bluuebunny/crossref_metadata_2025_split huggingface dataset:
Isolation of Human Placental Fibroblasts
Authors: Ilic Dusko , Kapidzic Mirhan , Genbacev Olga 
Published: June 2008

AbstractThe 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

AbstractAfter 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

Abstract
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

AbstractIn 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

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

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

Abstract
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

AbstractIn 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

Abstract
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

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

Abstract
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

AbstractSubgroup 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

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

Abstract
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

AbstractBecause 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

Abstract
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

AbstractIn 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

Abstract
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

Abstract
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

Abstract
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

AbstractRandomized 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.
Cell Line
Authors: Sivakumaran Theru A. , Iyengar Sudha K. 
Published: June 2008

AbstractA 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

AbstractCollecting 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.