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:Narrative electronic prescribing instructions (NEPIs) are text that convey information on the administration or co-administration of a drug as directed by a prescriber. For researchers, NEPIs have the potential to advance our understanding of the risks and benefits of medications in populations; however, due to their unstructured nature, they are not often utilized. The goal of this scoping review was to evaluate how NEPIs are currently employed in research, identify opportunities and challenges for their broader application, and provide recommendations on their future use. The scoping review comprised a comprehensive literature review and a survey of key stakeholders. From the literature review, we identified 33 primary articles that described the use of NEPIs. The majority of articles (n = 19) identified issues with the quality of information in NEPIs compared with structured prescribing information; nine articles described the development of novel algorithms that performed well in extracting information from NEPIs, and five described the used of manual or simpler algorithms to extract prescribing information from NEPIs. A survey of 19 stakeholders indicated concerns for the quality of information in NEPIs and called for standardization of NEPIs to reduce data variability/errors. Nevertheless, stakeholders believed NEPIs present an opportunity to identify prescriber's intent for the prescription and to study temporal treatment patterns. In summary, NEPIs hold much promise for advancing the field of pharmacoepidemiology. Researchers should take advantage of addressing important questions that can be uniquely answered with NEPIs, but exercise caution when using this information and carefully consider the quality of the data.
| S-EPMC8419095 | 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: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: Not available
| S-EPMC1118945 | biostudies-literature
Project description:In an introductory tutorial, we illustrated building cohort state-transition models (cSTMs) in R, where the state transition probabilities were constant over time. However, in practice, many cSTMs require transitions, rewards, or both to vary over time (time dependent). This tutorial illustrates adding 2 types of time dependence using a previously published cost-effectiveness analysis of multiple strategies as an example. The first is simulation-time dependence, which allows for the transition probabilities to vary as a function of time as measured since the start of the simulation (e.g., varying probability of death as the cohort ages). The second is state-residence time dependence, allowing for history by tracking the time spent in any particular health state using tunnel states. We use these time-dependent cSTMs to conduct cost-effectiveness and probabilistic sensitivity analyses. We also obtain various epidemiological outcomes of interest from the outputs generated from the cSTM, such as survival probability and disease prevalence, often used for model calibration and validation. We present the mathematical notation first, followed by the R code to execute the calculations. The full R code is provided in a public code repository for broader implementation.
| S-EPMC9844995 | 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-literature
Project description:Background: Pregnant and postpartum women have been historically excluded from clinical trials, with data on the safety of drugs relying on observational research. Methodological concerns regarding the timing and dosing of medications, data sources, study designs, and methods used for estimating associations are still problematic in observational studies. Answering causal questions is even more complex. Despite the increased interest in emulating target trials using observational data, little is known about this approach in perinatal pharmacoepidemiology. Objective: This scoping review protocol aims to describe the methodology for assessing the available literature concerning emulating target trials for studying outcomes in women exposed to medications in the preconception, perinatal, or postpartum periods. Methods and Analysis: We will follow the methods detailed in the Joanna Briggs Institute reviewer’s manual. We will adopt the six-stage framework recommended by Arksey and O'Malley and Levac and others. Web scraping techniques will be used for identifying relevant studies. Two authors will select articles based on the title and abstract, with discrepancies resolved by consensus, by a third reviewer. Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews flow diagram will be presented to reflect the search process. We will use existing statements to identify quality gaps in the current literature. Variables related to the content for perinatal pharmacoepidemiologic research will be included. The Risk Of Bias In Non-randomised Studies - of Interventions (ROBINS-I) will guide the assessment of the target trial emulation (i.e., treatment strategies compared, assignment procedures, follow-up period, outcome, and causal contrasts). Discussion: Data regarding the safety of drugs taken, prior to and during pregnancy and while lactating are lacking and it is necessary to understand how we can answer these questions using rigorous methods in observational research. Through this scoping review, we intend to understand to what extent the target trial approach is being used in perinatal pharmacoepidemiology and provide recommendations to improve its use in this field. Ethics and Dissemination: Secondary data from published scientific articles will be used, not requiring approval by the Research Ethics Committee with human beings. Findings will be submitted to a peer-reviewed journal.
| S-EPMC9354606 | biostudies-literature