scholarly journals Spatial and Temporal Variation of Mortality and Deprivation 2: Statistical Modelling

1998 ◽  
Vol 30 (10) ◽  
pp. 1815-1834 ◽  
Author(s):  
M L Senior ◽  
H C W L Williams ◽  
G Higgs
Keyword(s):  
Cross Sections ◽  
Repeated Measures ◽  
Fixed Effects ◽  
Linear Models ◽  
Nonlinear Effects ◽  
Statistical Modelling ◽  
Spatial And Temporal Variation ◽  
Fixed Effects Model ◽  
Poisson Models ◽  
Over Time

Building on the tabular analyses exemplified in our first paper and widely used in the medical literature, we use generalised linear models to provide a formal, statistical approach to the analysis of mortality and deprivation relationships, and their change over time. Three types of fixed effects model are specified and estimated with the same ward-level data sets for Wales examined in our first paper. They are: Poisson models for analysing mortality and deprivation at a single cross section in time; repeated-measures Poisson models for analysing mortality–deprivation relations, not only at cross sections in time, but also their changes over time; and logit models focusing on temporal changes in mortality–deprivation relationships. Nonlinear effects of deprivation on mortality have been explored by using dummy variables representing deprivation categories to establish the connection between formal statistical models and the tabular approach.

scholarly journals PSVIII-17 Assessment of conformational changes across three parities in sows

2020 ◽  
Vol 98 (Supplement_3) ◽  
pp. 221-222
Author(s):  
Melanie D Trenhaile-Grannemann ◽  
Ronald M Lewis ◽  
Stephen D Kachman ◽  
Kenneth J Stalder ◽  
Benny E Mote
Keyword(s):  
Body Size ◽  
Body Length ◽  
Conformational Changes ◽  
Repeated Measures ◽  
Fixed Effects ◽  
Random Effect ◽  
Joint Angles ◽  
Body Depth ◽  
Conformation Changes ◽  
Over Time

Abstract Conformation-based sow selection is performed prior to reaching mature size, yet little is known about how conformation changes as growth continues. To assess conformation changes, 9 conformational traits were objectively measured at 12 discrete time points between 112 d of age and parity 3 weaning on 622 sows in 5 cohorts. The 9 traits included 5 body size traits (body length, body depth at the shoulder and flank, and height at the shoulder and flank) and 4 joint angles (knee, hock, and front and rear pastern). Data were analyzed with a repeated measures model (SAS V 9.4) including cohort and time point as fixed effects, sire as a random effect, and heterogeneous compound symmetry as the covariance structure. Sire variance ranged from 0.16 (body depth shoulder) to 2.00 (body length) cm2 for body size traits and 2.28 (rear pastern) to 4.22 (front pastern) degrees2 for joint angles. Cohort had an effect on all traits (P < 0.05). All traits displayed changes over time (P < 0.001). Size traits increased between 112 d of age and parity 3 weaning (64.16 vs. 107.57 cm, 26.62 vs. 44.14 cm, 23.32 vs. 36.92 cm, 46.10 vs. 73.55 cm, 49.36 vs. 77.47 cm for body length, body depth shoulder and flank, and height shoulder and flank, respectively); however, they fluctuated within parity by increasing during gestation and decreasing at weaning. Knee angle decreased (164.12 vs. 150.72 degrees) while fluctuating within parity by decreasing in the second half of gestation and increasing after weaning. Front and rear pastern angles decreased over time (60.89 vs. 53.74 degrees and 64.64 vs. 55.50 degrees for front and rear pastern, respectively), while biologically negligible change was observed in hock angle (148.63 vs. 147.48 degrees). Sow conformation changes throughout life, and these changes may require consideration when making selection decisions.

scholarly journals 150PREGNANCY AND PARTURITION FOLLOWING TRANSFER OF BOVINE EMBRYOS CULTURED IN TWO MEDIA UNDER TWO OXYGEN CONCENTRATIONS

2004 ◽  
Vol 16 (2) ◽  
pp. 197 ◽  
Author(s):  
A. Fischer-Brown ◽  
R. Monson ◽  
D. Northey ◽  
T. Kuhlka ◽  
J. Rutledge
Keyword(s):  
Birth Weight ◽  
Pregnancy Rate ◽  
Repeated Measures ◽  
Fixed Effects ◽  
Linear Models ◽  
Statistical Significance ◽  
Bovine Embryos ◽  
Crown Rump Length ◽  
Oxygen Treatment

