Empirical models for radial and tangential fibre width in tree rings of Norway spruce in north-western Europe

Holzforschung ◽  
2012 ◽  
Vol 66 (2) ◽  
Author(s):  
Tony Franceschini ◽  
Sven-Olof Lundqvist ◽  
Jean-Daniel Bontemps ◽  
Thomas Grahn ◽  
Lars Olsson ◽  
...  
Keyword(s):  
Norway Spruce ◽  
Western Europe ◽  
Mixed Effects ◽  
Mixed Effects Models ◽  
Annual Rings ◽  
Fibre Width ◽  
Fixed Part ◽  
The Mean ◽  
North Western

Abstract Tracheid dimensions influence the quality of wood and that of pulp and paper. Both between- and within-ring variations are influenced by tree developmental stage, site, genetics, and forest management. To contribute to the knowledge in this regard, the radial and tangential tracheid dimensions on Norway spruce, defined as the lumens and double cell wall thickness, have been measured using a SilviScan device. Namely, 4947 annual rings from 35 trees from plantations in France, Norway, and Sweden were examined. Mixed-effects models were constructed concerning the radial and tangential tracheid widths for the total ring and in three within-ring compartments – earlywood (EW), transition wood (TW), and latewood (LW) – as functions of age, radius, height in the tree, and growth rate. Between-site and between-tree variations were also considered. The mean radial tracheid width was 34.2 μm (EW), 29.9 μm (TrW), and 22.1 μm (LW). The tangential tracheid width was on average 30.1 μm in all compartments. The radial and tangential tracheid widths in the rings and compartments increased from the pith to the bark and decreased with greater growth rates. Within a given ring, both properties decreased with height in the tree. The fixed part of the models of the radial tracheid width accounted for 68% (EW), 45% (TW), and 33% (LW) while for the models of the tangential fibre width it accounted for 42% of the variation in all compartments. Climate or hydraulic maintenance was hypothesised to be responsible for the variation of the radial tracheid width.

scholarly journals Testing heterogeneity in inter-rater reliability

2020 ◽  
Author(s):  
František Bartoš ◽  
Patrícia Martinková ◽  
Marek Brabec
Keyword(s):  
Variance Components ◽  
Generalized Additive Models ◽  
Mixed Effects ◽  
Additive Models ◽  
Mixed Effects Models ◽  
Rater Reliability ◽  
Linear Mixed Effects Models ◽  
Linear Mixed Effects ◽  
Wide Range

Estimating the inter-rater reliability (IRR) is important for assessing and improving the quality of ratings. In some cases, the IRR may differ between groups due to their features. To test heterogeneity in IRR, the second-order generalized estimating equations (GEE2) and linear mixed-effects models (LME) were already used. Another method capable of estimating the components for IRR is generalized additive models (GAM). This paper presents a simulation study evaluating the performance of these methods in estimating variance components and in testing heterogeneity in IRR. We consider a wide range of sample sizes and various scenarios leading to heterogenous IRR. The results show, that while the LME and GAM models perform similarly and yield reliable estimates, the GEE2 models may lead to incorrect results.

scholarly journals Hydrological response to climate change in the Lesse and the Vesdre catchments: contribution of a physically based model (Wallonia, Belgium)

2011 ◽  
Vol 15 (6) ◽  
pp. 1745-1756 ◽  
Author(s):  
A. Bauwens ◽  
C. Sohier ◽  
A. Degré
Keyword(s):  
Climate Change ◽  
Return Period ◽  
Western Europe ◽  
Distributed Model ◽  
Severe Drought ◽  
Physically Based Model ◽  
Physically Based ◽  
The Mean ◽  
Projected Changes ◽  
North Western

Abstract. The Meuse is an important rain-fed river in North-Western Europe. Nine million people live in its catchment, split over five countries. Projected changes in precipitation and temperature characteristics due to climate change would have a significant impact on the Meuse River and its tributaries. In this study, we focused on the impacts of climate change on the hydrology of two sub-catchments of the Meuse in Belgium, the Lesse and the Vesdre, placing the emphasis on the water-soil-plant continuum in order to highlight the effects of climate change on plant growth, and water uptake on the hydrology of two sub-catchments. These effects were studied using two climate scenarios and a physically based distributed model, which reflects the water-soil-plant continuum. Our results show that the vegetation will evapotranspirate between 10 and 17 % less at the end of the century because of water scarcity in summer, even if the root development is better under climate change conditions. In the low scenario, the mean minimal 7 days discharge value could decrease between 19 and 24 % for a two year return period, and between 20 and 35 % for a fifty year return period. It will lead to rare but severe drought in rivers, with potentially huge consequences on water quality.

