bluuebunny/crossref_metadata_2025 huggingface dataset:
Multivariate and Hyperspectral Image Analysis
Authors: Geladi Paul , Grahn Hans F.
Published: June 2008

Multivariate image analysis (MIA) and hyperspectral image analysis (HIA) are methodologies for analyzing multivariate images, where the image coordinates are position (two or three dimensions) and variable number. Multivariate/hyperspectral images can have typical sizes 1024 × 1024, 512 × 512, 256 × 256, etc. and have between two and many hundreds of variables. The variables can be wavelength, electron energy, particle mass, and many others. Classical image analysis concentrates mainly on spatial relationships between pixels in a gray level image. MIA/HIA concentrates on the correlation of structure between the variables to provide extra information useful for exploring images and classifying regions in them. The many variables can be transformed into a few latent variable images containing condensed information. The sheer size of the data arrays necessitates visualization of raw data, intermediate data, model parameters, and residuals. When the images consist of continuous spectra having more than 100 variables, the name hyperspectral is used and the emphasis of the analysis is often on the spectral interpretation.

All physical techniques for measuring materials can be made into imaging techniques, describing not only a property but also its position in a plane or volume. All imaging techniques can be expanded to become multivariate/hyperspectral. Multivariate imaging is used in many fields of research, but for practical reasons a subdivision in the classes remote sensing, medical imaging, and microscopy (including macroscopy) can be made. In microscopy, MIA/HIA can be used to study materials and biological processes by optical, electron, and charged particle techniques.
Agent‐Oriented Software Engineering
Authors: Perini Anna
Published: June 2008

Agent‐oriented software engineering (AOSE) is an approach to construct software systems based on the
agent
paradigm. An agent can be thought of as an
autonomous
and
social
entity, which can communicate, coordinate, and cooperate with other agents to achieve goals. The agent paradigm offers useful abstractions to design today's complex networked applications.

Several AOSE methodologies have been proposed so far, each one providing its own modeling language and analysis techniques. Efforts toward unification of AOSE methodologies and the definition of standards are in progress, in parallel with research to address challenging issues, such as designing software with autonomic properties.
Aspect‐Oriented Software Development: an Introduction
Authors: Brichau Johan , Chitchyan Ruzanna , Rashid Awais , D'Hondt Theo
Published: June 2008

Modularity of software has long been the primary mechanism used in software development to improve reuse and support maintainability. However, certain concerns do not fit the module boundaries in contemporary software‐development paradigms. The consequence of such crosscutting concerns, such as logging, persistence, security, and so forth, is that the responsibilities of the individual modules are not clearly demarcated. Not only is a module responsible for its core state and behavior (pertaining to its business functionality, for instance) but also for that of the crosscutting concern (such as logging). This phenomenon is referred to as tangling. At the same time, we observe the scattering effect whereby a given crosscutting concem,for example, logging, is not modularized in a single element and hence hampers our ability to reason about its effect and behavior with respect to the other modules; such reasoning requires inspection of all the modules where logging is realized. This requirement goes against the established best practice of separation of concerns because the crosscutting effect results in software that is difficult to understand and maintain. Addressing this problem of crosscutting is the main focus of aspect‐oriented software‐development (AOSD) techniques. In this article, we provide an introduction to AOSD by highlighting how it provides systematic support for identification, modularization, and composition of crosscutting concerns throughout the software lifecycle.
Finite Element Method
Authors: Bathe Klaus‐Jürgen
Published: June 2008

The objective of this article is to give an overview of finite element methods that currently are used extensively in academia and industry. The method is described in general terms, the basic formulation is presented, and some issues regarding effective finite element procedures are summarized. Various applications are given briefly to illustrate the current use of the method. Finally, the article concludes with key challenges for the additional development of the method.
Robot Kinematics
Authors: Zhang Dan , Bi Zhuming , Gao Zhen
Published: June 2008

In this article, kinematics of robot manipulators is presented with a systematic and general approach—Denavit‐Hartenberg convention. The basic mathematical concepts are explained in detail, including translational transformation, rotational transformation, homogeneous transformation, Euler angle, direct kinematics and inverse kinematics, and so on. The relation between joint velocities and end‐effector linear and angular velocities is described by the Jacobian matrix. As an example, a parallel manipulator with three degrees of freedom (DOFs) is presented. This 3DOF‐parallel manipulator can be used for machine tools, and it includes two rotations and one translation along the z axis. Both direct and inverse kinematic problems are conducted based on the theory described in the beginning of the article. The case study will allow readers to better understand the knowledge of robot kinematics.
Unified Modeling Language (UML)
Authors: Selic Bran
Published: June 2008

