bluuebunny/crossref_metadata_2025_split huggingface dataset:
Preference Trials
Authors: Campbell Marion K. , Torgerson David J. 
Published: September 2014

AbstractAlthough the randomized controlled trial is widely accepted as the design of choice for evaluating health‐care interventions, it requires that all participants are prepared to accept allocation to any of the interventions under study. When patients have a strong preference to receive one of the study interventions, this can lead to difficulties with compliance and potentially to biased assessment of outcomes. The patient preference trial has been proposed as a potential solution, with patients with strong preferences being allocated to their treatment of choice and those with no strong preference being randomized in the standard manner. All patients are followed up and outcomes assessed. We discuss the effects of patient preference and review the advantages and disadvantages of the patient preference design.
Optimal Biological Dose for Molecularly‐Targeted Therapies
Authors: Ahn Chul , Kang Seung‐Ho , Xie Yang 
Published: September 2014

Abstract
Phase I cancer clinical trials are small dose‐finding studies designed to rapidly identify the optimal dose of a new anticancer agent. The optimal dose has been defined as the maximum tolerated dose (MTD) for conventional cytotoxic anticancer drugs, and this dose is typically recommended as the phase II dose. In the past decade, a new approach for cancer treatment has emerged. This new approach, now widely referred to as “molecularly targeted therapy” is designed a priori, based on the knowledge of various physiological molecules that have been obtained by the development of molecular biology. Most molecularly targeted agents slow or stop the growth of tumors. Such agents are, in general, less toxic than conventional cytotoxic agents; as a result, the maximum therapeutic effect may occur at doses well below the MTD. With the emergence of a growing number of molecularly targeted agents, approaches to early drug development are changing. We present dose‐finding designs for phase I clinical trials of molecularly targeted drugs. There is an increasing need to develop designs to find the optimal biological dose for molecularly targeted agents, and the utility of designs needs to be prospectively evaluated in future clinical dose‐finding trials for a variety of molecularly targeted agents.
Optimizing Schedule of Administration in PhaseIClinical Trials
Authors: Braun Thomas M. , Thall Peter F. 
Published: September 2014

AbstractHistorically, phase I clinical trials of new agents have been designed to find a safe dose from a small set of possible doses. Typically, toxicity is defined based on one or possibly two cycles of chemotherapy so that it can be scored relatively quickly after the start of therapy. The study treats cohorts of patients according to an outcome‐adaptive statistical design or algorithm for choosing successive doses, and the resulting data are used to determine an acceptable or maximum tolerable dose (MTD). However, in most clinical settings, physicians administer multiple courses of an agent to a patient and monitor long‐term toxicity that may be related to the agent's cumulative effects. Furthermore, some patients may receive one or more modifications or delays of their planned treatment course due to low‐grade (non–dose limiting) toxicity from previous administrations. To address these situations, we have proposed a new paradigm for phase I clinical trials that determines a maximum tolerable schedule (MTS) of administration, rather than a conventional MTD (Braun, Yuan, and Thall, 14). The design is based on the time to toxicity of each patient rather than a binary indicator. The model and method account for the patient's entire sequence of administrations, with the overall hazard of toxicity modeled as the sum of a sequence of dose‐dependent hazards, each associated with one administration. As patients are enrolled, data monitoring and decision making are done continuously throughout the trial using an outcome‐adaptive Bayesian design that optimizes the schedule in terms of the overall risk of toxicity. We illustrate this new methodology with a clinical trial in allogeneic bone marrow transplantation.
Population Pharmacokinetic and Pharmacodynamic Methods
Authors: Bulitta Jürgen B , G Holford Nicholas H 
Published: September 2014

Abstract
Pharmacokinetic and pharmacodynamic models are widely used to provide the quantitative basis for understanding the clinical pharmacology of pharmaceuticals. These models are used to describe both the average behavior in the population and differences between individuals. Pharmacokinetic models describe the time course of absorption, distribution, and elimination of a drug and any metabolites. Pharmacodynamic models describe the relationship between drug concentration and biological response. The pharmacokinetic‐pharmacodynamic model often has to account for a delay between the time course of concentrations and the time course of pharmacodynamic effect. Statistical models can describe the predictable and the unpredictable variability between and within subject variability in response. Predictable variability is most commonly understood for drug elimination processes and typically uses subject factors such as weight, renal function, and other medications to account for between subject differences. Unpredictable variability is usually larger than predictable variability. A wide variety of software has been developed for implementing these pharmacological and statistical models; of these, NONMEM (GloboMax LLC, Hanover, MD) is the most widely used. Newer algorithms are actively being developed. Model building and parameter estimation are often computationally intensive. Population models are used by drug developers to create a rationale framework for development, by regulatory health authorities to evaluate clinical trials and enhance product labels and by clinical researchers to understand human biology and disease progression.
Robust Two‐Stage Model‐Guided Designs for Phase
I
Clinical Studies
Authors: Potter Douglas M. 
Published: September 2014

