Use of State Sequence Analysis in Pharmacoepidemiology: A Tutorial.
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ABSTRACT:
SUBMITTER: Vanoli J
PROVIDER: S-EPMC8705850 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundCeftazidime-avibactam has in vitro activity against some carbapenem-resistant gram-negative infections (GNIs), and therefore may be a useful alternative to more toxic antibiotics such as colistin. Understanding ceftazidime-avibactam uptake and usage patterns would inform hospital formularies, stewardship, and antibiotic development.MethodsA retrospective cohort study assessed inpatient encounters in the Vizient database. Ceftazidime-avibactam and colistin administrations were categorized into presumed empiric (3 consecutive days of therapy or less with qualifying exclusions) versus targeted therapy (≥4 consecutive days of therapy) for presumed carbapenem-resistant GNIs. Quarterly percentage change (QPC) using modified Poisson regression and relative change in frequency of targeted ceftazidime-avibactam to colistin encounters was calculated. Factors associated with preferentially receiving targeted ceftazidime-avibactam versus colistin were identified using generalized estimating equations.ResultsBetween 2015 quarter (q) 1 and 2017q4, ceftazidime-avibactam was administered 21 215 times across 1901 encounters. Inpatient prescriptions for ceftazidime-avibactam increased from 0.44/10 000 hospitalizations in 2015q1 to 7.7/10 000 in 2017q4 (QPC, +11%; 95% CI, 10-13%; P < .01), while conversely colistin prescriptions decreased quarterly by 5% (95% CI, 4-6%; P < .01). Ceftazidime-avibactam therapy was categorized as empiric 25% of the time, targeted 65% of the time, and indeterminate 10% of the time. Patients with chronic kidney disease were twice as likely to receive targeted ceftazidime-avibactam versus colistin (RR, 2.02; 95% CI, 1.82-2.25), whereas those on dialysis were less likely to receive ceftazidime-avibactam than colistin (RR, 0.71; 95% CI, .61-.83).ConclusionsSince approval in 2015, ceftazidime-avibactam use has grown for presumed carbapenem-resistant GNIs, while colistin has correspondingly declined. Renal function drove the choice between ceftazidime-avibactam and colistin as targeted therapy.
| S-EPMC7884805 | 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
Project description:Liquid state nuclear magnetic resonance is the only class of magnetic resonance experiments for which the simulation problem is solved comprehensively for spin systems of any size. This paper contains a practical walkthrough for one of the many available simulation packages - Spinach. Its unique feature is polynomial complexity scaling: the ability to simulate large spin systems quantum mechanically and with accurate account of relaxation, diffusion, chemical processes, and hydrodynamics. This paper is a gentle introduction written with a PhD student in mind.
| S-EPMC6001792 | biostudies-literature
Project description:Background:Instrumental variable analysis, for example with physicians' prescribing preferences as an instrument for medications issued in primary care, is an increasingly popular method in the field of pharmacoepidemiology. Existing power calculators for studies using instrumental variable analysis, such as Mendelian randomization power calculators, do not allow for the structure of research questions in this field. This is because the analysis in pharmacoepidemiology will typically have stronger instruments and detect larger causal effects than in other fields. Consequently, there is a need for dedicated power calculators for pharmacoepidemiological research. Methods and Results:The formula for calculating the power of a study using instrumental variable analysis in the context of pharmacoepidemiology is derived before being validated by a simulation study. The formula is applicable for studies using a single binary instrument to analyse the causal effect of a binary exposure on a continuous outcome. An online calculator, as well as packages in both R and Stata, are provided for the implementation of the formula by others. Conclusions:The statistical power of instrumental variable analysis in pharmacoepidemiological studies to detect a clinically meaningful treatment effect is an important consideration. Research questions in this field have distinct structures that must be accounted for when calculating power. The formula presented differs from existing instrumental variable power formulae due to its parametrization, which is designed specifically for ease of use by pharmacoepidemiologists.