Developmental aberrations following transfer of in vitro-produced bovine embryos can result in early gestational losses and offspring abnormalities. An ongoing study tests the hypothesis that such aberrations occur with equal frequency among commonly employed culture systems. In year 1, embryos were produced using oocytes from abattoir-derived ovaries (breed unspecified) and a proven Angus bull selected for low birth weight. IVC treatments were 2×2 factorial for medium (KSOMaa or SOFaa) and oxygen concentration (5% or 20%). Angus recipients (n=61; 32 cows, 29 heifers) were randomly allotted to treatments for Day 7 transfers. Pregnancy was diagnosed with ultrasound several times during gestation (Table 1). At parturition calf weight, shoulder height, chest circumference, crown-rump length, and humeral and femoral length data were collected. Statistical analyses (Statistical Analysis System, Cary, NC) were logistic regression with a binomial distribution for pregnancy rate, and the general linear models procedure for calf measurements; included were fixed effects of medium, oxygen, and their interaction, with additional fixed effects of dam parity and calf sex where appropriate. No significant effects of medium or oxygen were found for pregnancy rate or calf measurements other than birth weight. Mean birth weight was higher in the KSOM, 20% oxygen treatment (Table 1), and medium-oxygen interaction for calf weight was also significant (P<0.01). In year 2 embryos were produced using the same Angus bull and Angus oocytes. Angus recipients (n=38; 32 cows, 6 heifers) were randomly allotted to treatments. Fetal crown-rump lengths were measured by ultrasound weekly from Days 33 to 54 and were analyzed as repeated measures using the mixed procedure. Pregnancy outcome and LS means for crown-rump lengths are included in Table 1. Though insufficient recipient numbers preclude determination of statistical significance, of interest is the relatively small fetal size in early gestation and large birth weights in the KSOM, 20% oxygen treatment. This treatment also contained a Day 33 pregnancy, subsequently lost by Day 40, in which the fetus was too small to obtain an accurate measurement. Fetal growth will continue to be monitored throughout gestation. Data will be collected at parturition as in year 1, and pooled analyses will be done. Table 1

scholarly journals The Effect of Stay-at-Home Orders on COVID-19 Infections in the United States

2020 ◽  
Author(s):  
James H. Fowler ◽  
Seth J. Hill ◽  
Remy Levin ◽  
Nick Obradovich
Keyword(s):  
United States ◽  
Temporal Variation ◽  
Fixed Effects ◽  
National Level ◽  
The United States ◽  
Spatial And Temporal Variation ◽  
Full Potential ◽  
County Level ◽  
Fixed Effects Model ◽  
At Home

SummaryBackgroundIn March and April 2020, public health authorities in the United States acted to mitigate transmission of and hospitalizations from the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19). These actions were not coordinated at the national level, which raises the question of what might have happened if they were. It also creates an opportunity to use spatial and temporal variation to measure their effect with greater accuracy.MethodsWe combine publicly available data sources on the timing of stay-at-home orders and daily confirmed COVID-19 cases at the county level in the United States (N = 124,027). We then derive from the classic SIR model a two-way fixed-effects model and apply it to the data with controls for unmeasured differences between counties and over time. This enables us to estimate the effect of stay-at-home orders while accounting for local variation in factors like health systems and demographics, and temporal variation in national mitigation actions, access to tests, or exposure to media reports that could influence the course of the disease.FindingsMean county-level daily growth in COVID-19 infections peaked at 17.2% just before stay-at-home orders were issued. Two way fixed-effects regression estimates suggest that orders were associated with a 3.9 percentage point (95% CI 1.2 to 6.6) reduction in the growth rate after one week and a 6.9 percentage point (2.4 to 11.5) reduction after two weeks. By day 27 the reduction (22.6 percentage points, 14.8 to 30.5) had surpassed the growth at the peak, indicating that growth had turned negative and the number of new daily infections was beginning to decline. A hypothetical national stay-at-home order issued on March 13, 2020 when a national emergency was declared might have reduced cumulative infections by 63.3%, and might have helped to reverse exponential growth in the disease by April 10.InterpretationAlthough stay-at-home orders impose great costs to society, delayed responses and piecemeal application of these orders generate similar costs without obtaining the full potential benefits suggested by this analysis. The results here suggest that a coordinated nationwide stay-at-home order might have reduced by hundreds of thousands the current number of infections and by tens of thousands the total number of deaths from COVID-19. Future efforts in the United States and elsewhere to control pandemics should coordinate stay-at-home orders at the national level, especially for diseases for which local spread has already occurred and testing availability is delayed. Since stay-at-home orders reduce infection growth rates, early implementation when infection counts are still low would be most beneficial.FundingNone.

