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Biometrics Jun 2023In many randomized clinical trials of therapeutics for COVID-19, the primary outcome is an ordinal categorical variable, and interest focuses on the odds ratio (OR;... (Randomized Controlled Trial)
Randomized Controlled Trial
In many randomized clinical trials of therapeutics for COVID-19, the primary outcome is an ordinal categorical variable, and interest focuses on the odds ratio (OR; active agent vs control) under the assumption of a proportional odds model. Although at the final analysis the outcome will be determined for all subjects, at an interim analysis, the status of some participants may not yet be determined, for example, because ascertainment of the outcome may not be possible until some prespecified follow-up time. Accordingly, the outcome from these subjects can be viewed as censored. A valid interim analysis can be based on data only from those subjects with full follow-up; however, this approach is inefficient, as it does not exploit additional information that may be available on those for whom the outcome is not yet available at the time of the interim analysis. Appealing to the theory of semiparametrics, we propose an estimator for the OR in a proportional odds model with censored, time-lagged categorical outcome that incorporates additional baseline and time-dependent covariate information and demonstrate that it can result in considerable gains in efficiency relative to simpler approaches. A byproduct of the approach is a covariate-adjusted estimator for the OR based on the full data that would be available at a final analysis.
Topics: Humans; COVID-19; Odds Ratio; Treatment Outcome
PubMed: 34825704
DOI: 10.1111/biom.13603 -
Journal of Biopharmaceutical Statistics May 2021There has been limited research on the confidence intervals of the conditional odds ratio in matched-pairs design. This article investigates the interval estimation of...
There has been limited research on the confidence intervals of the conditional odds ratio in matched-pairs design. This article investigates the interval estimation of the conditional odds ratio. We described several confidence intervals, which are available in some situations, and they can produce different results. We tried to determine which method(s) should be recommended for different situations. We derived four confidence intervals from the delta test, the score test, the inferential model test, and the fiducial test, and employed four exact calculation studies to compare the performances of the four methods, in order to make recommendations for small and moderate-to-large sample sizes. All of the methods are illustrated using a real example. And we offered the recommendations for different situations.
Topics: Confidence Intervals; Humans; Odds Ratio; Research Design; Sample Size
PubMed: 33400607
DOI: 10.1080/10543406.2020.1858309 -
Oral Diseases Apr 2022
Topics: COVID-19; Dental Care; Humans; Odds Ratio; Stomatognathic Diseases
PubMed: 32989904
DOI: 10.1111/odi.13653 -
European Journal of Epidemiology Aug 1995This paper argues that the use of the odds ratio parameter in epidemiology needs to be considered with a view to the specific study design and the types of exposure and...
This paper argues that the use of the odds ratio parameter in epidemiology needs to be considered with a view to the specific study design and the types of exposure and disease data at hand. Frequently, the odds ratio measure is being used instead of the risk ratio or the incidence-proportion ratio in cohort studies or as an estimate for the incidence-density ratio in case-referent studies. Therefore, the analyses of epidemiologic data have produced biased estimates and the presentation of results has been misleading. However, the odds ratio can be relinquished as an effect measure for these study designs; and, the application of the case-base sampling approach permits the incidence ratio and difference measures to be estimated without any untenable assumptions. For the Poisson regression, the odds ratio is not a parameter of interest; only the risk or rate ratio and difference are relevant. For the conditional logistic regression in matched case-referent studies, the odds ratio remains useful, but only when it is interpreted as an estimate of the incidence-density ratio. Thus the odds ratio should, in general, give way to the incidence ratio and difference as the measures of choice for exposure effect in epidemiology.
Topics: Data Interpretation, Statistical; Epidemiologic Methods; Odds Ratio; Risk
PubMed: 8549701
DOI: 10.1007/BF01721219 -
Epidemiology (Cambridge, Mass.) Nov 2017
Topics: Odds Ratio; Outcome Assessment, Health Care
PubMed: 28816709
DOI: 10.1097/EDE.0000000000000733 -
Epidemiology (Cambridge, Mass.) Jul 2022Risk prediction models often need to be updated when applied to new settings. A simple updating method involves fixed odds ratio transformation of predicted risks to...
Risk prediction models often need to be updated when applied to new settings. A simple updating method involves fixed odds ratio transformation of predicted risks to adjust the model for outcome prevalence in the new setting. When a sample from the target population is available, the gold standard is to use a logistic regression model to estimate this odds ratio. A simpler method has been proposed that calculates this odds ratio from the prevalence estimates in the original and new samples. We show that the marginal odds ratio estimated in this way is generally closer to one than the correct (conditional) odds ratio; thus, the simpler method should be avoided when individual-level data are available. When such data are not available, we suggest an approximate method for recovering the conditional odds ratio from the variance of predicted risks in the development sample. Brief simulations and examples show that this approach reduces undercorrection, often substantially.
Topics: Humans; Logistic Models; Odds Ratio; Research Design
PubMed: 35394467
DOI: 10.1097/EDE.0000000000001489 -
Journal of Biopharmaceutical Statistics Sep 2021This paper proposes two new approximate confidence limit methods for the common odds ratio from multiple 2 × 2 tables. The two new procedures, based on the asymptotic...
This paper proposes two new approximate confidence limit methods for the common odds ratio from multiple 2 × 2 tables. The two new procedures, based on the asymptotic distribution of Woolf estimator and Mantel-Haenszel estimator, associate with inverse sinh transformation. We employ three pseudo-frequency methods to calculate confidence intervals in order to avoid the interval failure caused by the presence of zero cells in multiple 2 × 2 tables. We develop the modified inverse sinh intervals for the common odds ratio which add one pseudo-frequency () to all the cells before computing the point estimate of common odds ratio and another pseudo-frequency () to all the cells before computing the standard error estimate. The simulation is to evaluate the 22 confidence intervals, including Woolf, Mantel-Haenszel, their inverse sinh intervals, and their pseudo-frequency modified inverse sinh intervals, in terms of their coverage probabilities and average log lengths. Simulation results demonstrate that the adjusted inverse sinh intervals by two different pseudo-frequencies perform quite well when is slightly greater than since the coverage probabilities of them are closer to confidence level of 95%. Larger values of lead to narrow intervals and low coverage probabilities. We also find that inverse sinh intervals are shorter than untransformed intervals based on Woolf estimator and Mantel-Haenszel estimator, respectively. These procedures were illustrated with two clinical trials.
Topics: Computer Simulation; Confidence Intervals; Odds Ratio; Probability
PubMed: 34191672
DOI: 10.1080/10543406.2021.1934856 -
Chest Jul 2022
Topics: Gambling; Humans; Odds Ratio; Referral and Consultation
PubMed: 35809925
DOI: 10.1016/j.chest.2022.02.033 -
Journal of Visceral Surgery Dec 2022
Topics: Humans; Odds Ratio; Surgeons; Meta-Analysis as Topic
PubMed: 36333183
DOI: 10.1016/j.jviscsurg.2022.10.003 -
Statistics in Medicine Dec 2006The odds ratio is probably the most widely used measure of association for binary variables. It has many well-recognized advantages and disadvantages vis-à-vis the risk...
The odds ratio is probably the most widely used measure of association for binary variables. It has many well-recognized advantages and disadvantages vis-à-vis the risk ratio. We demonstrate that for chained or conditional probabilities, odds ratios can behave paradoxically, by failing to show a reinforcement effect that is expressed very clearly when risk ratios are used.
Topics: Adolescent; Condoms; Data Interpretation, Statistical; Humans; Odds Ratio; Risk; Sexual Behavior
PubMed: 16927451
DOI: 10.1002/sim.2683