The Unified Modeling Language is a language used for analysis and design of software‐based systems. This is an entry for the Software Engineering Encyclopedia that gives an overview of UML.
Visual Programming Languages
Authors: Cox Philip T.
Published: June 2008

Diagrams have always been used as aids in the design of programs, but because of the limitations of hardware and software, could not be directly incorporated into the software development process until the advent of low‐cost, high‐quality graphics in the 1980s. This enabled research on the pictorial representation of software structures, including an extensive study of visual programming languages. A visual programming language is one that uses diagrams to represent significant portions of the logic of a program, with the aim of making programs easier and faster to build and debug. We present several representative visual programming languages, discuss theoretical and cognitive issues, and provide a brief overview of current research in the area.
Wide Area Networks
Authors: Gu Yunhong
Published: June 2008

This article briefly describes wide area networks (WAN), which can be defined roughly as the computer networks that cover a broad geographical area. The Internet is the most typical WAN. Another example would be an intranet for a large enterprise that has multiple geographically distributed branches. This article explains WANs in the organization of the five‐layer TCP/IP layered model. For each layer, typical hardware and software (protocols) for WANs are discussed, especially these popular technologies used in WANs today.
Dynamic Programming
Authors: Oka Ryuichi , Wakamatsu Aizu
Published: June 2008

Dynamic programming (DP) is a powerful method for solving optimization problems. DP belongs to the mathematical programming field, which also includes linear programming. DP provides optimal algorithms for solving problems categorized by a sequential decision process. It was developed by R. Bellman in the 1950s and has been applied to various domains, including Markov decision processes, optimal control, variation methods, and numerical analysis. Recently, its fields of application have expanded to include speech recognition, bioinformatics, information retrieval, and many areas in artificial intelligence and pattern recognition. The reason for its widespread use is the simplicity of its algorithms, which are based on the principle of optimality. To emphasize the simplicity, we focus on a baseline algorithm of DP.
Process‐Aware Information Systems: Design, Enactment, and Analysis
Authors: van der Aalst Wil M.P.
Published: June 2008

Process‐aware information systems support operational business processes by combining advances in information technology with recent insights from management science. Workflow management systems are typical examples of such systems. However, many other types of information systems are also “process aware” even if their processes are hard‐coded or only used implicitly (e.g., ERP systems). The shift from data orientation to process orientation has increased the importance of process‐aware information systems. Moreover, advanced analysis techniques ranging from simulation and verification to process mining and activity monitoring allow for systems that support process improvement in various ways. This article provides an overview of process‐aware information systems and relates these to business process management, workflow management, process analysis techniques, and process flexibility.
Geometric Camera Calibration
Authors: Kannala Juho , Heikkilä Janne , Brandt Sami S.
Published: June 2008

Geometric camera calibration is a prerequisite for making accurate geometric measurements from image data, and hence it is a fundamental task in computer vision. This article gives a discussion about the camera models and calibration methods used in the field. The emphasis is on conventional calibration methods in which the parameters of the camera model are determined by using images of a calibration object whose geometric properties are known. The presented techniques are illustrated with real calibration examples in which several different kinds of cameras are calibrated using a planar calibration object.
Thrashing
Authors: Denning Peter J.
Published: June 2008

Thrashing is an unstable collapse of throughput of a system as the load is increased.
Isolation of Human Placental Fibroblasts
Authors: Ilic Dusko , Kapidzic Mirhan , Genbacev Olga
Published: June 2008

The first human embryonic stem cell lines (hESCs) were derived using mouse embryonic fibroblasts as feeder cells. In attempts to replace mouse embryonic fibroblasts with feeders of human origin, irradiated human placental fibroblasts were successfully used as feeder cells for the derivation and propagation of hESCs. Here we describe a protocol for the isolation and expansion of fibroblasts from placental villous stroma. We include a description of placental architecture to provide the background for a stepwise tissue digestion that leads to the isolation of villous stroma. Villous stroma from the first trimester tissue is different from term placenta and contains mesenchymal, fibroblast‐like cells, only a few blood vessels, and a network of matrix fibers. The fibroblasts isolated from a single placenta of 6‐ to 8‐weeks gestation proliferate rapidly and retain the ability to support hESC growth between passage doubling (PD) 8 and PD 12. Curr. Protoc. Stem Cell Biol. 5:1C.6.1‐1C.6.17. © 2008 by John Wiley & Sons, Inc.
Derivation of Human Skin Fibroblast Lines for Feeder Cells of Human Embryonic Stem Cells
Authors: Unger Christian , Felldin Ulrika , Nordenskjöld Agneta , Dilber M. Sirac , Hovatta Outi
Published: June 2008

