Regression models with asymmetric data for estimating thyroglobulin levels one year after the ablation of thyroid cancer

2018 ◽  
Vol 28 (8) ◽  
pp. 2258-2275 ◽  
Author(s):  
Carlos A Alvear Rodriguez ◽  
José Rafael Tovar Cuevas
Keyword(s):  
Thyroid Cancer ◽  
Regression Models ◽  
Differentiated Thyroid Cancer ◽  
Probability Distributions ◽  
Ordinary Least Squares ◽  
Parameter Estimates ◽  
Least Squares Regression ◽  
Generalized Gamma ◽  
Before And After ◽  
Log Normal

A key biomarker in the study of differentiated thyroid cancer is thyroglobulin. Measurements of the levels of this protein in the blood are determined using laboratory instruments that cannot detect very small concentrations below a threshold, generating left-censored measurements. In the presence of censoring, ordinary least-squares regression models generate biased parameter estimates; therefore, it is necessary to resort to more complex models that consider the censored observations and the behavior of the distribution of the response variable, such as censored and mixed regression models. These techniques were used to model the relationship between thyroglobulin levels in individuals with differentiated thyroid cancer before and after treatment with radioactive iodine (I-131). Log-normal, log-skew-normal, log-power-normal, and log-generalized-gamma probability distributions were used to model the behavior of errors in the adjusted models. Log-generalized-gamma distribution yielded the best results according to the established model selection criteria.

scholarly journals Least Likely Observations in Regression Models for Categorical Outcomes

2002 ◽  
Vol 2 (3) ◽  
pp. 296-300 ◽  
Author(s):  
Jeremy Freese
Keyword(s):  
Logistic Regression ◽  
Regression Models ◽  
Binary Logistic Regression ◽  
Positive Outcome ◽  
Ordinary Least Squares ◽  
Parameter Estimates ◽  
Least Squares Regression ◽  
Binary Logistic Regression Model ◽  
Ordered Logistic Regression ◽  
Negative Outcome

This article presents a method and program for identifying poorly fitting observations for maximum-likelihood regression models for categorical dependent variables. After estimating a model, the program leastlikely will list the observations that have the lowest predicted probabilities of observing the value of the outcome category that was actually observed. For example, when run after estimating a binary logistic regression model, leastlikely will list the observations with a positive outcome that had the lowest predicted probabilities of a positive outcome and the observations with a negative outcome that had the lowest predicted probabilities of a negative outcome. These can be considered the observations in which the outcome is most surprising given the values of the independent variables and the parameter estimates and, like observations with large residuals in ordinary least squares regression, may warrant individual inspection. Use of the program is illustrated with examples using binary and ordered logistic regression.

Linear regression models for biomass table construction, using cluster samples

1989 ◽  
Vol 19 (5) ◽  
pp. 664-673 ◽  
Author(s):  
Andrew J. R. Gillespie ◽  
Tiberius Cunia
Keyword(s):  
Least Squares ◽  
Regression Models ◽  
Predictor Variable ◽  
Ordinary Least Squares ◽  
Cluster Sampling ◽  
Parameter Estimates ◽  
Least Squares Regression ◽  
Linear Regression Models ◽  
Estimation Procedures ◽  
Biased Estimates

Biomass tables are often constructed from cluster samples by means of ordinary least squares regression estimation procedures. These procedures assume that sample observations are uncorrelated, which ignores the intracluster correlation of cluster samples and results in underestimates of the model error. We tested alternative estimation procedures by simulation under a variety of cluster sampling methods, to determine combinations of sampling and estimation procedures that yield accurate parameter estimates and reliable estimates of error. Modified, generalized, and jack-knife least squares procedures gave accurate parameter and error estimates when sample trees were selected with equal probability. Regression models that did not include height as a predictor variable yielded biased parameter estimates when sample trees were selected with probability proportional to tree size. Models that included height did not yield biased estimates. There was no discernible gain in precision associated with sampling with probability proportional to size. Random coefficient regressions generally gave biased point estimates with poor precision, regardless of sampling method.

