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Fundamentals of Clinical Research: Bridging Medicine, Statistics and Operations

Antonella Bacchieri Giovanni Della Cioppa

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Institución detectada Año de publicación Navegá Descargá Solicitá
No detectada 2007 SpringerLink

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Tipo de recurso:

libros

ISBN impreso

978-88-470-0491-7

ISBN electrónico

978-88-470-0492-4

Editor responsable

Springer Nature

País de edición

Reino Unido

Fecha de publicación

Información sobre derechos de publicación

© Springer-Verlag Italia, Milano 2007

Tabla de contenidos

Viability of Biological Phenomena and Measurement Errors

The variability of biological phenomena, as we perceive them, can be divided into three main components: phenotypic, temporal and measurement-related. Measurement-related variability includes every source of variation that occurs as a consequence of measurements and interpretation of results.

Palabras clave: Allergic Rhinitis; Blood Urea Nitrogen; Measurement Scale; Biological Phenomenon; Ratio Scale.

Pp. 1-13

Distinctive Aspects of a Biomedical Study Observational and Experimental Studies

The aims of this chapter are, firstly, to introduce aspects which are common to all biomedical studies, and, secondly, to discuss the features that distinguish observational from experimental studies, the two fundamental categories of biomedical studies.

Palabras clave: Medical Study; Experimental Factor; Distinctive Aspect; Statistical Aspect; Medical Objective.

Pp. 14-27

Observational Studies

In this chapter we give a brief overview of observational studies, also referred to as epidemiological studies (as mentioned above, we use these terms interchangeably). The rest of the book is dedicated to clinical trials, which belong to the category of experimental studies (see chapter 2). We decided to devote some space to observational studies for three reasons: first, data from epidemiological studies are often required to plan and interpret clinical trials; second, to better understand the basic principles of experimental studies it is useful to understand those of observational studies; third, some of the methods of data analysis are common to both types of study. We will present a comparison between observational and experimental studies later in the book, once we have discussed experimental studies in detail (chapter 9). The reader who has a specific interest in epidemiology will find only general concepts in this chapter. For more on this topic we recommend the textbooks by Hennekens and Buring [57], Lilienfeld and Lilienfeld [67] and Miettinen [71] among many others.

Palabras clave: Lung Cancer; Confounding Factor; Transient Ischemic Attack; High Density Lipoprotein; Biological Plausibility.

Pp. 28-57

Defining the Treatment Effect

In this book we will use the term “signal” to define the summary variable which, at a group level and in comparative terms, is used to formulate the hypothesis to be tested, in order to evaluate the effect of the experimental treatment under study. It should be noted that in clinical research the term signal is often used with a different meaning, to indicate an increased frequency of a given adverse event in the experimental treatment group over the control group, to the extent that the adverse event is suspected to be causally related to the experimental treatment. In this book we will use this term in the literal sense of “a sign to convey a command, direction, or warning” (Webster’s New World Dictionary, [103]).

Palabras clave: Experimental Treatment; Therapeutic Level; Peak Expiratory Flow Rate; Group Indicator; Severe Attack.

Pp. 58-89

Probability, Inference and Decision Making

In writing this chapter we have three special debts of gratitude to acknowledge. One is to Professor Theodore Colton for the frequentist approach, outlined with simplicity and rigor in his textbook on clinical trials [26]. The second is to Professor Ludovico Piccinato, of the University of Rome, since our outline of the Bayesian approach is based on one of his books [76] and on a presentation he and one of the authors gave to a medical audience on the comparison between the Bayesian and frequentist approaches. The third is to Professor Adelchi Azzalini, of the University of Padua, for his precious suggestions and advice.

Palabras clave: Probability Distribution; Null Hypothesis; Unknown Parameter; Alternative Hypothesis; Likelihood Function.

Pp. 90-156

The Choice of the Sample

The sample is the group of subjects on which the study is performed. Two aspects, one qualitative and the other quantitative, must be considered when choosing the sample.

Palabras clave: Sample Distribution; Sample Size Increase; Estimate Sample Size; Birth Control Method; Patient Selection Criterion.

Pp. 157-171

The Choice of Treatments

aIn line with the terminology introduced in section 2.4, in this chapter we shall use the term “experimental treatment” when referring to the main object of a clinical trial (often a novel treatment), and the term “study treatments” when referring to both experimental and control treatments.

Palabras clave: Active Control; Experimental Treatment; COX2 Inhibitor; Angiotensin Converting Enzyme Inhibitor; Treatment Logistics.

Pp. 172-182

Experimental Design: Fallacy of “Before-After” Comparisons in Uncontrolled Studies

The design of an experiment is defined as the method by which subjects (experimental units) are assigned to treatments. The chosen method of assignment in turn determines the way in which the data collected in the study are analyzed.

Palabras clave: Amyotrophic Lateral Sclerosis; Experimental Treatment; Allergic Rhinitis; Plasma Cholesterol; Placebo Effect.

Pp. 183-199

Experimental Design: the Randomized Blinded Study as an Instrument to Reduce Bias

From the previous chapter it should be clear that in most cases the before-after comparison in a single group of patients is an inadequate experimental design, as it fails to achieve comparisons free from bias. “Before” is not a good control for “after”, since the effects of many factors are mixed with the effect of the treatment, introducing all kinds of systematic errors. Generally, in this type of experimental design, bias has the effect of simulating or exaggerating the effect of the treatment.

Palabras clave: Randomization List; Assessment Bias; Allocation Ratio; Simple Randomization; Stratify Randomization.

Pp. 200-227

Experimental Designs

A preliminary remark for those readers who master some statistics: this chapter is limited to the linear model, which anyway is a sufficiently broad platform to cover most of the clinical applications.

Palabras clave: Factorial Design; Period Effect; Pollen Season; Parallel Group; Randomize Block Design.

Pp. 228-276