scholarly journals Risk Adjustment for Lumbar Dysfunction: Comparison of Linear Mixed Models With and Without Inclusion of Between-Clinic Variation as a Random Effect

2015 ◽  
Vol 95 (12) ◽  
pp. 1692-1702 ◽  
Author(s):  
Sheng-Che Yen ◽  
Marie B. Corkery ◽  
Kevin K. Chui ◽  
Justin Manjourides ◽  
Ying-Chih Wang ◽  
...  
Keyword(s):  
Physical Therapy ◽  
Patient Outcomes ◽  
Risk Adjustment ◽  
Mixed Models ◽  
Linear Mixed Models ◽  
Random Effect ◽  
Fixed Effect Model ◽  
The United States ◽  
Fixed Effect ◽  
Effect Model

BackgroundValid comparison of patient outcomes of physical therapy care requires risk adjustment for patient characteristics using statistical models. Because patients are clustered within clinics, results of risk adjustment models are likely to be biased by random, unobserved between-clinic differences. Such bias could lead to inaccurate prediction and interpretation of outcomes.PurposeThe purpose of this study was to determine if including between-clinic variation as a random effect would improve the performance of a risk adjustment model for patient outcomes following physical therapy for low back dysfunction.DesignThis was a secondary analysis of data from a longitudinal cohort of 147,623 patients with lumbar dysfunction receiving physical therapy in 1,470 clinics in 48 states of the United States.MethodsThree linear mixed models predicting patients' functional status (FS) at discharge, controlling for FS at intake, age, sex, number of comorbidities, surgical history, and health care payer, were developed. Models were: (1) a fixed-effect model, (2) a random-intercept model that allowed clinics to have different intercepts, and (3) a random-slope model that allowed different intercepts and slopes for each clinic. Goodness of fit, residual error, and coefficient estimates were compared across the models.ResultsThe random-effect model fit the data better and explained an additional 11% to 12% of the between-patient differences compared with the fixed-effect model. Effects of payer, acuity, and number of comorbidities were confounded by random clinic effects.LimitationsModels may not have included some variables associated with FS at discharge. The clinics studied may not be representative of all US physical therapy clinics.ConclusionsRisk adjustment models for functional outcome of patients with lumbar dysfunction that control for between-clinic variation performed better than a model that does not.

Racial disparity in OncotypeDX score in early stage, hormone receptor positive, HER-2 negative, node negative breast cancers: A systemic review and meta-analysis.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e12555-e12555
Author(s):  
Yi Lee ◽  
Ruolin Liu ◽  
Alexis K. Bean ◽  
Madison J. Garshasebi ◽  
Qasim Jehangir ◽  
...  
Keyword(s):  
High Risk ◽  
Random Effects ◽  
Racial Disparity ◽  
Random Effect ◽  
Recurrence Score ◽  
Fixed Effect Model ◽  
Fixed Effect ◽  
Random Effects Model ◽  
Effect Model ◽  
To Receive