scholarly journals Improving the Computation of Brier Scores for Evaluating Expert-Elicited Judgements

2021 ◽  
Vol 7 ◽  
Author(s):  
Gayan Dharmarathne ◽  
Anca Hanea ◽  
Andrew P. Robinson
Keyword(s):  
Experimental Design ◽  
Multiple Imputation ◽  
Prediction Accuracy ◽  
Mixed Effects ◽  
Expert Elicitation ◽  
Mixed Effects Models ◽  
Standard Errors ◽  
Support Design ◽  
Correlation Structures

Structured expert judgement (SEJ) is a suite of techniques used to elicit expert predictions, e.g. probability predictions of the occurrence of events, for situations in which data are too expensive or impossible to obtain. The quality of expert predictions can be assessed using Brier scores and calibration questions. In practice, these scores are computed from data that may have a correlation structure due to sharing the effects of the same levels of grouping factors of the experimental design. For example, asking common questions from experts may result in correlated probability predictions due to sharing common question effects. Furthermore, experts commonly fail to answer all the needed questions. Here, we focus on (i) improving the computation of standard error estimates of expert Brier scores by using mixed-effects models that support design-based correlation structures of observations, and (ii) imputation of missing probability predictions in computing expert Brier scores to enhance the comparability of the prediction accuracy of experts. We show that the accuracy of estimating standard errors of expert Brier scores can be improved by incorporating the within-question correlations due to asking common questions. We recommend the use of multiple imputation to correct for missing data in expert elicitation exercises. We also discuss the implications of adopting a formal experimental design approach for SEJ exercises.

Randomized trial of remote cancer symptom monitoring during COVID-19: Impact on symptoms, QoL, and unplanned health care utilization.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 12000-12000
Author(s):  
Kathi Mooney ◽  
Eli Iacob ◽  
Christina M. Wilson ◽  
Jennifer Lloyd ◽  
Heidi Nielson ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Health Care ◽  
Health Care Utilization ◽  
Symptom Burden ◽  
Mixed Effects ◽  
Health Care Use ◽  
Mixed Effects Models ◽  
Care Utilization ◽  
Symptom Monitoring

12000 Background: Unplanned health care utilization due to poorly controlled cancer symptoms is common and important to avoid during the Covid-19 pandemic. In a randomized trial we evaluated whether remote symptom monitoring and management utilizing Symptom Care at Home (SCH), would reduce symptom burden, improve quality of life, and decrease unplanned health care use in cancer patients receiving active treatment. Methods: Patients (n = 252) receiving chemotherapy and/or radiation therapy were randomized to the SCH intervention (n = 128) or usual care (UC) (n = 124). Daily, those in the intervention group, utilized the SCH system to report the presence and severity of 9 common symptoms during treatment. For symptoms endorsed, SCH participants received immediate, tailored automated self-management coaching. Symptoms at moderate to severe levels were automatically reported to oncology nurse practitioners who called the SCH patients to improve symptom management based on a decision support dashboard. Participants from both groups were assessed at baseline and monthly for up to 5 months on symptom burden (MDASI), mental health well-being and social isolation (PROMIS; HADS) and Health-related Quality of Life (HRQoL) (Penedo Covid-19 HRQoL subscale). Unplanned health care use was extracted from the EHR. Descriptive statistics examined equivalency between groups. Mixed effects models with random intercepts were utilized to examine group differences over time with post-hoc analyses to determine specific timepoint differences. Results: Participants did not differ on demographic or baseline measures. On average participants were 61 years of age, predominantly female (61%) and white (93%). A variety of cancers were represented with colon, breast and ovarian most common and 60% had stage 3 or 4 disease. Longitudinal mixed effects models found significant effects for lower symptom burden (p =.018) and better HRQoL (p =.007) for SCH participants versus UC at months 1 and 2 with improvements subsiding over the remaining months. Mental health wellbeing and social isolation were not significantly different. There were a total of 71 unplanned health care episodes with 28 for SCH and 43 for UC. Unplanned episode types included: unplanned clinic visit- 3 SCH vs 2 UC; ED visit- 10 SCH vs 16 UC and unplanned hospitalizations-15 SCH vs 25 UC. More SCH participants had no unplanned health care episodes than UC participants (χ2 4.08; p =.04). Conclusions: Remote monitoring and management of patients’ cancer and treatment-related symptoms during the Covid-19 pandemic reduced symptom burden and improved quality of life during the first two months of monitoring. Unplanned health care utilization trended lower for those remotely monitored. Extending care to the home during the pandemic can decrease demand on the health care system and improve cancer patients’ symptom experience. Clinical trial information: NCT04464486.