Abstract
The standard rule‐based cohorts‐of‐three phase I design, originally proposed for dose‐finding studies of chemotherapeutic agents in cancer patients, has well‐known limitations; these include treating a large fraction of the patients accrued at low, presumably ineffective doses. Model‐guided designs, such as the continual reassessment method, attempt to address some of these limitations. Such schemes can work well if the initially specified range of doses to be investigated brackets the true maximum tolerated dose (MTD); however, if this condition is not met, and additional doses must be added as the study progresses, these schemes can find an MTD in the absence of the observation of any dose‐limiting toxicity (DLT). In first‐in‐human studies, the latter situation is a particular concern. Robust two‐stage model‐guided designs use a rule‐based first stage that ends upon observation of the first DLT, and a second stage that uses a flexible model that is initialized with the initial DLT data. These designs have the robustness of rule‐based designs and the advantages of the model‐guided designs. Three such designs are reviewed and compared.
Maximum Tolerable Dose (
MTD
)
Authors: Chevret Sylvie 
Published: September 2014

Abstract
The “Maximum Tolerable Dose” (MTD), also known as the “Maximum Tolerated Dose” or the “Maximally Tolerated Dose”, is defined as the dose that produces an “acceptable” level of toxicity or that, if exceeded, would put animals or patients at “unacceptable” risk for toxicity. Besides determining animal toxicology, establishing the MTD is the main objective of Phase I clinical trials, mostly in cancer and HIV treatment in which relatively high doses of drugs are usually chosen to achieve the greatest possible beneficial antitumor effect. Definition of the MTD usually relies on the sample, as MTD is defined as the dose level at which more than two patients over six experienced dose‐limiting toxicity (DLT). More recently, the MTD has been defined as the dose that produces a certain frequency of DLT within the treated patient population. In this framework, the MTD is estimated from the data using Bayes or maximum likelihood inference. In all these designs, the MTD is established for one initial administration or treatment course of a cytotoxic experimental agent, ignoring efficacy. To address these issues, the maximum tolerated schedule and the most successful dose have been proposed to be used rather than a conventional MTD. Finally, the concept of MTD that uses toxicity as a surrogate endpoint for efficacy in cytotoxic Phase I trials has been also controversial. Interests in alternatives to MTD have gained recently when dealing with new cytostatic agents that may produce relatively minimal organ toxicity, compared with standard cytotoxics. New optimal doses should be defined in the near future.
Stability Study Designs
Authors: Lin Tsae‐Yun Daphne , Chen Chi Wan 
Published: September 2014

AbstractA good stability study design is the key to a successful stability program. In this article, stability designs including full, bracketing, and matrixing designs will be exemplified. A full design involves complete testing of all factor combinations at all time points, which can be costly. The pharmaceutical industry would like to apply bracketing or matrixing design so that not every factor combination will be tested at every time point. The statistical aspects of these stability designs will be discussed in relation to the recent Internation Conference on Harmonization (ICH) guidelines. Several commonly used criteria for design comparison will be presented. The statistical and regulatory considerations on the selection of stability design will also be discussed. The stability study should be well designed so the shelf life of the drug product can be estimated with the high degree of accuracy and precision.
Sample Size Calculation for Comparing Proportions
Authors: Wang Hansheng , Chow Shein‐Chung 
Published: September 2014

Abstract
In practice, the objective of sample size calculation is to select the minimum sample size in such a way that the desired power can be obtained for detection of a clinically meaningful difference at the prespecified significance level. Therefore, the selection of the sample size depends on the magnitude of the clinically meaningful difference, the desired power, the prespecified significance level, and the hypotheses of interest under the study of choice (e.g., a parallel design or a crossover design).
Response Adaptive Randomization
Authors: Coad D.Stephen 
Published: September 2014

AbstractWhen two or more treatments are being compared in a clinical trial, information accrues on the treatments as the trial proceeds. However, there may be evidence suggesting that some treatments are more promising than others, and consequently, there may be interest in applying response adaptive randomization, which uses the current data to assign more patients to the treatments that are performing better thus far in the trial. In this way, patients have a higher probability of being assigned to these treatments. Examples of this type of randomization are given, such as urn models and sequential maximum likelihood estimation rules, and some of their properties are described. The issue of inference after trials that use response adaptive randomization is then discussed. For example, this approach can lead to a loss in power when using standard tests and the problem of estimation can be considerably more complicated than in a trial that uses complete randomization. The use of stopping rules is also addressed, since, in practice, there may be interest in stopping a trial early if there is convincing evidence of a treatment difference or less promising treatments may be dropped from additional consideration. An indication is given of possible future work.
Minimum Effective Dose (MinED)
Authors: Thomas Neal , Ting Naitee 
Published: September 2014