e12555 Background: Oncotype DX Breast Recurrence Score (RS) is the currently used risk-assessment tool for early-stage, hormone receptor-positive, HER-2 negative, node-negative breast cancer in the US. Studies showed inconsistency in RS distribution and treatment among races. Causes may include variations in somatic mutations like Ki-67, which have been reported to express higher in African American (AA) and Asian populations than in Non-Hispanic White (NHW) population, germline mutations in BRCA and TP53, that are not in the RS algorithm, and financial burden of the testing. We analyzed data from different countries to investigate racial disparity in RS. Methods: We searched Medline, EMBASE, Web of Science, and Cochrane Central Register of Controlled Trials, indexed from January 2010 to January 2021. More than 85% of studies were conducted in the pre-TAILORx study phase. To include data that are available and better represent different races, we included studies that used the previous cutoff value, low-risk ( < 18), intermediate-risk (18-30), high-risk ( > 30). Retrospective studies using Surveillance, Epidemiology, and End Results or National Cancer Database were excluded to avoid overlap data. A total of 17 studies, 9789 patients from seven countries (US, Japan, China, Taiwan, Kuwait, UAE, Israel) were pooled. The Odds Ratio (OR) was extracted with a 95% confidence interval (CI) for RS distribution and post-RS treatment. Both fixed-effect and random-effect meta-analysis were performed. Results: Among AA and NHW, AA were 1.7 times more likely to have high recurrence score (OR = 1.75; 95% CI = 1.46 - 2.10; P < 0.0001), with no heterogeneity among studies (I2 = 0%, heterogeneity P = 0.59). Asian were 1.59 more likely than NHW to be high-risk using a random effects model (OR = 1.59; 95% CI = 1.06 - 2.40; P = 0.0259). High-risk Asian were two times more likely to receive adjuvant chemotherapy post-RS comparing to NHW (OR: 2.31, CI: 1.07 - 4.98, fixed effect model; OR: 2.85, CI: 0.48, 17.05, random effects model), while high-risk AA were less likely to receive chemotherapy comparing to NHW (OR: 0.74, CI: 0.54-1.01, fixed effect model; OR: 0.73, CI: 0.54-0.99, random effects model). Intermediate-risk Asian and AA were more likely to receive chemotherapy compared to NHW (Asian to NHW; OR: 1.68, CI: 1.16-2.43, with fixed effect model, OR: 1.68, CI: 0.94-3.02, with random effects model; AA to NHW; OR: 1.16, CI: 0.93-1.46 with fixed effect model; OR: 1.06, CI: 0.62-1.79 with random effect model). Conclusions: We identified racial disparity in RS and post-RS treatment. Future research is required to elucidate the causes for AA and Asian receiving higher recurrence scores, a need for tailoring RS cutoffs for different races, and the utilization in adequate post-RS treatment.

Effect of Dividend on Stock Price: A Case of Fuel and Power Industries in Bangladesh

2019 ◽  
Vol 9 (2) ◽  
pp. 87
Author(s):  
Mir Md Nazrul Islam
Keyword(s):  
Stock Price ◽  
Dividend Policy ◽  
Random Effect ◽  
Random Effect Model ◽  
Fixed Effect Model ◽  
Fixed Effect ◽  
Return On Equity ◽  
Share Prices ◽  
Earnings Per Share ◽  
Effect Model

Dividend policy is an extensively researched topic in the arena of investments but still it remains an enigmatic that whether Dividend Policy affects the Stock Prices or not. The consequences of researches conducted in different stock markets are different. In Bangladesh, capital market investment is very essential and significant for the growth and market capitalization of domestic industry, trade and commerce. In current years Bangladesh had faced many precarious situations in its stock market. The Stock price reactions to the declaration of dividend of the fuel and power industry of Bangladesh are empirically examined. This study examines stock price reactions of listed dividend paying fuel and power industries in Dhaka stock exchange, Bangladesh for period of 11 years from of 2008-2018. This study will help us to make effective dividend decisions and effective implementation of dividend policies. In this study, Fixed Effect Model along with Random Effect Model have been used to estimate results. Both Models are implemented on panel data for explaining the association between dividend payments and share prices while controlling logarithm value of Profit after Tax, Earnings per Share and Return on Equity. The research is accompanied with a view to find whether the dividend announcement convey any evidence to the market that results a stock price volatility for adjusting the dividend announcement information while controlling the variables like Profit After Tax Earnings, Per Share and Return on Equity. The study also tested both the Models and found Random Effect Model is more significant than Fixed Effect Model. The result documented on the Random Effect Model shows that there are significant relationship with Retention Ratio, dividend per share and Return on Equity. In addition, Profit after tax shows the negative significant association and Earning per Shares insignificant with the share prices in Bangladesh Fuel and Power sector. 

scholarly journals Is MGNREG Scheme Complementary to Fishing Activities?

