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Archives of Iranian Medicine Oct 2015Binary outcomes are common in prospective studies such as randomized controlled trials and cohort studies. Logistic regression is the most popular regression model for... (Review)
Review
BACKGROUND
Binary outcomes are common in prospective studies such as randomized controlled trials and cohort studies. Logistic regression is the most popular regression model for binary outcomes. Logistic regression yields an odds ratio that approximates the risk ratio when the risk of outcome is low. A consensus has been reached in an extensive argument in much of the literature that the risk ratio is preferred over the odds ratio for prospective studies. To obtain a model-based estimate of risk ratios, log-binomial regression has been recommended. However, this model may fail to converge and many methods have been provided as an alternative in these situations.
METHODS
In this paper, we discuss the methods to obtain adjusted risk ratios in settings with independent and clustered data and we will review the results of comparisons between these methods based on simulation studies, especially a large simulation study which was conducted by the authors. We use hypothetical examples to show how log-Poisson regression with modified standard errors can be used to estimate risk ratio in practice using popular statistical software.
CONCLUSION
The potential misinterpretation of odds ratios should be considered by researchers, especially when the risk of the outcome is high. When researchers want to estimate the effect of exposure or intervention by controlling potential covariates, the misinterpretation of odds ratios can be avoided using regression models that can estimate risk ratios instead of logistic regression. The log-Poisson regression with modified standard errors can be considered to estimate risk ratios in both independent and clustered data settings.
Topics: Logistic Models; Odds Ratio; Prospective Studies; Risk; Software
PubMed: 26443254
DOI: No ID Found -
Obstetrics and Gynecology Oct 2001To 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...
OBJECTIVE
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.
Topics: Data Interpretation, Statistical; Gynecology; Obstetrics; Odds Ratio; Periodicals as Topic; Risk
PubMed: 11576589
DOI: 10.1016/s0029-7844(01)01488-0 -
Biometrical Journal. Biometrische... Mar 2023Meta-analysis of binary data is challenging when the event under investigation is rare, and standard models for random-effects meta-analysis perform poorly in such... (Meta-Analysis)
Meta-Analysis
Meta-analysis of binary data is challenging when the event under investigation is rare, and standard models for random-effects meta-analysis perform poorly in such settings. In this simulation study, we investigate the performance of different random-effects meta-analysis models in terms of point and interval estimation of the pooled log odds ratio in rare events meta-analysis. First and foremost, we evaluate the performance of a hypergeometric-normal model from the family of generalized linear mixed models (GLMMs), which has been recommended, but has not yet been thoroughly investigated for rare events meta-analysis. Performance of this model is compared to performance of the beta-binomial model, which yielded favorable results in previous simulation studies, and to the performance of models that are frequently used in rare events meta-analysis, such as the inverse variance model and the Mantel-Haenszel method. In addition to considering a large number of simulation parameters inspired by real-world data settings, we study the comparative performance of the meta-analytic models under two different data-generating models (DGMs) that have been used in past simulation studies. The results of this study show that the hypergeometric-normal GLMM is useful for meta-analysis of rare events when moderate to large heterogeneity is present. In addition, our study reveals important insights with regard to the performance of the beta-binomial model under different DGMs from the binomial-normal family. In particular, we demonstrate that although misalignment of the beta-binomial model with the DGM affects its performance, it shows more robustness to the DGM than its competitors.
Topics: Odds Ratio; Computer Simulation; Models, Statistical; Linear Models
PubMed: 36216590
DOI: 10.1002/bimj.202200132 -
Journal of Clinical Epidemiology Feb 2022A recent paper by Doi et al. advocated completely replacing the relative risk (RR) with the odds ratio (OR) as the effect measure in clinical trials and meta-analyses... (Meta-Analysis)
Meta-Analysis
Controversy and Debate: Questionable utility of the relative risk in clinical research: Paper 2: Is the Odds Ratio "portable" in meta-analysis? Time to consider bivariate generalized linear mixed model.
OBJECTIVES
A recent paper by Doi et al. advocated completely replacing the relative risk (RR) with the odds ratio (OR) as the effect measure in clinical trials and meta-analyses with binary outcomes. Besides some practical advantages of RR over OR, Doi et al.'s key assumption that the OR is "portable" in the meta-analysis, that is, study-specific ORs are likely not correlated with baseline risks, was not well justified.
