Application of Mixed-Effect Regression Models for Compiling a Land Value Map

Abstract There are considerable individual differences in the rates of cognitive decline across later adulthood. Personality traits are among the factors that may account for some of these differences. The current project investigated whether personality traits were associated with trajectories of cognitive decline, and whether the associations were different before and after dementia diagnosis. The data were analyzed using linear mixed effect regression models. Across study aims was a focus on replicability and generalizability. Each question was address in four independent longitudinal studies (EAS, MAP, ROS, SATSA), and then meta-analyzed using random effects meta-analysis, providing estimates of heterogeneity. As expected, we detected evidence for cognitive decline in all four samples. Results also indicated that neuroticism and openness were associated with total cognitive function. and openness was associated with decline post dementia diagnosis.

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Dual-Parent Joblessness, Household Work and Its Moderating Role on Children’s Joblessness as Young Adults

Journal of Family Issues ◽

10.1177/0192513x19894353 ◽

2020 ◽

Vol 41 (9) ◽

pp. 1569-1596 ◽

Cited By ~ 1

Author(s):

Irma Mooi-Reci ◽

Lyn Craig

Keyword(s):

Young Adults ◽

Young Adulthood ◽

Household Income ◽

Regression Models ◽

Household Work ◽

Work Time ◽

Mixed Effect ◽

Effect Regression ◽

Using Data ◽

Moderating Role

Using data from the Household, Income, and Labour Dynamics in Australia (HILDA) Survey, we examine whether living in jobless families where parents devote more time to household work shields children against their own joblessness in the future. We draw on a representative sample of young adults who were aged between 4 and 17 years in 2001 and lived with both parents through to 2007 ( N = 1,852). A series of mixed-effect regression models suggest that dual-parent joblessness is associated with an increase in families’ overall household production. The extra household work of fathers has a moderating role on young people’s later joblessness in young adulthood; young adults raised in households in which fathers increase their household work time during jobless periods are less likely to themselves become jobless as adults. This effect is not found if mothers increase their household work time.

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Comparison of linear and mixed-effect regression models and a k-nearest neighbour approach for estimation of single-tree biomass

Canadian Journal of Forest Research ◽

10.1139/x07-119 ◽

2008 ◽

Vol 38 (1) ◽

pp. 1-9 ◽

Cited By ~ 38

Author(s):

Lutz Fehrmann ◽

Aleksi Lehtonen ◽

Christoph Kleinn ◽

Erkki Tomppo

Keyword(s):

Mixed Models ◽

Regression Models ◽

Tree Height ◽

Biomass Estimation ◽

Data Sets ◽

Tree Biomass ◽

Mixed Effect ◽

Single Tree ◽

Breast Height ◽

Effect Regression

Allometric biomass models for individual trees are typically specific to site conditions and species. They are often based on a low number of easily measured independent variables, such as diameter in breast height and tree height. A prevalence of small data sets and few study sites limit their application domain. One challenge in the context of the actual climate change discussion is to find more general approaches for reliable biomass estimation. Therefore, nonparametric approaches can be seen as an alternative to commonly used regression models. In this pilot study, we compare a nonparametric instance-based k-nearest neighbour (k-NN) approach to estimate single-tree biomass with predictions from linear mixed-effect regression models and subsidiary linear models using data sets of Norway spruce ( Picea abies (L.) Karst.) and Scots pine ( Pinus sylvestris L.) from the National Forest Inventory of Finland. For all trees, the predictor variables diameter at breast height and tree height are known. The data sets were split randomly into a modelling and a test subset for each species. The test subsets were not considered for the estimation of regression coefficients nor as training data for the k-NN imputation. The relative root mean square errors of linear mixed models and k-NN estimations are slightly lower than those of an ordinary least squares regression model. Relative prediction errors of the k-NN approach are 16.4% for spruce and 14.5% for pine. Errors of the linear mixed models are 17.4% for spruce and 15.0% for pine. Our results show that nonparametric methods are suitable in the context of single-tree biomass estimation.

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Types of Vocalizations in Self-Expression and Self-Inquiry

Психология Журнал Высшей школы экономики ◽

10.17323/1813-8918-2021-1-224-239 ◽

2021 ◽

Vol 18 (1) ◽

pp. 224-239

Author(s):

Alena Zirko

Keyword(s):

Regression Models ◽

T Test ◽

Emotional Involvement ◽

Mixed Effect ◽

Safe Place ◽

Perceived Satisfaction ◽

Effect Regression ◽

Authentic Voice

The author discusses vocalizations as using non-verbal voice sounds in self-expression and self-inquiry. The purpose of the study was to investigate the experience of self-expression and self-inquiry through vocalizations in the situations of valuing and evaluating. The researcher hypothesized that placing an individual in a safe place for self-expression on the conditions of valuing creates more authentic and genuine feelings, helping to reveal their authentic voice. On the contrary, placing a person under conditions of evaluating and impressiveness leads to a less authentic feeling and sounding. Two groups of participants were separated. The expressive group was created using the condition of valuing. The impressive group was created using the condition of evaluating. Participants in both groups used their voices to express themselves performing research tasks and then filled out the survey applications reflecting the sounder’s body, voice, feelings and listener’s feelings during the research. The application’s indicators were grouped into six factors: “Psychophysiological authenticity”, “Psychological authenticity”, “Satisfaction”, “Vocalization change”, “Perceived emotional involvement”, and “Perceived satisfaction”. The multilinear mixed effect regression models were built to investigate the influence of the research conditions on these factors and their dynamics. The t-test was used to compare the results between the groups. Significant differences were revealed with the factors “Psychophysiological authenticity”, “Psychological authenticity”, “Satisfaction”, and “Perceived satisfaction”. They were greater in the expressive group than they were in the impressive group. The indicators of “Perceived satisfaction” were growing.

