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Biometrical Journal. Biometrische... Nov 2016For the calculation of relative measures such as risk ratio (RR) and odds ratio (OR) in a single study, additional approaches are required for the case of zero events....
For the calculation of relative measures such as risk ratio (RR) and odds ratio (OR) in a single study, additional approaches are required for the case of zero events. In the case of zero events in one treatment arm, the Peto odds ratio (POR) can be calculated without continuity correction, and is currently the relative effect estimation method of choice for binary data with rare events. The aim of this simulation study is a variegated comparison of the estimated OR and estimated POR with the true OR in a single study with two parallel groups without confounders in data situations where the POR is currently recommended. This comparison was performed by means of several performance measures, that is the coverage, confidence interval (CI) width, mean squared error (MSE), and mean percentage error (MPE). We demonstrated that the estimator for the POR does not outperform the estimator for the OR for all the performance measures investigated. In the case of rare events, small treatment effects and similar group sizes, we demonstrated that the estimator for the POR performed better than the estimator for the OR only regarding the coverage and MPE, but not the CI width and MSE. For larger effects and unbalanced group size ratios, the coverage and MPE of the estimator for the POR were inappropriate. As in practice the true effect is unknown, the POR method should be applied only with the utmost caution.
Topics: Biometry; Computer Simulation; Humans; Models, Statistical; Odds Ratio; Risk
PubMed: 27546483
DOI: 10.1002/bimj.201600034 -
The Angle Orthodontist May 2022To map the statistical methods applied to assess reliability in orthodontic publications and to identify possible trends over time.
OBJECTIVES
To map the statistical methods applied to assess reliability in orthodontic publications and to identify possible trends over time.
MATERIALS AND METHODS
Original research articles published in 2009 and 2019 in a subset of orthodontic journals were downloaded. Publication characteristics, including publication year, number of authors, single vs multicenter study, geographic origin of the study, statistician involvement, study category, subject category, types of reliability assessment, and statistical methods applied to assess reliability, were recorded. Descriptive statistics, Chi-square tests, and logistic regression analyses were performed to investigate associations between reliability analysis and study characteristics.
RESULTS
A total of 768 original research articles were analyzed. The most prevalent study category was observational (69%) with a statistician involved in 16% of studies. Overall, reliability was assessed in 47% of studies, and the most frequent methods applied to assess reliability were intraclass correlation coefficients or kappa statistics (60.4%). The odds of applying appropriate methods were greater in 2019 than in 2009 (odds ratio [OR]: 2.43; 95% confidence interval [CI]: 1.75, 3.37; P < .001). Involvement of a statistician resulted in greater odds of applying appropriate methods compared to no statistician involvement (OR: 1.88; 95% CI: 1.23, 2.87; P < .01).
CONCLUSIONS
Over the past decade (2009 vs 2019), reliability assessment became more common in the orthodontic literature, and studies applying correct statistical methods to assess reliability significantly increased. This trend was more apparent in studies that involved a statistician, which may highlight the role of the statistician.
Topics: Odds Ratio; Reproducibility of Results; Research Design
PubMed: 35099528
DOI: 10.2319/081021-625.1 -
Occupational and Environmental Medicine Feb 1995
Topics: Cross-Sectional Studies; Odds Ratio; Proportional Hazards Models
PubMed: 7757169
DOI: 10.1136/oem.52.2.143 -
Journal of Clinical Epidemiology Feb 2022Recently Doi et al. argued that risk ratios should be replaced with odds ratios in clinical research. We disagreed, and empirically documented the lack of portability... (Meta-Analysis)
Meta-Analysis
Controversy and Debate : Questionable utility of the relative risk in clinical research: Paper 4 :Odds Ratios are far from "portable" - A call to use realistic models for effect variation in meta-analysis.
OBJECTIVE
Recently Doi et al. argued that risk ratios should be replaced with odds ratios in clinical research. We disagreed, and empirically documented the lack of portability of odds ratios, while Doi et al. defended their position. In this response we highlight important errors in their position.
STUDY DESIGN AND SETTING
We counter Doi et al.'s arguments by further examining the correlations of odds ratios, and risk ratios, with baseline risks in 20,198 meta-analyses from the Cochrane Database of Systematic Reviews.
