233 research outputs found
Using Lexis Objects for Multi-State Models in R
No full textThe Lexis class in the R package Epi provides tools for creation, manipulation and display of data from multi-state models. Transitions between states are described by rates (intensities); Lexis objects represent this kind of data and provide tools to show states and transitions annotated by relevant summary numbers. Data can be transformed to a form that allows modelling of several transition rates with common parameters.
Lexis: An R Class for Epidemiological Studies with Long-Term Follow-Up
No full textThe Lexis class in the R package Epi provides an object-based framework for managing follow-up time on multiple time scales, which is an important feature of prospective epidemiological studies with long duration. Follow-up time may be split either into fixed time bands, or on individual event times and the split data may be used in Poisson regression models that account for the evolution of disease risk on multiple time scales. The summary and plot methods for Lexis objects allow inspection of the follow-up times.
Bendix Kansas City Division technological spinoff through 1978
No full textThe results of work of Bendix Kansas City Division are made available in the form of technical reports that are processed through the DOE Technical Information Center in Oak Ridge. The present report lists the documents released by the Division, along with author and subject indexes. Drawing sets released are also listed. Locations of report collections in the U.S., other countries, and international agencies are provided. (RWR
Log of Apollo Lunar Surface Experiments Package technical memoranda (ATM)
No full textGives the ATM number, issue date, author, and title for the ALSEP technical memoranda (ATM) from Bendix
Statistical models for assessing agreement for quantitative data with heterogeneous random raters and replicate measurements
No full textAgreement between methods for quantitative measurements are typically assessed by computing limits of agreement between pairs of methods and/or by illustration through Bland–Altman plots. We consider the situation where the observed measurement methods are considered a random sample from a population of possible methods, and discuss how the underlying linear mixed effects model can be extended to this situation. This is relevant when, for example, the methods represent raters/judges that are used to score specific individuals or items. In the case of random methods, we are not interested in estimates pertaining to the specific methods, but are instead interested in quantifying the variation between the methods actually involved making measurements, and accommodating this as an extra source of variation when generalizing to the clinical performance of a method. In the model we allow raters to have individual precision/skill and permit linked replicates (i.e., when the numbering, labeling or ordering of the replicates within items is important). Applications involving estimation of the limits of agreement for two datasets are shown: A dataset of spatial perception among a group of students as well as a dataset on consumer preference of French chocolate. The models are implemented in the MethComp package for R [Carstensen B, Gurrin L, Ekstrøm CT, Figurski M. MethComp: functions for analysis of agreement in method comparison studies; 2013. R package version 1.22, R Core Team. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2012].Keywords: agreement; limits og agreement; mixed models; random raters; method comparisonAgreement between methods for quantitative measurements are typically assessed by computing limits of agreement between pairs of methods and/or by illustration through Bland-Altman plots. We consider the situation where the observed measurement methods are considered a random sample from a population of possible methods, and discuss how the underlying linear mixed effects model can be extended to this situation. This is relevant when, for example, the methods represent raters/judges that are used to score specific individuals or items. In the case of random methods, we are not interested in estimates pertaining to the specific methods, but are instead interested in quantifying the variation between the methods actually involved making measurements, and accommodating this as an extra source of variation when generalizing to the clinical performance of a method. In the model we allow raters to have individual precision/skill and permit linked replicates (i.e., when the numbering, labeling or ordering of the replicates within items is important). Applications involving estimation of the limits of agreement for two datasets are shown: A dataset of spatial perception among a group of students as well as a dataset on consumer preference of French chocolate. The models are implemented in the MethComp package for R [Carstensen B, Gurrin L, Ekstrøm CT, Figurski M. MethComp: functions for analysis of agreement in method comparison studies; 2013. R package version 1.22, R Core Team. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2012].</p
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