A comparison of seven random-effects models for meta-analyses that estimate the summary odds ratio.
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ABSTRACT:
SUBMITTER: Jackson D
PROVIDER: S-EPMC5841569 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundConsider a meta-analysis where a 'head-to-head' comparison of diagnostic tests for a disease of interest is intended. Assume there are two or more tests available for the disease, where each test has been studied in one or more papers. Some of the papers may have studied more than one test, hence the results are not independent. Also the collection of tests studied may change from one paper to the other, hence incomplete matched groups.MethodsWe propose a model, the proportional odds ratio (POR) model, which makes no assumptions about the shape of ORp, a baseline function capturing the way OR changes across papers. The POR model does not assume homogeneity of ORs, but merely specifies a relationship between the ORs of the two tests. One may expand the domain of the POR model to cover dependent studies, multiple outcomes, multiple thresholds, multi-category or continuous tests, and individual-level data.ResultsIn the paper we demonstrate how to formulate the model for a few real examples, and how to use widely available or popular statistical software (like SAS, R or S-Plus, and Stata) to fit the models, and estimate the discrimination accuracy of tests. Furthermore, we provide code for converting ORs into other measures of test performance like predictive values, post-test probabilities, and likelihood ratios, under mild conditions. Also we provide code to convert numerical results into graphical ones, like forest plots, heterogeneous ROC curves, and post test probability difference graphs.ConclusionsThe flexibility of POR model, coupled with ease with which it can be estimated in familiar software, suits the daily practice of meta-analysis and improves clinical decision-making.
| S-EPMC539279 | biostudies-literature
Project description:The homogeneity tests of odds ratios are used in clinical trials and epidemiological investigations as a preliminary step of meta-analysis. In recent studies, the severity or mortality of COVID-19 in relation to demographic characteristics, comorbidities, and other conditions has been popularly discussed by interpreting odds ratios and using meta-analysis. According to the homogeneity test results, a common odds ratio summarizes all of the odds ratios in a series of studies. If the aim is not to find a common odds ratio, but to find which of the sub-characteristics/groups is different from the others or is under risk, then the implementation of a multiple comparison procedure is required. In this article, the focus is placed on the accuracy and reliability of the homogeneity of odds ratio tests for multiple comparisons when the odds ratios are heterogeneous at the omnibus level. Three recently proposed multiple comparison tests and four homogeneity of odds ratios tests with six adjustment methods to control the type-I error rate are considered. The reliability and accuracy of the methods are discussed in relation to COVID-19 severity data associated with diabetes on a country-by-country basis, and a simulation study to assess the powers and type-I error rates of the tests is conducted.
| S-EPMC9415621 | biostudies-literature
Project description:This study was done to clarify the optimal number and type of casual urine specimens required to estimate urinary sodium/potassium (Na/K) ratio in individuals with high blood pressure. A total of 74 individuals with high blood pressure, 43 treated and 31 untreated, were recruited from the Japanese general population. Urinary sodium, potassium and Na/K ratio were measured in both casual urine samples and 7-day 24-h urine samples and then analyzed by correlation and Bland-Altman analyses. Mean Na/K ratio from random casual urine samples on four or more days strongly correlated with the Na/K ratio of 7-day 24-h urine (r=0.80-0.87), which was similar to the correlation between 1 and 2-day 24-h urine and 7-day 24-h urine (r=0.75-0.89). The agreement quality for Na/K ratio of seven random casual urine for estimating the Na/K ratio of 7-day 24-h urine was good (bias: -0.26, limits of agreements: -1.53-1.01), and it was similar to that of 2-day 24-h urine for estimating 7-day 24-h values (bias: 0.07, limits of agreement: -1.03 to 1.18). Stratified analyses comparing individuals using antihypertensive medication and individuals not using antihypertensive medication showed similar results. Correlations of the means of casual urine sodium or potassium concentrations with 7-day 24-h sodium or potassium excretions were relatively weaker than those for Na/K ratio. The mean Na/K ratio of 4-7 random casual urine specimens on different days provides a good substitute for 1-2-day 24-h urinary Na/K ratio for individuals with high blood pressure.