RESULTS
Doi et al.'s claim that odds ratios are portable is invalid because 1) their reasoning is circular: they assume a model under which the odds ratio is constant and show that under such a model the odds ratio is portable; 2) the method they advocate to convert odds ratios to risk ratios is biased; 3) their empirical example is readily-refuted by counter-examples of meta-analyses in which the risk ratio is portable but the odds ratio isn't; and 4) they fail to consider the causal determinants of meta-analytic inclusion criteria: Doi et al. mistakenly claim that variation in odds ratios with different baseline risks in meta-analyses is due to collider bias. Empirical comparison between the correlations of odds ratios, and risk ratios, with baseline risks show that the portability of odds ratios and risk ratios varies across settings.
CONCLUSION
The suggestion to replace risk ratios with odds ratios is based on circular reasoning and a confusion of mathematical and empirical results. It is especially misleading for meta-analyses and clinical guidance. Neither the odds ratio nor the risk ratio is universally portable. To address this lack of portability, we reinforce our suggestion to report variation in effect measures conditioning on modifying factors such as baseline risk; understanding such variation is essential to patient-centered practice.
Topics: Bias; Causality; Humans; Odds Ratio; Risk; Systematic Reviews as Topic
PubMed: 34390790
DOI: 10.1016/j.jclinepi.2021.08.002 -
Obstetrics and Gynecology Feb 2008Odds and odds ratios are hard for many clinicians to understand. Odds are the probability of an event occurring divided by the probability of the event not occurring. An...
Odds and odds ratios are hard for many clinicians to understand. Odds are the probability of an event occurring divided by the probability of the event not occurring. An odds ratio is the odds of the event in one group, for example, those exposed to a drug, divided by the odds in another group not exposed. Odds ratios always exaggerate the true relative risk to some degree. When the probability of the disease is low (for example, less than 10%), the odds ratio approximates the true relative risk. As the event becomes more common, the exaggeration grows, and the odds ratio no longer is a useful proxy for the relative risk. Although the odds ratio is always a valid measure of association, it is not always a good substitute for the relative risk. Because of the difficulty in understanding odds ratios, their use should probably be limited to case-control studies and logistic regression, for which odds ratios are the proper measures of association.
Topics: Data Interpretation, Statistical; Humans; Incidence; Odds Ratio; Probability; Risk
PubMed: 18238982
DOI: 10.1097/01.AOG.0000297304.32187.5d -
Prognostic value of capillary refill time in adult patients: a systematic review with meta-analysis.Critical Care (London, England) Dec 2023Acute circulatory failure leads to tissue hypoperfusion. Capillary refill time (CRT) has been widely studied, but its predictive value remains debated. We conducted a... (Meta-Analysis)
Meta-Analysis Review
PURPOSE
Acute circulatory failure leads to tissue hypoperfusion. Capillary refill time (CRT) has been widely studied, but its predictive value remains debated. We conducted a meta-analysis to assess the ability of CRT to predict death or adverse events in a context at risk or confirmed acute circulatory failure in adults.
METHOD
MEDLINE, EMBASE, and Google scholar databases were screened for relevant studies. The pooled area under the ROC curve (AUC ROC), sensitivity, specificity, threshold, and diagnostic odds ratio using a random-effects model were determined. The primary analysis was the ability of abnormal CRT to predict death in patients with acute circulatory failure. Secondary analysis included the ability of CRT to predict death or adverse events in patients at risk or with confirmed acute circulatory failure, the comparison with lactate, and the identification of explanatory factors associated with better accuracy.
RESULTS
A total of 60,656 patients in 23 studies were included. Concerning the primary analysis, the pooled AUC ROC of 13 studies was 0.66 (95%CI [0.59; 0.76]), and pooled sensitivity was 54% (95%CI [43; 64]). The pooled specificity was 72% (95%CI [55; 84]). The pooled diagnostic odds ratio was 3.4 (95%CI [1.4; 8.3]). Concerning the secondary analysis, the pooled AUC ROC of 23 studies was 0.69 (95%CI [0.65; 0.74]). The prognostic value of CRT compared to lactate was not significantly different. High-quality CRT was associated with a greater accuracy.
CONCLUSION
CRT poorly predicted death and adverse events in patients at risk or established acute circulatory failure. Its accuracy is greater when high-quality CRT measurement is performed.
Topics: Humans; Adult; Prognosis; Hemodynamics; Shock; Odds Ratio
PubMed: 38042855
DOI: 10.1186/s13054-023-04751-9 -
Journal of Clinical Epidemiology Apr 2017
Topics: Data Interpretation, Statistical; Odds Ratio
PubMed: 28161452
DOI: 10.1016/j.jclinepi.2016.12.013 -
Statistical Methods in Medical Research Aug 2015This paper presents an approximate closed form sample size formula for determining non-inferiority in active-control trials with binary data. We use the odds-ratio as...