2016 ◽  
Vol 5 (1) ◽  
pp. 47-53
Author(s):  
Chiranjib Neogi ◽  
Kamal Ray ◽  
Ramesh Chandra Das
Keyword(s):  
Freshwater Fish ◽  
Rainwater Harvesting ◽  
Random Effect ◽  
Random Effect Model ◽  
Fixed Effect Model ◽  
Fixed Effect ◽  
Series Data ◽  
Component Part ◽  
Indian States ◽  
Effect Model

Freshwater fish output is taken as a proxy variable for empirical assessment of indirect benefits in terms of enhanced quantity of freshwater fish (output) cultivation. It is not unlikely to assess empirically the productivity of subsidized public scheme when rural development or rural asset generations are underlined in the said scheme, MGNREG Act, 2005. Rainwater harvesting is a major component part of the scheme since about 49.5 per cent of the total fund is already utilized on water conservation and obviously it has an impact on the cultivation of freshwater fish output. Time series data on annual expenditure on MGNREG and corresponding freshwater fish output at the state level are taken during the period 2006-07 to 2013-14 for 16 major Indian states. Fixed effect model and random effect models are being applied and the Hausman specification test suggests that fixed effect model is more appropriate than random effect model. Significant differences among the intercepts of the selected states are revealed as per F test. The results of fixed effect panel regression establish that fish output is enhanced by 0.000257 thousand tones or 0.26 tones if MGNREG expenditure rises by one crore or 10 million rupees. 

scholarly journals Inequality and Economic Growth: An Analysis of 8-Panels

2017 ◽  
Vol 3 (2) ◽  
pp. 173
Author(s):  
Khadijah A. Idowu ◽  
Yusuf Bababtunde Adeneye
Keyword(s):  
Economic Growth ◽  
Panel Data Analysis ◽  
Random Effect ◽  
Random Effect Model ◽  
Specific Effect ◽  
Developed Countries ◽  
Fixed Effect Model ◽  
Fixed Effect ◽  
Effect Model ◽  
The World

<p><em>Purpose: This paper investigates the effects of inequality on economic growth in the world using continental approach.</em><em></em></p><p><em>Design/methodology:<strong> </strong>Gini Coefficient and Gross Domestic Products (GDP) per capita were used to measure inequality and economic growth respectively. The study conducted a panel data analysis of the relationship between inequality and economic growth. The data span from 1991-2015. Five countries were selected each from seven continents and were also pooled together to constitute a single panel for 35 countries, thus establishing 8 panels. The Hausman test was conducted to determine whether a random or fixed effect model best fit pooled countries analysis or not.</em><em></em></p><p><em>Findings: Findings revealed that for the developing countries, high income inequality retards economic growth while for the developed countries such as Europe countries; the situation seems to be different. European countries as revealed in the findings showed that developed countries have benefited from inequality which has significantly and positively affected their economic growth. The results for Panel II (Asia countries) and Panel III (Europe countries) are in line with the study of Forbes (2000) and Li and Zou (1998) that documented that inequality boosts economic growth. Importantly, we found that inequality positively affects economic growth for Panels/Continents with fixed effect model while inequality negatively affects economic growth for Panels/Continents with random effect model.</em></p><p><em>Research Limitation: The study did not control for each continent differences. For African countries, weak institutional settings and environment is a key factor contributing to high inequality.</em><em></em></p><p><em>Originality: The paper was able to know the specific effect of inequality on economic growth in each continent in the World. This documents continents that have benefited from inequality and those that inequality has greatly affected their economies negatively.</em><em></em></p>

Estimasi Fixed Effect Model dari Likuiditas Aset, Financial Leverage dan Likuiditas Saham di Indonesia

2019 ◽  
Vol 4 (3) ◽  
pp. 412
Author(s):  
Irdha Yusra ◽  
Awidi Mulfita
Keyword(s):  
Panel Data ◽  
Random Effect ◽  
Random Effect Model ◽  
Fixed Effect Model ◽  
Fixed Effect ◽  
Financial Leverage ◽  
Stock Liquidity ◽  
Common Effect ◽  
Effect Model ◽  
Asset Liquidity