Living Arrangements and Sleep-Related Outcomes Among Older Adults in China

2019 ◽  
Vol 91 (2) ◽  
pp. 111-126 ◽  
Author(s):  
Yen-Han Lee ◽  
Yen-Chang Chang ◽  
Timothy Chiang ◽  
Ching-Ti Liu ◽  
Mack Shelley
Keyword(s):  
Older Adults ◽  
Living Arrangements ◽  
Odds Ratio ◽  
Regression Models ◽  
Family Members ◽  
Ordinary Least Squares ◽  
Least Squares Regression ◽  
Quality Of Sleep ◽  
Nationally Representative

It has been discussed previously that older adults’ living arrangements are associated with mortality. This study investigated the relationships between older adults’ living arrangements and sleep-related outcomes in China. The nationally representative sample included 4,731 participants who participated on two different occasions, with a total of 9,462 observations (2012 and 2014 waves). Panel logistic regression and panel ordinary least squares regression models were estimated with outcomes of sleep quality and average hours of sleep daily, respectively. Approximately 62% of individuals reported good quality of sleep. We observed that older adults who lived with family members had 17% greater odds of reporting good quality of sleep (adjusted odds ratio = 1.17, 95% confidence interval [1.03, 1.34], p < .05) and reported longer sleep duration daily (β = .334, standard error = .069, p < .01), compared with those who lived alone. Social support is needed to strengthen the residential relationship, especially with family members.

Reconciling temporal conflicts in innovation ambidexterity: the role of TMT temporal leadership

2020 ◽  
Vol 24 (8) ◽  
pp. 1899-1920
Author(s):  
Jiawen Chen ◽  
Linlin Liu
Keyword(s):  
Regression Models ◽  
Ordinary Least Squares ◽  
Least Squares Regression ◽  
Exploration And Exploitation ◽  
Temporal Perspective ◽  
Content Type ◽  
Temporal Aspects ◽  
Positive Effect ◽  
Practical Implications

Purpose This study aims to extend the temporal perspective on ambidexterity by investigating how and under what conditions top management team (TMT) temporal leadership improves innovation ambidexterity. Design/methodology/approach Using a questionnaire survey, data were collected from 165 small- and medium-sized enterprises in China. Ordinary least squares regression models were applied to test the hypotheses. Findings The findings show that TMT temporal leadership has a positive effect on innovation ambidexterity and temporal conflict mediates this relationship. Market dynamism and institutional support moderate the indirect effect of TMT temporal leadership on innovation ambidexterity. Practical implications Managers wishing to promote exploration and exploitation simultaneously should pay attention to the temporal aspects of their innovation strategy and improve their temporal leadership activities. Originality/value This study highlights the temporal conflicts in ambidexterity and clarifies the enabling role of TMT temporal leadership. It contributes new insights to the research on organizational ambidexterity and strategic leadership.

scholarly journals Extreme and Inconsistent: A Case-Oriented Regression Analysis of Health, Inequality, and Poverty

2020 ◽  
Vol 6 ◽  
pp. 237802312090606
Author(s):  
Simone Rambotti ◽  
Ronald L. Breiger
Keyword(s):  
Regression Analysis ◽  
Income Inequality ◽  
Comparative Research ◽  
Regression Models ◽  
Ordinary Least Squares ◽  
Multiple Model ◽  
Least Squares Regression ◽  
Coefficient Estimates ◽  
Regression Methods ◽  
Inside Out