scholarly journals Leave‐Out Estimation of Variance Components

Econometrica ◽  
2020 ◽  
Vol 88 (5) ◽  
pp. 1859-1898 ◽  
Author(s):  
Patrick Kline ◽  
Raffaele Saggio ◽  
Mikkel Sølvsten
Keyword(s):  
Wage Inequality ◽  
Quadratic Forms ◽  
Fixed Effects ◽  
Linear Models ◽  
Large Data ◽  
Wage Determination ◽  
Fixed Effects Model ◽  
Relative Contribution ◽  
Estimation Of Variance ◽  
Linear Restrictions

We propose leave‐out estimators of quadratic forms designed for the study of linear models with unrestricted heteroscedasticity. Applications include analysis of variance and tests of linear restrictions in models with many regressors. An approximation algorithm is provided that enables accurate computation of the estimator in very large data sets. We study the large sample properties of our estimator allowing the number of regressors to grow in proportion to the number of observations. Consistency is established in a variety of settings where plug‐in methods and estimators predicated on homoscedasticity exhibit first‐order biases. For quadratic forms of increasing rank, the limiting distribution can be represented by a linear combination of normal and non‐central χ 2 random variables, with normality ensuing under strong identification. Standard error estimators are proposed that enable tests of linear restrictions and the construction of uniformly valid confidence intervals for quadratic forms of interest. We find in Italian social security records that leave‐out estimates of a variance decomposition in a two‐way fixed effects model of wage determination yield substantially different conclusions regarding the relative contribution of workers, firms, and worker‐firm sorting to wage inequality than conventional methods. Monte Carlo exercises corroborate the accuracy of our asymptotic approximations, with clear evidence of non‐normality emerging when worker mobility between blocks of firms is limited.

Optimizing dosing in navigation interventions across the cancer continuum: A planned secondary data analysis from the Patient Care Connect implementation.

2017 ◽  
Vol 35 (31_suppl) ◽  
pp. 174-174
Author(s):  
Elizabeth Ann Kvale ◽  
Maria J Pisu ◽  
Courtney Williams ◽  
Kelly Kenzik ◽  
Andres Azuero ◽  
...  
Keyword(s):  
Repeated Measures ◽  
Linear Models ◽  
Secondary Data ◽  
Administrative Claims ◽  
Cost Models ◽  
Total Cost ◽  
Baseline Cost ◽  
Optimal Approach ◽  
The Impact ◽  
Over Time

174 Background: Patient navigation programs in cancer care have historically focused on assisting persons to overcome barriers to accessing care. Evidence is emerging to support the impact of navigation interventions across the cancer continuum. However, navigation programs have varied designs, resulting in a lack of clarity about the optimal approach to delivering services to patients, and a lack of evidence linking program design to outcomes. Methods: A planned retrospective analysis of Medicare administrative claims for a population of older beneficiaries diagnosed with cancer: The main exposure was the number of contacts in person or over the phone with PCCP navigators in the 6 month period starting from the quarter in which patients enrolled in the PCCP. Repeated measures generalized linear models with normal distribution were used to evaluate trends in total cost over time based on: number of contacts, quarters post-enrollment (TIME), and the interaction between number of contacts and TIME. Intra-correlation was controlled for repeated measures. Results: 4,337 patients were included in this analysis. 17.9% had one contact, 17.7% had two, 22.2% had 3-4, 24.2% had 5-10, and 18.0% had more than 10 contacts. African Americans had a greater number of participants with more than 10 navigator contacts, as stage 4 cancers, and initial or end-of-life phase of care. Patients who received more than 3 contacts had significantly higher levels of baseline cost. Models to evaluate total cost over time demonstrate an effect of navigator contact on cost that is associated with number of contacts. This trend is statistically significant at 3-4 contacts or more, and remains significant at 10 or more contacts. Conclusions: Increased navigator contact is associated with increased slope of decline in utilization and cost indicates that navigation programs should be adequately resourced to deliver care that enables navigators to have contact with patients a minimum of 3-4 contacts over a six month period.