After the first derivations of human embryonic stem cell (hESC) lines on fetal mouse feeder cell layers, the idea of using human cells instead of mouse cells as feeder cells soon arouse. Mouse cells bear a risk of microbial contamination, and nonhuman immunogenic proteins are absorbed from the feeders to hESCs. Human skin fibroblasts can be effectively used as feeder cells for hESCs. The same primary cell line, which can be safely used for up to 15 passages after stock preparations, can be expanded and used for large numbers of hESC derivations and cultures. These cells are relatively easy to handle and maintain. No animal facilities or animal work is needed. Here, we describe the derivation, culture, and cryopreservation procedures of research grade human skin fibroblast lines. We also describe how to make feeder layers for hESC using these fibroblasts. Curr. Protoc. Stem Cell Biol. 5:1C.7.1‐1C.7.10. © 2008 by John Wiley & Sons, Inc.
Block Randomization
Authors: McEntegart Damian
Published: June 2008

Simple unconstrained randomization is the yardstick for randomization techniques in clinical trials as it is completely unpredictable and provides an unambiguous basis for inference from statistical analysis. But simple randomization is rarely used because of the need to protect the trial from imbalance in the overall numbers allocated to treatment and from temporal bias in the allocations. For these reasons the randomization procedure is often restricted by some form of blocking, the most common being random permuted blocks. The methodology, issues and practical considerations relating to the use of blocked randomization are described. Particular attention is paid to the issue of potential selection bias, which may arise from the predictability of tail allocations towards the end of any block of treatment allocations if the past allocations in the block are known or can be guessed. The merits of using unequally sized blocks to avoid selection bias are discussed in detail. Inference following blocked randomization is also discussed. Alternative forms of blocking are described; these include lists generated from the maximal procedure, the use of rows from Hadamard matrices, Zelen's balanced blocks procedure and mixed randomization. Practical considerations relating to the use of blocked randomization are described including software used to generate random blocks. Also covered are considerations relating to the use of electronic systems to automatically perform allocation from a pre‐defined randomization list; the use of such systems is becoming increasingly common with the advent of telephone interactive voice response (IVR) and web based randomization systems.
Multiple Evaluators
Authors: Liao Jason J. Z. , Capen Robert C.
Published: June 2008

In medical and other related sciences, the clinical or experimental measurements obtained from multiple evaluators usually serve as a basis for diagnostic, prognostic, therapeutic, and performance evaluations. Thus, it is important to assess interevaluator agreement. The measurement scale can be continuous, binary, nominal, or ordinal. In this article, we review some commonly used approaches for both continuous and categorical outcomes.
Masking
Authors: Donahue Rafe Michael Joseph
Published: June 2008

Blinding, 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.
Assay Sensitivity
Authors: Chuang‐Stein Christy
Published: June 2008

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.
Coherence in Phase
I
Clinical Trials
Authors: Cheung Ying Kuen (Ken)
Published: June 2008

This article reviews the coherence principles in phase I dose‐finding studies, in which several doses of a new treatment are tested for safety and the maximum dose exhibiting acceptable toxicity level is identified. Because most phase I designs are outcome‐adaptive, dose assignments for future patients depend on the previous observations in the trial. The coherence principles are ethical stipulations to ensure that dose escalation for a new patient will occur only when the most recent patient does not show sign of toxicity, and de‐escalation will occur only when a toxic outcome has just been reported. In this article, we give examples of coherent designs in the literature, and we discuss related concepts such as group coherence and real‐time coherence in more general clinical situations. We also review the application of the coherence concepts to the calibration of a two‐stage design.
Accelerated Approval
Authors: Cabanilla Louis A. , Milne Christopher P.
Published: June 2008