A methodological paradox characterizes macro-comparative research: it routinely violates the assumptions underlying its dominant method, multiple regression analysis. Comparative researchers have substantive interest in cases, but cases are largely rendered invisible in regression analysis. Researchers seldom recognize the mismatch between the goals of macro-comparative research and the demands of regression methods, and sometimes they end up engaging in strenuous disputes over particular variable effects. A good example is the controversial relationship between income inequality and health. Here, the authors offer an innovative method that combines variable-oriented and case-oriented approaches by turning ordinary least squares regression models “inside out.” The authors estimate case-specific contributions to regression coefficient estimates. They reanalyze data on income inequality, poverty, and life expectancy across 20 affluent countries. Multiple model specifications are dependent primarily on two countries with values on the outcome that are extreme in magnitude and inconsistent with conventional theoretical expectations.

scholarly journals A Randomized Controlled Trial for the Effectiveness of Aromatherapy in Decreasing Salivary Gland Damage following Radioactive Iodine Therapy for Differentiated Thyroid Cancer

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Michihiro Nakayama ◽  
Atsutaka Okizaki ◽  
Koji Takahashi
Keyword(s):  
Thyroid Cancer ◽  
Salivary Gland ◽  
Differentiated Thyroid Cancer ◽  
Radioactive Iodine ◽  
Control Group ◽  
Parotid Glands ◽  
Before And After ◽  
Group A ◽  
Gland Function ◽  
Group B

Objective. The aim of this study was to investigate effects of aromatherapy in decreasing salivary gland damage for patients undergoing radioactive iodine (RAI) therapy with differentiated thyroid cancer (DTC). Materials and Methods. The subjects were 71 patients with DTC. They were divided into aromatherapy group (group A, n=35) and a control group (group B, n=36). We blended 1.0 mL of lemon and 0.5 mL of ginger essential oils. The patients in the inhalation aromatherapy group inhaled this blend oil and those in the control group inhaled distilled water as placebo for 10 min during admission. We statistically compared salivary gland function before and after treatment between groups A and B. Results. In comparison with group B, the rate of change of the accumulation rate was significantly higher in the parotid glands and submandibular glands of group A (P<0.05). In comparison with group B, a significant increase in rate of secretion change before and after treatment was noted in the bilateral parotid glands in group A (P<0.05). Conclusion. Because an amelioration of salivary gland function was observed in the present study, our results suggest the efficacy of aromatherapy in the prevention of treatment-related salivary gland disorder. This trial is registered with UMIN Clinical Trial Registry: UMIN000013968.

Explaining procedural justice during police-suspect encounters

2017 ◽  
Vol 40 (3) ◽  
pp. 574-586 ◽  
Author(s):  
John D. McCluskey ◽  
Michael Reisig
Keyword(s):  
Police Officers ◽  
Regression Models ◽  
Ordinary Least Squares ◽  
Situational Factors ◽  
Self Control ◽  
Social Characteristics ◽  
Observation Data ◽  
Least Squares Regression ◽  
Content Type ◽  
Police Encounters

Purpose The purpose of this paper is to develop and test a series of hypotheses regarding the use of procedurally just policing during suspect encounters. Design/methodology/approach Systematic social observation data from police encounters with suspects are used (N=939). Ordinary least-squares regression models are estimated to evaluate the effects of four variable clusters (i.e. suspect self-presentation, situational factors, suspect social characteristics, and officer characteristics) on procedurally just policing practices. Findings Results from the regression models show that the most salient predictors of police officers exercising authority in a procedurally just manner include the level of self-control displayed by suspects, the number of citizen onlookers, whether the encounter involved a traffic problem, the race/ethnicity of suspects, and suspects’ social status. Research limitations/implications This study focused only on police-suspects encounters where compliance requests were made. While the size of the sample is relatively large, the results from this study do not generalize to all types of police encounters with members of the public. Originality/value This research adds to an emerging body of research focused on predicting procedurally just practices in police encounters. The findings support increased attention to theories that explain police-citizens interactions, and also indicate that further consideration to the measurement of police behavior is warranted.

