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The Journal of Clinical Psychiatry Jul 2015Risk, and related measures of effect size (for categorical outcomes) such as relative risks and odds ratios, are frequently presented in research articles. Not all...
Risk, and related measures of effect size (for categorical outcomes) such as relative risks and odds ratios, are frequently presented in research articles. Not all readers know how these statistics are derived and interpreted, nor are all readers aware of their strengths and limitations. This article examines several measures, including absolute risk, attributable risk, attributable risk percent, population attributable risk percent, relative risk, odds, odds ratio, and others. The concept and method of calculation are explained for each of these in simple terms and with the help of examples. The interpretation of each is presented in plain English rather than in technical language. Clinically useful notes are provided, wherever necessary.
Topics: Biostatistics; Humans; Odds Ratio; Risk
PubMed: 26231012
DOI: 10.4088/JCP.15f10150 -
Deutsches Arzteblatt International Feb 2023Refeeding syndrome (RFS) can occur in malnourished patients when normal, enteral, or parenteral feeding is resumed. The syndrome often goes unrecognized and may, in the... (Review)
Review
BACKGROUND
Refeeding syndrome (RFS) can occur in malnourished patients when normal, enteral, or parenteral feeding is resumed. The syndrome often goes unrecognized and may, in the most severe cases, result in death. The diagnosis of RFS can be crucially facilitated by the use of clinical decision support systems (CDSS).
METHODS
The literature in PubMed was searched for current treatment recommendations, randomized intervention studies, and publications on RFS and CDSS. We also took account of insights gained from the development and implementation of our own CDSS for the diagnosis of RFS.
RESULTS
The identification of high-risk patients and the recognition of manifest RFS is clinically challenging due to the syndrome's unspecific symptoms and physicians' lack of awareness of the risk of this condition. The literature shows that compared to patients without RFS, malnourished patients with RFS have significantly greater 6-month mortality (odds ratio 1.54, 95% confidence interval: [1.04; 2.28]) and an elevated risk of admission to intensive care (odds ratio 2.71 [1.01; 7.27]). In a prospective testing program, use of our own CDSS led to correct diagnosis in two thirds of cases.
CONCLUSION
RFS is difficult to detect and represents a high risk to the patients affected. Appropriate CDSS can identify such patients and ensure proper professional care.
Topics: Humans; Hospitalization; Malnutrition; Odds Ratio; Prospective Studies; Refeeding Syndrome
PubMed: 36482748
DOI: 10.3238/arztebl.m2022.0381 -
Journal of Evidence-based Medicine Sep 2022The odds ratio (OR) has been misunderstood in evidence based medicine and clinical epidemiology. Currently, "noncollapsibility" is considered a problem with...
The odds ratio (OR) has been misunderstood in evidence based medicine and clinical epidemiology. Currently, "noncollapsibility" is considered a problem with interpretation of the OR and it is thought that the OR is rarely the parameter of interest for causal inference or interpretation of effect modification. The current focus on the relative risk (RR) and risk difference (RD) suffers from an important limitation: they are not solely measures of effect and vary numerically with baseline risk. In this paper, generalized linear models are examined in terms of the three binary effect measures commonly used in epidemiology to demonstrate that ORs may be the only way to interpret effect modification and have properties that should make them the parameter of interest for causal inference. We look forward to discussion, debate, and counter-views on this issue from the epidemiology community.
Topics: Causality; Linear Models; Odds Ratio; Risk
PubMed: 36138553
DOI: 10.1111/jebm.12495 -
Neurosurgery Nov 2023General large language models (LLMs), such as ChatGPT (GPT-3.5), have demonstrated the capability to pass multiple-choice medical board examinations. However,...
BACKGROUND AND OBJECTIVES
General large language models (LLMs), such as ChatGPT (GPT-3.5), have demonstrated the capability to pass multiple-choice medical board examinations. However, comparative accuracy of different LLMs and LLM performance on assessments of predominantly higher-order management questions is poorly understood. We aimed to assess the performance of 3 LLMs (GPT-3.5, GPT-4, and Google Bard) on a question bank designed specifically for neurosurgery oral boards examination preparation.
