Essential EBP Statistics: Odds Ratios and Measures of Risk Reduction

Introduction

The primary focus of EBP in health care is the use of best evidence, clinical expertise, and patient values to determine decisions regarding patient care better. At the core of the idea of EBP, one should be able to comprehend and understand statistical measures implied in research findings. In this regard, the impact of basic statistical indicators cannot be overestimated while considering the evidence of significance and relevance of research outcomes. Exploring basic EBP statistics, including odds ratios, relative risk reduction, and absolute risk reduction, supplies healthcare practitioners with the tools to assess the practical value of research results and make evidence-based decisions in clinical practice.

Essential EBP Statistics

Healthcare professionals involved in evidence-based practice should learn the basics of EBP statistics and understand essential data foundations such as odds ratios and risk reduction measures (Sheldon, 2001). Odds ratios play an important role in causal research because they describe how the likelihood of having an outcome is related to exposure to a risk factor. It can be determined, and hence, the strength and direction of the relationship can be studied by ascertaining the odds of an event between two groups. Of particular importance is that this statistical measure is well-suited to case-control studies since they use it to infer the relation between the exposures and outcomes in a retrospective manner. Interpretation of the odds ratios is based on whether it is greater than, equal to, or less than 1, representing increased, no change, or decreased risks whereby one individual is likely to develop a particular outcome compared with another.

In clinical practice, risk reduction measures are also important to measure the impact of interventions, including absolute risk reduction (ARR) and relative risk reduction (RRR), as well as odds ratios. The effectiveness of ARR is in quantifying the difference between the risk of a group treated with an intervention and one that has only been given a placebo; this allows clinicians to measure the absolute impact of the intervention in reducing adverse outcomes. At the same time, RRR specificates the proportionate reduction in risk achieved by the intervention compared to the baseline risk in the control group. Both measures provide meaningful information regarding the clinical importance of interventions for providers to determine the practical implications of research results and make the right decision regarding its implementation into therapeutic care.

In addition, clinical mastery of fundamental EBP statistics helps healthcare practitioners to read and search for relevant literature on research critically and differentiate statistically significant findings from clinically important results. The principles behind odds ratios and risk reduction measures may provide a framework for evaluating the magnitude of treatment effects and allow clinical practitioners to determine whether potential benefits outweigh the harms associated with various interventions. Notably, this competency not only increases the quality of care to patients but also enables healthcare professionals to participate meaningfully in evidence-based decision-making processes, leads to better results for patients, and contributes positively to medicine.

Computing Risk Ratios

For the new drug group, the risk of low hemoglobin is calculated as follows:

Risk of Low Hgb (New Drug) = Number of Patients with Low Hgb (New Drug) / Total Number of Patients (New Drug)

= 476 / 10,052

≈ 0.047

Similarly, for the old drug group:

Risk of Low Hgb (Old Drug) = Number of Patients with Low Hgb (Old Drug) / Total Number of Patients (Old Drug)

= 798 / 10,050

≈ 0.079

Analysis of Research Article

As Barratt et al. (2004) proposed, the paper’s hypothesis concerns how science presents essential concepts of evidence-based medicine such as RRR, ARR, and NNT. In the authors’ attempt to reveal these principles and offer working strategies for practitioners to understand and apply them in practice, they aim to clarify these ideals and formulate practical guidelines for clinicians to understand and use them effectively. In this context, the dependent variable, or the object of interest in the study, is the understanding and performance of clinicians regarding risk reduction measures. In contrast, the independent variables are measures and a presentation way to convey them.

In the context of how the findings presented in the article are achieved through hypothetical scenarios and calculations, relative risk reduction, absolute risk reduction, and the number needed to treat are demonstrated and determined. They offer illustrative cases from clinical trials and systematic reviews of how these ideas are used in practical circumstances. For example, they provide the tables and equations that enable clinicians to calculate and interpret these parameters according to the data drawn from published studies. Therefore, The study results underscore the significance of considering treatments’ relative and absolute impacts in the clinical deduction.

The presented information is intended to assist clinicians in understanding the risk reduction measures and their application in practice. The authors use practical tips and exercises to answer that being the offspring of scientific work, clinicians should be able to critically evaluate research findings and make well-founded decisions based on individual patient needs. Nevertheless, it is important to note that the statistical significance of the findings may differ in cases of using other specific data and analyses. As a result, the clinical significance lies in the relevance of patient care and treatment strategies. Barratt et al. provide an interesting perspective regarding a toolkit that can close the divide between research evidence and practice in evidence-based medicine.

References

Barratt, A., Wyer, P. C., Hatala, R., McGinn, T., Dans, A. L., Keitz, S., & Moyer, V. (2004). Tips for learners of evidence-based medicine: 1. Relative risk reduction, absolute risk reduction, and number needed to treat. Cmaj, 171(4), 353-358. https://www.cmaj.ca/content/171/4/353.short

Sheldon, T. A. (2001). Biostatistics and study design for evidence-based practice. AACN Advanced Critical Care, 12(4), 546-559. https://aacnjournals.org/aacnacconline/article-abstract/12/4/546/13942