A Calibration Tutorial for Spectral Data. Part 1: Data Pretreatment and Principal Component Regression Using Matlab

1996 ◽  
Vol 4 (1) ◽  
pp. 225-242 ◽  
Author(s):  
Paul Geladi ◽  
Harald Martens
Keyword(s):  
Least Squares ◽  
Spectral Data ◽  
Partial Least Squares ◽  
Regression Models ◽  
Principal Component Regression ◽  
Principal Component ◽  
Ordinary Least Squares ◽  
Least Squares Regression ◽  
Statistical Parameters ◽  
Calibration Samples

Regression and calibration play an important role in analytical chemistry. All analytical instrumentation is dependent on a calibration that uses some regression model for a set of calibration samples. The ordinary least squares (OLS) method of building a multivariate linear regression (MLR) model has strict limitations. Therefore, biased or regularised regression models have been introduced. Some selected ones are ridge regression (RR), principal component regression (PCR) and partial least squares regression (PLS or PLSR). Also, artificial neural networks (ANN) based on back-propagation can be used as regression models. In order to understand regression models more is needed than just a set of statistical parameters. A deeper understanding of the underlying chemistry and physics is always equally important. For spectral data this means that a basic understanding of spectra and their errors is useful and that spectral representation should be included in judging the usefulness of the data treatment. A “constructed” spectrometric example is introduced. It consists of real spectrometric measurements in the range 408–1176 nm for 26 calibration samples and 10 test samples. The main response variable is litmus concentration, but other constituents such as bromocresolgreen and ZnO are added as interferents and also the pH is changed. The example is introduced as a tutorial. All calculations are shown in detail in Matlab. This makes it easy for the reader to follow and understand the calculations. It also makes the calculations completely traceable. The raw data are available as a file. In Part 1, the emphasis is on pretreatment of the data and on visualisation in different stages of the calculations. Part 1 ends with principal component regression calculations. Partial least squares calculations and some ANN results are presented in Part 2.

scholarly journals Lymph node characteristics of 6279 N1 differentiated thyroid cancer patients

2020 ◽  
Vol 9 (3) ◽  
pp. 201-210 ◽  
Author(s):  
C Sui ◽  
Q He ◽  
R Du ◽  
D Zhang ◽  
F Li ◽  
...  
Keyword(s):  
Thyroid Cancer ◽  
Lymph Node ◽  
Tumor Size ◽  
Regression Models ◽  
Differentiated Thyroid Cancer ◽  
Nodular Goiter ◽  
Clinicopathological Characteristics ◽  
Tumor Diameter ◽  
Linear Regression Models ◽  
Neck Metastasis

Purpose This study examined the clinicopathological characteristics of 6279 N1 differentiated thyroid cancer (DTC) patients who underwent operations in our center. Methods This was a retrospective longitudinal analysis. We categorized the DTC patients on the basis of various lymph node (LN) characteristics. Logistic regression models and multiple linear regression models were used for the correlation analysis. Results A total of 3693 (58.8%) N1a patients and 2586 (41.2%) N1b patients were included. Patients with N1b disease had larger metastatic foci (0.5 vs 0.15 cm), a greater number of metastatic LNs (5 vs 2), a greater number of dissected LNs (25 vs 7), and a smaller lymph node ratio (NR, number of positive LNs/number of sampled LNs) (23.1% vs 28.6%) than patients in stage N1a. Comparing the clinicopathological features, we found that male, increased tumor size, multifocality, and thyroiditis increased the risk of stage N1b disease (P < 0.05). Sex, multifocality, capsular infiltration, and tumor size were associated with the size of the metastatic LNs (P < 0.05). Sex, capsular infiltration, and nodular goiter were associated with the NR (P < 0.05). Female sex, tumor located in inferior lobe, maximal tumor diameter (MTD) < 1 cm, and nodular goiter were independent predictors for skip metastases (P < 0.05). MTD > 1 cm, central neck metastasis and age were independent predictors for bilateral lateral neck metastasis (BLNM) (P < 0.05). Conclusion The LN characteristics of stage N1a and N1b disease were associated with significantly different features, such as sex, tumor size, multifocality, capsular infiltration, and nodular goiter.

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