Abstract 12688: Age and Sex Disparities in Hypertension Control: The Multi-ethnic Study of Atherosclerosis (mesa)

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Nkiru Osude ◽  
Ramon A Durazo-Arvizu ◽  
Talar Markossian ◽  
Kiang Liu ◽  
Erin D Michos ◽  
...  
Keyword(s):  
Mixed Effects ◽  
Mixed Effects Models ◽  
Age Group ◽  
Hypertension Control ◽  
Ethnic Study ◽  
Age Related ◽  
Oscillometric Device ◽  
Sex Disparities ◽  
The Mean ◽  
Age And Sex

Introduction: Blood pressure (BP) control may decline with age and degree of decline may differ by sex. Methods: The Multi-Ethnic Study of Atherosclerosis (MESA) recruited 6814 men and women, age 45 to 84 years, from six communities in the U.S. during 2000-2002 and follow-up exams occurred every two years for a total of 6 exams from 2000-2016. We assessed the association of age and sex with hypertension control using MESA data among participants receiving treatment for hypertension at any of the first five exams At each exam, resting BP was measured in triplicate at 1-minute intervals using an automated oscillometric device; we used the mean of last two measurements. Hypertension control was defined as BP < 140/90 mmHg among adults with treated hypertension. Mixed-effects models were used to examine the association of sex with BP control by age group (45-64, 65-84, 85+ yrs) while accounting for the clustering within sites and intra-individual correlation and adjusting for demographics, co-morbidities, smoking, alcohol use, and education. Results: Among the 2,017 adults receiving treatment for hypertension (63.1% controlled), the mean age at exam 1 was 64.0 (9.1) yrs, 43.3% male; race/ethnicity was 33.5% White, 9.2% Chinese, 37.2% Black and 20.1% Hispanic. There was a significant interaction of sex*age group (P < 0.001) in mixed-effects models after adjustment for all covariates. The adjusted probability of BP control was then calculated for each sex and age group. Among women, the probability of BP control declined from 74.6% (95% CI 70.8%, 78.5%) for age 45-64 yrs to 55.9% (50.0%, 61.8%) for age 85+ yrs. Among men, probability of BP control declined from 74.0% (70.0%, 78.0%) for age 45-64 yrs to 70.6% (65.7%, 75.5%) for age 85+ yrs. The figure shows the probability of hypertension control by sex and age at a given exam. Conclusion: Hypertension control differs by sex. Interventions are needed to address age-related sex disparities in hypertension control.

scholarly journals Rating Norms Should be Calculated from Cumulative Link Mixed Effects Models

2021 ◽  
Author(s):  
Jack Edward Taylor ◽  
Guillaume A Rousselet ◽  
Christoph Scheepers ◽  
Sara C Sereno
Keyword(s):  
Standard Deviation ◽  
Rank Order ◽  
Mixed Effects ◽  
Mixed Effects Models ◽  
Summary Statistics ◽  
Ordinal Scales ◽  
The Mean ◽  
Statistical Issues ◽  
Response Biases

Studies which provide norms of Likert ratings typically report per-item summary statistics. Traditionally, these summary statistics comprise the mean and the standard deviation (SD) of the ratings, and the number of observations. Such summary statistics can preserve the rank order of items, but provide distorted estimates of the relative distances between items because of the ordinal nature of Likert ratings. Inter-item relations in such ordinal scales can be more appropriately modelled by cumulative-link mixed effects models (CLMMs). In a series of simulations, and with a reanalysis of an existing rating norms dataset, we show that CLMMs can be used to more accurately norm items, and can provide summary statistics analogous to the traditionally reported means and SDs, but which are disentangled from participants’ response biases. CLMMs can be applied to solve important statistical issues that exist for more traditional analyses of rating norms.

scholarly journals A comparative study of R functions for clustered data analysis

Trials ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Wei Wang ◽  
Michael O. Harhay
Keyword(s):  
Data Analysis ◽  
Confidence Intervals ◽  
Simulation Study ◽  
Generalized Estimating Equations ◽  
Mean Squared Error ◽  
Mixed Effects ◽  
Mixed Effects Models ◽  
Intra Class Correlation ◽  
Squared Error ◽  
The Mean

