State the number of subjects studied and why this number was chosen. Describe the sources of subjects, how the subjects were selected and the inclusion or exclusion criteria that were employed. Present information on subjects who declined to participate, withdrawals and subjects with incomplete follow-up. Describe in detail how measurements were made and techniques used. All statistical methods should be mentioned and, when necessary, (for unusual methods) referenced; for every statistical result, the method used should be clearly described.
All tests should be two-sided, unless the use of one-sided tests is specifically justified. No data should be removed, imputed, weighted, adjusted or trimmed unless this action is specifically described and justified and its consequences are presented. Use non-parametric techniques when data have been measured on an ordinal scale or on an interval scale or non-normality is suspected and normality cannot be induced by transformation. In addition, for small unbalanced data sets with many ties or a poor distribution, exact methods may be needed to produce reliable result Matched data should be analyzed using conditional techniques, e.g. paired t-test, Wilcoxon’s signed ranks test, McNemar’s test or conditional logistic regression.
When measurements are repeated on the same subject, they should not be treated as independent observations; use repeated measures ANOVA or multilevel models. A possible alternative would be to summarize all values from each subject into an individual estimate of a clinically relevant entity, e.g. the magnitude of a peak value, area under curve, doubling time, etc., and then use these estimates as input in an analysis with one observation per subject. When multiple hypothesis testing is performed in a study with the aim of confirming a pre-specified hypothesis, care should be taken to avoid spurious significance by using techniques for simultaneous inference.
Acta Orthopaedica 2008 - Last modified: 2010-08-08 - Webmaster: webmaster@actaorthop.org