Safety profile of ripretinib, including impact of alopecia, and Palmar-Plantar Erythrodysesthesia Syndrome (PPES) on patient-reported outcomes (PROs), in ≥ fourth-line advanced gastrointestinal stromal tumors (GIST): Analyses from INVICTUS.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 11539-11539
Author(s):  
Suzanne George ◽  
Michael C. Heinrich ◽  
John Raymond Zalcberg ◽  
Sebastian Bauer ◽  
Hans Gelderblom ◽  
...  
Keyword(s):  
Safety Profile ◽  
Repeated Measures ◽  
Fixed Effects ◽  
Statistical Significance ◽  
P Value ◽  
Tolerability Profile ◽  
Patient Reported ◽  
The Impact ◽  
Advanced Gist ◽  
Over Time

11539 Background: Ripretinib is a novel switch-control TKI that broadly inhibits KIT and PDGFRA kinase signaling. In INVICTUS (NCT03353753), a randomized, double-blind, placebo (PBO)-controlled trial of ripretinib in ≥4th-line advanced GIST, ripretinib reduced the risk of disease progression or death by 85% vs PBO with a favorable overall safety profile. Common ( > 20%) adverse events (AEs) included, but were not limited to, alopecia and PPES. Exploratory analyses evaluated the impact of alopecia and PPES on quality of life (QoL). Methods: Patients (pts) with advanced GIST previously treated with at least imatinib, sunitinib, and regorafenib were randomized (2:1) to ripretinib 150 mg QD or PBO. AEs were graded using CTCAE v4 and PROs collected using EQ-5D-5L (EQ5D) and EORTC QLQ-C30 (C30). Repeated measures (RM) models assessed the impact of alopecia and PPES on 5 PROs (EQ5D visual analogue scale; and C30 physical functioning, role functioning, and the overall health and overall QoL questions) within the ripretinib arm. Fixed effects were sex, alopecia/PPES, and ECOG scores at baseline. Results: 128/129 randomized pts received treatment (85 ripretinib 150 mg QD; 43 PBO). Alopecia, regardless of causality, occurred in 44 (51.8%) on ripretinib (34 [40.0%] grade 1; 10 [11.8%] grade 2) and 2 (4.7%) on PBO (both grade 1). PPES occurred in 18 (21.2%) on ripretinib (11 [12.9%] grade 1; 7 [8.2%] grade 2); none on PBO. The median times in days to first occurrence and worst severity grade with ripretinib were 57.0 and 62.5 for alopecia; 56.5 and 57.0 for PPES. The RM models showed a slight trend towards improvement in PRO score over time for pts with alopecia; the only association reaching a P-value of < 0.05 was between alopecia and increased overall QoL. None of the associations between PPES and PRO scores reach P < 0.05. All PRO p-values are nominal, and no statistical significance is being claimed. Conclusions: Ripretinib had a favorable overall safety and tolerability profile. When stratified by alopecia and PPES, patient-reported assessments of function, overall health, and overall QoL were maintained over time. For both alopecia and PPES, onset and maximum severity occurred almost simultaneously, indicating that these events generally did not progressively worsen. These results suggest that alopecia and PPES are manageable and do not have a negative effect on function, overall health, and QoL. Clinical trial information: NCT03353753 .

scholarly journals Repeated measures ASCA+ for analysis of longitudinal intervention studies with multivariate outcome data

2020 ◽  
Author(s):  
Torfinn S. Madssen ◽  
Guro F. Giskeødegård ◽  
Age K. Smilde ◽  
Johan A. Westerhuis
Keyword(s):  
Clinical Trials ◽  
Repeated Measures ◽  
Intervention Studies ◽  
Linear Models ◽  
Principal Component ◽  
Component Analysis ◽  
Outcome Data ◽  
Biological Data ◽  
Changes Over Time ◽  
Over Time