| S-EPMC5837396 | biostudies-literature
Project description: Not available
| S-EPMC1118945 | 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:Background and objectives: Many brain-computer interfaces (BCIs) for people with severe disabilities present stimuli in the visual modality with little consideration of the visual skills required for successful use. The primary objective of this tutorial is to present researchers and clinical professionals with basic information about the visual skills needed for functional use of visual BCIs, and to offer modifications that would render BCI technology more accessible for persons with vision impairments.Methods: First, we provide a background on BCIs that rely on a visual interface. We then describe the visual skills required for BCI technologies that are used for augmentative and alternative communication (AAC), as well as common eye conditions or impairments that can impact the user's performance. We summarize screening tools that can be administered by the non-eye care professional in a research or clinical setting, as well as the role of the eye care professional. Finally, we explore potential BCI design modifications to compensate for identified functional impairments. Information was generated from literature review and the clinical experience of vision experts.Results and conclusions: This in-depth description culminates in foundational information about visual skills and functional visual impairments that affect the design and use of visual interfaces for BCI technologies. The visual interface is a critical component of successful BCI systems. We can determine a BCI system for potential users with visual impairments and design BCI visual interfaces based on sound anatomical and physiological visual clinical science.Implications for RehabilitationAs brain-computer interfaces (BCIs) become possible access methods for people with severe motor impairments, it is critical that clinicians have a basic knowledge of the visual skills necessary for use of visual BCI interfaces.Rehabilitation providers must have a knowledge of objectively gathering information regarding a potential BCI user's functional visual skills.Rehabilitation providers must understand how to modify BCI visual interfaces for the potential user with visual impairments.Rehabilitation scientists should understand the visual demands of BCIs as they develop and evaluate these new access methods.
| S-EPMC7529781 | biostudies-literature
Project description:Distributional cost-effectiveness analysis (DCEA) is a framework for incorporating health inequality concerns into the economic evaluation of health sector interventions. In this tutorial, we describe the technical details of how to conduct DCEA, using an illustrative example comparing alternative ways of implementing the National Health Service (NHS) Bowel Cancer Screening Programme (BCSP). The 2 key stages in DCEA are 1) modeling social distributions of health associated with different interventions, and 2) evaluating social distributions of health with respect to the dual objectives of improving total population health and reducing unfair health inequality. As well as describing the technical methods used, we also identify the data requirements and the social value judgments that have to be made. Finally, we demonstrate the use of sensitivity analyses to explore the impacts of alternative modeling assumptions and social value judgments.
| S-EPMC4853814 | biostudies-other
Project description:When analyzing experimental chemical data, it is often necessary to incorporate the structure of the study design into the chemometric/statistical models to effectively address the research questions of interest. ANOVA-Simultaneous Component Analysis (ASCA) is one of the most prominent methods to include such information in the quantitative analysis of multivariate data, especially when the number of variables is large. This tutorial review intends to explain in a simple way how ASCA works, how it is operated and how to correctly interpret ASCA results, with approachable mathematical and visual descriptions. Two examples are given: the first, a simulated chemical reaction, serves to illustrate the ASCA steps and the second, from a real chemical ecology data set, the interpretation of results. An overview of methods closely related to ASCA is also provided, pointing out their differences and scope, to give a wide-ranging picture of the available options to build multivariate models that take experimental design into account. Graphical abstract Image 1 Highlights • ASCA is a multivariate method for analysis of multi-factor data.• An overview of the (mathematical) principles of ASCA is presented.• Key aspects for practical application of ASCA are discussed.• Detailed explanation of ASCA output in terms of score and loading plots is given.• Literature review of other multivariate techniques for analysis of multi-factor data.
| S-EPMC7772684 | biostudies-literature
Project description:In this article, we provide a brief tutorial on the estimation, analysis, and simulation on attitude networks using the programming language R. We first discuss what a network is and subsequently show how one can estimate a regularized network on typical attitude data. For this, we use open-access data on the attitudes toward Barack Obama during the 2012 American presidential election. Second, we show how one can calculate standard network measures such as community structure, centrality, and connectivity on this estimated attitude network. Third, we show how one can simulate from an estimated attitude network to derive predictions from attitude networks. By this, we highlight that network theory provides a framework for both testing and developing formalized hypotheses on attitudes and related core social psychological constructs.
| S-EPMC5582642 | biostudies-literature