METHODS
The 149-question Self-Assessment Neurosurgery Examination Indications Examination was used to query LLM accuracy. Questions were inputted in a single best answer, multiple-choice format. χ 2 , Fisher exact, and univariable logistic regression tests assessed differences in performance by question characteristics.
RESULTS
On a question bank with predominantly higher-order questions (85.2%), ChatGPT (GPT-3.5) and GPT-4 answered 62.4% (95% CI: 54.1%-70.1%) and 82.6% (95% CI: 75.2%-88.1%) of questions correctly, respectively. By contrast, Bard scored 44.2% (66/149, 95% CI: 36.2%-52.6%). GPT-3.5 and GPT-4 demonstrated significantly higher scores than Bard (both P < .01), and GPT-4 outperformed GPT-3.5 ( P = .023). Among 6 subspecialties, GPT-4 had significantly higher accuracy in the Spine category relative to GPT-3.5 and in 4 categories relative to Bard (all P < .01). Incorporation of higher-order problem solving was associated with lower question accuracy for GPT-3.5 (odds ratio [OR] = 0.80, P = .042) and Bard (OR = 0.76, P = .014), but not GPT-4 (OR = 0.86, P = .085). GPT-4's performance on imaging-related questions surpassed GPT-3.5's (68.6% vs 47.1%, P = .044) and was comparable with Bard's (68.6% vs 66.7%, P = 1.000). However, GPT-4 demonstrated significantly lower rates of "hallucination" on imaging-related questions than both GPT-3.5 (2.3% vs 57.1%, P < .001) and Bard (2.3% vs 27.3%, P = .002). Lack of question text description for questions predicted significantly higher odds of hallucination for GPT-3.5 (OR = 1.45, P = .012) and Bard (OR = 2.09, P < .001).
CONCLUSION
On a question bank of predominantly higher-order management case scenarios for neurosurgery oral boards preparation, GPT-4 achieved a score of 82.6%, outperforming ChatGPT and Google Bard.
Topics: Humans; Neurosurgery; Neurosurgical Procedures; Odds Ratio; Search Engine; Self-Assessment; Natural Language Processing
PubMed: 37306460
DOI: 10.1227/neu.0000000000002551 -
Korean Journal of Radiology Aug 2022
Topics: Humans; Logistic Models; Odds Ratio; Proportional Hazards Models
PubMed: 35695319
DOI: 10.3348/kjr.2022.0249 -
American Journal of Epidemiology Nov 2023The classical Cornfield inequalities state that if a third confounding variable is fully responsible for an observed association between the exposure and the outcome...
The classical Cornfield inequalities state that if a third confounding variable is fully responsible for an observed association between the exposure and the outcome variables, then the association between both the exposure and the confounder, and the confounder and the outcome, must be at least as strong as the association between the exposure and the outcome, as measured by the risk ratio. The work of Ding and VanderWeele on assumption-free sensitivity analysis sharpens this bound to a bivariate function of the 2 risk ratios involving the confounder. Analogous results are nonexistent for the odds ratio, even though the conversion from odds ratios to risk ratios can sometimes be problematic. We present a version of the classical Cornfield inequalities for the odds ratio. The proof is based on the mediant inequality, dating back to ancient Alexandria. We also develop several sharp bivariate bounds of the observed association, where the 2 variables are either risk ratios or odds ratios involving the confounder.