<p><em>In investing, investors don’t assess the expected return, but also liquidity in shares. Because the aspect of liquidity is very important for investors to decide which stocks are attractive investments. This study aims to examine the effect of asset liquidity and financial leverage on stock liquidity. The population is all companies which are listed in Indonesia Stock Exchange in 2013-2017 periods. The sampling technique uses a purposive sampling method with predetermined criteria and obtained a sample of 58 companies with 290 observations. The data of the financial statement of the companies has been obtained from the official website of IDX. The analytical method used is regression analysis of panel data with the help of application E-Views 8. Panel data regression can be estimated using three models, namely Common Effect Model (CEM), Fixed Effect Model (FEM), and Random Effect Model (REM). From the results of the estimation model, it is found that FEM is the best model in this study. Furthermore, the results of the study show that asset liquidity has a positive and not significant effect on stock liquidity, while financial leverage has a negative and significant effect on stock liquidity.</em></p><p>Dalam berinvestasi, investor tidak hanya menilai dari return yang diharapkan, namun juga likuiditas pada saham. Karena aspek likuiditas sangat penting bagi investor untuk memutuskan mana saham yang menarik investasi. Penelitian ini bertujuan untuk menguji pengaruh likuiditas aset dan financial leverage terhadap likuiditas saham. Populasi dalam penelitian ini adalah perusahaan yang terdaftar di Bursa Efek Indonesia (BEI) periode 2013-2017. Teknik pengambilan sampel menggunakan metode purposive sampling dengan kriteria yang telah ditentukan dan diperoleh sampel sebanyak 58 perusahaan. Data laporan keuangan diperoleh dari website resmi BEI. Metode analisis yang dipakai adalah analisis regresi data panel dengan bantuan aplikasi E-Views 8. Regresi data panel dapat diestimasi menggunakan tiga model, yaitu Common Effect Model (CEM), Fixed Effect Model (FEM), dan Random Effect Model (REM). Untuk mendapatkan model terbaik digunakan uji lanjut, yaitu Uji Chow dan Uji Hausman. Dari hasil estimasi model diperoleh bahwa FEM sebagai model terbaik dalam penelitian ini. Lebih lanjut, hasil penelitian menemukan bahwa likuiditas aset berpengaruh positif dan tidak signifikan terhadap likuiditas saham, sedangkan financial leverage berpengaruh negatif dan signifikan terhadap likuiditas saham.</p>

Comparative Evaluation of Efficacy of Ritodrine Versus Nifedipine in Prevention of Preterm Labour in Direct Head to Head Randomized Control Trials: A Systematic Review

2018 ◽  
Vol 2 (S1) ◽  
pp. e000153
Author(s):  
Viraj Panchal ◽  
Nishita Darji ◽  
Devang Rana
Keyword(s):  
Preterm Labour ◽  
Random Effect ◽  
Random Effect Model ◽  
Fixed Effect Model ◽  
Fixed Effect ◽  
Mesh Terms ◽  
Effect Model ◽  
Direct Head ◽  
The Difference ◽  
Term Labour

Aims and Objectives: To compare the efficacy of ritodrine versus nifedipine in prevention of preterm labour at day two and seven. Methodology: All randomised control trials which follows PRISMA guidelines 2009 and in which Ritodrine and Nifedipine was compared head to head for the treatment of Pre-term labour. Clinical trial registries,MEDLINE, SCOPUS, EMBASE database were searched for MeSH terms Ritodrine, Nifedipine, pre-term labour and having primary outcome as number of delivery at day 2 and 7. Observational studies, unpublished studies, RCTs not following PRISMA guidelines were excluded. Data was analyzed using RevMan 5.3 version® and Odd’s Ratio was calculated to determine the difference at day 2 and 7. Both fixed effect and Random effect model was utilized to calculate the difference. P value less than 0.05 was considered as statistically significant. The I2 will be used to measure the heterogeneity between studies and a value > 30.0 will be considered to reflect heterogeneity. Results: A total of 6 Head to head RCTs were included in the studies. At day 2, according to fixed effect model, statistically ritodrine was having more likelihood for delivery as compared to nifedipine(Odd’s ratio=1.492, CI=1.013-2.197, P=0.043) but according to random effect model the difference was not statistically significant(Odd’s ratio=1.468, CI=0.919-2.344, P=0.108). At day 7, according to fixed effect model, ritodrine was having more likelihood for delivery as compared to nifedipine(Odd’s ratio=1.196, CI=0.852-1.679, P=0.302) and according to random effect model the difference was not statistically significant(Odd’s ratio=1.143, CI=0.720-1.815, P=0.572). Conclusion: Ritodrine causes more deliveries at day 2 and 7, so nifedipine is a better tocolytic as compared to ritodrine.