STUDY DESIGNS AND SETTINGS
We summarized Spearman's rank correlation coefficient between study-specific ORs and baseline risks in 40,243 meta-analyses from the Cochrane Database of Systematic Reviews.
RESULTS
Study-specific ORs tend to be higher in studies with lower baseline risks of disease for most meta-analyses in Cochrane Database of Systematic Reviews. Using an actual meta-analysis example, we demonstrate that there is a strong negative correlation between OR (RR or RD) with the baseline risk and the conditional effects notably vary with baseline risks.
CONCLUSIONS
Replacing RR or RD with OR is currently unadvisable in clinical trials and meta-analyses. It is possible that no effect measure is "portable" in a meta-analysis. In addition to the overall (or marginal) effect, we suggest presenting the conditional effect based on the baseline risk using a bivariate generalized linear mixed model.
Topics: Humans; Linear Models; Odds Ratio; Risk; Systematic Reviews as Topic
PubMed: 34384876
DOI: 10.1016/j.jclinepi.2021.08.004 -
Research Synthesis Methods Sep 2023For estimation of heterogeneity variance in meta-analysis of log-odds-ratio, we derive new mean- and median-unbiased point estimators and new interval estimators based... (Meta-Analysis)
Meta-Analysis
For estimation of heterogeneity variance in meta-analysis of log-odds-ratio, we derive new mean- and median-unbiased point estimators and new interval estimators based on a generalized statistic, , in which the weights depend on only the studies' effective sample sizes. We compare them with familiar estimators based on the inverse-variance-weights version of , In an extensive simulation, we studied the bias (including median bias) of the point estimators and the coverage (including left and right coverage error) of the confidence intervals. Most estimators add to each cell of the table when one cell contains a zero count; we include a version that always adds . The results show that: two of the new point estimators and two of the familiar point estimators are almost unbiased when the total sample size and the probability in the Control arm ( ) is 0.1, and when and is 0.2 or 0.5; for , all estimators have negative bias for small to medium sample sizes, but for larger sample sizes some of the new median-unbiased estimators are almost median-unbiased; choices of interval estimators depend on values of parameters, but one of the new estimators is reasonable when and another, when or ; and lack of balance between left and right coverage errors for small and/or implies that the available approximations for the distributions of and are accurate only for larger sample sizes.
Topics: Odds Ratio; Probability; Computer Simulation; Sample Size; Bias
PubMed: 37381621
DOI: 10.1002/jrsm.1647 -
Nutrition (Burbank, Los Angeles County,... Jun 2000
Topics: Health Status; Humans; Nutritional Status; Odds Ratio
PubMed: 10869907
DOI: 10.1016/s0899-9007(00)00286-0 -
International Journal of Epidemiology Dec 2023
Topics: Odds Ratio; Regression Analysis
PubMed: 37244649
DOI: 10.1093/ije/dyad077 -
International Journal of Epidemiology Dec 2023
Topics: Humans; Odds Ratio; Algorithms
PubMed: 37253586
DOI: 10.1093/ije/dyad076 -
International Journal of Cardiology Mar 2013
Topics: Humans; Meta-Analysis as Topic; Odds Ratio; Risk
PubMed: 22795708
DOI: 10.1016/j.ijcard.2012.06.078 -
American Journal of Epidemiology Sep 2022Previous papers have mentioned that conditioning on a binary collider would introduce an association between its causes in at least 1 stratum. In this paper, we prove...
Previous papers have mentioned that conditioning on a binary collider would introduce an association between its causes in at least 1 stratum. In this paper, we prove this statement and, along with intuitions, formally examine the direction and magnitude of the associations between 2 risk factors of a binary collider using interaction contrasts. Among level one of the collider, 2 variables are independent, positively associated, and negatively associated if multiplicative risk interaction contrast is equal to, more than, and less than 0, respectively; the same results hold for the other level of the collider if the multiplicative survival interaction contrast, equal to multiplicative risk interaction contrast minus the additive risk interaction contrast, is compared with 0. The strength of the association depends on the magnitude of the interaction contrast: The stronger the interaction is, the larger the magnitude of the association will be. However, the common conditional odds ratio under the homogeneity assumption will be bounded. A figure is presented that succinctly illustrates our results and helps researchers to better visualize the associations introduced upon conditioning on a collider.
Topics: Bias; Causality; Humans; Odds Ratio; Risk Factors
PubMed: 35689644
DOI: 10.1093/aje/kwac103