AbstractThe minimum effective dose (MinED or MED) defines a lower bound for therapeutically useful doses. The MinED is important because most drugs have increasing potential for safety problems as dose is increased. The MinED is typically studied during phase II of drug development. A concise definition of the MinED from the International Conference on Harmonization (ICH) guidelines is “the smallest dose with a discernible useful effect.” We discuss methods to give operational meaning to the descriptors “useful” and “discernible.”
Dose Ranging Crossover Designs
Authors: Patterson Scott D. , Jones Byron , Zariffa Névine 
Published: September 2014

Abstract
Dose ranging trials are used frequently in drug development to learn about dose response for both desirable and undesirable effects and to provide sponsors with confidence that investment in confirmatory trials for market access is likely to be successful. The design of crossover studies and analysis of data that develops from such crossover trials are discussed.
Allocation Concealment
Authors: Schulz Kenneth F. 
Published: September 2014

AbstractAllocation concealment, as an integral part of randomization, implements an allocation sequence. Inadequate allocation concealment can lead to exaggerated estimates of treatment effect, but with scope for bias in both directions. Without adequate allocation concealment, even proper random assignment sequences can be subverted. Investigators must recognize the inquisitiveness and ingeniousness of human nature and “immunize” trials against selection and confounding biases with proper allocation concealment.
Assessment Bias in Clinical Trials
Authors: Gøtzsche Peter C. 
Published: September 2014

Abstract
Assessment bias in a clinical trial occurs if bias in the assessment of the outcome exists. It is also called ascertainment bias, diagnostic bias, or detection bias. A major cause of assessment bias is lack of blinding. Other problems relate to differential identification of harmless or false‐positive cases of disease, bias in assessment of disease‐specific mortality, the use of composite outcomes, competing risks, timing of the assessments, and bias in assessment of harms.
Non‐Inferiority Trial
Authors: James Hung H. M. , Wang Sue‐Jane , O'Neill Robert 
Published: September 2014

Abstract
Noninferiority trials employ a selected active‐control treatment, the effect of which has been established in historical placebo‐controlled trials. The active control serves as a comparator for evaluating the effect of an experimental treatment. A placebo arm may be either present in or absent from the noninferiority trial, depending on whether use of the placebo arm is feasible. In contrast with showing superiority, it is much more challenging to design a “show noninferiority” trial. The notion of noninferiority is extremely difficult to define, and a threshold termed “noninferiority margin” must be determined to define the noninferiority hypothesis for testing. The margin determination depends on many key factors: the objective of the noninferiority trial, critical assumptions pertaining to the ability of a trial to distinguish between an effective and an ineffective treatments, and the applicability of the historical estimate of the active control's effect to the noninferiority trial when the placebo cannot be used, and so forth. This article is intended to pinpoint the factors critically needing consideration for the viability of using a noninferiority trial to evaluate the effect of an experimental treatment.
Masking
Authors: Joseph Donahue Rafe Michael 
Published: September 2014

AbstractBlinding, along with allocation concealment and randomization, is a primary procedure used to control biases in clinical trials. Blinding may refer to both (1) the procedures that are used to keep persons associated with the trial (participants, investigators, and such) ignorant of the treatments to which the individual participants have been assigned and (2) the results of such efforts. Different individuals associated with a clinical trial bring different potential biases to the trial if they are not blinded. Blinding and allocation concealment both help to reduce biases in clinical trials, but allocation concealment seems even more important than blinding. Blinding may take place at varying levels from no one blinded (“open‐label”), to single‐blind, to double‐ and triple‐blind. The International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use guidelines and the Consolidated Standards of Reporting Clinical Trials guidelines encourage the reporting of details of blinding procedures used in clinical trials and the results they attained, including information about who may have been unblinded during the trials and what biases that may have generated therefrom.
Multinational (Global) Trial
Authors: Snapinn Steven M. 
Published: September 2014

