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Expression and interpretation of relative risk and odds ratio in biomedical research studies
Indian Journal of Community Health
Relative risk and odds ratio are commonly used in the biomedical research studies; however, expression and interpretation must be done very carefully. A risk ratio and an odds ratio are used in cohort studies but only odds ratio is used in case control studies. However, relative risk or risk ratio is found to be frequently used in the interventional biomedical research studies. The relative risk and odds ratio provide important information regarding the effect of a risk factor on the outcome of interest. The relative risk and odds ratio of 1 suggests that there is no difference between two groups. A value >1 suggests increase risk, while a value <1 suggest reduction of risk. If the confidence interval meets or includes value 1.00 (line of no difference) indicates there is no difference between the groups.
Statistics review 11: assessing risk
Critical care (London, England), 2004
Relative risk and odds ratio have been introduced in earlier reviews (see Statistics reviews 3, 6 and 8). This review describes the calculation and interpretation of their confidence intervals. The different circumstances in which the use of either the relative risk or odds ratio is appropriate and their relative merits are discussed. A method of measuring the impact of exposure to a risk factor is introduced. Measures of the success of a treatment using data from clinical trials are also considered.
International Journal of Environmental Research and Public Health
Evidence-based medicine is predicated on the integration of best available research evidence with clinical expertise and patient values to inform care. In medical research, several distinct measures are commonly used to describe the associations between variables, and a sound understanding of these pervasive measures is foundational in the clinician’s ability to interpret, synthesize, and apply available evidence from the medical literature. Accordingly, this article aims to provide an educational tutorial/topic primer on some of the most ubiquitous measures of association and risk quantification in medical research, including odds ratios, relative risk, absolute risk, and number needed to treat, using several real-world examples from the medical literature.
Measures of effect: relative risks, odds ratios, risk difference, and ‘number needed to treat’
2007
Epidemiological studies aim at assessing the relationship between exposures and outcomes. Clinicians are interested in knowing not only whether a link between a given exposure (e.g. smoking) and a certain outcome (e.g. myocardial infarction) is statistically significant, but also the magnitude of this relationship. The 'measures of effect' are indexes that summarize the strength of the link between exposures and outcomes and can help the clinician in taking decisions in every day clinical practice. In epidemiological studies, the effect of exposure can be measured both in relative and absolute terms. The risk ratio, the incidence rate ratio, and the odds ratio are relative measures of effect. Risk difference is an absolute measure of effect and it is calculated by subtracting the risk of the outcome in exposed individuals from that of unexposed.
Evaluating and Interpreting Epidemiological Risk
Hospital Pharmacy, 2000
This special feature sets forth the basic skills pharmacists need to evaluate the primary literature with regard to epidemiological risk. The author discusses (1) the three key types of observational study designs used in epidemiological literature; (2) common measures of association and and their utility; (3) calculation of attributable risk, relative risk, and odds ratio; (4) the limitations of using relative risk and odds ratio; and (5) inappropriate use of risk in the primary literature.
Annals of Epidemiology, 2002
In cohort studies of common outcomes, odds ratios (ORs) may seriously overestimate the true effect of an exposure on the outcome of interest (as measured by the risk ratio [RR]). Since few study designs require ORs (most frequently, case-control studies), their popularity is due to the widespread use of logistic regression. Because ORs are used to approximate RRs so frequently, methods have been published in the general medical literature describing how to convert ORs to RRs; however, these methods may produce inaccurate confidence intervals (CIs). The authors explore the use of binomial regression as an alternative technique to directly estimate RRs and associated CIs in cohort studies of common outcomes. METHODS: Using actual study data, the authors describe how to perform binomial regression using the SAS System for Windows, a statistical analysis program widely used by US health researchers. RESULTS: In a sample data set, the OR for the exposure of interest overestimated the RR more than twofold. The 95% CIs for the OR and converted RR were wider than for the directly estimated RR. CONCLUSIONS: The authors argue that for cohort studies, the use of logistic regression should be sharply curtailed, and that instead, binomial regression be used to directly estimate RRs and associated CIs.
