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American Journal of Epidemiology Feb 2021Case-control studies are an important part of the epidemiologic literature, yet confusion remains about how to interpret estimates from different case-control study...
Case-control studies are an important part of the epidemiologic literature, yet confusion remains about how to interpret estimates from different case-control study designs. We demonstrate that not all case-control study designs estimate odds ratios. On the contrary, case-control studies in the literature often report odds ratios as their main parameter even when using designs that do not estimate odds ratios. Only studies using specific case-control designs should report odds ratios, whereas the case-cohort and incidence-density sampled case-control studies must report risk ratio and incidence rate ratios, respectively. This also applies to case-control studies conducted in open cohorts, which often estimate incidence rate ratios. We also demonstrate the misinterpretation of case-control study estimates in a small sample of highly cited case-control studies in general epidemiologic and medical journals. We therefore suggest that greater care be taken when considering which parameter is to be reported from a case-control study.
Topics: Case-Control Studies; Data Interpretation, Statistical; Humans; Odds Ratio; Research Design
PubMed: 32889542
DOI: 10.1093/aje/kwaa167 -
Biometrics Sep 2020Odds ratios approximate risk ratios when the outcome under consideration is rare but can diverge substantially from risk ratios when the outcome is common. In this...
Odds ratios approximate risk ratios when the outcome under consideration is rare but can diverge substantially from risk ratios when the outcome is common. In this paper, we derive optimal analytic conversions of odds ratios and hazard ratios to risk ratios that are minimax for the bias ratio when outcome probabilities are specified to fall in any fixed interval. The results for hazard ratios are derived under a proportional hazard assumption for the exposure. For outcome probabilities specified to lie in symmetric intervals centered around 0.5, it is shown that the square-root transformation of the odds ratio is the optimal minimax conversion for the risk ratio. General results for any nonsymmetric interval are given both for odds ratio and for hazard ratio conversions. The results are principally useful when odds ratios or hazard ratios are reported in papers, and the reader does not have access to the data or to information about the overall outcome prevalence.
Topics: Bias; Odds Ratio; Probability; Proportional Hazards Models
PubMed: 31808145
DOI: 10.1111/biom.13197 -
Biostatistics (Oxford, England) Apr 2021Measuring a biomarker in pooled samples from multiple cases or controls can lead to cost-effective estimation of a covariate-adjusted odds ratio, particularly for...
Measuring a biomarker in pooled samples from multiple cases or controls can lead to cost-effective estimation of a covariate-adjusted odds ratio, particularly for expensive assays. But pooled measurements may be affected by assay-related measurement error (ME) and/or pooling-related processing error (PE), which can induce bias if ignored. Building on recently developed methods for a normal biomarker subject to additive errors, we present two related estimators for a right-skewed biomarker subject to multiplicative errors: one based on logistic regression and the other based on a Gamma discriminant function model. Applied to a reproductive health dataset with a right-skewed cytokine measured in pools of size 1 and 2, both methods suggest no association with spontaneous abortion. The fitted models indicate little ME but fairly severe PE, the latter of which is much too large to ignore. Simulations mimicking these data with a non-unity odds ratio confirm validity of the estimators and illustrate how PE can detract from pooling-related gains in statistical efficiency. These methods address a key issue associated with the homogeneous pools study design and should facilitate valid odds ratio estimation at a lower cost in a wide range of scenarios.
Topics: Bias; Biomarkers; Female; Humans; Logistic Models; Odds Ratio; Pregnancy; Research Design
PubMed: 31373355
DOI: 10.1093/biostatistics/kxz028 -
International Journal of Epidemiology Feb 2023The risk ratio (RR) is the ratio of the outcome among the exposed to risk of the outcome among the unexposed. This is a simple concept, which makes one wonder why it has...
The risk ratio (RR) is the ratio of the outcome among the exposed to risk of the outcome among the unexposed. This is a simple concept, which makes one wonder why it has not gained the same popularity as the odds ratio. Using logistic regression to estimate the odds ratio is quite common in epidemiology and interpreting the odds ratio as a risk ratio, under the assumption that the outcome is rare, is also common. On one hand, estimating the odds ratio is simple but interpreting it is hard. On the other, estimating the risk ratio is challenging but its interpretation is straightforward. Issues with estimating risk ratio still remain after four decades. These issues include convergence of the algorithm, the choice of regression specification (e.g. log-binomial, Poisson) and many more. Various new computational methods are available which help overcome the issue of convergence and provide doubly robust estimates of RR.