Abstract
Multinational clinical trials are trials conducted in more than one country using a common protocol. They have become increasingly common and have many advantages, such as access to a large patient population and the potential to obtain a broadly generalizable conclusion. However, these trials also have a number of practical difficulties, such as differences among the countries in language, import/export procedures, and regulatory and legal requirements. In addition, country‐by‐treatment interactions have frequently been found in these trials, leading to difficulties in interpreting the results. For example, the MERIT‐HF trial, which evaluated metoprolol in patients with heart failure, found an overall hazard ratio for total mortality of 0.66, but a hazard ratio of 1.05 in the United States. There is particular concern that differences among countries hinder the ability to evaluate the cost effectiveness of an experimental treatment.
Assay Sensitivity
Authors: Chuang‐Stein Christy 
Published: September 2014

Abstract
The assay sensitivity of a clinical trial is defined as the trial's ability to distinguish an effective treatment from a less effective or an ineffective one. It is an important consideration when designing, conducting, and analyzing a clinical trial. The lack of adequate assay sensitivity has different implications for superiority trials and noninferiority trials. This article discusses factors that could impact assay sensitivity. It also discusses the increasing use of a three‐arm design to help evaluate assay sensitivity of a trial.
Superiority Trial
Authors: Sackett David L. 
Published: September 2014

AbstractWhen busy clinicians encounter a new treatment, they ask themselves two questions. First, is itbetterthan (“superior to”) what they are using now? Second, if is not superior, is itas good aswhat they are using now (“noninferior”) and preferable for some other reason (e.g., fewer side effects or more affordable)? In statistical terms, these clinical questions are “one‐sided.” However, traditional statistical concepts call for two‐sided tests of statistical significance and require rejection of the null hypothesis. A solution to both this incompatibility (between one‐sided clinical reasoning and two‐sided statistical testing) and confusion (about the clinical interpretation of statistically nonsignificant results) is now gaining widespread recognition and application. It is encompassed by the concepts of “superiority” and “noninferiority,” and these inextricable concepts are the subjects of this entry.
Primary Efficacy Endpoint
Authors: Follmann Dean A 
Published: September 2014

AbstractAt its core, a clinical trial is designed to test a specific primary hypothesis. The primary efficacy endpoint in a clinical trial is a clinical or laboratory outcome measured in an individual after randomization that allows one to test the primary hypothesis and provides the means of assessing whether a therapy is effective compared with its control. A primary endpoint can be measured once or repeatedly over time, or may comprise several kinds of individual measurements. Once a primary endpoint is selected, statistical methods of analysis to test the primary hypothesis can be determined and sample size calculations can be performed to ensure the trial is properly powered. Because of the key role of the primary endpoint in the design and analysis of a trial, it is critical that it be chosen carefully. A primary efficacy endpoint must be precisely specified in advance and should(1)address the primary objective,(2)be ascertainable in all patients,(3)be “fair” to each study arm,(4)have demonstrated or accepted relevance for the population and intervention(s) of the trial, and(5)be sensitive to meaningful changes in a patient's health.
Enrichment Design
Authors: Fedorov Valerii V. , Liu Tao 
Published: September 2014

AbstractEnrichment designs usually consist of two or three stages, where the first stage serves as a screening process for selecting a certain subpopulation, and the succeeding stages serve to distinguish the treatment effect from the placebo effect, within the selected (enriched) subpopulation. The efficiency of its efficacy (response) detection comes at the expense of the ability to make inferences for the general population; that is, the results are valid only for the selected subpopulation. At the same time, if a valid partitioning of the population is proven, then it might be viewed as evidence of “latent” classifiers, which can be targeted for a search. Statistical analysis commonly makes explicit use of the outcomes from only the selected subpopulation (final stage). Some simple models can take advantage of the outcomes from all stages. Most published studies using these designs have been conducted in the field of clinical oncology, but enrichment designs have become increasingly popular in various therapeutic areas.
Historical Control: Overview
Authors: Thomas Neal 
Published: September 2014

AbstractHistorical control refers to the practice of using data from past studies and administrative databases to estimate potential response to placebo or standard‐of‐care treatment among patients in an ongoing study. The historical control information may come from patient‐specific data or from aggregate published estimates. Studies that project response to a control regimen based on historical data rather than concurrently randomized patients are most common in settings where there are ethical barriers to the use of either placebo or an older treatment widely believed to have little benefit. Historical controlled trials (HCTs) are subject to several potentially important sources of bias. HCTs are examples from a broader class of nonrandomized comparative designs called observational studies. Numerous methods have been developed to reduce bias in observational studies, and these methods are sometimes applicable in HCTs, for example, covariate adjustment and matching [Cochran and Rubin (1)], and propensity score methods [Rosenbaum and Rubin (2)]. Bayesian statistical methods can be used to create hybrid designs that use some historical control data and some concurrently collected randomized control data. Times series are another source of historical control information commonly used in economics and marketing as well as, occasionally, in clinical research.