| S-EPMC4827004 | biostudies-literature
Project description:Multiple imputation is a practically useful approach to handling incompletely observed data in statistical analysis. Parameter estimation and inference based on imputed full data have been made easy by Rubin's rule for result combination. However, creating proper imputation that accommodates flexible models for statistical analysis in practice can be very challenging. We propose an imputation framework that uses conditional semiparametric odds ratio models to impute the missing values. The proposed imputation framework is more flexible and robust than the imputation approach based on the normal model. It is a compatible framework in comparison to the approach based on fully conditionally specified models. The proposed algorithms for multiple imputation through the Markov chain Monte Carlo sampling approach can be straightforwardly carried out. Simulation studies demonstrate that the proposed approach performs better than existing, commonly used imputation approaches. The proposed approach is applied to imputing missing values in bone fracture data.
| S-EPMC3135790 | biostudies-literature
Project description:Genome-wide association studies (GWAS) have identified thousands of loci that are robustly associated with complex diseases. The use of linear mixed model (LMM) methodology for GWAS is becoming more prevalent due to its ability to control for population structure and cryptic relatedness and to increase power. The odds ratio (OR) is a common measure of the association of a disease with an exposure (e.g., a genetic variant) and is readably available from logistic regression. However, when the LMM is applied to all-or-none traits it provides estimates of genetic effects on the observed 0-1 scale, a different scale to that in logistic regression. This limits the comparability of results across studies, for example in a meta-analysis, and makes the interpretation of the magnitude of an effect from an LMM GWAS difficult. In this study, we derived transformations from the genetic effects estimated under the LMM to the OR that only rely on summary statistics. To test the proposed transformations, we used real genotypes from two large, publicly available data sets to simulate all-or-none phenotypes for a set of scenarios that differ in underlying model, disease prevalence, and heritability. Furthermore, we applied these transformations to GWAS summary statistics for type 2 diabetes generated from 108,042 individuals in the UK Biobank. In both simulation and real-data application, we observed very high concordance between the transformed OR from the LMM and either the simulated truth or estimates from logistic regression. The transformations derived and validated in this study improve the comparability of results from prospective and already performed LMM GWAS on complex diseases by providing a reliable transformation to a common comparative scale for the genetic effects.
| S-EPMC5887138 | biostudies-literature
Project description:Case-control studies are an important part of the epidemiologic literature, yet there remains a lot of confusion about how to interpret estimates from different case-control study designs. We demonstrate that not all case-control study designs estimate odds ratios. In contrast, case-control studies in the literature often report odds ratios as their main parameter even when using designs that do not estimate odds ratios. Therefore, 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 greater care be taken when considering which parameter is to be reported from a case-control study.
| S-EPMC7850067 | biostudies-literature
Project description:Meta-analysis is a statistical methodology for combining information from diverse sources so that a more reliable and efficient conclusion can be reached. It can be conducted by either synthesizing study-level summary statistics or drawing inference from an overarching model for individual participant data (IPD) if available. The latter is often viewed as the "gold standard." For random-effects models, however, it remains not fully understood whether the use of IPD indeed gains efficiency over summary statistics. In this paper, we examine the relative efficiency of the two methods under a general likelihood inference setting. We show theoretically and numerically that summary-statistics-based analysis is at most as efficient as IPD analysis, provided that the random effects follow the Gaussian distribution, and maximum likelihood estimation is used to obtain summary statistics. More specifically, (i) the two methods are equivalent in an asymptotic sense; and (ii) summary-statistics-based inference can incur an appreciable loss of efficiency if the sample sizes are not sufficiently large. Our results are established under the assumption that the between-study heterogeneity parameter remains constant regardless of the sample sizes, which is different from a previous study. Our findings are confirmed by the analyses of simulated data sets and a real-world study of alcohol interventions.
| S-EPMC7955582 | biostudies-literature
Project description:The evaluation of cultivars using multi-environment trials (MET) is an important step in plant breeding programs. One of the objectives of these evaluations is to understand the genotype by environment interaction (GEI). A method of determining the effect of GEI on the performance of cultivars is based on studies of adaptability and stability. Initial studies were based on linear regression; however, these methodologies have limitations, mainly in trials with genetic or statistical unbalanced, heterogeneity of residual variances, and genetic covariance. An alternative would be the use of random regression models (RRM), in which the behavior of the genotypes is characterized as a reaction norm using longitudinal data or repeated measurements and information regarding a covariance function. The objective of this work was the application of RRM in the study of the behavior of common bean cultivars using a MET, based on Legendre polynomials and genotype-ideotype distances. We used a set of 13 trials, which were classified as unfavorable or favorable environments. The results revealed that RRM enables the prediction of the genotypic values of cultivars in environments where they were not evaluated with high accuracy values, thereby circumventing the unbalanced of the experiments. From these values, it was possible to measure the genotypic adaptability according to ideotypes, according to their reaction norms. In addition, the stability of the cultivars can be interpreted as variation in the behavior of the ideotype. The use of ideotypes based on real data allowed a better comparison of the performance of cultivars across environments. The use of RRM in plant breeding is a good alternative to understand the behavior of cultivars in a MET, especially when we want to quantify the adaptability and stability of genotypes.
