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Journal of Clinical Epidemiology Feb 2022In a recent paper we suggest that the relative risk (RR) be replaced with the odds ratio (OR) as the effect measure of choice in clinical epidemiology. In response, Chu,... (Meta-Analysis)
Meta-Analysis
OBJECTIVES
In a recent paper we suggest that the relative risk (RR) be replaced with the odds ratio (OR) as the effect measure of choice in clinical epidemiology. In response, Chu, and colleagues raise several points that argue for the status quo. In this paper, we respond to their response.
STUDY DESIGNS AND SETTINGS
We use the same examples given by Chu and colleagues to recompute estimates of effect and demonstrate the problem with the RR.
RESULTS
We reaffirm the following findings: a) the OR and RR measure different things and their numerical difference is only important if misinterpreted b) this potential misinterpretation is a trivial issue compared to the lack of portability of the RR c) the same examples reaffirm non-portability of the RR and demonstrate how misleading the results might be in contrast to the OR, which is independent of the baseline risk d) the concept of non-collapsibility for the OR should be expected in the presence of a non-confounding risk factor, and is not a bias e) the log link in regression models that generate RRs as well as the use of RRs in meta-analysis is shown to be problematic using the same examples.
CONCLUSION
The OR should replace the RR in clinical research and meta-analyses though there should be conversion of the end product into ratios or differences of risk, solely, for interpretation. To this end we provide a Stata module (logittorisk) for this purpose.
Topics: Humans; Odds Ratio; Risk
PubMed: 34380019
DOI: 10.1016/j.jclinepi.2021.08.003 -
Controlled Clinical Trials Aug 1997
Topics: Humans; Odds Ratio; Predictive Value of Tests; Reference Values
PubMed: 9257075
DOI: 10.1016/s0197-2456(97)90013-1 -
Postgraduate Medicine May 2015Use of odds ratio (OR) in randomized controlled trials (RCTs) has been criticized because it overestimates the effect size, if incorrectly interpreted as risk ratio...
OBJECTIVE
Use of odds ratio (OR) in randomized controlled trials (RCTs) has been criticized because it overestimates the effect size, if incorrectly interpreted as risk ratio (RR). To what extent does this make a difference in the context of clinical research is unclear. We, therefore, aimed to address this issue considering its importance in evidence-based practice of medicine.
METHODS
We reviewed 580 RCTs published in the New England Journal of Medicine between January 2004 and June 2014 and identified 107 RCTs that reported unadjusted RR (n = 76) or OR (n = 31) for the primary outcome. For studies reporting ORs, we calculated RRs, and vice versa, using Stata software. The percentage of divergence between the reported and calculated effect size estimates was analyzed.
RESULTS
None of the RCTs showed a statistically significant result becoming insignificant or vice versa depending on the effect size estimate. OR exaggerated the RR in 62% of the RCTs. The percentage of overestimation was > 50% in 28 RCTs and > 100% in 13 RCTs. The degree of overestimation was positively correlated with the prevalence of outcomes (spearman's rho = 0.84 and 0.66, p < 0.001).
CONCLUSION
Use of OR instead of RR in RCTs does not change the qualitative inference of results. However, the use of OR can markedly exaggerate the effect size in RCTs if misinterpreted as RR and, hence, has the potential to mislead clinicians.
Topics: Evidence-Based Medicine; Odds Ratio; Randomized Controlled Trials as Topic; Statistics as Topic
PubMed: 25746068
DOI: 10.1080/00325481.2015.1022494 -
BMC Infectious Diseases Jan 2014Greater use of antibiotics during the past 50 years has exerted selective pressure on susceptible bacteria and may have favoured the survival of resistant strains.... (Meta-Analysis)
Meta-Analysis Review
BACKGROUND
Greater use of antibiotics during the past 50 years has exerted selective pressure on susceptible bacteria and may have favoured the survival of resistant strains. Existing information on antibiotic resistance patterns from pathogens circulating among community-based patients is substantially less than from hospitalized patients on whom guidelines are often based. We therefore chose to assess the relationship between the antibiotic resistance pattern of bacteria circulating in the community and the consumption of antibiotics in the community.
METHODS
Both gray literature and published scientific literature in English and other European languages was examined. Multiple regression analysis was used to analyse whether studies found a positive relationship between antibiotic consumption and resistance. A subsequent meta-analysis and meta-regression was conducted for studies for which a common effect size measure (odds ratio) could be calculated.