In 1992, the Food and Drug Administration (FDA) promulgated regulations for the Accelerated Approval program, which allowed drugs to gain approval based on unvalidated surrogate endpoints, or an effect on a clinical endpoint other than survival, or approval with restricted distribution. Initially, the program was primarily targeted at AIDS and cancer treatments, but it is now available for a wide array of diseases, and it has grown beyond small molecules to encompass biologic products, vaccines, and other product areas. Because of the severity of the illnesses being addressed and the potential for a “meaningful clinical benefit,” the FDA is generally willing to accept a higher risk to benefit ratio for an accelerated approval to provide earlier treatment for patients despite the lack of definitive evidence of the ultimate clinical benefit. Although a drug that is given accelerated approval might reach the market, the company sponsoring the drug is required to complete the necessary research to determine efficacy with a “Phase IV” study with “due diligence.” A problem with the definition of diligence, as well as enforcement mechanisms, has lead to some contention about the level of compliance by the drug industry. Despite these issues, the program alone, or in concert with other FDA approval programs, has to date brought some 50 lifesaving drugs to market months or years faster than would have happened through the traditional process. This information, together with advances in the science and technology of the clinical evaluation of biopharmaceuticals and the more powerful voice of patient advocacy in determining regulatory agency policy, ought to ensure that accelerated approval will expand its reach both therapeutically and geographically.
Regression to the Mean
Authors: Davis Clarence E.
Published: June 2008

When a person is selected for having an extreme measurement (e.g., high blood pressure), a second measure of the variable will, on average, be closer to the mean of the population distribution. This function is called regression to the mean. It is used as a justification for a randomized control group in clinical trials. It may also affect recruitment in clinical trials in which potential participants are selected because they hold an extreme value on some variable.
Historical Control
Authors: Thomas Neal
Published: June 2008

Historical 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.
Gene Therapy
Authors: Ginn Samantha L , Alexander Ian E
Published: June 2008

Conducting a clinical trial poses a unique set of challenges that must be addressed to ensure the safety of human subjects. In this article, we discuss issues that relate in particular to gene therapy clinical trials. These issues include the choice of gene delivery system, the need for extensive preclinical testing to ensure the feasibility and safety of the approach, and careful monitoring of subjects for short‐ and long‐term toxicity associated with the gene transfer protocol.
Subgroup Analysis
Authors: Simon Richard M.
Published: June 2008

Subgroup analysis refers to the practice of attempting to determine whether and how treatment effects vary among subgroups of subjects who are studied in intervention studies. This article reviews the variety of statistical methods that have been used and developed for subgroup analyses. These methods include testing null hypotheses of no treatment effects within subgroups,P‐value adjustments for multiplicity, confidence intervals for subgroup‐specific treatment effects, interaction tests, qualitative interaction tests, and empirical Bayes and fully Bayesian methods. The article addresses the distinction between preplanned subgroup analyses in which experiment‐wise type I error rate is controlled and an unplanned data‐dredging exercise for the purpose of generating hypotheses to be tested in subsequent studies. The article emphasizes the importance of restricting inferential methods to a very limited number of prespecified subgroups based on strong biological rationale or reports from other clinical trials. The article discusses the central nature of a limited number of planned highly focused subgroup analyses for pharmacogenomic clinical trials. The article also addresses the limited reliably evaluating consistency of treatment effects among subgroups in positive clinical trials, including center effects in multi‐center trials, and the article discusses limitations imposed by sample size for subgroup analyses and the role of meta‐analysis in subgroup analyses.
Dose Ranging Crossover Designs
Authors: Patterson Scott D. , Jones Byron , Zariffa Névine
Published: June 2008

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.
Confirmatory Trials
Authors: Donahue Rafe Michael Joseph
Published: June 2008

Confirmatory clinical trials are defined by the ICH as adequately controlled trials in which the hypotheses are stated in advance and evaluated. They can be used to demonstrate or confirm efficacy, establish a safety profile, investigate benefit/risk, or establish dose‐response. They always have (at least) one prespecified primary objective. Confirmatory trials are not exploratory trials.
Multiple Comparisons
Authors: Proschan Michael A.
Published: June 2008

Because clinical trials are very expensive, investigators often want to make several comparisons in the same trial. But, performing several statistical tests at significance level α will increase the probability of at least one false positive declaration. The situation is analogous to throwing many darts at a target; even a completely unskilled dart thrower will eventually hit the bull's eye. Many multiple comparison adjustment methods control the probability of at least one false positive by “raising the bar” for each separate comparison. However, raising the bar increases the chance of missing a true effect and increases the sample size needed to maintain the same power. The tension between controlling the chance of at least one false positive and missing a true effect makes multiplicity adjustment a subject of great controversy in clinical trials. Although clinical trialists have differing opinions about how much adjustment is needed, certain settings exist most would agree that adjustment is needed and others in which most would agree that it is not. This article attempts to shed light on situations in which multiplicity adjustments are needed and the most common methods of adjustment in different clinical trial scenarios, which include multiple arms, multiple endpoints, subgroup analysis, and interim monitoring.
Sample Size Considerations for Morbidity/Mortality Trials
Authors: Shih Joanna H.
Published: June 2008

