Volume 179 - Issue 2
Statistical methods in clinical trials
Authors: Val J Gebski and Anthony C Keech
Med J Aust 2003; 179 (2): 119-120. || doi: 10.5694/j.1326-5377.2003.tb05461.x
Published online: 21 July 2003
Published online: 21 July 2003
Val J Gebski,* Anthony C Keech†
* Principal Research Fellow, † Deputy Director, NHMRC Clinical Trials Centre, University of Sydney, Locked Bag 77, Camperdown, NSW 1450. valATctc.usyd.edu.au
In reply: While one can view the world as being "discrete", the assumptions underpinning most common statistical methods in analysis of clinical studies are "continuous" distributions. In fact, statisticians go to enormous lengths to approximate discrete systems as continuous ones (lifetime analysis, normal approximations, etc).
The measurement scale by which study outcomes are assessed needs careful consideration (to ensure consistent precision and units of measurement). However, both practical and statistical considerations allow for the more common definitions of continuous and discrete measurements to be just as effective for statistical comparisons. Indeed, there is frequently little loss of statistical efficiency when "continuous" variables are appropriately categorised into ordinal groups.1
Resampling methods randomly sample the data repeatedly to estimate the underlying population distribution parameters (eg, mean, standard deviation, etc). They can be very useful in solving specific problems in which the underlying properties of the data used to make treatment comparisons are unknown and using other statistical approximations is deemed to be inappropriate. However, these are specialised computer-intensive techniques for use by trained biostatisticians, rather than commonly used analysis methods. Problems arise with resampling techniques (eg, obtaining confidence intervals), which require specialised statistical expertise.
References
- McNeil DR. Quick powerful tests with grouped data. Biometrika 1968; 55: 264-268. BJDGHGGH