AbstractLongitudinal intervention studies with repeated measurements over time are an important type of experimental design in biomedical research. Due to the advent of “omics”-sciences (genomics, transcriptomics, proteomics, metabolomics), longitudinal studies generate increasingly multivariate outcome data. Analysis of such data must take both the longitudinal intervention structure and multivariate nature of the data into account. The ASCA+-framework combines general linear models with principal component analysis, and can be used to separate and visualize the multivariate effect of different experimental factors. However, this methodology has not yet been developed for the more complex designs often found in longitudinal intervention studies, which may be unbalanced, involve randomized interventions, and have substantial missing data. Here we describe a new methodology, repeated measures ASCA+ (RM-ASCA+), and show how it can be used to model metabolic changes over time, and compare metabolic changes between groups, in both randomized and non-randomized intervention studies. Tools for both visualization and model validation are discussed. This approach can facilitate easier interpretation of data from longitudinal clinical trials with multivariate outcomes.Author summaryClinical trials are increasingly generating large amounts of complex biological data. Examples can include measuring metabolism or gene expression in tissue or blood sampled repeatedly over the course of a treatment. In such cases, one might wish to compare changes in not one, but hundreds, or thousands of variables simultaneously. In order to effectively analyze such data, both the study design and the multivariate nature of the data should be considered during data analysis. ANOVA simultaneous component analysis+ (ASCA+) is a statistical method which combines general linear models with principal component analysis, and provides a way to separate and visualize the effects of different factors on complex biological data. In this work, we describe how repeated measures linear mixed models, a class of models commonly used when analyzing changes over time and treatment effects in longitudinal studies, can be used together with ASCA+ for analyzing clinical trials in a novel method called repeated measures-ASCA+ (RM-ASCA+).

scholarly journals Climate Associations with Headwater Streamflow in Managed Forests over 16 Years and Projections of Future Dry Headwater Stream Channels

Forests ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 968 ◽  
Author(s):  
Olson ◽  
Burton
Keyword(s):  
Forest Management ◽  
Repeated Measures ◽  
Linear Models ◽  
Headwater Streams ◽  
Stream Habitat ◽  
Spatial And Temporal Variation ◽  
Spatial And Temporal Variability ◽  
Stream Channels ◽  
Moisture Index

Integrating climate-smart principles into riparian and upland forest management can facilitate effective and efficient land use and conservation planning. Emerging values of forested headwater streams can help forge these links, yet climate effects on headwaters are little studied. We assessed associations of headwater discontinuous streams with climate metrics, watershed size, and forest-harvest treatments. We hypothesized that summer streamflow would decrease in warm, dry years, with possible harvest interactions. We field-collected streamflow patterns from 65 discontinuous stream reaches at 13 managed forest sites in Western Oregon, USA over a 16-year period. We analyzed spatial and temporal variability in field-collected stream habitat metrics using non-metric multidimensional scaling ordination. Relationships between streamflow, climate metrics, basin size, and harvest treatments were analyzed with simple linear models and mixed models with repeated measures. Using past effects of climate variation on streamflow, we projected effects to 2085 under three future scenarios, then quantified implications on headwater networks for a case-study landscape. Ordination identified the percent dry length of stream reaches as a top predictor of spatial and temporal variation in discontinuous stream-habitat types. In our final multivariate model, the percent dry length was associated with heat: moisture index, mean minimum summer temperature, and basin area. Across future climate scenarios in years 2055–2085, a 4.5%–11.5% loss in headwater surface streamflow was projected; this resulted in 597–2058 km of additional dry channel lengths of headwater streams in our case study area, the range of the endemic headwater-associated Cascade torrent salamander (Rhyacotriton cascadae Good and Wake) in the Oregon Cascade Range, a species proposed for listing under the US Threatened and Endangered Act. Implications of our study for proactive climate-smart forest-management designs in headwaters include restoration to retain surface flows and managing over-ridge wildlife dispersal habitat from areas with perennial surface water flow, as stream reaches with discontinuous streamflow were projected to have reduced flows in the future with climate change projections.