scholarly journals MODEL REGRESI DATA PANEL UNTUK MENGETAHUI FAKTOR YANG MEMPENGARUHI TINGKAT KEMISKINAN DI PULAU MADURA

2020 ◽  
Vol 9 (3) ◽  
pp. 355-363
Author(s):  
Artanti Indrasetianingsih ◽  
Tutik Khalimatul Wasik
Keyword(s):  
Panel Data ◽  
Random Effect ◽  
Random Effect Model ◽  
Fixed Effect Model ◽  
Fixed Effect ◽  
Series Data ◽  
Poverty Level ◽  
Effect Model ◽  
Panel Data Regression ◽  
Data Regression

Poverty arises when a person or group of people is unable to meet the level of economic prosperity which is considered a minimum requirement of a certain standard of living or poverty is understood as a state of lack of money and goods to ensure survival. Panel data regression is the development of regression analysis which is a combination of time series data and cross section data. Panel data regression is usually used to make observations of data that is examined continuously for several periods. The purpose of this study is to determine the factors that influence the level of poverty in Madura Island in the period 2008 - 2017. In this study the variables used in this study are life expectancy (X1), average length of school (X2), level open unemployment (X3), and labor force participation (X4) with the Comman Effect Model (CEM) approach, Fixed Effect Model and Random Effect Model (REM). To choose the best model from the three is the chow test, the hausman test and the breusch-pagan test. In this study, the best model chosen was the Fixed Effect Model. Keywords: CEM, Fixed Effect Model, Data Panel Regression, REM, Poverty level.

scholarly journals PENGARUH KEPEMILIKAN MANAJERIAL, STRUKTUR AKTIVA, UKURAN PERUSAHAAN, PERTUMBUHAN PENJUALAN DAN PROFITABILITAS TERHADAP KEBIJAKAN UTANG PADA PERUSAHAAN SEKTOR ANEKA INDUSTRI YANG TERDAFTAR DI BURSA EFEK INDONESIA (BEI) PERIODE 2012 – 2016

2018 ◽  
Vol 9 (1) ◽  
pp. 105-124
Author(s):  
Umi Mardiyati ◽  
Qothrunnada Qothrunnada ◽  
Destria Kurnianti
Keyword(s):  
Random Effect ◽  
Random Effect Model ◽  
Fixed Effect Model ◽  
Fixed Effect ◽  
Effect Model ◽  
Robustness Check

Penelitian ini bertujuan untuk mengetahui pengaruh kepemilikan manajerial, struktur aktiva, ukuran perusahaan, pertumbuhan penjualan dan profitabilitas terhadap kebijakan utang pada perusahaan aneka industri yang terdaftar di BEI periode 2012-2016 dengan 96 unit sampel. Model yang digunakan dalam penelitian ini adalah data panel dengan pendekatan Fixed Effect Model dan Random Effect Model. Hasil penelitian menunjukkan bahwa kepemilikan manajerial dan struktur aktiva tidak berpengaruh terhadap kebijakan utang (DER dan DAR), ukuran perusahaan positif dan signifikan terhadap DER dan negatif dan signifikan terhadap DAR, sedangkan pertumbuhan penjualan dan profitabilitas positif dan signifikan terhadap DER tetapi tidak berpengaruh terhadap DAR. Hasil robustness check menunjukkan kepemilikan manajerial, struktur aktiva dan ukuran perusahaan memiliki hasil robust. Sedangkan pertumbuhan penjualan dan profitabilitas memiliki hasil tidak robust.

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