A narrative review of using prescription drug databases for comorbidity adjustment: A less effective remedy or a prescription for improved model fit?

ObjectiveTo determine whether apathy or depression predicts all-cause dementia in small vessel disease (SVD) patients.MethodsAnalyses used two prospective cohort studies of SVD: St. George’s Cognition and Neuroimaging in Stroke (SCANS; n=121) and Radboud University Nijmegen Diffusion Tensor and Magnetic Resonance Cohort (RUN DMC; n=352). Multivariate Cox regressions were used to predict dementia using baseline apathy and depression scores in both datasets. Change in apathy and depression was used to predict dementia in a subset of 104 participants with longitudinal data from SCANS. All models were controlled for age, education and cognitive function.ResultsBaseline apathy scores predicted dementia in SCANS (HR 1.49, 95% CI 1.05 to 2.11, p=0.024) and RUN DMC (HR 1.05, 95% CI 1.01 to 1.09, p=0.007). Increasing apathy was associated with dementia in SCANS (HR 1.53, 95% CI 1.08 to 2.17, p=0.017). In contrast, baseline depression and change in depression did not predict dementia in either dataset. Including apathy in predictive models of dementia improved model fit.ConclusionsApathy, but not depression, may be a prodromal symptom of dementia in SVD, and may be useful in identifying at-risk individuals.

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Cosinor Analysis for Temperature Time Series Data of Long Duration

Biological Research For Nursing ◽

10.1177/1099800407303509 ◽

2007 ◽

Vol 9 (1) ◽

pp. 30-41 ◽

Cited By ~ 7

Author(s):

Nikhil S. Padhye ◽

Sandra K. Hanneman

Keyword(s):

Time Series ◽

Time Series Data ◽

Model Fit ◽

Series Data ◽

Model Parameters ◽

Cosinor Analysis ◽

Long Time ◽

Improved Model ◽

Data Length ◽

Cosinor Models

The application of cosinor models to long time series requires special attention. With increasing length of the time series, the presence of noise and drifts in rhythm parameters from cycle to cycle lead to rapid deterioration of cosinor models. The sensitivity of amplitude and model-fit to the data length is demonstrated for body temperature data from ambulatory menstrual cycling and menopausal women and from ambulatory male swine. It follows that amplitude comparisons between studies cannot be made independent of consideration of the data length. Cosinor analysis may be carried out on serial-sections of the series for improved model-fit and for tracking changes in rhythm parameters. Noise and drift reduction can also be achieved by folding the series onto a single cycle, which leads to substantial gains in the model-fit but lowers the amplitude. Central values of model parameters are negligibly changed by consideration of the autoregressive nature of residuals.

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Improved unit hydrograph characterisation of the daily flow regime (including low flows) for the River Teifi, Wales: towards better rainfall-streamflow models for regionalisation

Hydrology and Earth System Sciences ◽

10.5194/hess-6-899-2002 ◽

2002 ◽

Vol 6 (5) ◽

pp. 899-911 ◽

Cited By ~ 21

Author(s):

I.G. Littlewood

Keyword(s):

Time Delay ◽

Model Fit ◽

Temperature Modulation ◽

Rainfall Runoff ◽

Unit Hydrograph ◽

New Model ◽

Low Flows ◽

Starting Point ◽

Improved Model ◽

Pure Time Delay

Abstract. An established rainfall-streamflow modelling methodology employing a six-parameter unit hydrograph-based rainfall-runoff model structure is developed further to give an improved model-fit to daily flows for the River Teifi at Glan Teifi. It is shown that a previous model of this type for the Teifi, which (a) accounted for 85% of the variance in observed streamflow, (b) incorporated a pure time delay of one day and (c) was calibrated using a trade-off between two model-fit statistics (as recommended in the original methodology), systematically over-estimates low flows. Using that model as a starting point the combined application of a non-integer pure time delay and further adjustment of a temperature modulation parameter in the loss module, using the flow duration curve as an additional model-fit criterion, gives a much improved model-fit to low flows, while leaving the already good model-fit to higher flows essentially unchanged. The further adjustment of the temperature modulation loss module parameter in this way is much more effective at improving model-fit to low flows than the introduction of the non-integer pure time delay. The new model for the Teifi accounts for 88% of the variance in observed streamflow and performs well over the 5 percentile to 95 percentile range of flows. Issues concerning the utility and efficacy of the new model selection procedure are discussed in the context of hydrological studies, including regionalisation. Keywords: unit hydrographs, rainfall-runoff modelling, low flows, regionalisation.

