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International Urology and Nephrology Dec 2023The assessment of risk and effect size of a specific endpoint associated to the presence/absence of a certain exposure is a hallmark in clinical and epidemiological... (Review)
Review
The assessment of risk and effect size of a specific endpoint associated to the presence/absence of a certain exposure is a hallmark in clinical and epidemiological research. In fact, before recommending any treatment, it is mandatory to investigate the magnitude of the benefits and harms between the exposure under investigation (e.g. a given treatment) and a specific disease or event. To do this, clinicians and statisticians use absolute (risk differences, number needed to treat, likelihood to be helped or harmed) and relative (risk ratio, incidence rate ratio, hazard ratio and odds ratio) measures of effect. Herein, using a series of clinical examples, we aim to present a step by step methodologic approach of measures of effect in the area of nephrology and urology.
Topics: Humans; Probability; Odds Ratio; Incidence; Language
PubMed: 37162698
DOI: 10.1007/s11255-023-03626-w -
International Journal of Cardiology Jun 2013
Topics: Humans; Meta-Analysis as Topic; Odds Ratio; Risk
PubMed: 23084114
DOI: 10.1016/j.ijcard.2012.09.139 -
Statistical Methods in Medical Research Nov 2018Determining conditional dependence is a challenging but important task in both model building and in applications such as genetic association studies and graphical...
Determining conditional dependence is a challenging but important task in both model building and in applications such as genetic association studies and graphical models. Research on this topic has focused on kernel-based methods or has used categorical conditioning variables because of the challenge of the curse of dimensionality. To overcome this challenge, we propose a class of tests for conditional independence without any restriction on the distribution of the conditioning variables. The proposed test statistic can be treated as a generalized weighted Kendall's tau, in which the generalized odds ratio is utilized as a weight function to account for the distance between different values of the conditioning variables. The test procedure has desirable asymptotic properties and is easy to implement. We evaluate the finite sample performance of the proposed test through simulation studies and illustrate it using two real data examples.
Topics: Genetic Association Studies; Models, Statistical; Odds Ratio; Statistics, Nonparametric
PubMed: 29298614
DOI: 10.1177/0962280217695345 -
Research Synthesis Methods Nov 2021The Peto odds ratio is a well-known effect measure in meta-analysis of binary outcomes. For pairwise comparisons, the Peto odds ratio estimator can be severely biased in... (Meta-Analysis)
Meta-Analysis
The Peto odds ratio is a well-known effect measure in meta-analysis of binary outcomes. For pairwise comparisons, the Peto odds ratio estimator can be severely biased in the situation of unbalanced sample sizes in the two treatment groups or large treatment effects. In this publication, we evaluate Peto odds ratio estimators in the setting of multi-arm studies and in network meta-analysis using illustrative examples. We observe that Peto odds ratio estimators in a multi-arm study are inconsistent if the observed event probabilities are different or the sample sizes of treatment groups are unbalanced. The same problem emerges in network meta-analysis including only two-arm studies and translates to indirect comparisons of pairwise meta-analyses. We conclude that the Peto odds ratio should not be used as effect measure in network meta-analysis or indirect comparisons of pairwise meta-analyses.
Topics: Network Meta-Analysis; Odds Ratio
PubMed: 34133079
DOI: 10.1002/jrsm.1503 -
American Journal of Epidemiology Jun 2013Epidemiologic studies often aim to estimate the odds ratio for the association between a binary exposure and a binary disease outcome. Because confounding bias is of...
Epidemiologic studies often aim to estimate the odds ratio for the association between a binary exposure and a binary disease outcome. Because confounding bias is of serious concern in observational studies, investigators typically estimate the adjusted odds ratio in a multivariate logistic regression which conditions on a large number of potential confounders. It is well known that modeling error in specification of the confounders can lead to substantial bias in the adjusted odds ratio for exposure. As a remedy, Tchetgen Tchetgen et al. (Biometrika. 2010;97(1):171-180) recently developed so-called doubly robust estimators of an adjusted odds ratio by carefully combining standard logistic regression with reverse regression analysis, in which exposure is the dependent variable and both the outcome and the confounders are the independent variables. Double robustness implies that only one of the 2 modeling strategies needs to be correct in order to make valid inferences about the odds ratio parameter. In this paper, I aim to introduce this recent methodology into the epidemiologic literature by presenting a simple closed-form doubly robust estimator of the adjusted odds ratio for a binary exposure. A SAS macro (SAS Institute Inc., Cary, North Carolina) is given in an online appendix to facilitate use of the approach in routine epidemiologic practice, and a simulated data example is also provided for the purpose of illustration.
