In discussing design and results of randomized clinical trials, in particular with clinical oncologists, one often encounters the opinion that a phase III trial is a complicated, highly costly, and difficult task. Part of this opinion seems to originate in myths around underlying biostatistical principles such as randomization, sampling and sample size, statistical hypotheses, statistical error probabilities, and statistical power. This work clarifies basic statistical issues of randomized clinical trials and the interpretation of their results. ⋯ They concern choice of study design, choice of participating centers, and recruitment of patients as well as statistical questions of establishing study hypotheses and interpreting p values. These myths are shown to be caused primarily through a misunderstanding of statistical inference and statistical thinking that can be avoided when a rational understanding of statistical principles is translated into a clinical research approach. We also conclude that before clinical evidence is summarized from different studies each study should be examined thoroughly.
ZE Biostatistik, Deutsches Krebsforschungszentrum Heidelberg, Germany. edler@dkfz.de
Onkologie. 2003 Dec 1;26(6):551-6.
AbstractIn discussing design and results of randomized clinical trials, in particular with clinical oncologists, one often encounters the opinion that a phase III trial is a complicated, highly costly, and difficult task. Part of this opinion seems to originate in myths around underlying biostatistical principles such as randomization, sampling and sample size, statistical hypotheses, statistical error probabilities, and statistical power. This work clarifies basic statistical issues of randomized clinical trials and the interpretation of their results. Six issues ('myths') relevant for the design of clinical trials and the interpretation of their results are addressed. They concern choice of study design, choice of participating centers, and recruitment of patients as well as statistical questions of establishing study hypotheses and interpreting p values. These myths are shown to be caused primarily through a misunderstanding of statistical inference and statistical thinking that can be avoided when a rational understanding of statistical principles is translated into a clinical research approach. We also conclude that before clinical evidence is summarized from different studies each study should be examined thoroughly.Copyright 2003 S. Karger GmbH, Freiburg