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Quantifying status and trends from monitoring surveys: application to Pygmy Whitefish (Prosopium coulterii) in Lake Superior

Canadian Journal of Fisheries and Aquatic Sciences ◽

10.1139/cjfas-2021-0155 ◽

2021 ◽

Author(s):

Adam S. van der Lee ◽

Mark R. Vinson ◽

Marten A. Koops

Keyword(s):

Community Dynamics ◽

Lake Superior ◽

Temporal Trends ◽

Model Fit ◽

Population Projections ◽

Biomass Density ◽

The Status ◽

Random Structures ◽

Improved Model ◽

The Impact

Population assessments of fish species often rely on data from surveys with different objectives such as measuring biodiversity or community dynamics. These surveys often contain spatial-temporal dependencies that can greatly influence conclusions drawn from analyses. Pygmy whitefish (PWF, Prosopium coulterii) populations in Lake Superior were recently assessed as Threatened by the Committee on the Status of Endangered Species in Canada which motivated a thorough analysis of available data to improve our understanding of its population status. The U.S. Geological Survey conducts annual bottom trawl surveys in Lake Superior that commonly captures PWF. We used these data (1989-2018) to model temporal trends in PWF biomass-density and make lake-wide population projections. We used a Bayesian approach, Integrated Nested Laplace Approximation (INLA), and compared the impact of including different random structures on model fit. Inclusion of spatial structure improved model fit and conclusions differed from models omitting random effects. PWF populations have experienced periodic fluctuations in biomass-density since 1989, though 2018 may represent the lowest density in the 30-year time series. Lake-wide biomass was estimated to be 71.5t.

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Effects of metric hierarchy and rhyme predictability on word duration in The Cat in the Hat

10.31234/osf.io/26evg ◽

2018 ◽

Author(s):

Mara Breen

Keyword(s):

Syntactic Structure ◽

Spoken Word ◽

Model Fit ◽

Semantic Structure ◽

Interval Duration ◽

Frequency Word ◽

Children's Book ◽

Work Factors ◽

Word Duration ◽

Improved Model

Word durations convey many types of linguistic information, including intrinsic lexical features like length and frequency and contextual features like syntactic and semantic structure. The current study was designed to investigate whether hierarchical metric structure and rhyme predictability account for durational variation over and above other features in productions of a rhyming, metrically-regular children's book: The Cat in the Hat (Dr. Seuss, 1957). One-syllable word durations and inter-onset intervals were modeled as functions of segment number, lexical frequency, word class, syntactic structure, repetition, and font emphasis. Consistent with prior work, factors predicting longer word durations and inter-onset intervals included more phonemes, lower frequency, first mention, alignment with a syntactic boundary, and capitalization. A model parameter corresponding to metric grid height improved model fit of word durations and inter-onset intervals. Specifically, speakers realized five levels of metric hierarchy with inter-onset intervals such that interval duration increased linearly with increased height in the metric hierarchy. Conversely, speakers realized only three levels of metric hierarchy with word duration, demonstrating that they shortened the highly predictable rhyme resolutions. These results further understanding of the factors that affect spoken word duration, and demonstrate the myriad cues that children receive about linguistic structure from nursery rhymes.

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Regulating The Use Of Prescription Drug Databases In Health Insurance Underwriting

Journal of Business & Economics Research (JBER) ◽

10.19030/jber.v10i8.7174 ◽

2012 ◽

Vol 10 (8) ◽

pp. 455

Author(s):

Kevin L. Eastman ◽

Joseph S. Ruhland ◽

Alan D. Eastman

Keyword(s):

Health Insurance ◽

Prescription Drug ◽

Small Group ◽

Font Size ◽

Group Health ◽

Advantages And Disadvantages ◽

Drug Databases ◽

Group Health Insurance ◽

Prescription Databases

This paper examines the use of commercially-available prescription drug profiles in the underwriting of individual and small-group health insurance plans. It explains how these profiles are developed and used by insurers and analyzes their potential advantages and disadvantages to both insurers and consumers. Current and pending legislation and regulations governing the use of these prescription databases are also explained.

