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Journal of Global Health Sep 2023We noted that there remains some confusion in the health-science literature on reporting sample odds ratios as estimated rate ratios in case-control studies.
BACKGROUND
We noted that there remains some confusion in the health-science literature on reporting sample odds ratios as estimated rate ratios in case-control studies.
METHODS
We recap historical literature that definitively answered the question of when sample odds ratios (ORs) from a case-control study are consistent estimators for population rate ratios. We use numerical examples to illustrate the magnitude of the disparity between sample ORs in a case-control study and population rate ratios when sufficient conditions for them to be equal are not satisfied.
RESULTS
We stress that in a case-control study, sampling controls from those still at risk at the time of outcome event of the index case is not sufficient for a sample OR to be a consistent estimator for an intelligible rate ratio. In such studies, constancy of the exposure prevalence together with constancy of the hazard ratio (HR) (i.e., the instantaneous rate ratio) over time is sufficient for this result if sampling time is not controlled; if time is controlled, constancy of the HR will suffice. We present numerical examples to illustrate how failure to satisfy these conditions adds a small systematic error to sample ORs as estimates of population rate ratios.
CONCLUSIONS
We recommend that researchers understand and critically evaluate all conditions used to interpret their estimates as consistent for a population parameter in case-control studies.
Topics: Humans; Case-Control Studies; Odds Ratio; Research Personnel
PubMed: 37712381
DOI: 10.7189/jogh.13.04101 -
Evidence-based Nursing Oct 2015
Topics: Data Interpretation, Statistical; Humans; Nursing Research; Odds Ratio; Risk Factors
PubMed: 26361782
DOI: 10.1136/eb-2015-102206 -
International Journal of Epidemiology Dec 1993The use of the term 'odds ratio' in reporting the findings of case-control studies is technically correct, but is often misleading. The meaning of the odds ratio... (Comparative Study)
Comparative Study
The use of the term 'odds ratio' in reporting the findings of case-control studies is technically correct, but is often misleading. The meaning of the odds ratio estimates obtained in a case-control study differs according to whether controls are selected from person-time at risk (the study base), persons at risk (the base-population at risk at the beginning of follow-up), or survivors (the population at risk at the end of follow-up). These three methods of control selection correspond to estimating the rate ratio, risk ratio, or the odds ratio respectively, by means of calculating the odds ratio in the subjects actually studied. None of these estimation procedures depends on any rare disease assumption. Where the rare disease assumption is relevant is whether the effect which is estimated (e.g. the odds ratio) is approximately equal to some other effect measure of interest (e.g. the risk ratio or rate ratio) in the underlying study base. To avoid confusion on this issue, authors should be encouraged to not only specify the manner in which controls have been selected (e.g. by density sampling) but also the corresponding effect measure which is being estimated (e.g. the rate ratio) by the 'odds ratio' which is obtained in a case-control analysis.
Topics: Case-Control Studies; Cohort Studies; Humans; Incidence; Odds Ratio
PubMed: 8144304
DOI: 10.1093/ije/22.6.1189 -
Pharmaceutical Statistics 2023The win odds and the net benefit are related directly to each other and indirectly, through ties, to the win ratio. These three win statistics test the same null...
The win odds and the net benefit are related directly to each other and indirectly, through ties, to the win ratio. These three win statistics test the same null hypothesis of equal win probabilities between two groups. They provide similar p-values and powers, because the Z-values of their statistical tests are approximately equal. Thus, they can complement one another to show the strength of a treatment effect. In this article, we show that the estimated variances of the win statistics are also directly related regardless of ties or indirectly related through ties. Since its introduction in 2018, the stratified win ratio has been applied in designs and analyses of clinical trials, including Phase III and Phase IV studies. This article generalizes the stratified method to the win odds and the net benefit. As a result, the relations of the three win statistics and the approximate equivalence of their statistical tests also hold for the stratified win statistics.
Topics: Humans; Probability; Odds Ratio
PubMed: 36808217
DOI: 10.1002/pst.2293 -
Acta Medica Portuguesa 2013It is very important to review the meaning of the Odds Ratio as a measure of effect and association, as well as, the bias of the Odds Ratio when it is assumed as a risk...
INTRODUCTION
It is very important to review the meaning of the Odds Ratio as a measure of effect and association, as well as, the bias of the Odds Ratio when it is assumed as a risk ratio or a prevalence ratio in the case of frequent disease or frequent health outcome.
MATERIAL AND METHODS
We simulated in a cohort of 200 individuals with 100 exposed and 100 non-exposed to a risk factor, a first setting of rare disease and a second setting of a more frequent disease. In both settings the risk ratios were similar. We computed the Odds Ratio and Relative Risks by the classical approach (standard method) and respectively by logistic regression and Poisson regression. After these, we introduced in the cohort a confounding variable and then we computed the Odds Ratio and Relative Risk by Mantel-Hanszel stratified analysis (standard method) and respectively by multiple logistic regression and multiple Poisson regression. We used the 95% confidence interval in parameter estimation and SPSS V20 was used in statistical analysis.