scholarly journals Maybe maximal: Good enough mixed models optimize power while controlling Type I error

2019 ◽  
Author(s):  
Michael Seedorff ◽  
Jacob Oleson ◽  
Bob McMurray
Keyword(s):  
Random Effects ◽  
Mixed Models ◽  
Repeated Measures ◽  
Fixed Effects ◽  
Degrees Of Freedom ◽  
Linear Models ◽  
Type I Error ◽  
Logistic Models ◽  
Information Criteria ◽  
Type I

Mixed effects models have become a critical tool in all areas of psychology and allied fields. This is due to their ability to account for multiple random factors, and their ability to handle proportional data in repeated measures designs. While substantial research has addressed how to structure fixed effects in such models there is less understanding of appropriate random effects structures. Recent work with linear models suggests the choice of random effects structures affects Type I error in such models (Barr, Levy, Scheepers, &amp; Tily, 2013; Matuschek, Kliegl, Vasishth, Baayen, &amp; Bates, 2017). This has not been examined for between subject effects, which are crucial for many areas of psychology, nor has this been examined in logistic models. Moreover, mixed models expose a number of researcher degrees of freedom: the decision to aggregate data or not, the manner in which degrees of freedom are computed, and what to do when models do not converge. However, the implications of these choices for power and Type I error are not well known. To address these issues, we conducted a series of Monte Carlo simulations which examined linear and logistic models in a mixed design with crossed random effects. These suggest that a consideration of the entire space of possible models using simple information criteria such as AIC leads to optimal power while holding Type I error constant. They also suggest data aggregation and the d.f, computation have minimal effects on Type I Error and Power, and they suggest appropriate approaches for dealing with non-convergence.

scholarly journals Understanding Convergence Between Non-Hispanic Black and White COVID-19 Mortality: A County-Level Approach

2021 ◽  
Author(s):  
Ralph Lawton ◽  
Kevin Zheng ◽  
Daniel Zheng ◽  
Erich Huang
Keyword(s):  
Fixed Effects ◽  
Regional Distribution ◽  
Specific Effect ◽  
Mortality Data ◽  
County Level ◽  
Fixed Effects Model ◽  
Black And White ◽  
Mortality Ratio ◽  
Per Capita ◽  
Over Time

BackgroundNon-Hispanic Black populations have suffered greater per capita COVID-19 mortality at more than 1.5 times that of White populations. Previous work has established that, over time, rates of Black and White mortality have converged; however, some studies suggest that regional shifts in COVID-19 prevalence may play a role in the relative change between racial groups. This study’s objective was to investigate changes in Black and White COVID-19 mortality over time and uncover potential mechanisms driving these changes.Methods and FindingsUsing county-level COVID-19 mortality data stratified by race, we investigate the trajectory of non-Hispanic Black mortality, White mortality, and the Black/White per capita mortality ratio from June 2020–January 2021. Over this period, in the counties studied, cumulative mortality rose by 56.7% and 82.8% for Black and White populations respectively, resulting in a decrease in mortality ratio of 0.369 (23.8%). These trends persisted even when a county-level fixed-effects model was used to estimate changes over time within counties (controlling for all time-invariant county level characteristics and removing the effects of changes in regional distribution of COVID-19). Next, we leverage county-level variation over time in COVID-19 prevalence to show that the decline in the Black/White mortality ratio can be explained by changes in COVID-19 prevalence. Finally, we study heterogeneity in the time trend, finding that convergence occurs most significantly in younger populations, areas with less dense populations, and outside of the Northeast. Limitations include suppressed data in counties with fewer than 10 deaths in a racial category, and the use of provisional COVID-19 death data that may be incomplete.ConclusionsThe results of this study suggest that convergence in Black/White mortality is not driven by county-level characteristics or changes in the regional dispersion of COVID-19, but instead by changes within counties. Further, declines in the Black/White mortality ratio appear strongly linked to changes in COVID-19 prevalence, rather than a time-specific effect. Further studies on changes in exposure by race over time, or on the vulnerability of individuals who died at different points in the pandemic, may provide crucial insight on mechanisms and strategies to further reduce COVID-19 mortality disparities.

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