Practitioner's Guide to Latent Class Analysis: Methodological Considerations and Common Pitfalls.
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ABSTRACT:
SUBMITTER: Sinha P
PROVIDER: S-EPMC7746621 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundPneumonitis is one of the most common adverse events induced by the use of immune checkpoint inhibitors (ICI), accounting for a 20% of all ICI-associated deaths. Despite numerous efforts to identify risk factors and develop predictive models, there is no clinically deployed risk prediction model for patient risk stratification or for guiding subsequent monitoring. We believe this is due to systemic suboptimal approaches in study designs and methodologies in the literature. The nature and prevalence of different methodological approaches has not been thoroughly examined in prior systematic reviews.MethodsThe PubMed, medRxiv and bioRxiv databases were used to identify studies that aimed at risk factor discovery and/or risk prediction model development for ICI-induced pneumonitis (ICI pneumonitis). Studies were then analysed to identify common methodological pitfalls and their contribution to the risk of bias, assessed using the QUIPS and PROBAST tools.ResultsThere were 51 manuscripts eligible for the review, with Japan-based studies over-represented, being nearly half (24/51) of all papers considered. Only 2/51 studies had a low risk of bias overall. Common bias-inducing practices included unclear diagnostic method or potential misdiagnosis, lack of multiple testing correction, the use of univariate analysis for selecting features for multivariable analysis, discretization of continuous variables, and inappropriate handling of missing values. Results from the risk model development studies were also likely to have been overoptimistic due to lack of holdout sets.ConclusionsStudies with low risk of bias in their methodology are lacking in the existing literature. High-quality risk factor identification and risk model development studies are urgently required by the community to give the best chance of them progressing into a clinically deployable risk prediction model. Recommendations and alternative approaches for reducing the risk of bias were also discussed to guide future studies.
| S-EPMC10560723 | biostudies-literature
Project description:IntroductionHealth disparities arise from biological-environmental interactions. Neuroimaging cohorts are reaching sufficiently large sample sizes such that analyses could evaluate how the environment affects the brain. We present a practical guide for applying geospatial methods to a neuroimaging cohort.MethodsWe estimated brain age gap (BAG) from structural magnetic resonance imaging (MRI) from 239 city-dwelling participants in St. Louis, Missouri. We compared these participants to population-level estimates from the American Community Survey (ACS). We used geospatial analysis to identify neighborhoods associated with patterns of altered brain structure. We also evaluated the relationship between Area Deprivation Index (ADI) and BAG.ResultsWe identify areas in St. Louis, Missouri that were significantly associated with higher BAG from a spatially representative cohort. We provide replication code.ConclusionWe observe a relationship between neighborhoods and brain health, which suggests that neighborhood-based interventions could be appropriate. We encourage other studies to geocode participant information to evaluate biological-environmental interaction.
| S-EPMC10019584 | biostudies-literature
Project description:UnlabelledMucopolysaccharidosis II (MPS II), or Hunter syndrome, is an X-linked lysosomal storage disorder caused by a deficiency in the enzyme iduronate-2-sulfatase. Affected patients suffer progressive damage to multiple organ systems and early mortality. Two thirds of patients also manifest cognitive impairment and developmental delays. MPS II can be extremely difficult to diagnose before irreversible organ and tissue damage has occurred because of an insidious onset and the overlap in signs and symptoms with common childhood complaints. This is particularly true of patients without cognitive impairment (attenuated phenotype). Although not curative, early treatment with enzyme replacement therapy before irreversible organ damage has occurred may result in the greatest clinical benefit. Here, the signs, symptoms, and surgical history that should trigger suspicion of MPS II are described, and the diagnostic process is reviewed with a focus on practical considerations and the avoidance of common diagnostic pitfalls. Once a diagnosis is made, multidisciplinary management with an extended team of pediatric specialists is essential and should involve the pediatrician or family practice physician as facilitator and medical home for the patient and family.ConclusionBecause routine newborn screening is not yet available for MPS II, the involvement and awareness of pediatricians, family practice physicians, and pediatric specialists is critical for early identification, diagnosis, and referral in order to help optimize patient outcomes.
| S-EPMC3306562 | biostudies-literature
Project description:ObjectivePredicting how the brain can be driven to specific states by means of internal or external control requires a fundamental understanding of the relationship between neural connectivity and activity. Network control theory is a powerful tool from the physical and engineering sciences that can provide insights regarding that relationship; it formalizes the study of how the dynamics of a complex system can arise from its underlying structure of interconnected units.ApproachGiven the recent use of network control theory in neuroscience, it is now timely to offer a practical guide to methodological considerations in the controllability of structural brain networks. Here we provide a systematic overview of the framework, examine the impact of modeling choices on frequently studied control metrics, and suggest potentially useful theoretical extensions. We ground our discussions, numerical demonstrations, and theoretical advances in a dataset of high-resolution diffusion imaging with 730 diffusion directions acquired over approximately 1 h of scanning from ten healthy young adults.Main resultsFollowing a didactic introduction of the theory, we probe how a selection of modeling choices affects four common statistics: average controllability, modal controllability, minimum control energy, and optimal control energy. Next, we extend the current state-of-the-art in two ways: first, by developing an alternative measure of structural connectivity that accounts for radial propagation of activity through abutting tissue, and second, by defining a complementary metric quantifying the complexity of the energy landscape of a system. We close with specific modeling recommendations and a discussion of methodological constraints.SignificanceOur hope is that this accessible account will inspire the neuroimaging community to more fully exploit the potential of network control theory in tackling pressing questions in cognitive, developmental, and clinical neuroscience.
