Contiene: bibliografia y glosario.

What do we mean by complementary medicine in this book? The science behind clinical medicine. Major areas of research. Topics in clinical research. Evidence-based medicine. Why do we need research on complementary therapies?. Is research into complementary therapies special?. Why research into complementary therapies is somewhat different?. Strategic approaches to research into complementary medicine. Why it is difficult to realize strategic approaches?. Aims, target audience and structure of this book. I Theory – Things you should know before embarking on a clinical study. Basic study design. When is a treatment effective?. Why do we need control or comparison groups?. Types of controls and comparisons. Specific and non-specific effects. Bias – threats to internal validity. Prognostic and baseline differences between groups – why randomization is so desirable. Differences between groups after treatment has started – why blinding is so desirable. Attrition. Bias during analysis and reporting. Clinical studies and the real world – external validity. The need for balancing internal and external validity. Selection of study participants. Selection of study interventions. Selection of outcome measures. What study design for what purpose?. Studies without a control group. Studies with a non-randomized comparison group. Randomized trials. Establishing an evidence picture. Basic statistics. Why statistics?. Types of variables and their distribution. Categorical variables. Continuous variables. Categorical or continuous?. Summarizing your data. Calculating the mean and the median. The distribution of your data. Measuring variation within your study population. Measuring sampling variation. Standard deviation, standard error and confidence interval. Comparing two groups or two time points for one variable. Calculating summary measures for dichotomous variables. Calculating summary measures for continuous variables. Testing a hypothesis. Relevance of the p-value. Statistical tests for comparing means. Statistical tests for comparing proportions. Confidence intervals. Type I and type II errors. How to deal with multiple testing. Comparing more than two groups for one variable. Statistical models and tests for comparing more than two groups for one variable. Comparing two or more groups for more than one variable. Correcting for baseline differences. Logistic regression and statistical modelling. Analysis populations. Dealing with missing values. Interval hypotheses: equivalence/non-inferiority and superiority. Planning, managing, analyzing and publishing a clinical study. Formulating the research question. Why a clear research question is so crucial. Practical steps. Case studies. Study Protocol. What is a study protocol?. Develop your study protocol – step by step. Interventions and Controls. Theoretical background. Define your control and interventions. Case studies. Randomization. Individual and cluster randomization. Practical steps. Case studies. What to do if you do not randomize. Taking baseline differences into account. Matching – practical steps. Adjusting analyses for imbalances – practical steps. Case study. Blinding. Should you go for blinding?. Practical steps. Case studies. Study participants. Who should be included in your study?. Practical steps. Case studies. Outcome measurement. Theoretical background. Define your outcome measures and prepare your CRF. Case studies on outcomes. Sample size calculation. What does sample size calculation mean? . Practical steps. Case studies. Ethics and regulatory aspects. Theoretical background on ethics. Getting approval from the IRB/Ethics Committee. Regulatory aspects. Study and data management. Project management.