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Natural selection influenced the genetic architecture of brain structure, behavioral and neuropsychiatric traits

10.1101/2020.02.26.966531 ◽

2020 ◽

Cited By ~ 1

Author(s):

Frank R Wendt ◽

Gita A Pathak ◽

Cassie Overstreet ◽

Daniel S Tylee ◽

Joel Gelernter ◽

...

Keyword(s):

Natural Selection ◽

Complex Traits ◽

Association Studies ◽

Model Fit ◽

Human Populations ◽

Genome Wide Association Studies ◽

Loss Of Function ◽

Risk Alleles ◽

Genome Wide ◽

Improved Model

AbstractNatural selection has shaped the phenotypic characteristics of human populations. Genome-wide association studies (GWAS) have elucidated contributions of thousands of common variants with small effects on an individual’s predisposition to complex traits (polygenicity), as well as wide-spread sharing of risk alleles across traits in the human phenome (pleiotropy). It remains unclear how the pervasive effects of natural selection influence polygenicity in brain-related traits. We investigate these effects by annotating the genome with measures of background (BGS) and positive selection, indications of Neanderthal introgression, measures of functional significance including loss-of-function (LoF) intolerant and genic regions, and genotype networks in 75 brain-related traits. Evidence of natural selection was determined using binary annotations of top 2%, 1%, and 0.5% of selection scores genome-wide. We detected enrichment (q<0.05) of SNP-heritability at loci with elevated BGS (7 phenotypes) and in genic (34 phenotypes) and LoF-intolerant regions (67 phenotypes). BGS (top 2%) significantly predicted effect size variance for trait-associated loci (σ2 parameter) in 75 brain-related traits (β=4.39×10−5, p=1.43×10−5, model r2=0.548). By including the number of DSM-5 diagnostic combinations per psychiatric disorder, we substantially improved model fit (σ2 ~ BTop2% × Genic × diagnostic combinations; model r2=0.661). We show that GWAS with larger variance in risk locus effect sizes are collectively predicted by the effects of loci under strong BGS and in regulatory regions of the genome. We further show that diagnostic complexity exacerbates this relationship and perhaps dampens the ability to detect psychiatric risk loci.

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The Development of a Measure of Psychological Responses to Injury

Journal of Sport Rehabilitation ◽

10.1123/jsr.17.1.21 ◽

2008 ◽

Vol 17 (1) ◽

pp. 21-37 ◽

Cited By ~ 12

Author(s):

Lynne Evans ◽

Lew Hardy ◽

Ian Mitchell ◽

Tim Rees

Keyword(s):

Sport Injury ◽

Factor Model ◽

Five Factor Model ◽

Model Fit ◽

Factorial Validity ◽

Psychological Responses ◽

Variable Model ◽

Initial Development ◽

Improved Model ◽

Injured Athletes

Objective:The current paper reports the initial development of a theoretically derived measure to assess the psychological responses of injured athletes.Design:The paper comprises two studies. The first examines the factorial validity of the Psychological Responses to Sport Injury Inventory (PRSII) originally reported by Evans, Hardy, and Mullen.1 The second reexamines the factorial validity of the PRSII following scale refinement. Confirmatory factor analysis was employed in both studies.Setting:Sport injury clinics.Participants:Study 1 comprised repeated observations (n = 486) on 56 injured athletes. Study 2 comprised single observations on 418 injured athletes.Measure:Psychological Responses to Sport Injury Inventory (PRSII).Results:The five factor model from the first study demonstrated variable model fit. The six factor model that emerged from the second study showed improved model fit.Conclusions:The study provides some support for the PRSII as a measure of athletes’ psychological responses to injury.