Topics: Humans; Odds Ratio; Logistic Models
PubMed: 36416437
DOI: 10.1093/ije/dyac220 -
Accident; Analysis and Prevention Oct 2021It is widely believed that with higher levels of vehicle automation and especially with the advent of fully automatic vehicles, the currently typical forward-facing,...
It is widely believed that with higher levels of vehicle automation and especially with the advent of fully automatic vehicles, the currently typical forward-facing, upright position will give way to a more relaxed and reclined seating posture. Therefore, the current study investigates the influence of a reclined sitting position on crash injury severity by analyzing real-world crash data from the German in-depth accident study (GIDAS). We compared reclined to upright occupants and focused on effect sizes regarding odds ratios at different injury severity levels. We used the abbreviated injury scale (AIS 2015) for injury scaling and the maximum AIS (MAIS) at the levels 2+, 3+, and 4+ to convert injury severity into a dichotomous metric. Two different analyses were conducted, one looking at the occupant MAIS and one focusing on selected body regions. The body regions investigated are head/face/neck (HFN), thorax, abdomen, pelvis/hip/lower extremities (PHL), and upper extremities. We computed odds ratios greater than one indicating a higher odds of injury at a given injury severity level in the reclined group compared to the upright group. The odds ratios for belted, reclined occupants compared to belted, upright sitting occupants are 2.07, 3.09, and 3.66 for the injury severity levels MAIS2+, MAIS3+, and MAIS4+, respectively. When looking at the body regions, the spread of the odds ratios is wider: At the MAIS2+ level, the odds ratios range between 1.6 and 7.1; at the MAIS3+ level, the odds ratios span from 1.5 to 8.7, with the latter value representing the PHL region. No odds ratio could be computed for the upper extremity injuries at this level. At the MAIS4+ injury severity level, only the HFN odds ratio was statistically significant with a value of 5.6. This study is among the first to show an association between body posture and injury severity at MAIS3+ and MAIS4+ injury level in real-world crashes for reclined seating postures.
Topics: Abbreviated Injury Scale; Accidents, Traffic; Automation; Humans; Odds Ratio; Sitting Position; Wounds and Injuries
PubMed: 34464840
DOI: 10.1016/j.aap.2021.106357 -
Pharmacoepidemiology and Drug Safety Aug 2023As measures of association between an adverse drug reaction (ADR) and exposure to a drug the reporting odds ratio (ROR) and the information component (IC) can be used....
PURPOSE
As measures of association between an adverse drug reaction (ADR) and exposure to a drug the reporting odds ratio (ROR) and the information component (IC) can be used. We sought to test the reliability of signal detection with these.
METHODS
We simulated ADR counts as binomially distributed random numbers for different expected ADR frequencies and theoretical reporting odds ratios (RORs). We then calculated the empirical IC and the empirical ROR and their confidence intervals. The rate of signals that was detected despite a theoretical ROR of 1 represented the false positive rate, and represented the sensitivity if the ROR was >1.
RESULTS
For expected case counts below 1 the false positive rate oscillates from 0.01 to 0.1 even though 0.025 were intended. Even beyond expected case counts of 5 oscillations can cover a range of 0.018 to 0.035. The first n oscillations with the largest amplitude are eliminated if a minimum case count of n is required. To detect an ROR of 2 with a sensitivity of 0.8, a minimum of 12 expected ADRs are required. In contrast, 2 expected ADRs suffice to detect an ROR of 4.
CONCLUSION
Summaries of measures for disproportionality should include the expected number of cases in the group of interest if a signal was detected. If no signal was detected the sensitivity for the detection of a representative ROR or the minimum ROR that could be detected with probability 0.8 should be reported.