Value in Health, 2002
Introduction: Statistical analyses of data and making sense of medical data have received much attention in the medical literature, but nevertheless have caused confusion among practitioners. Each researcher provides a different method for comparing treatments. For example, when the end point is binary, such as disease versus no disease, the common measures are odds ratios, relative risk, relative risk reduction, absolute risk reduction, and the number needed to treat. The question faced by the practitioner is then: Which one will help me in choosing the best treatment for my patient?
On the estimation of relative risk from vital statistical data
Journal of Epidemiology & Community Health, 1979
A method is described for the determination of a measure of relative risk from vital statistical data. If the frequency of disease in a population is linearly related to the level of exposure to a given factor, then a measure of the relative risk can be estimated from the slope and intercept of the regression line. For example, when the exposure is measured in terms of the proportion of the population exposed to the factor, then the relative risk is equal to slope +1
Quantitative Health Risk Assessment
New South Wales Public Health Bulletin, 2003
This issue of the NSW Public Health Bulletin focuses on the application of quantitative health risk assessment in public health decision-making. Over the last decade, these assessments have become a common currency for government, industry, and public health officials. Risk assessment, however, is not a value-free science. Underpinning the practice are notions of what health is, what risks are tolerable, what constitutes evidence, and the legitimacy of government intervention in the management of risk. Prioritising health risk assessment: National and international practice Chemicals used in food production, household products, textiles, medicines, and automobiles, underpin modern life. Global production of industrial chemicals has increased from one million tonnes in 1930 to 400 million tonnes today. The number of chemicals marketed in TABLE 2 THE LONDON PRINCIPLES FOR EVALUATING EPIDEMIOLOGIC DATA IN REGULATORY RISK ASSESSMENT Principles for Evaluating an Epidemiologic Report for Cause-Effect Relationship A1 The population studied should be pertinent to the risk assessment at hand, and it should be representative of a well-defined underlying cohort or population at risk. A2 Study procedures should be described in sufficient detail, or available from the study's written protocol, to determine whether appropriate methods were used in the design and conduct of the investigation. A3 The measures of exposure(s) or exposure surrogates should be: • conceptually relevant to the risk assessment being conducted; • based on principles that are biologically sound in light of present knowledge; • properly quantitated to assess dose-response relationships. A4 Study outcomes (endpoints) should be clearly defined, properly measured, and ascertained in an unbiased manner. A5 The analysis of the study's data should provide both point and interval estimates of the exposure's effect, including adjustment for confounding, assessment of interaction (for example, effect of multiple exposures or differential susceptibility), and an evaluation of the possible influence of study bias. A6 The reporting of the study should clearly identify both its strengths and limitations, and the interpretation of its findings should reflect not only an honest consideration of those factors, but also its relationship to the current state of knowledge in the area. The overall study quality should be sufficiently high that it would be judged publishable in a peer-reviewed scientific journal.
An odd measure of risk: use and misuse of the odds ratio
Obstetrics & Gynecology, 2001
To determine how often the odds ratio, as used in clinical research of obstetrics and gynecology, differs substantially from the risk ratio estimate and to assess whether the difference in these measures leads to misinterpretation of research results. METHODS: Articles from 1998 through 1999 in Obstetrics & Gynecology and the American Journal of Obstetrics and Gynecology were searched for the term "odds ratio." The key odds ratio in each article was identified, and, when possible, an estimated risk ratio was calculated. The odds ratios and the estimated risk ratios were compared quantitatively and graphically. RESULTS: Of 151 studies using odds ratios, 107 were suitable to estimate a risk ratio. The difference between the odds ratio and the estimated risk ratio was greater than 20% in 47 (44%) of these articles. An odds ratio appears to magnify an effect compared with a risk ratio. In 39 (26%) articles the odds ratio was interpreted as a risk ratio without explicit justification. CONCLUSION: The odds ratio is frequently used, and often misinterpreted, in the current literature of obstetrics and gynecology.