# Statistics

**General information**
Statistical evaluation is a vital part of many communications. These guidelines have been written for the benefit of sound scientific work and to help authors prepare their manuscripts in accordance with good statistical standards. The guidelines are applicable to retrospective clinical studies as well as to experimental studies, randomized clinical trials and epidemiological studies. However, all aspects are not equally important for all types of studies. For instance, randomized clinical trials typically include a given number of patients based on calculations of statistical power. In exploratory experimental studies, the number of units studied may be based on other considerations, but may still be justified.
The following general principles should also be followed: The investigator should ensure that his data are of high quality. All data should also be stored and retrievable at request. The use of a statistical method presupposes appropriate knowledge and understanding. Presentation of statistical results should focus on their clinical, not statistical, importance.

**Introduction**
State clearly the aim of the study and the studied hypothesis.

**Patients and methods**
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.

**Statistical report**
The relation between the studied hypothesis and the presented results from null hypothesis testing (p-values) should be clearly explained in the manuscript. The tests should be used with a defined effect size and the estimation uncertainty should be considered in the results presentation. Unless the use of 1-sided tests is specifically justified, the tests should be 2-sided. Present p-values with real numbers, if these are greater than 0.001 (1 digit except zeros), otherwise use ‘p < 0.001’. Do not use ‘ns’, ‘p > 0.05’ or asterisks. Note that statistical significance does in itself not support that a finding is clinically relevant, and statistical non-significance is not sufficient to show equivalence or absence of differences or effects. Acta Orthopaedica recommends authors to present analysis results with 95% confidence intervals instead of p-values.

All statistical methods should be clearly specified and, when necessary, (for unusual methods) referenced; for every statistical result, the method used should be clearly described. The fulfillment of the assumptions underlying the statistical methods is an important issue to address. No data should be removed, imputed, weighted, adjusted or trimmed unless this is clearly described and justified, and its consequences are explained. All null hypothesis 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 distribution-free techniques for testing non-parametric null hypotheses, e.g. 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.

Repeated measurements on the same subject, are correlated, not statistically independent. A statistical method allowing correlated observations should be used, e.g. a mixed model repeated measures ANOVA. 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 null hypotheses are tested with the aim of confirming a pre-specified hypothesis, care should be taken to avoid spurious significance by using techniques for simultaneous inference. Pre-specification is, however, necessary for confirmation. The use of techniques for simultaneous inference without a pre-specified null hypothesis should be explained and motivated.