2010, English, Book, Illustrated edition: Clinical research in complementary and integrative medicine : a practical training book / Claudia M. Witt, Klaus Linde ; in collaboration with Adrian White ; preface by George Lewith and Brian Berman. Witt, Claudia.

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Clinical research in complementary and integrative medicine : a practical training book / Claudia M. Witt, Klaus Linde ; in collaboration with Adrian White ; preface by George Lewith and Brian Berman
 
Bookmark: http://trove.nla.gov.au/version/181098494
Edition
  • 1st ed.
Physical Description
  • xiv, 189 p. : ill. ; 24 cm.
Published
  • Munich : Elsevier, [2010]
Language
  • English

Edition details

Title
  • Clinical research in complementary and integrative medicine : a practical training book /​ Claudia M. Witt, Klaus Linde ; in collaboration with Adrian White ; preface by George Lewith and Brian Berman.
Author
  • Witt, Claudia.
Other Authors
  • Linde, Klaus.
Edition
  • 1st ed.
Published
  • Munich : Elsevier, [2010]
Physical Description
  • xiv, 189 p. : ill. ; 24 cm.
Subjects
Contents
  • Machine generated contents note: 1.Introduction
  • 1.1.What do we mean by complementary medicine in this book?
  • 1.2.The science behind clinical medicine
  • 1.2.1.Major areas of research
  • 1.2.2.Topics in clinical research
  • 1.2.3.Evidence-based medicine
  • 1.3.Why do we need research on complementary therapies?
  • 1.4.Is research into complementary therapies special?
  • 1.4.1.Why research into complementary therapies is somewhat different?
  • 1.4.2.Strategic approaches to research into complementary medicine
  • 1.4.3.Why it is difficult to realize strategic approaches?
  • 1.5.Aims, target audience and structure of this book
  • I.Theory - Things you should know before embarking on a clinical study
  • 2.Basic study design
  • 2.1.When is a treatment effective?
  • 2.1.1.Why do we need control or comparison groups?
  • 2.1.2.Types of controls and comparisons
  • 2.1.3.Specific and non-specific effects
  • 2.2.Bias - threats to internal validity
  • Contents note continued: 2.2.1.Prognostic and baseline differences between groups - why randomization is so desirable
  • 2.2.2.Differences between groups after treatment has started - why blinding is so desirable
  • 2.2.3.Attrition
  • 2.2.4.Bias during analysis and reporting
  • 2.3.Clinical studies and the real world - external validity
  • 2.3.1.The need for balancing internal and external validity
  • 2.3.2.Selection of study participants
  • 2.3.3.Selection of study interventions
  • 2.3.4.Selection of outcome measures
  • 2.4.What study design for what purpose?
  • 2.4.1.Studies without a control group
  • 2.4.2.Studies with a non-randomized comparison group
  • 2.4.3.Randomized trials
  • 2.5.Establishing an evidence picture
  • 3.Basic statistics
  • 3.1.Why statistics?
  • 3.2.Types of variables and their distribution
  • 3.2.1.Categorical variables
  • 3.2.2.Continuous variables
  • 3.2.3.Categorical or continuous?
  • 3.3.Summarizing your data
  • Contents note continued: 3.3.1.Calculating the mean and the median
  • 3.3.2.The distribution of your data
  • 3.3.3.Measuring variation within your study population
  • 3.3.4.Measuring sampling variation
  • 3.3.5.Standard deviation, standard error and confidence interval
  • 3.4.Comparing two groups or two time points for one variable
  • 3.4.1.Calculating summary measures for dichotomous variables
  • 3.4.2.Calculating summary measures for continuous variables
  • 3.4.3.Testing a hypothesis
  • 3.4.4.Relevance of the p-value
  • 3.4.5.Statistical tests for comparing means
  • 3.4.6.Statistical tests for comparing proportions
  • 3.4.7.Confidence intervals
  • 3.4.8.Type I and type II errors
  • 3.4.9.How to deal with multiple testing
  • 3.5.Comparing more than two groups for one variable
  • 3.5.1.Statistical models and tests for comparing more than two groups for one variable
  • 3.6.Comparing two or more groups for more than one variable
  • 3.6.1.Correcting for baseline differences
  • Contents note continued: 3.6.2.Logistic regression and statistical modelling
  • 3.7.Analysis populations
  • 3.8.Dealing with missing values
  • 3.9.Interval hypotheses: equivalence/​non-inferiority and superiority
  • II.Practice - Planning, managing, analyzing and publishing a clinical study
  • 4.Planning
  • 4.1.Formulating the research question
  • 4.1.1.Why a clear research question is so crucial
  • 4.1.2.Practical steps
  • 4.1.3.Case studies
  • 4.2.Study Protocol
  • 4.2.1.What is a study protocol?
  • 4.2.2.Develop your study protocol - step by step
  • 4.3.Interventions and Controls
  • 4.3.1.Theoretical background
  • 4.3.2.Define your control and interventions
  • 4.3.3.Case studies
  • 4.4.Randomization
  • 4.4.1.Individual and cluster randomization
