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Clinically useful measures of the effects of treatment
  1. Alba DiCenso, RN, PhD1
  1. School of Nursing and Department of Clinical Epidemiology and Biostatistics Faculty of Health Sciences, McMaster University Hamilton, Ontario, Canada

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In the EBN notebooks that have appeared in the previous 2 issues of the journal, we outlined 3 steps to help us to determine whether to apply the results of a research study to our patients.1 Firstly, we should consider whether the study results are valid. For articles about the effectiveness of healthcare interventions, the 3 key validity issues are whether the patients were randomly assigned to different treatments, whether they were analysed according to the groups to which they were assigned, and the extent of follow up. Secondly, if we judge the study to be valid, we examine the study results to determine whether the new treatment is effective, the size of the effect, and whether the effect is clinically important. When determining the clinical significance of effective treatments, findings can be expressed in 3 ways: as a change in relative risk, change in absolute risk and number needed to treat (NNT). Abstracts in Evidence-Based Nursing that describe effective treatments include these numbers, when data permit their calculation. The third step, the application to an individual patient, requires knowledge about both the study and the patient. This involves consideration of both the extent to which the patient resembles those who were enrolled in the study and the patient's risk for the event for which the treatment was designed.2 This notebook will explain the concepts that help us to determine whether study findings should be applied to our own individual patients.

Let's work through a randomised controlled trial abstracted in this issue of the journal (p52) that evaluates the effectiveness of a cognitive behavioural family intervention in reducing psychological distress and depression in caregivers of patients with Alzheimer's disease.3 Addressing first the validity of this trial, we find that patient-caregiver dyads were randomly assigned to the 14 …

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Footnotes

  • * In this article, we use RRR to illustrate treatment effects. 2 other terms which are used to illustrate treatment effects are: relative benefit increase (RBI), defined as the proportional increase in rates of good outcomes between experimental and control participants in a trial, and relative risk increase (RRI), defined as the proportional increase in rates of bad outcomes between experimental and control participants in a trial. Both of these are calculated identically to RRR (ie, CER–EER/CER).4

  • Although we use ARR to illustrate treatment effects in this paper, 2 other terms which reflect absolute differences are absolute benefit increase (ABI), defined as the absolute arithmetic difference in rates of good outcomes between experimental and control participants in a trial, and absolute risk increase (ARI), defined as the absolute arithmetic difference in rates of bad outcomes between experimental and control participants in a trial. Both of these are calculated identically to ARR (ie, CER–EER).4