Topics: Environmental Exposure; Logistic Models; Odds Ratio
PubMed: 23558352
DOI: 10.1093/aje/kws377 -
Journal of Clinical Epidemiology Nov 2003Diagnostic testing can be used to discriminate subjects with a target disorder from subjects without it. Several indicators of diagnostic performance have been proposed,...
Diagnostic testing can be used to discriminate subjects with a target disorder from subjects without it. Several indicators of diagnostic performance have been proposed, such as sensitivity and specificity. Using paired indicators can be a disadvantage in comparing the performance of competing tests, especially if one test does not outperform the other on both indicators. Here we propose the use of the odds ratio as a single indicator of diagnostic performance. The diagnostic odds ratio is closely linked to existing indicators, it facilitates formal meta-analysis of studies on diagnostic test performance, and it is derived from logistic models, which allow for the inclusion of additional variables to correct for heterogeneity. A disadvantage is the impossibility of weighing the true positive and false positive rate separately. In this article the application of the diagnostic odds ratio in test evaluation is illustrated.
Topics: Diagnosis; Humans; Logistic Models; Meta-Analysis as Topic; Odds Ratio; Predictive Value of Tests; Sensitivity and Specificity
PubMed: 14615004
DOI: 10.1016/s0895-4356(03)00177-x -
BMJ (Clinical Research Ed.) Mar 1998
Review
Topics: Data Interpretation, Statistical; Humans; Odds Ratio; Risk Assessment
PubMed: 9550961
DOI: 10.1136/bmj.316.7136.989 -
The American Journal of Emergency... Oct 2020
Topics: Carbon Monoxide Poisoning; Humans; Odds Ratio; Outcome Assessment, Health Care; Retrospective Studies; Risk Factors
PubMed: 32811711
DOI: 10.1016/j.ajem.2020.07.087 -
Anesthesia and Analgesia Dec 2017Epidemiology is the study of how disease is distributed in populations and the factors that influence or determine this distribution. Clinical epidemiology denotes the... (Review)
Review
Epidemiology is the study of how disease is distributed in populations and the factors that influence or determine this distribution. Clinical epidemiology denotes the application of epidemiologic methods to questions relevant to patient care and provides a highly useful set of principles and methods for the design and conduct of quantitative clinical research. Validly analyzing, correctly reporting, and successfully interpreting the findings of a clinical research study often require an understanding of the epidemiologic terms and measures that describe the patterns of association between the exposure of interest (treatment or intervention) and a health outcome (disease). This statistical tutorial thus discusses selected fundamental epidemiologic concepts and terminology that are applicable to clinical research. Incidence is the occurrence of a health outcome during a specific time period. Prevalence is the existence of a health outcome during a specific time period. The relative risk can be defined as the probability of the outcome of interest (eg, developing the disease) among exposed individuals compared to the probability of the same event in nonexposed individuals. The odds ratio is a measure of risk that compares the frequency of exposure to a putative causal factor in the individuals with the health outcome (cases) versus those individuals without the health outcome (controls). Factors that are associated with both the exposure and the outcome of interest need to be considered to avoid bias in your estimate of risk. Because it takes into consideration the contribution of extraneous variables (confounders), the adjusted odds ratio provides a more valid estimation of the association between the exposure and the health outcome and thus is the preferably reported measure. The odds ratio closely approximates the risk ratio in a cohort study or a randomized controlled trial when the outcome of interest does not occur frequently (<10%). The editors, reviewers, authors, and readers of journal articles should be aware of and make the key distinction between the absolute risk reduction and the relative risk reduction. In assessing the findings of a clinical study, the investigators, reviewers, and readers must determine if the findings are not only statistically significant, but also clinically meaningful. Furthermore, in deciding on the merits of a new medication or other therapeutic intervention, the clinician must balance the benefits versus the adverse effects in individual patients. The number needed to treat and the number needed to harm can provide this needed additional insight and perspective.
Topics: Epidemiologic Studies; Humans; Odds Ratio; Probability; Terminology as Topic
PubMed: 29028741
DOI: 10.1213/ANE.0000000000002554 -
Journal of Advanced Nursing Dec 2020
Meta-Analysis
Topics: Humans; Odds Ratio; Outcome Assessment, Health Care
PubMed: 33047856
DOI: 10.1111/jan.14528