  • 4.4.2.Practical steps
  • 4.4.3.Case studies
  • 4.5.What to do if you do not randomize
  • 4.5.1.Taking baseline differences into account
  • 4.5.2.Matching - practical steps
  • Contents note continued: 4.5.3.Adjusting analyses for imbalances - practical steps
  • 4.5.4.Case study
  • 4.6.Blinding
  • 4.6.1.Should you go for blinding?
  • 4.6.2.Practical steps
  • 4.6.3.Case studies
  • 4.7.Study participants
  • 4.7.1.Who should be included in your study?
  • 4.7.2.Practical steps
  • 4.7.3.Case studies
  • 4.8.Outcome measurement
  • 4.8.1.Theoretical background
  • 4.8.2.Define your outcome measures and prepare your CRF
  • 4.8.3.Case studies on outcomes
  • 4.9.Sample size calculation
  • 4.9.1.What does sample size calculation mean?
  • 4.9.2.Practical steps
  • 4.9.3.Case studies
  • 4.10.Ethics and regulatory aspects
  • 4.10.1.Theoretical background on ethics
  • 4.10.2.Getting approval from the IRB/​Ethics Committee
  • 4.10.3.Regulatory aspects
  • 5.Study and data management
  • 5.1.Project management
  • 5.1.1.Phases of project management
  • 5.1.2.Taking notes
  • 5.2.Guidelines for clinical trials
  • 5.3.Study management
  • 5.3.1.Theoretical background
  • Contents note continued: 5.3.2.Manage your study
  • 5.4.Data management
  • 5.4.1.Theoretical background
  • 5.4.2.Manage your data
  • 5.5.Case studies
  • 6.Data analysis
  • 6.1.Analysing your own data step by step
  • 6.1.1.Step 1: Define the analysis populations and the handling of missing values
  • 6.1.2.Step 2: Identify the different types of variable
  • 6.1.3.Step 3: Clarify what you can calculate from your data
  • 6.1.4.Step 4: Choose suitable statistical methods
  • 6.1.5.Step 5: Perform the statistical analysis
  • 6.1.6.Step 6: Decide how you will present your results
  • 6.1.7.Step 7: Interpret your results and draw your conclusions
  • 6.2.Case studies
  • 7.Publication
  • 7.1.General issues
  • 7.1.1.Why is publication so crucial?
  • 7.1.2.Basic things to keep in mind
  • 7.2.Early preparatory work
  • 7.2.1.Define your aims
  • 7.2.2.Deciding on authorship
  • 7.2.3.Selecting a journal
  • 7.2.4.Checking instructions for authors
  • Contents note continued: 7.2.5.Checking general guidelines for reporting
  • 7.3.Writing the manuscript
  • 7.3.1.Results section
  • 7.3.2.Methods section
  • 7.3.3.Introduction
  • 7.3.4.Discussion
  • 7.3.5.Abstract
  • 7.3.6.References
  • 7.3.7.Additional statements
  • 7.3.8.Internal revision
  • 7.4.Getting your manuscript accepted
  • 7.4.1.Preparing the submission
  • 7.4.2.Submitting the manuscript
  • 7.4.3.What happens at the journal?
  • 7.4.4.Revision
  • 7.4.5.After rejection
  • 7.5.After acceptance
  • 7.5.1.Proofreading
  • 7.5.2.Finally: Publication
  • 7.5.3.Dealing with mass media
  • III.Putting a clinical study into context
  • 8.Qualitative research
  • 8.1.Research question and examples
  • 8.2.Qualitative approaches
  • 8.2.1.Ethnography
  • 8.2.2.Field research
  • 8.2.3.Case studies
  • 8.3.Qualitative methods and types of data
  • 8.3.1.Interviews
  • 8.3.2.Observation
  • 8.3.3.Written documents or pictures
  • 8.4.Qualitative data analysis
  • 8.4.1.Content analysis
  • Contents note continued: 8.4.2.Grounded theory
  • 8.5.Quality assurance in qualitative research
  • 8.6.Combining qualitative and quantitative research methods
  • 9.Economic studies
  • 9.1.Principles of economic analysis
  • 9.1.1.Costing
  • 9.1.2.Measuring the benefit
  • 9.2.Types of economic evaluations
  • 9.2.1.Cost of disease analysis
  • 9.2.2.Cost-benefit analysis
  • 9.2.3.Cost-effectiveness-analysis
  • 9.2.4.Cost-utility analysis
  • 9.2.5.Cost-minimization analysis
  • 9.3.Examples of economic analysis
  • 9.4.Critical appraisal
  • 10.Single-case research
  • 10.1.Why case reports are not scientific
  • 10.2.Why single-case research is important?
  • 10.3.Assessing causality in single cases
  • 10.4.Experimental single-case studies
  • 10.4.1.General aspects
  • 10.4.2.Progressive approaches
  • 10.4.3.Repetitive approaches
  • 10.5.Observational single case studies
  • 10.5.1.Retrospective case studies
  • 10.5.2.Prospective case studies
  • 10.5.3.Semi-prospective case studies
  • Contents note continued: 10.6.Cross-roads between research on single cases and groups of patients
  • 10.6.1.Best case series
  • 10.6.2.Multiple baseline studies
  • 10.6.3.Single-case studies embedded in studies of groups of patients
  • 10.6.4.Meta-analysis of single case studies
  • 11.A brief look at other study designs
  • 11.1.Cross-sectional studies/​surveys
  • 11.2.Diagnostic studies
  • 11.3.Etiological and prognostic studies.
Notes
  • Includes bibliographical references and index.
Language
  • English
ISBN
  • 9780702034763
  • 0702034762
Dewey Number
  • 615.5072
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