, such as the risk of assigning highly toxic dose combinations
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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