A Tutorial of Bland Altman Analysis in A Bayesian Framework.
Ontology highlight
ABSTRACT:
SUBMITTER: Alari KM
PROVIDER: S-EPMC8133695 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Bland-Altman method comparison studies are common in the medical sciences and are used to compare a new measure to a gold-standard (often costlier or more invasive) measure. The distribution of these differences is summarized by two statistics, the 'bias' and standard deviation, and these measures are combined to provide estimates of the limits of agreement (LoA). When these LoA are within the bounds of clinically insignificant differences, the new non-invasive measure is preferred. Very often, multiple Bland-Altman studies have been conducted comparing the same two measures, and random-effects meta-analysis provides a means to pool these estimates. We provide a framework for the meta-analysis of Bland-Altman studies, including methods for estimating the LoA and measures of uncertainty (i.e., confidence intervals). Importantly, these LoA are likely to be wider than those typically reported in Bland-Altman meta-analyses. Frequently, Bland-Altman studies report results based on repeated measures designs but do not properly adjust for this design in the analysis. Meta-analyses of Bland-Altman studies frequently exclude these studies for this reason. We provide a meta-analytic approach that allows inclusion of estimates from these studies. This includes adjustments to the estimate of the standard deviation and a method for pooling the estimates based upon robust variance estimation. An example is included based on a previously published meta-analysis. Copyright © 2017 John Wiley & Sons, Ltd.
| S-EPMC5585060 | biostudies-literature
Project description:The Bland-Altman Limits of Agreement is a popular and widespread means of analyzing the agreement of two methods, instruments, or raters in quantitative outcomes. An agreement analysis could be reported as a stand-alone research article but it is more often conducted as a minor quality assurance project in a subgroup of patients, as a part of a larger diagnostic accuracy study, clinical trial, or epidemiological survey. Consequently, such an analysis is often limited to brief descriptions in the main report. Therefore, in several medical fields, it has been recommended to report specific items related to the Bland-Altman analysis. The present study aimed to identify the most comprehensive and appropriate list of items for such an analysis. Seven proposals were identified from a MEDLINE/PubMed search, three of which were derived by reviewing anesthesia journals. Broad consensus was seen for the a priori establishment of acceptability benchmarks, estimation of repeatability of measurements, description of the data structure, visual assessment of the normality and homogeneity assumption, and plotting and numerically reporting both bias and the Bland-Altman Limits of Agreement, including respective 95% confidence intervals. Abu-Arafeh et al. provided the most comprehensive and prudent list, identifying 13 key items for reporting (Br. J. Anaesth. 2016, 117, 569-575). An exemplification with interrater data from a local study accentuated the straightforwardness of transparent reporting of the Bland-Altman analysis. The 13 key items should be applied by researchers, journal editors, and reviewers in the future, to increase the quality of reporting Bland-Altman agreement analyses.
| S-EPMC7278016 | biostudies-literature
Project description:ObjectiveA common form of validation study compares alternative methods for collecting data. The Bland-Altman plot pairs observations across methods and plots their mean values vs. their difference. This method provides only limited information, however, when the range of observed values is small relative to the number of observations. This brief report shows how adding a simple bar chart to a Bland-Altman plot adds essential additional information.Study design and settingThe methodological approach is illustrated using data from a randomized controlled clinical trial of patients in a U.S. county health system.ResultsWhen the number of unique values is small, a Bland-Altman plot alone may provide inadequate information. Adding a bar chart yields new and essential information about agreement, bias, and heteroscedasticity.ConclusionStudies validating one data-collection method against another can be performed successfully even when the number of unique values is small.
