Statistical presentation and analysis of ordered categorical outcome data in rheumatology journals

Abstract

Objective. To assess the appropriateness of presentation of summary measures and analysis of ordered categorical (ordinal) data in three rheumatology journals in 1999, and to consider differences between basic and clinical science articles. Methods. Six hundred forty-four full-length articles from the 1999 editions of 3 rheumatology journals were evaluated for inclusion of an ordinal outcome. Articles were classified as basic or clinical science, and the appropriateness of presentation and analysis of the ordinal outcome were assessed. Chi-square tests were used to evaluate difference in percentages. Results. Ordinal outcomes were identified in 175 (27.2%) of 644 articles. Only 69 (39.4%) had appropriate data presentation, and 111 (63.4%) had appropriate data analysis. Appropriate presentation was seen less commonly in the basic science rather than the clinical science articles, but differences in the occurrence of appropriate analysis were not seen. Conclusion.Ordinal data are common in rheumatology articles, but presentation usually does not conform to recommended guidelines. KEY WORDS. Ordinal; Summary statistics; Hypothesis tests; Estimation.Ordinal data are generated when observations are placed into ordered categories. Such data are often generated by scoring radiographs or histologic slides, or from evaluating questionnaire responses. Ordinal data contain more information than categorical data without ordering (nominal data), but do not contain as much information as continuously measured data. This makes presentation of summary measures and hypothesis testing with ordinal data challenging.Previous analyses of medical research articles have suggested that ordinal outcome data is often presented or analyzed in ways that do not account for either the ordering or the categorical structure of the data (1-3). This can lead to biased estimates and reduced ability (low power) to detect important effects. Ordinal variables may be dichotomized as being above or below a fixed cut-off value and treated as binary (0/1), but this combines different levels together and can sacrifice information from the original scale (1). Contingency table methods that are appropriate for unordered categorical data do not take advantage of ordering in the data, resulting in loss of information and difficulty in interpretation (1). Methods for continuous data, such as the mean, standard deviation, Student's ttest, and F test, make several assumptions (e.g., consistent spacing, symmetry, and normality of the data distribution) that are generally not satisfied by ordinal data. As noted by Altman and Bland, "Although some statistical methods, such as the t-test, are not sensitive to moderate departures from normality, it is generally preferable not to rely on this feature"(4).To use the order information in ordinal data, but to avoid unnecessary assumptions, biostatistics textbooks (5) and journal articles (1,3,4,6 -9) have recommended that nonparametric methods based on ranking the data be used. These methods include use of percentiles,...

Frequency of appropriate and inappropriate presentation and analysis methods of ordered categorical data in the veterinary dermatology literature from January 2003 to June 2006

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“…Clinical outcomes measured with ordinal scales are often presented and analysed with inappropriate statistical methods in the medical literature. In the fields of anaesthesia, rheumatology, and nursing, several studies indicated that ordinal data were presented appropriately in only 39–49% and analysed appropriately in 57–63% of journal articles 4–6 …”

Impact of your results: Beyond the relative risk

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Clinical studies utilising ordinal data: Pitfalls in the analysis and interpretation of clinical grading systems

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