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Different Version, Similar Result? A Critical Analysis of the Multiplicity of Shortened Versions of the Zimbardo Time Perspective Inventory

SAGE Open ◽

10.1177/2158244020923351 ◽

2020 ◽

Vol 10 (2) ◽

pp. 215824402092335

Author(s):

John L. Perry ◽

Elizabeth C. Temple ◽

Frank C. Worrell ◽

Urška Zivkovic ◽

Zena R. Mello ◽

...

Keyword(s):

Individual Differences ◽

Critical Analysis ◽

Time Perspective ◽

Model Fit ◽

Data Driven ◽

Internal Reliability ◽

Psychometric Scales ◽

Improved Model ◽

The Cost ◽

The Impact

The Zimbardo Time Perspective Inventory (ZTPI) has been extensively used, with more than 1,400 citations in Scopus alone. After identifying psychometric problems however, several authors have attempted to overcome limitations by shortening the scale. As such, there now exist multiple. shortened versions of the ZTPI, all using some of the original 56 items. Although each shorter version reports various broadly acceptable validity parameters using the group with which it was developed, these are often sample specific and at the cost of reliability, generalizability, and ability to detect individual differences in the construct. To examine this more closely, we reviewed the psychometric properties of the ZTPI and some of its derivatives, and found that data-driven approaches to creating these shortened versions of the scale prioritized improved model fit over internal reliability and sensitivity. In conclusion, we suggest that it is time for a new collaborative strategy to address conceptual and measurement concerns with the ZTPI, and discourage data-driven and sample-specific solutions to the psychometric concerns of the scale’s scores. More broadly, we recommend that researchers consider the impact on reliability, generalizability, and ability to detect individual differences when developing short psychometric scales.

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The Value of L-Band Soil Moisture and Vegetation Optical Depth Estimates in the Prediction of Vegetation Phenology

Remote Sensing ◽

10.3390/rs13071343 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1343

Author(s):

Bonan Li ◽

Stephen P. Good ◽

Dawn R. URycki

Keyword(s):

Soil Moisture ◽

Optical Depth ◽

Environmental Conditions ◽

Temporal Dynamics ◽

Model Fit ◽

Vegetation Phenology ◽

Area Index ◽

Model Predictions ◽

L Band ◽

Improved Model

Vegetation phenology is a key ecosystem characteristic that is sensitive to environmental conditions. Here, we examined the utility of soil moisture (SM) and vegetation optical depth (VOD) observations from NASA’s L-band Soil Moisture Active Passive (SMAP) mission for the prediction of leaf area index (LAI), a common metric of canopy phenology. We leveraged mutual information theory to determine whether SM and VOD contain information about the temporal dynamics of LAI that is not contained in traditional LAI predictors (i.e., precipitation, temperature, and radiation) and known LAI climatology. We found that adding SMAP SM and VOD to multivariate non-linear empirical models to predict daily LAI anomalies improved model fit and reduced error by 5.2% compared with models including only traditional LAI predictors and LAI climatology (average R2 = 0.22 vs. 0.15 and unbiased root mean square error [ubRMSE] = 0.130 vs. 0.137 for cross-validated models with and without SM and VOD, respectively). SMAP SM and VOD made the more improvement in model fit in grasslands (R2 = 0.24 vs. 0.16 and ubRMSE = 0.118 vs. 0.126 [5.7% reduction] for models with and without SM and VOD, respectively); model predictions were least improved in shrublands. Analysis of feature importance indicates that LAI climatology and temperature were overall the two most informative variables for LAI anomaly prediction. SM was more important in drier regions, whereas VOD was consistently the second least important factor. Variations in total LAI were mostly explained by local daily LAI climatology. On average, the R2s and ubRMSE of total LAI predictions by the traditional drivers and its climatology are 0.81 and 0.137, respectively. Adding SMAP SM and VOD to these existing predictors improved the R2s to 0.83 (0.02 improvement in R2s) and reduced the ubRMSE to 0.13 (5.2% reduction). Though these improvements were modest on average, in locations where LAI climatology is not reflective of LAI dynamics and anomalies are larger, we find SM and VOD to be considerably more useful for LAI prediction. Overall, we find that L-band SM and VOD observations can be useful for prediction of LAI, though the informational contribution varies with land cover and environmental conditions.

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