| S-EPMC2818014 | biostudies-literature
Project description:This article is a how-to guide on Bayesian computation using Gibbs sampling, demonstrated in the context of Latent Class Analysis (LCA). It is written for students in quantitative psychology or related fields who have a working knowledge of Bayes Theorem and conditional probability and have experience in writing computer programs in the statistical language R . The overall goals are to provide an accessible and self-contained tutorial, along with a practical computation tool. We begin with how Bayesian computation is typically described in academic articles. Technical difficulties are addressed by a hypothetical, worked-out example. We show how Bayesian computation can be broken down into a series of simpler calculations, which can then be assembled together to complete a computationally more complex model. The details are described much more explicitly than what is typically available in elementary introductions to Bayesian modeling so that readers are not overwhelmed by the mathematics. Moreover, the provided computer program shows how Bayesian LCA can be implemented with relative ease. The computer program is then applied in a large, real-world data set and explained line-by-line. We outline the general steps in how to extend these considerations to other methodological applications. We conclude with suggestions for further readings.
| S-EPMC6364555 | biostudies-literature
Project description:The Bland-Altman plot is the most common method to analyze and visualize agreement between raters or methods of quantitative outcomes in health research. While very useful for studies with two raters, a limitation of the classical Bland-Altman plot is that it is specifically used for studies with two raters. We propose an extension of the Bland-Altman plot suitable for more than two raters and derive the approximate limits of agreement with 95% confidence intervals. We validated the suggested limit of agreement by a simulation study. Moreover, we offer suggestions on how to present bias, heterogeneity among raters, as well as the uncertainty of the limits of agreement. The resulting plot could be utilized to investigate and present agreement in studies with more than two raters.
| S-EPMC7824071 | biostudies-literature
Project description:Statistical mediation analysis is used to investigate intermediate variables in the relation between independent and dependent variables. Causal interpretation of mediation analyses is challenging because randomization of subjects to levels of the independent variable does not rule out the possibility of unmeasured confounders of the mediator to outcome relation. Furthermore, commonly used frequentist methods for mediation analysis compute the probability of the data given the null hypothesis, which is not the probability of a hypothesis given the data as in Bayesian analysis. Under certain assumptions, applying the potential outcomes framework to mediation analysis allows for the computation of causal effects, and statistical mediation in the Bayesian framework gives indirect effects probabilistic interpretations. This tutorial combines causal inference and Bayesian methods for mediation analysis so the indirect and direct effects have both causal and probabilistic interpretations. Steps in Bayesian causal mediation analysis are shown in the application to an empirical example.
| S-EPMC5999040 | biostudies-literature
Project description:ObjectiveSince the inflammatory process has been implicated in the pathophysiology of psychiatric disorder, an important issue emerging is to assess the test-retest reliability of cytokine measurement in healthy individuals and patients with schizophrenia. The objective of the present study was to investigate the test-retest reliability of bead-based multiplex immunoassay technology (BMIT) for cytokine measurement by using a Bland-Altman plot (BAP).MethodsTwenty healthy individuals and twenty patients with schizophrenia were enrolled, and a 17-plex cytokine assay was used to measure inflammatory biomarkers at baseline and two weeks later. The test-retest reliability was examined by BAP, 95% limits of agreement (LOA), intraclass correlation coefficient (ICC), and coefficient of repeatability (CoR).ResultsIn the healthy controls, only interleukin (IL)-2, IL-13, IL-10, IL-17, and macrophage inflammatory protein-1β showed excellent ICC. The BAP with 95% LOA determined that 13 cytokines showed acceptable 95% LOA for a 2-week test-retest reliability, and only IL-1β, IL-12 and tumor necrosis factor (TNF)-α had significant test-retest bias. The CoR of cytokines varied significantly, ranging from 1.72 to 218.1. Compared with healthy controls, patients with schizophrenia showed significantly higher levels of IL-5, IL-13, and TNF-α and significantly lower levels of IL-4, IL-12, and interferon-gamma (IFN-γ). Of these six cytokines, IL-12 and TNF-α were considered suboptimal reliability.ConclusionThe findings from ICC and CoR implied that the test-retest reliability of BMIT for cytokine measurement were suboptimal. However, the BAP with 95% LOA confirmed that BMIT can reliably distinguish schizophrenia from healthy individuals in cytokine measurement, while significant within-subject variation and between-group overlapping were evident in cytokine expression.
