scholarly journals Fixed or random? On the reliability of mixed-effect models for a small number of levels in grouping variables

2021 ◽  
Author(s):  
Johannes Oberpriller ◽  
Melina de Souza Leite ◽  
Maximilian Pichler
Keyword(s):  
Type I Error ◽  
Random Effect ◽  
Fixed Effect Model ◽  
Mixed Effects ◽  
Fixed Effect ◽  
Statistical Properties ◽  
Type I ◽  
Mixed Effect ◽  
Mixed Effect Models ◽  
Effect Model

Biological data are often intrinsically hierarchical. Due to their ability to account for such dependencies, mixed-effect models have become a common analysis technique in ecology and evolution. While many questions around their theoretical foundations and practical applications are solved, one fundamental question is still highly debated: When having a low number of levels should we model a grouping variable as a random or fixed effect? In such situation, the variance of the random effect is presumably underestimated, but whether this affects the statistical properties of the fixed effects is unclear. Here, we analyze the consequences of including a grouping variable as fixed or random effect and possible other modeling options (over and underspecified models) for data with small number of levels in the grouping variable (2 - 8). For all models, we calculated type I error rates, power and coverage. Moreover, we show the influence of possible study designs on these statistical properties. We found that mixed-effect models already for two groups correctly estimate variance for two groups. Moreover, model choice does not influence the statistical properties when there is no random slope in the data-generating process. However, if an ecological effect differs among groups, using a random slope and intercept model, and switching to a fixed-effect model only in case of a singular fit avoids overconfidence in the results. Additionally, power and type I error are strongly influenced by the number of and the difference between groups. We conclude that inferring the correct random effect structure is of high importance to get correct statistical properties. When in doubt, we recommend starting with the simpler model and using model diagnostics to identify missing components. When having identified the correct structure, we encourage to start with a mixed-effects model independent of the number of groups and only in case of a singular fit switch to a fixed-effect model. With these recommendations, we allow for more informative choices about study design and data analysis and thus make ecological inference with mixed-effects models more robust for low number of groups.

scholarly journals Meta-analysis of SNP-environment interaction with heterogeneity for overlapping data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Qinqin Jin ◽  
Gang Shi
Keyword(s):  
Meta Analysis ◽  
Random Effect ◽  
Random Effect Model ◽  
Fixed Effect Model ◽  
Fixed Effect ◽  
Type I ◽  
Environment Interaction ◽  
Genome Wide Association Studies ◽  
Overlapping Data ◽  
Effect Model

AbstractMeta-analysis is a popular method used in genome-wide association studies, by which the results of multiple studies are combined to identify associations. This process generates heterogeneity. Recently, we proposed a random effect model meta-regression method (MR) to study the effect of single nucleotide polymorphism (SNP)-environment interactions. This method takes heterogeneity into account and produces high power. We also proposed a fixed effect model overlapping MR in which the overlapping data is taken into account. In the present study, a random effect model overlapping MR that simultaneously considers heterogeneity and overlapping data is proposed. This method is based on the random effect model MR and the fixed effect model overlapping MR. A new way of solving the logarithm of the determinant of covariance matrices in likelihood functions is also provided. Tests for the likelihood ratio statistic of the SNP-environment interaction effect and the SNP and SNP-environment joint effects are given. In our simulations, null distributions and type I error rates were proposed to verify the suitability of our method, and powers were applied to evaluate the superiority of our method. Our findings indicate that this method is effective in cases of overlapping data with a high heterogeneity.

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 Consequences of power transforms as a statistical solution in linear mixed-effects models of chronometric data

2018 ◽  
Author(s):  
Van Rynald T Liceralde ◽  
Peter C. Gordon
Keyword(s):  
Type I Error ◽  
Response Times ◽  
Random Effect ◽  
Mixed Effects ◽  
Error Rates ◽  
Mixed Effects Models ◽  
Type I ◽  
Statistical Solution ◽  
Linear Mixed Effects Models ◽  
Linear Mixed Effects

Power transforms have been increasingly used in linear mixed-effects models (LMMs) of chronometric data (e.g., response times [RTs]) as a statistical solution to preempt violating the assumption of residual normality. However, differences in results between LMMs fit to raw RTs and transformed RTs have reignited discussions on issues concerning the transformation of RTs. Here, we analyzed three word-recognition megastudies and performed Monte Carlo simulations to better understand the consequences of transforming RTs in LMMs. Within each megastudy, transforming RTs produced different fixed- and random-effect patterns; across the megastudies, RTs were optimally normalized by different power transforms, and results were more consistent among LMMs fit to raw RTs. Moreover, the simulations showed that LMMs fit to optimally normalized RTs had greater power for main effects in smaller samples, but that LMMs fit to raw RTs had greater power for interaction effects as sample sizes increased, with negligible differences in Type I error rates between the two models. Based on these results, LMMs should be fit to raw RTs when there is no compelling reason beyond nonnormality to transform RTs and when the interpretive framework mapping the predictors and RTs treats RT as an interval scale.

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.

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