RESULTS
In the case of rare disease the Odds Ratio was very close to the Relative Risk. For more frequent disease the Odds Ratio overestimated the Relative Risk. In this situation and with a confounding variable, the relative Risk adjusted by Poisson regression was more valid then the Odds Ratio to represent a risk ratio. The confidence intervals of the Relative Risk adjusted by Poisson regression were always greater than Mantel-Hanszel confidence intervals.
CONCLUSIONS
The Odds Ratio and multiple logistic regression were valid analytic procedures in several epidemiological designs such as case-control studies and exploratory prospective studies as well as exploratory cross-sectional studies. The Odds Ratio should not be interpreted as a risk ratio or a prevalence ratio in the case of a health outcome that it is not rare. The multiple Poisson regression should be considered as an alternative procedure to logistic regression, especially if we want to estimate the effect of a specific exposure to a risk factor.
Topics: Epidemiologic Studies; Humans; Odds Ratio; Risk Assessment
PubMed: 24192088
DOI: No ID Found -
Statistics in Medicine Feb 2023The relative risk and odds ratio are widely used in many fields, including biomedical research, to compare two treatments. Extensive research has been done to infer the...
The relative risk and odds ratio are widely used in many fields, including biomedical research, to compare two treatments. Extensive research has been done to infer the two parameters through approximate or exact confidence intervals. However, these intervals may be liberal or conservative. A natural question is whether the intervals can be further improved in maintaining the correct confidence coefficient of an approximate interval or shortening an exact but conservative interval. In this article, when two independent binomials are observed we offer an effort to improve any of the existing intervals by applying the -function method. In particular, if the given interval is approximate, then the improved interval is exact; if the given interval is exact, then the improved interval is a subset of the given interval. This method is also applied multiple times to the improved intervals until the final resultant interval cannot be shortened any further. To demonstrate the effectiveness of the method, we use three real datasets to illustrate in detail how several good intervals in practice are improved. Two exact intervals are then recommended for estimating each of the two parameters in different scenarios.
Topics: Humans; Risk; Odds Ratio; Confidence Intervals; Sample Size; Biomedical Research
PubMed: 36470679
DOI: 10.1002/sim.9617 -
Anaesthesia Jan 2017
Topics: Odds Ratio; Risk
PubMed: 27988953
DOI: 10.1111/anae.13775 -
The Journal of Clinical Psychiatry Jun 2023Categorical outcome analyses in randomized controlled trials (RCTs) and observational studies are commonly presented as relative risks (RRs) and odds ratios (ORs). In...
Categorical outcome analyses in randomized controlled trials (RCTs) and observational studies are commonly presented as relative risks (RRs) and odds ratios (ORs). In some situations, these RRs and ORs may be misunderstood, resulting in wrong conclusions. How this may happen is explained in the context of a hypothetical RCT that compares potentially lifesaving drugs A and B with placebo. In this RCT, the RR for survival is 1.67 for A vs placebo and 1.42 for B vs placebo. Using these RR data, as a challenge, readers are invited to answer 2 questions either intuitively or by other means. First, by how much is A better than B? Second, if the absolute survival rate with B is 8.5%, using the answer obtained from the previous question, what is the absolute survival rate with A? In this same RCT, the OR for survival is 1.74 for A vs placebo and 1.46 for B vs placebo. Using the OR data instead of the RR data, readers are again invited to answer the 2 questions listed above. This article explains why it is easy for readers and even authors to arrive at wrong answers to the 2 questions and draw wrong conclusions about the results. This article also explains what the correct answers are and how they may be obtained. The explanations involve simple concepts and even simpler arithmetic.
Topics: Humans; Risk; Odds Ratio
PubMed: 37339361
DOI: 10.4088/JCP.23f14943 -
Clinical Nurse Specialist CNS
Topics: Humans; Odds Ratio; Confidence Intervals; Postoperative Complications
PubMed: 38079139
DOI: 10.1097/NUR.0000000000000787 -
Community Dentistry and Oral... Dec 2019This commentary explains and exemplifies a method to estimate the Odds Ratio-OR with a correction for possible errors in diagnosis. This procedure allows reassessing...
This commentary explains and exemplifies a method to estimate the Odds Ratio-OR with a correction for possible errors in diagnosis. This procedure allows reassessing hypotheses of association between health outcomes and exposures when the database entails lack of accuracy, and the estimates of sensitivity and specificity of the diagnostic tool are available. Misclassification is not uncommon in dental public health research. Classification errors should not be ignored; they should instead be subject of measurement and adjustment. However, the lack of diagnostic validity is an elusive error, which cannot be entirely controlled. The method described here is solely an attempt to explore further the assessment of factors impacting on health outcomes.
Topics: Dentistry; Humans; Odds Ratio; Public Health; Research Design; Sensitivity and Specificity
PubMed: 31389620
DOI: 10.1111/cdoe.12489