| S-EPMC6710413 | biostudies-literature
Project description:BACKGROUND/OBJECTIVES:Bland-Altman methods for assessing the agreement between two measures are highly cited. However, these methods may often not be used to assess agreement, and when used, they are not always presented or interpreted correctly. Our objective was to evaluate the use and the quality of reporting of Bland-Altman analyses in studies that compare self-reported with measured weight and height. METHODS:We evaluated the use of Bland-Altman methods in 394 published articles that compared self-reported and measured weight and height data for adolescents or adults. Six reporting criteria were developed: assessment of the normality of the distribution of differences, a complete and correctly labeled Bland-Altman plot displaying the mean difference and limits of agreement (LOA), numerical values and confidence intervals, standard errors, or standard deviations for mean difference, numerical values of LOA, confidence intervals for LOA, and prespecified criteria for acceptable LOA. RESULTS:Only 72/394 (18%) studies comparing self-reported with measured weight and height or BMI used some form of Bland-Altman analyses. No study using Bland-Altman analyses satisfied more than four of the six criteria. Of the 72 studies, 64 gave mean differences along with confidence intervals or standard deviations, 55 provided complete Bland-Altman plots that were appropriately labeled and described, 37 provided numerical values for LOA, 4 reported that they examined the normality of the distribution of differences, 3 provided confidence intervals for LOA, and 3 had prespecified criteria for agreement. CONCLUSIONS:Bland-Altman methods appear to be infrequently used in studies comparing measured with self-reported weight, height, or BMI, and key information is missing in many of those that do use Bland-Altman methods. Future directions would be defining acceptable LOA values and improving the reporting and application of Bland-Altman methods in studies of self-reported anthropometry.
| S-EPMC7261640 | biostudies-literature
Project description:BackgroundTo assess the agreement of continuous measurements between a number of observers, Jones et al. introduced limits of agreement with the mean (LOAM) for multiple observers, representing how much an individual observer can deviate from the mean measurement of all observers. Besides the graphical visualisation of LOAM, suggested by Jones et al., it is desirable to supply LOAM with confidence intervals and to extend the method to the case of multiple measurements per observer.MethodsWe reformulate LOAM under the assumption the measurements follow an additive two-way random effects model. Assuming this model, we provide estimates and confidence intervals for the proposed LOAM. Further, this approach is easily extended to the case of multiple measurements per observer.ResultsThe proposed method is applied on two data sets to illustrate its use. Specifically, we consider agreement between measurements regarding tumour size and aortic diameter. For the latter study, three measurement methods are considered.ConclusionsThe proposed LOAM and the associated confidence intervals are useful for assessing agreement between continuous measurements.
| S-EPMC7730774 | biostudies-literature
Project description:This tutorial provides a step-by-step guide to performing cost-effectiveness analysis using a multi-state modeling approach. Alongside the tutorial, we provide easy-to-use functions in the statistics package R. We argue that this multi-state modeling approach using a package such as R has advantages over approaches where models are built in a spreadsheet package. In particular, using a syntax-based approach means there is a written record of what was done and the calculations are transparent. Reproducing the analysis is straightforward as the syntax just needs to be run again. The approach can be thought of as an alternative way to build a Markov decision-analytic model, which also has the option to use a state-arrival extended approach. In the state-arrival extended multi-state model, a covariate that represents patients' history is included, allowing the Markov property to be tested. We illustrate the building of multi-state survival models, making predictions from the models and assessing fits. We then proceed to perform a cost-effectiveness analysis, including deterministic and probabilistic sensitivity analyses. Finally, we show how to create 2 common methods of visualizing the results-namely, cost-effectiveness planes and cost-effectiveness acceptability curves. The analysis is implemented entirely within R. It is based on adaptions to functions in the existing R package mstate to accommodate parametric multi-state modeling that facilitates extrapolation of survival curves.
| S-EPMC5424858 | biostudies-literature