Sample size and power determination plays a crucial role in the design of morbidity/mortality clinical trials. To ensure that a trial is adequately sized so that the true treatment effect can be detected with a desired power, it is important to evaluate the assumptions used in the sample‐size calculation procedure. Various close‐form sample‐size formulas have been proposed for the analysis of morbidity/mortality trials with survival endpoints. However, most of them are formed based on restrictive assumptions and do not take into consideration factors that may affect the estimate of the treatment effect. These factors include drop‐in, drop‐out, staggered entry, loss to follow‐up, lags in treatment effects, and informative noncompliance. This article reviews the sample‐size calculation methods for testing the treatment effect in two‐arm survival trials. For each method considered, its basic features, underlying assumptions, and limitations are discussed. Presented first are the test statistics and associated methods that yield close‐form sample‐size formulas. Then attention will be focused on methods with aforementioned complexities in sample‐size calculation.
Treatment‐by‐Center Interaction
Authors: Gallo Paul
Published: June 2008

In multicenter clinical trials, in addition to the main study objectives that address effects of the therapies being evaluated, it is important to consider the level of consistency of treatment effects that is observed across the centers. Investigating whether evidence of treatment‐by‐center interaction exists and, if so, gaining greater understanding of what has led to this nonhomogeneity of results across sites can be an important aspect of most fully comprehending the trial results. This understanding may shed light, for example, on conditions under which the trial therapies can be used most effectively or may help identify characteristics of patients most likely to benefit. Important questions meriting additional clinical investigation may be identified. Considering treatment‐by‐center interaction may also impact the strength of the overall conclusions to be drawn from the trial and the extent to which its results can be generalized.
Investigator/Institution
Authors: Getz Kenneth A.
Published: June 2008

The market for study conduct service providers is in constant flux. During the past decade, this landscape has privatized and has changed structurally in response to economic forces, regulatory reforms, and opportunistic institutional behaviors. In recent years, increasing competition, more challenging patient recruitment and retention, tighter financial controls by research sponsors, and mounting regulatory compliance requirements have stimulated a more difficult operating environment for U.S.‐based investigative sites. These operating conditions are now driving the migration of FDA (U.S. Food and Drug Administration) regulated clinical trials overseas to investigative sites based in Central and Eastern Europe, Latin America, India, and China.
Over the Counter (
OTC
) Drugs
Authors: Brass Eric P.
Published: June 2008

Regulatory decision making and public health efforts are dependent on clinical research to define the ability of consumers to self‐manage treatment regimens with over‐the‐counter drugs. Label compression studies evaluate the ability of consumers to understand the key communication messages of a prototype drug label. Self‐selection studies assess the ability of consumers to use the label to determine whether use of the proposed drug is appropriate for them based on their individual health status. Actual use studies allow assessment of consumers' ability to decide whether to use a proposed over‐the‐counter drug, and they evaluate how consumers manage the treatment regimen. These clinical research methods pose unique challenges as they balance the need for rigorous assessments with efforts to facilitate unbiased participant decision making to understand how the drug would be used in the open marketplace without professional supervision.
Disease Trials on Pediatric Patients
Authors: Bauchner Howard
Published: June 2008

Randomized clinical trials are the cornerstone of the Cochrane Collaboration and recommendations from the USPSTF. Data suggest that there are less RCTs involving children than adults. Unique obstracts that researchers face when conducting RCTs which include children as subjects include: limited number of children with major medical diseases; issues related to ethics, and measurable objective health outcomes.
Lan‐DeMetsAlpha‐Spending Function
Authors: DeMets David L. , Lan K. K. Gordon
Published: June 2008

Randomized clinical trials are typically monitored periodically for early evidence of convincing benefit or of potential harm for both scientific and ethical reasons by a data monitoring committee. After increments in the number of patients randomized or in the number of observed events, interim statistical analyses may be conducted to evaluate emerging results. This process may be referred to as a group sequential design. However, repeated analysis of accumulating data with conventional statistical testing can lead to substantially increased Type I or false positive error rates. The alpha spending function provides a flexible approach to control the Type I error while allowing the data monitoring committee to review the data as needed without having to specifya prioriwhen and how many such interim analyses will be conducted. Other earlier group sequential methods had these somewhat restrictive requirements. This alpha spending function approach can be applied to several spending functions that approximate some of these earlier methods.
Stratified Randomization
Authors: Perevozskaya Inna
Published: June 2008