| S-EPMC7710123 | biostudies-literature
Project description:BackgroundIn meta-analysis, the normal distribution assumption has been adopted in most systematic reviews of random-effects distribution models due to its computational and conceptual simplicity. However, this restrictive model assumption is possibly unsuitable and might have serious influences in practices.MethodsWe provide two examples of real-world evidence that clearly show that the normal distribution assumption is explicitly unsuitable. We propose new random-effects meta-analysis methods using five flexible random-effects distribution models that can flexibly regulate skewness, kurtosis and tailweight: skew normal distribution, skew t-distribution, asymmetric Subbotin distribution, Jones-Faddy distribution, and sinh-arcsinh distribution. We also developed a statistical package, flexmeta, that can easily perform these methods.ResultsUsing the flexible random-effects distribution models, the results of the two meta-analyses were markedly altered, potentially influencing the overall conclusions of these systematic reviews.ConclusionsThe restrictive normal distribution assumption in the random-effects model can yield misleading conclusions. The proposed flexible methods can provide more precise conclusions in systematic reviews.
| S-EPMC9424185 | biostudies-literature
Project description:ObjectivesTo summarise the range, strength, and validity of reported associations between environmental risk factors and non-Hodgkin's lymphoma, and to evaluate the concordance between associations reported in meta-analyses of summary level data and meta-analyses of individual participant data.DesignUmbrella review and comparison of meta-analyses of summary and individual participant level data.Data sourcesMedline, Embase, Scopus, Web of Science Core Collection, Cochrane Library, and Epistemonikos, from inception to 23 July 2021.Eligibility criteria for selecting studiesEnglish language meta-analyses of summary level data and of individual participant data evaluating associations between environmental risk factors and incident non-Hodgkin's lymphoma (overall and subtypes).Data extraction and synthesisSummary effect estimates from meta-analyses of summary level data comparing ever versus never exposure that were adjusted for the largest number of potential confounders were re-estimated using a random effects model and classified as presenting evidence that was non-significant, weak (P<0.05), suggestive (P<0.001 and >1000 cases), highly suggestive (P<0.000001, >1000 cases, largest study reporting a significant association), or convincing (P<0.000001, >1000 cases, largest study reporting a significant association, I2 <50%, 95% prediction interval excluding the null value, and no evidence of small study effects and excess significance bias) evidence. When the same exposures, exposure contrast levels, and outcomes were evaluated in meta-analyses of summary level data and meta-analyses of individual participant data from the International Lymphoma Epidemiology (InterLymph) Consortium, concordance in terms of direction, level of significance, and overlap of 95% confidence intervals was examined. Methodological quality of the meta-analyses of summary level data was assessed by the AMSTAR 2 tool.ResultsWe identified 85 meta-analyses of summary level data reporting 257 associations for 134 unique environmental risk factors and 10 subtypes of non-Hodgkin's lymphoma nearly all (79, 93%) were classified as having critically low quality. Most associations (225, 88%) presented either non-significant or weak evidence. The 11 (4%) associations presenting highly suggestive evidence were primarily for autoimmune or infectious disease related risk factors. Only one association, between history of coeliac disease and risk of non-Hodgkin's lymphoma, presented convincing evidence. Of 40 associations reported in meta-analyses of summary level data that were also evaluated in InterLymph meta-analyses of individual participant data, 22 (55%) pairs were in the same direction, had the same level of statistical significance, and had overlapping 95% confidence intervals; 28 (70%) pairs had summary effect sizes from the meta-analyses of individual participant data that were more conservative.ConclusionThis umbrella review suggests evidence of many meta-analyses of summary level data reporting weak associations between environmental risk factors and non-Hodgkin's lymphoma. Improvements to primary studies as well as evidence synthesis in evaluations of evironmental risk factors and non-Hodgkin's lymphoma are needed.Review registration numberPROSPERO CRD42020178010.
| S-EPMC9978687 | biostudies-literature