| S-EPMC7734595 | biostudies-literature
Project description:The translation of emerging genomic knowledge into public health and clinical care is one of the major challenges for the coming decades. At the moment, genome-based prediction of common diseases, such as type 2 diabetes, coronary heart disease and cancer, is still not informative. Our understanding of the genetic basis of multifactorial diseases is improving, but the currently identified susceptibility variants contribute only marginally to the development of disease. At the same time, an increasing number of companies are offering personalized lifestyle and health recommendations on the basis of individual genetic profiles. This discrepancy between the limited predictive value and the commercial availability of genetic profiles highlights the need for a critical appraisal of the usefulness of genome-based applications in clinical and public health care. Anticipating the discovery of a large number of genetic variants in the near future, we need to prepare a framework for the design and analysis of studies aiming to evaluate the clinical validity and utility of genetic tests. In this article, we review recent studies on the predictive value of genetic profiling from a methodological perspective and address issues around the choice of the study population, the construction of genetic profiles, the measurement of the predictive value, calibration and validation of prediction models, and assessment of clinical utility. Careful consideration of these issues will contribute to the knowledge base that is needed to identify useful genome-based applications for implementation in clinical and public health practice.
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Project description:Correct and, hence, valid classifications of individuals are of high importance in the social sciences as these classifications are the basis for diagnoses and/or the assignment to a treatment. The via regia to inspect the validity of psychological ratings is the multitrait-multimethod (MTMM) approach. First, a latent variable model for the analysis of rater agreement (latent rater agreement model) will be presented that allows for the analysis of convergent validity between different measurement approaches (e.g., raters). Models of rater agreement are transferred to the level of latent variables. Second, the latent rater agreement model will be extended to a more informative MTMM latent class model. This model allows for estimating (i) the convergence of ratings, (ii) method biases in terms of differential latent distributions of raters and differential associations of categorizations within raters (specific rater bias), and (iii) the distinguishability of categories indicating if categories are satisfyingly distinct from each other. Finally, an empirical application is presented to exemplify the interpretation of the MTMM latent class model.
| S-EPMC4584970 | biostudies-literature
Project description:Logistic regression analysis is a statistical technique to evaluate the relationship between various predictor variables (either categorical or continuous) and an outcome which is binary (dichotomous). In this article, we discuss logistic regression analysis and the limitations of this technique.
| S-EPMC5543767 | biostudies-other
Project description:To answer frequently asked questions surrounding the use of the new herpes zoster (HZ) vaccine.Published results of clinical trials and other studies, recommendations from the Canadian National Advisory Committee on Immunization, and the US Advisory Committee on Immunization Practices; data were also obtained from the vaccine's Health Canada-approved product monograph.Herpes zoster results from reactivation of the varicella-zoster virus; postherpetic neuralgia (PHN) is its most common and serious complication. The incidence of PHN after HZ is directly related to age, with 50% of affected individuals older than 60 years experiencing persistent and unrelieved pain. The live virus HZ vaccine reduces the incidence of HZ by about 50% and the occurrence of PHN by two-thirds, with vaccinated individuals experiencing attenuated or shortened symptoms. The vaccine is contraindicated in many immunocompromised patients and might not be effective in patients taking antiviral medications active against the HZ virus. Physicians should be aware of the different recommendations for these groups.The HZ vaccine is a safe and effective preventive measure for reducing the overall burden and severity of HZ in older adults. The vaccine appears to be cost-effective when administered to adults aged 60 years and older.
| S-EPMC3192074 | biostudies-other
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:BackgroundAtopic dermatitis (AD) is a heterogeneous disease with a multifactorial aetiology and complex pathophysiology. This heterogeneity translates into different trajectories of disease progression with respect to severity, persistence and risk of development of atopic comorbidities. Determining which possible disease trajectories or comorbidities any individual child might develop is challenging in clinical practice. Tools that help identify paediatric patients at higher risk of disease progression would greatly aid clinicians.MethodsWe reviewed recent cohort studies to synthesize and simplify the epidemiological data to try to identify shared clinically relevant characteristics that may help physicians estimate the risk of disease progression in paediatric patients with AD.ResultsDespite the variability in data collection and methods of analysis and their limitations, there are common patterns of early-childhood AD that may aid in the estimation of risk for disease progression. Factors associated with risk of AD progression include younger age of onset, family history of atopy, greater AD severity, filaggrin mutations, urban environment and polysensitization and/or allergic multimorbidity. Based on these factors, we provide a practitioner's guide for identifying, counselling and/or referring infants and children with AD at potentially higher risk of developing persistent AD and atopic comorbidities. We also present clinical scenarios to illustrate how these data relate to real-life situations.ConclusionsUseful insights are provided for physicians and patients to inform them better about the risk of AD progression and to help guide care pathways for the paediatric population with AD. What's already known about this topic? The complex pathophysiology of atopic dermatitis (AD) translates into a heterogeneous clinical presentation and trajectories of disease progression. Although the consensus is that most paediatric patients with AD will eventually 'outgrow' the disease or follow the longitudinal trajectory known as the 'atopic march', a significant proportion will develop persistent AD and/or other atopic conditions. No known factors conclusively predict the risk of progression or development of comorbidities. What does this study add? Recent analyses of data from large cohorts of paediatric patients with AD have suggested the existence of potentially discrete clusters of patients who present with relatively common AD phenotypes. These studies have shed some light onto the factors associated with risk of progression, which we review in this article. A practitioner's guide with clinical scenarios is provided to help identify patients at high risk of progression to determine whether a patient should be monitored and/or would require specialist referral.
| S-EPMC6899789 | biostudies-literature