This article outlines the situations where stratified randomization may be necessary and points out the reader to the “Randomization” article where the issue is discussed in detail.
Cell Line
Authors: Sivakumaran Theru A. , Iyengar Sudha K.
Published: June 2008

A cell line is obtained after the first subculture of primary cell culture that is established from freshly isolated tissues. Until the establishment of a primary culture, cells are usually heterogeneous and still closely represent the parent cell types and express tissue‐specific properties. After several passages, the cell line will die out because of senescence or escape senescence to become a continuous cell line, then to become a valuable resource, particularly if it is preserved after characterization and validation. Cell lines are used to study the normal physiology or biochemistry of the cells in disease and health, in drug screening as well as in studies of cell toxicity mechanisms. Today, cell lines may also assist stem‐cell therapy.
Analysis Population
Authors: Mugglin Andrew S. , Connett John E.
Published: June 2008

Collecting data on humans in clinical trials is very complicated, for scientific, logistic, ethical, and financial reasons. Analyzing such data is no less complicated, and it is seldom a simple exercise to determine which data to analyze, let alone how to analyze them and report the findings. In this article, we seek to describe some issues faced by the data analyst in a clinical trial. We define several terms that are often used to describe succinctly a data set created according to certain principles. We identify several issues and themes that frequently develop when discussing the selection of data for analysis. We also provide some principles that we have found to be useful approaches to dealing with the difficulty of selecting data for analysis in a complex but real setting. Finally, we provide an illustrative example of how an analysis dataset was actually chosen.
Postmenopausal Estrogen/Progestin Intervention Trial (
PEPI
)
Authors: Langer Robert D.
Published: June 2008

The Postmenopausal Estrogen/Progestin Interventions (PEPI) trial was a 3‐year randomized double‐masked placebo controlled trial conducted between 1989 and 1994. This aim of this study was to test the effects of four active hormone replacement treatments (HRT) on cardiovascular risk factors in women between 1 and 10 years postmenopausal after multiple observational studies suggested that HRT might reduce cardiovascular disease. Primary endpoints were changes in HDL cholesterol, systolic blood pressure (SBP), 2‐hour post‐challenge insulin, and fibrinogen. It enrolled 875 women into five groups defined by estrogen, progestin, and dosing pattern. Groups were placebo/placebo, daily conjugated equine estrogen 0.625 mg (CEE)/placebo, CEE/medroxyprogesterone acetate (MPA) 10 mg for 12 days per month, CEE/MPA 2.5 mg daily, and CEE/micronized progesterone (MP) 200 mg for 12 days per month. Compared with placebo, all treatments improved HDL except CEE alone; CEE/MP was significantly better than CEE/either MPA. All treatments reduced fibrinogen. No significant differences were observed in SBP or insulin. Endometrial safety was a secondary endpoint for the 60% of subjects with a uterus. CEE/placebo was associated with > 20% annual incidence of hyperplasia, whereas all regimens with a progestin were effective in preventing disease. Bone mineral density, which was another secondary endpoint, was improved by all treatments.
Women's Health Initiative Dietary Trial
Authors: Mossavar‐Rahmani Yasmin , Tinker Lesley F.
Published: June 2008

The Women's Health Initiative (WHI) Dietary Modification Randomized Controlled Trial (DMT) was the largest randomized, controlled clinical trial to test the effects of a low‐fat dietary pattern on decreasing the risk of incident breast cancer, colorectal cancer, and heart disease. In all, 48,835 healthy post‐menopausal women aged 50–79 years were recruited across 40 clinical centers throughout the United States. Among postmenopausal women, a low‐fat dietary pattern did not result in a statistically significant reduction in invasive breast cancer risk, colorectal cancer risk, or heart disease risk over an 8.1 year average follow‐up period; however, some evidence suggests that risk for breast cancer was reduced among women who entered the study consuming dietary fat within the highest quartile and who demonstrated the greatest overall reduction in dietary fat intake in response to the dietary intervention. Furthermore, the nonsignificant trends observed suggest that any reduced risk associated with a low‐fat dietary pattern may only be evident after a longer follow‐up period. Thus, a planned, nonintervention follow‐up is underway to allow for continued longitudinal evaluation and potentially a more definitive comparison.
Global Assessment Variables
Authors: Tilley Barbara , Huang Peng , O'Brien Peter C.
Published: June 2008