Abstract Background Clustered or correlated outcome data is common in medical research studies, such as the analysis of national or international disease registries, or cluster-randomized trials, where groups of trial participants, instead of each trial participant, are randomized to interventions. Within-group correlation in studies with clustered data requires the use of specific statistical methods, such as generalized estimating equations and mixed-effects models, to account for this correlation and support unbiased statistical inference. Methods We compare different approaches to estimating generalized estimating equations and mixed effects models for a continuous outcome in R through a simulation study and a data example. The methods are implemented through four popular functions of the statistical software R, “geese”, “gls”, “lme”, and “lmer”. In the simulation study, we compare the mean squared error of estimating all the model parameters and compare the coverage proportion of the 95% confidence intervals. In the data analysis, we compare estimation of the intervention effect and the intra-class correlation. Results In the simulation study, the function “lme” takes the least computation time. There is no difference in the mean squared error of the four functions. The “lmer” function provides better coverage of the fixed effects when the number of clusters is small as 10. The function “gls” produces close to nominal scale confidence intervals of the intra-class correlation. In the data analysis and the “gls” function yields a positive estimate of the intra-class correlation while the “geese” function gives a negative estimate. Neither of the confidence intervals contains the value zero. Conclusions The “gls” function efficiently produces an estimate of the intra-class correlation with a confidence interval. When the within-group correlation is as high as 0.5, the confidence interval is not always obtainable.

scholarly journals Evaluating the Carrot Rewards app, a population-level incentive-based intervention promoting step counts across two Canadian provinces: a quasi-experimental study (Preprint)

2018 ◽  
Author(s):  
Marc Mitchell ◽  
Lauren White ◽  
Erica Lau ◽  
Tricia Leahey ◽  
Marc Adams ◽  
...  
Keyword(s):  
Goal Achievement ◽  
Mixed Effects ◽  
Mixed Effects Models ◽  
Daily Step ◽  
Step Counts ◽  
Walking Program ◽  
Quasi Experimental ◽  
The Mean ◽  
Canadian Provinces ◽  
Physically Inactive

BACKGROUND The Carrot Rewards application (‘app’) was developed as part of an innovative public-private partnership to reward Canadians with loyalty points, exchangeable for retail goods, travel rewards and groceries, for engaging in healthy behaviors such as walking. OBJECTIVE The purpose of this study was to examine whether a multi-component intervention including goal setting, graded tasks, biofeedback and very small incentives tied to daily step goal achievement (assessed by built-in smartphone accelerometers) could increase physical activity in two Canadian provinces, British Columbia (BC) and Newfoundland and Labrador (NL). METHODS A 12-week quasi-experimental (single group pre/post) study was conducted. Among eligible participants (n=78,882), 44.39% (n=35,014) enrolled in the Carrot Rewards “Steps” walking program during the recruitment period (June 13th – July 10th 2016). During the two-week baseline (or ‘run-in’) period, mean steps/day were calculated for participants. Thereafter, participants earned incentives in the form of loyalty points (worth $0.04 CAD) every day they reached their personalized daily step goal (i.e. baseline mean + 1,000 steps = level of first daily step goal). Participants earned additional points (worth $0.40 CAD) for meeting their step goal 10+ non-consecutive times in a 14-day period (called a “Step Up Challenge”). Participants could earn up to $5.00 CAD during the 12-week evaluation period. Upon meeting the 10-day contingency, participants could increase their daily goal by 500 steps, with the objective of gradually increasing the number of steps participants take each day by 3,000. Only participants with five or more valid days (days with step counts between 1,000 and 40,000) during the baseline period were included in the analysis (n=32,229).The primary study outcome was mean steps/day (by week), and was analyzed using linear mixed-effects models. RESULTS Of the 32,229 participants with valid baseline data, the mean age was 33.7  11.6 years and 66.11% (21,306/32,229) were female. The mean daily step count at baseline was 6,511.22. Just over half of users (50.69%, 16,336/32,229) were categorized as “physically inactive”, accumulating less than 5,000 daily steps at baseline. Results from the mixed-effects models revealed statistically significant increases in mean daily step counts when comparing baseline with each Study Week (P<.0001). Compared to baseline, participants walked 115.70 more steps (95% CI: 74.59,156.81; P<.0001) at Study Week 12. Users classified as “high engagers” (app engagement above the sample median; 48.13%, 15,511/32,229) in BC and NL walked 738.70 (95% CI: 673.81, 803.54; P<.0001) and 346.00 (95% CI: 239.26, 452.74; P<.0001) more steps, respectively. Among physically inactive, high engagers (21.08%; 7,022/32,229) an average increase of 1,224.66 steps per day (95% CI: 1160.69, 1288.63; P<.0001) was observed. Effect sizes were modest CONCLUSIONS Providing very small but immediate rewards for personalized daily step goal achievement as part of a multi-component intervention increased daily step counts on a population-scale, especially for physically inactive individuals and individuals who engaged more with the walking program. Positive effects in both BC and NL provide evidence of replicability.

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