Topics: Humans; Odds Ratio; Reproducibility of Results; Adverse Drug Reaction Reporting Systems; Databases, Factual; Drug-Related Side Effects and Adverse Reactions; Pharmacovigilance
PubMed: 36966482
DOI: 10.1002/pds.5624 -
Neuroscience and Biobehavioral Reviews Feb 2023The main objective of this meta-analysis was to investigate handedness in post-traumatic stress disorder on a meta-analytical level. For this purpose, articles were... (Meta-Analysis)
Meta-Analysis Review
The main objective of this meta-analysis was to investigate handedness in post-traumatic stress disorder on a meta-analytical level. For this purpose, articles were identified via a search in PubMed, PsychInfo, PubPsych, ResearchGate, and Google Scholar. Studies reporting findings relating to handedness in PTSD patients and healthy controls were considered eligible. In total, k = 14 studies with an overall N of 2939 (747 PTSD patients and 2192 controls) were included in the study. Random-effects meta-analyses, as well as robust Bayes meta-analyses (RoBMA), were conducted for three comparisons: (a) non-right-handedness, (b) left-handedness, and (c) mixed-handedness. Results showed significantly higher frequencies of non-right-handedness (odds ratio = 1.81) and mixed-handedness (odds ratio = 2.42) in PTSD patients compared to controls. No differences were found for left-handedness. This specific effect of mixed-handedness is in line with findings for other disorders, such as schizophrenia. Future studies should investigate common neurodevelopmental origins for the relationship between mixed-handedness and psychopathology and aim at investigating both handedness direction and handedness strength.
Topics: Humans; Functional Laterality; Stress Disorders, Post-Traumatic; Bayes Theorem; Schizophrenia; Odds Ratio
PubMed: 36549376
DOI: 10.1016/j.neubiorev.2022.105009 -
Statistics in Medicine May 2022Minimal sufficient balance (MSB) is a recently suggested method for adaptively controlling covariate imbalance in randomized controlled trials in a manner which reduces...
Minimal sufficient balance (MSB) is a recently suggested method for adaptively controlling covariate imbalance in randomized controlled trials in a manner which reduces the impact on randomness of allocation over other approaches by only intervening when the imbalance is sufficiently significant. Despite its improvements, the approach is unable to consider the relative clinical importance or magnitude of imbalance in each covariate weight, and ignores any imbalance which is not statistically significant, even when these imbalances may collectively justify intervention. We propose the common scale MSB (CS-MSB) method which addresses these limitations, and present simulation studies comparing our proposed method to MSB. We demonstrate that CS-MSB requires less intervention than MSB to achieve the same level of covariate balance, and does not adversely impact either statistical power or Type-I error.
Topics: Computer Simulation; Humans; Odds Ratio; Random Allocation; Research Design
PubMed: 35176811
DOI: 10.1002/sim.9332 -
Genetic Epidemiology May 2007Genome-wide association studies are carried out to identify unknown genes for a complex trait. Polymorphisms showing the most statistically significant associations are...
Genome-wide association studies are carried out to identify unknown genes for a complex trait. Polymorphisms showing the most statistically significant associations are reported and followed up in subsequent confirmatory studies. In addition to the test of association, the statistical analysis provides point estimates of the relationship between the genotype and phenotype at each polymorphism, typically an odds ratio in case-control association studies. The statistical significance of the test and the estimator of the odds ratio are completely correlated. Selecting the most extreme statistics is equivalent to selecting the most extreme odds ratios. The value of the estimator, given the value of the statistical significance depends on the standard error of the estimator and the power of the study. This report shows that when power is low, estimates of the odds ratio from a genome-wide association study, or any large-scale association study, will be upwardly biased. Genome-wide association studies are often underpowered given the low alpha levels required to declare statistical significance and the small individual genetic effects known to characterize complex traits. Factors such as low allele frequency, inadequate sample size and weak genetic effects contribute to large standard errors in the odds ratio estimates, low power and upwardly biased odds ratios. Studies that have high power to detect an association with the true odds ratio will have little or no bias, regardless of the statistical significance threshold. The results have implications for the interpretation of genome-wide association analysis and the planning of subsequent confirmatory stages.
Topics: Computer Simulation; Gene Frequency; Genetic Predisposition to Disease; Genome, Human; Genotype; Humans; Models, Genetic; Models, Statistical; Odds Ratio; Phenotype; Polymorphism, Genetic
PubMed: 17266119
DOI: 10.1002/gepi.20209 -
JAMA Nov 2018
Topics: Odds Ratio
PubMed: 30458484
DOI: 10.1001/jama.2018.14417