Multiple outcomes are often collected for an evaluation of a treatment's effect on disease or quality of life or other underlying condition. When multiple outcomes are being assessed, and no predefined and validated composite outcome (summary measure) exists, two classes of statistical tests have been developed that address two different scientific questions. Tests in one class are designed to test whether a treatment effect is observed for any of the outcomes. Tests in the other class are designed to test whether a treatment confers an overall benefit (or harm) to patients. We provide a brief survey of global statistical tests (GST) of the second class. Each GST is discussed in terms of its motivation, approach to conducting the test, and some basic properties of the test. To facilitate the interpretation of the GST, we use the concept of a global treatment effect (GTE) to quantify the treatment's overall effect on multiple outcomes. We provide examples of the application of GSTs to clinical trials along with recommendations for choosing outcomes to include in the GST.
Drift (For Interim Analysis)
Authors: Reboussin David M.
Published: June 2008

Drift, in the context of the design and analysis of group sequential plans for clinical trials, refers to the mean of a Brownian motion process describing the accumulating evidence on the treatment difference. Although the implementation of group sequential testing does not require explicit reference to Brownian motion process, some understanding of it simplifies the discussion of certain aspects, especially sample size determination, estimation, and stochastic curtailment.
Mother to Child Human Immunodeficiency Virus Transmission Trials
Authors: Shapiro David E.
Published: June 2008

Mother‐to‐child transmission (MTCT) of human immunodeficiency virus type 1 (HIV) can occur through three major routes: through the placental barrier during gestation, via contact with maternal bodily fluids during labor and delivery, and by ingestion of breast milk after delivery; most MTCT is believed to occur close to the time of or during childbirth. Prevention of MTCT (PMTCT) is one of the most successful areas in HIV clinical research. Interventions to prevent MTCT have been developed for two different settings: the United States and other developed countries with access to medications, medical infrastructure, and safe replacement feeding so that breastfeeding can be discouraged; and resource‐limited countries with limited access to medications and clean water, and where breastfeeding is critical. This article describes four important PMTCT randomized trials, including two trials of key interventions used in developed countries and two trials of interventions feasible in resource‐limited settings.
Stepped Wedge Design
Authors: Hughes James P.
Published: June 2008

Stepped wedge trials are randomized trials in which each randomization unit first receives the control condition and then crosses over to receive the intervention. However, the time at which the intervention is provided to each unit is randomized. Stepped wedge trials may be either individually or cluster‐randomized. The stepped wedge design is particularly useful when it is not feasible to provide the intervention to everyone or every community at once, and for evaluating the effectiveness of interventions that have been shown to be efficacious in a more limited, research setting and are now being scaled up to the community level. This design is also useful for evaluating temporal changes in the intervention effect.
Cox Proportional Hazard Model
Authors: Cai Jianwen , Zeng Donglin
Published: June 2008

In this article, we provide an overview on the Cox proportional hazards model. We discuss the partial likelihood approach for parameter estimation and inference. Some practical issues in using the Cox regression model are discussed, including how to handle tied data, incorporate time‐dependent covariates, and fit the Cox regression model in stratified population. The article also reviews some practical ways for assessing proportional hazards assumption; one authentic example is used to illustrate data analysis using the Cox regression model. Finally, we list a few extensions of the Cox regression model.
Intraclass Correlation Coefficient
Authors: Campbell Marion K. , Grimshaw Jeremy M. , Piaggio Gilda
Published: June 2008

Increasingly researchers are recognizing that in many situations, using a cluster‐randomized trial (in which groups of individuals are randomized rather than individuals directly) may be more appropriate than an individually randomized trial. Cluster‐randomized trials are more complex to design, however, primarily because observations on individuals within the same cluster tend to be correlated. The statistical measure of this correlation within clusters is known as the intraclass or intracluster correlation coefficient (ICC). In this article, we discuss the measurement and impact of the ICC on the design and analysis of cluster‐randomized trials. In particular we outline how an ICC can be calculated, the likely magnitude of ICCs in specific settings, the factors that influence the ICC estimate, how an ICC should be reported, and the impact of the ICC on sample‐size requirements and analysis.
Regulatory Definitions
Authors: Traverso Kelly L.
Published: June 2008

Since President Roosevelt signed the Federal Food, Drug and Cosmetic law in 1938, drugs in clinical development must be tested for safety before they can be marketed. As a result, the role of safety in premarketing development is to determine that the product benefit exceeds the risks, which contributes in the efficacy of clinical drug development. This assessment is done throughout the phases of clinical development (See “Study Design: Phases I–V”). For the product benefit to be evaluated, safety information needs to be collected on the patients that are involved in the clinical studies. One of the markers that is evaluated is whether the patient had an adverse event (often referred as a side effect) while enrolled in the study. Sponsors of clinical studies are mandated by the Food and Drug Administration (FDA) and other global health authorities to report these adverse events (see also “Regulatory Issues”). These events are reported in timeframes outlined in the regulations and differ depending on the type of adverse event. This section will define what an adverse event is, the timeframe that sponsors have to report the event, and how the sponsor reports the information collected to the regulatory agencies.
Sensitivity, Specificity and Receiver Operator Characteristic (
ROC
) Methods
Authors: Kraemer Helena Chmura
Published: June 2008