Topics: Humans; Odds Ratio; Confounding Factors, Epidemiologic
PubMed: 37312597
DOI: 10.1093/aje/kwad137 -
Statistics in Medicine Sep 2022Factorial trials offer an efficient method to evaluate multiple interventions in a single trial, however the use of additional treatments can obscure research... (Randomized Controlled Trial)
Randomized Controlled Trial
Factorial trials offer an efficient method to evaluate multiple interventions in a single trial, however the use of additional treatments can obscure research objectives, leading to inappropriate analytical methods and interpretation of results. We define a set of estimands for factorial trials, and describe a framework for applying these estimands, with the aim of clarifying trial objectives and ensuring appropriate primary and sensitivity analyses are chosen. This framework is intended for use in factorial trials where the intent is to conduct "two-trials-in-one" (ie, to separately evaluate the effects of treatments A and B), and is comprised of four steps: (i) specifying how additional treatment(s) (eg, treatment B) will be handled in the estimand, and how intercurrent events affecting the additional treatment(s) will be handled; (ii) designating the appropriate factorial estimator as the primary analysis strategy; (iii) evaluating the interaction to assess the plausibility of the assumptions underpinning the factorial estimator; and (iv) performing a sensitivity analysis using an appropriate multiarm estimator to evaluate to what extent departures from the underlying assumption of no interaction may affect results. We show that adjustment for other factors is necessary for noncollapsible effect measures (such as odds ratio), and through a trial re-analysis we find that failure to consider the estimand could lead to inappropriate interpretation of results. We conclude that careful use of the estimands framework clarifies research objectives and reduces the risk of misinterpretation of trial results, and should become a standard part of both the protocol and reporting of factorial trials.
Topics: Data Interpretation, Statistical; Humans; Models, Statistical; Odds Ratio; Research Design
PubMed: 35751568
DOI: 10.1002/sim.9510 -
Indian Pediatrics Jun 2022Observational study designs are those where the investigator/researcher just observes and does not carry out any intervention(s)/actions to alter the outcome. The three... (Observational Study)
Observational Study
Observational study designs are those where the investigator/researcher just observes and does not carry out any intervention(s)/actions to alter the outcome. The three most common types of observational studies are cross-sectional, case control and cohort (or longitudinal). In cross-sectional studies, both the exposure/risk factor(s) and the outcome(s) are determined at a single time point. They can provide information on prevalence of a condition and snapshot of probable associations that can be used to generate hypothesis. Case-control studies are where subjects are selected based on presence/absence of outcome and the risk factors are determined during the study after enrolment of study subjects. The association between exposure and outcome is reported as odds ratio. These studies; however, have high risk of bias, which must be taken care of during study design. Cohort studies are prospective in nature, where subjects are selected based on presence/absence of exposure, and the outcome(s) is determined at the end of study. These studies can provide incidence of disease/outcome and the association between exposure and outcome is reported as relative risk. They are useful to ascertain causality. High dropouts of study participants and confounding can be problems encountered in these studies.
Topics: Case-Control Studies; Cohort Studies; Cross-Sectional Studies; Humans; Odds Ratio; Prospective Studies
PubMed: 35481482
DOI: No ID Found -
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 -
American Journal of Epidemiology Sep 2022Previous papers have mentioned that conditioning on a binary collider would introduce an association between its causes in at least 1 stratum. In this paper, we prove...
Previous papers have mentioned that conditioning on a binary collider would introduce an association between its causes in at least 1 stratum. In this paper, we prove this statement and, along with intuitions, formally examine the direction and magnitude of the associations between 2 risk factors of a binary collider using interaction contrasts. Among level one of the collider, 2 variables are independent, positively associated, and negatively associated if multiplicative risk interaction contrast is equal to, more than, and less than 0, respectively; the same results hold for the other level of the collider if the multiplicative survival interaction contrast, equal to multiplicative risk interaction contrast minus the additive risk interaction contrast, is compared with 0. The strength of the association depends on the magnitude of the interaction contrast: The stronger the interaction is, the larger the magnitude of the association will be. However, the common conditional odds ratio under the homogeneity assumption will be bounded. A figure is presented that succinctly illustrates our results and helps researchers to better visualize the associations introduced upon conditioning on a collider.
Topics: Bias; Causality; Humans; Odds Ratio; Risk Factors
PubMed: 35689644
DOI: 10.1093/aje/kwac103