This paper presents an approximate closed form sample size formula for determining non-inferiority in active-control trials with binary data. We use the odds-ratio as the measure of the relative treatment effect, derive the sample size formula based on the score test and compare it with a second, well-known formula based on the Wald test. Both closed form formulae are compared with simulations based on the likelihood ratio test. Within the range of parameter values investigated, the score test closed form formula is reasonably accurate when non-inferiority margins are based on odds-ratios of about 0.5 or above and when the magnitude of the odds ratio under the alternative hypothesis lies between about 1 and 2.5. The accuracy generally decreases as the odds ratio under the alternative hypothesis moves upwards from 1. As the non-inferiority margin odds ratio decreases from 0.5, the score test closed form formula increasingly overestimates the sample size irrespective of the magnitude of the odds ratio under the alternative hypothesis. The Wald test closed form formula is also reasonably accurate in the cases where the score test closed form formula works well. Outside these scenarios, the Wald test closed form formula can either underestimate or overestimate the sample size, depending on the magnitude of the non-inferiority margin odds ratio and the odds ratio under the alternative hypothesis. Although neither approximation is accurate for all cases, both approaches lead to satisfactory sample size calculation for non-inferiority trials with binary data where the odds ratio is the parameter of interest.
Topics: Likelihood Functions; Odds Ratio; Sample Size
PubMed: 24492793
DOI: 10.1177/0962280214520729 -
World Neurosurgery Jul 2022Clinical research questions are commonly answered using a case-control design. The decision to use this design is usually justified due to low cost, feasibility, and... (Review)
Review
OBJECTIVE
Clinical research questions are commonly answered using a case-control design. The decision to use this design is usually justified due to low cost, feasibility, and ease of execution. However, the case-control design presents challenges in execution, selection of cases/controls, and interpretation of effect measures (odds ratios, among others). In this paper, we clarify for a neurosurgical audience the design and appropriate effect size measures obtained from case-control studies.
METHODS
A narrative review was conducted of published literature on the topic. The future implementation of such studies was discussed and highlighted with several examples from neurosurgical practice.
RESULTS
In a case-control design, participants are selected for a study based on their outcome status. Some participants have the outcome of interest (cases), whereas others do not (controls). Controls can be selected from a variety of sources, such as the general population, relatives/friends, or hospital patients without the disease under investigation. The most important criterion is that these controls come from the same study base as cases. Furthermore, it is essential to realize that measures of association obtained from a case-control study depend on the sampling strategy of the controls and, as such, have equivalent counterparts available from cohort studies. We delineate traditional case-control, case-cohort, and incidence density-sampled case-control studies and their applicability to common conditions encountered in daily neurosurgical practice (e.g., glioblastoma, aneurysms, and epilepsy).
CONCLUSIONS
Neurosurgeons must understand the types of case-control studies and their associated effect measures to properly conduct research and incorporate research findings into clinical practice.
Topics: Case-Control Studies; Cohort Studies; Epilepsy; Humans; Neurosurgery; Odds Ratio
PubMed: 35367643
DOI: 10.1016/j.wneu.2022.03.097 -
Statistics in Medicine Feb 2020We propose a semiparameteric model for multivariate clustered competing risks data when the cause-specific failure times and the occurrence of competing risk events...
We propose a semiparameteric model for multivariate clustered competing risks data when the cause-specific failure times and the occurrence of competing risk events among subjects within the same cluster are of interest. The cause-specific hazard functions are assumed to follow Cox proportional hazard models, and the associations between failure times given the same or different cause events and the associations between occurrences of competing risk events within the same cluster are investigated through copula models. A cross-odds ratio measure is explored under our proposed models. Two-stage estimation procedure is proposed in which the marginal models are estimated in the first stage, and the dependence parameters are estimated via an expectation-maximization algorithm in the second stage. The proposed estimators are shown to yield consistent and asymptotically normal under mild regularity conditions. Simulation studies are conducted to assess finite sample performance of the proposed method. The proposed technique is demonstrated through an application to a multicenter Bone Marrow transplantation dataset.
Topics: Algorithms; Computer Simulation; Odds Ratio; Proportional Hazards Models
PubMed: 31799731
DOI: 10.1002/sim.8413