RESULTS
Electronic searches identified 974 studies but only 243 studies were considered eligible for inclusion by the two independent reviewers who extracted the data. A binomial test revealed a positive relationship between antibiotic consumption and resistance (p < .001) but multiple regression modelling did not produce any significant predictors of study outcome. The meta-analysis generated a significant pooled odds ratio of 2.3 (95% confidence interval 2.2 to 2.5) with a meta-regression producing several significant predictors (F(10,77) = 5.82, p < .01). Countries in southern Europe produced a stronger link between consumption and resistance than other regions.
CONCLUSIONS
Using a large set of studies we found that antibiotic consumption is associated with the development of antibiotic resistance. A subsequent meta-analysis, with a subsample of the studies, generated several significant predictors. Countries in southern Europe produced a stronger link between consumption and resistance than other regions so efforts at reducing antibiotic consumption may need to be strengthened in this area. Increased consumption of antibiotics may not only produce greater resistance at the individual patient level but may also produce greater resistance at the community, country, and regional levels, which can harm individual patients.
Topics: Anti-Bacterial Agents; Drug Resistance, Microbial; Europe; Humans; Odds Ratio
PubMed: 24405683
DOI: 10.1186/1471-2334-14-13 -
American Journal of Epidemiology Nov 2023The classical Cornfield inequalities state that if a third confounding variable is fully responsible for an observed association between the exposure and the outcome...
The classical Cornfield inequalities state that if a third confounding variable is fully responsible for an observed association between the exposure and the outcome variables, then the association between both the exposure and the confounder, and the confounder and the outcome, must be at least as strong as the association between the exposure and the outcome, as measured by the risk ratio. The work of Ding and VanderWeele on assumption-free sensitivity analysis sharpens this bound to a bivariate function of the 2 risk ratios involving the confounder. Analogous results are nonexistent for the odds ratio, even though the conversion from odds ratios to risk ratios can sometimes be problematic. We present a version of the classical Cornfield inequalities for the odds ratio. The proof is based on the mediant inequality, dating back to ancient Alexandria. We also develop several sharp bivariate bounds of the observed association, where the 2 variables are either risk ratios or odds ratios involving the confounder.
Topics: Humans; Odds Ratio; Confounding Factors, Epidemiologic
PubMed: 37312597
DOI: 10.1093/aje/kwad137 -
Jornal Brasileiro de Pneumologia :... Apr 2020
Topics: Clinical Trials as Topic; Humans; Logistic Models; Odds Ratio; Risk Factors
PubMed: 32321148
DOI: 10.36416/1806-3756/e20200137 -
Journal of Evidence-based Medicine Sep 2022The odds ratio (OR) has been misunderstood in evidence based medicine and clinical epidemiology. Currently, "noncollapsibility" is considered a problem with...
The odds ratio (OR) has been misunderstood in evidence based medicine and clinical epidemiology. Currently, "noncollapsibility" is considered a problem with interpretation of the OR and it is thought that the OR is rarely the parameter of interest for causal inference or interpretation of effect modification. The current focus on the relative risk (RR) and risk difference (RD) suffers from an important limitation: they are not solely measures of effect and vary numerically with baseline risk. In this paper, generalized linear models are examined in terms of the three binary effect measures commonly used in epidemiology to demonstrate that ORs may be the only way to interpret effect modification and have properties that should make them the parameter of interest for causal inference. We look forward to discussion, debate, and counter-views on this issue from the epidemiology community.
Topics: Causality; Linear Models; Odds Ratio; Risk
PubMed: 36138553
DOI: 10.1111/jebm.12495 -
Pharmacoepidemiology and Drug Safety Aug 2004
Topics: Drug-Related Side Effects and Adverse Reactions; Humans; Odds Ratio
PubMed: 15317032
DOI: 10.1002/pds.1002 -
Tidsskrift For Den Norske Laegeforening... Apr 2019
Topics: Data Interpretation, Statistical; Humans; Odds Ratio; Risk
PubMed: 30969040
DOI: 10.4045/tidsskr.19.0011 -
American Journal of Orthodontics and... Dec 2012
Topics: Odds Ratio
PubMed: 23195376
DOI: 10.1016/j.ajodo.2012.08.003