By Steven Piantadosi
This e-book is particularly certain. easy statistical thoughts are in actual fact provided yet merely these thoughts which are vital in scientific trials. the writer provides all of the concerns with medical trials together with moral concerns with a few old point of view. ideas of randomization and statistical layout are in actual fact provided. It deals dialogue of Bayesian concepts and meta-analyses, cross-over designs and team sequential equipment (interim analyses). For statisticians doing scientific study like myself, it is a useful reference resource.
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Additional info for Clinical Trials: A Methodologic Perspective Second Edition(Wiley Series in Probability and Statistics)
These criticisms of randomization are extreme and I believe them to be incorrect. I will return to this discussion in Chapter 13. 2 Clinical Trials Are Rigorously Defined An experiment is a series of observations made under conditions controlled by the scientist. To define an investigation as an experiment, the scientist must control the application of treatment (or intervention). In other words, the essential characteristic that distinguishes experimental from nonexperimental studies is whether or not the scientist controls or manipulates the treatment (factors) under investigation.
Trials are more common now than ever before because they are the most reliable means of making correct inferences about treatments in many commonly encountered clinical circumstances. When treatment differences are about the same size as patient-to-patient variability and/or the bias from flawed research designs, a rigorous clinical trial (especially a randomized design) is the only reliable way to separate the treatment effect from the noise. Physicians should be genuinely interested in treatments that improve standard therapy only moderately.
In this way number crunching becomes less important than sound statistical reasoning. The student of clinical trials should also understand that the field is growing and changing in response to both biological and statistical developments. A picture of good methodology today may be inadequate in the near future. This is probably more true of analytic methods than design, where the fundamentals will change more slowly. Analysis methods often will depend on new statistical developments or theory.