Three of the most common problems in medical research are as follows: (
1
) evaluating a medical test against a diagnostic criterion in a population, (
2
) choosing the best among available tests, and finally (
3
) developing the best combination of medical tests. In 1979, a
Lancet
editorial commented that
“The assessment of diagnostic methods belongs to the backwoods of clinical research. New treatments are submitted to thorough testing and books are written on the methodology of the controlled therapeutic trial; but no standards have been fixed for the assessment of diagnostic methods.”
Since 1979, some progress has been reflected in the Standards for Reporting of Diagnostic Accuracy (STARD) guidelines, but statistical methods remain much less developed than the methods for the assessment of treatments in randomized clinical trials. As a result, medical tests often enter general clinical use with little or no convincing documentation of their accuracy, which then has major repercussions on patient health and medical costs.
Repeatability and Reproducibility
Authors: Connett John E.
Published: June 2008

Repeatability and reproducibility are defined, the distinction between them is described, and examples are provided. The use of these measures in quantifying the precision of laboratory procedures is discussed. The role of reproducibility in the estimation of sample size for clinical trials with quantitative outcomes is explained.
Microarray
Authors: Page Grier P. , Cui Xiangqin
Published: June 2008

Microarray technology had a troubling beginning, but many problems resulted from the growing pains of this technology that, like many new things, was initially more promise than delivery. However, the promise has finally come to fruition, and microarray technology has developed into a mature technology that can generate valid and useful results in basic, translational, clinical trials, and now clinical practice. Much maturity resulted from the realization that microarrays are not some magic tool, but rather are like any other measurement device and that unless microarray experimentation is coupled with good experimental practices, it will not yield valid results or, worse yet, may lead to misleading results.
Treatment Interruption
Authors: Grund Birgit
Published: June 2008

Interruption of active drug treatment in clinical trials can be classified broadly as therapeutic treatment interruption (TI), in which the potential therapeutic benefit of supervised interruption of treatment is investigated, and analytic TI, in which participants are randomized to stopping versus continuing active treatment; the TI arm serves as control group. Design issues for therapeutic TI trials are discussed in the example of a trial in HIV/AIDS.

Analytic TIs are used in therapeutic HIV vaccine trials, in randomized discontinuation designs, and in other situations. The use of a randomized discontinuation design for a Phase II drug trial in cancer is discussed.
Hypothesis Testing
Authors: LaVallee Nicole M. , Stepanians Miganush N.
Published: June 2008

The objectives and methods of hypothesis testing are described. Key concepts in hypothesis testing are defined and explained, including null and alternative hypotheses, one‐sided and two‐sided tests, Type I and Type II errors, significance level, power, test statistic, critical value, statistical significance, and
P
‐value. Examples of one‐sample and two‐sample tests of population means are provided to illustrate how hypothesis tests are performed and interpreted.
Dermatology Trials
Authors: Feldman Steven R. , Clark Adele R.
Published: June 2008

One characteristic of many skin disease clinical trials is that they involve topical treatments. The direct application of potent topical medications can have excellent efficacy while minimizing the potential for systemic side effects. A limitation of trials in dermatology is that topical treatment tends to be more difficult for patients to use than oral medications. An advantage of dermatologic trials is that the outcomes are readily visible on the skin. Investigator and patient assessed outcomes can be used. A trial of a topical clobetasol illustrates the rapid effects of the drug and the relative safety over the short term.
Logrank Test
Authors: Hopkins Alan
Published: June 2008

The nonparametric log‐rank test compares two or more independent estimated time‐to‐event curves (survival functions) based on censored data. Right‐censored data originates in clinical trials when measuring time‐to‐an‐event. Time‐to‐infection, time‐to‐disease progression, and time‐to‐death are examples of clinical endpoints that may not be observed on all subjects randomized in a clinical trial because of a fixed follow‐up period or dropout. We shall refer to time‐to‐event data with right censoring as survival data. The log‐rank test is the most commonly used test for comparing survival functions among a class of weighted log‐rank statistics. The log‐rank test is the most powerful nonparametric test to detect proportional hazards alternatives. It is closely related to the Cox regression model.