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Different Effects of Aging on Intraocular Pressures Measured by Three Different Tonometers

Journal of Clinical Medicine ◽

10.3390/jcm10184202 ◽

2021 ◽

Vol 10 (18) ◽

pp. 4202

Author(s):

Kazunobu Sugihara ◽

Masaki Tanito

Keyword(s):

Linear Regression ◽

Regression Models ◽

Corneal Thickness ◽

Regression Analyses ◽

Mixed Effect ◽

Effect Regression ◽

Older Subjects ◽

Young Subjects ◽

Highly Correlated ◽

Effects Of Aging

This study aimed to compare intraocular pressures (IOP) using different tonometers, Goldmann applanation (IOPGAT), non-contact (IOPNCT), and rebound (IOPRBT), and to assess the effects of aging and central corneal thickness (CCT) on the measurements. The IOPGAT, IOPNCT, IOPRBT, mean patient age (65.1 ± 16.2 years), and CCT (521.7 ± 39.2 µm) were collected retrospectively from 1054 eyes. The differences among IOPs were compared by the paired t-test. Possible correlations between devices, age, and CCT were assessed by linear regression analyses. The effects of age and CCT on the IOP reading were assessed by mixed-effects regression models. The IOPGAT values were 2.4 and 1.4 mmHg higher than IOPNCT and IOPRBT, respectively; the IOPNCT was 1.0 mmHg lower than IOPRBT (p < 0.0001 for all comparisons). The IOPs measured by each tonometer were highly correlated with each other (r = 0.81–0.90, t = 45.2–65.5). The linear regression analyses showed that age was negatively correlated with IOPNCT (r = −0.12, t = −4.0) and IOPRBT (r = −0.14, t = −4.5) but not IOPGAT (r = 0.00, t = −0.2); the CCT was positively correlated with IOPGAT (r = 0.13, t = 4.3), IOPNCT (r = 0.29, t = 9.8), and IOPRBT (r = 0.22, t = 7.2). The mixed-effect regression models showed significant negative correlations between age and IOPNCT (t = −2.6) and IOPRBT (t = −3.4), no correlation between age and IOPGAT (t = 0.2), and a significant positive correlation between CCT and the tonometers (t = 3.4–7.3). No differences between IOPGAT and IOPRBT were seen at the age of 38.8 years. CCT affects IOPs from all tonometers; age affects IOPNCT and IOPRBT in different degrees. IOPRBT tended to be higher than IOPGAT in young subjects, but this stabilized in middle age and became higher in older subjects.

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Potential of community-based risk estimates for improving hospital performance measures and discharge planning

BMJ Open Quality ◽

10.1136/bmjoq-2020-001230 ◽

2021 ◽

Vol 10 (2) ◽

pp. e001230

Author(s):

Michael Reid ◽

George Kephart ◽

Pantelis Andreou ◽

Alysia Robinson

Keyword(s):

Discharge Planning ◽

Care Transitions ◽

Hospital Setting ◽

Hospital Performance ◽

Case Mix ◽

Readmission Rates ◽

Mixed Effect ◽

Adjusted Risk ◽

Community Risk ◽

Effect Regression

BackgroundRisk-adjusted rates of hospital readmission are a common indicator of hospital performance. There are concerns that current risk-adjustment methods do not account for the many factors outside the hospital setting that can affect readmission rates. Not accounting for these external factors could result in hospitals being unfairly penalized when they discharge patients to communities that are less able to support care transitions and disease management. While incorporating adjustments for the myriad of social and economic factors outside of the hospital setting could improve the accuracy of readmission rates as a performance measure, doing so has limited feasibility due to the number of potential variables and the paucity of data to measure them. This paper assesses a practical approach to addressing this problem: using mixed-effect regression models to estimate case-mix adjusted risk of readmission by community of patients’ residence (community risk of readmission) as a complementary performance indicator to hospital readmission rates.MethodsUsing hospital discharge data and mixed-effect regression models with a random intercept for community, we assess if case-mix adjusted community risk of readmission can be useful as a quality indicator for community-based care. Our outcome of interest was an unplanned repeat hospitalisation. Our primary exposure was community of residence.ResultsCommunity of residence is associated with case-mix adjusted risk of unplanned repeat hospitalisation. Community risk of readmission can be estimated and mapped as indicators of the ability of communities to support both care transitions and long-term disease management.ConclusionContextualising readmission rates through a community lens has the potential to help hospitals and policymakers improve discharge planning, reduce penalties to hospitals, and most importantly, provide higher quality care to the people that they serve.

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