scholarly journals Pengaruh ukuran sampel dan intraclass correlation coefficients (ICC) terhadap bias estimasi parameter multilevel latent variable modeling: studi dengan simulasi Monte Carlo

2017 ◽  
Vol 21 (1) ◽  
pp. 34-50
Author(s):  
Muhammad Dwirifqi Kharisma Putra ◽  
Jahja Umar ◽  
Bahrul Hayat ◽  
Agung Priyo Utomo
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Sample Size ◽  
Latent Variable ◽  
Intraclass Correlation ◽  
Correlation Coefficients ◽  
Latent Variable Modeling ◽  
Intraclass Correlation Coefficients ◽  
The Impact

Studi ini menggunakan simulasi Monte Carlo dilakukan untuk melihat pengaruh ukuran sampel dan intraclass correlation coefficients (ICC) terhadap bias estimasi parameter multilevel latent variable modeling. Kondisi simulasi diciptakan dengan beberapa faktor yang ditetapkan yaitu lima kondisi ICC (0.05, 0.10, 0.15, 0.20, 0.25), jumlah kelompok (30, 50, 100 dan 150), jumlah observasi dalam kelompok (10, 20 dan 50) dan diestimasi menggunakan lima metode estimasi: ML, MLF, MLR, WLSMV dan BAYES. Jumlah kondisi keseluruhan sebanyak 300 kondisi dimana tiap kondisi direplikasi sebanyak 1000 kali dan dianalisis menggunakan software Mplus 7.4. Kriteria bias yang masih dapat diterima adalah < 10%. Hasil penelitian ini menunjukkan bahwa bias yang terjadi dipengaruhi oleh ukuran sampel dan ICC, penelitian ini juga menujukkan bahwa metode estimasi WLSMV dan BAYES berfungsi lebih baik pada berbagai kondisi dibandingkan dengan metode estimasi berbasis ML.Kata kunci: multilevel latent variable modeling, intraclass correlation coefficients, Metode Markov Chain Monte Carlo THE IMPACT OF SAMPLE SIZE AND INTRACLASS CORRELATION COEFFICIENTS (ICC) ON THE BIAS OF PARAMETER ESTIMATION IN MULTILEVEL LATENT VARIABLE MODELING: A MONTE CARLO STUDYAbstractA monte carlo study was conducted to investigate the effect of sample size and intraclass correlation coefficients (ICC) on the bias of parameter estimates in multilevel latent variable modeling. The design factors included (ICC: 0.05, 0.10, 0.15, 0.20, 0.25), number of groups in between level model (NG: 30, 50, 100 and 150), cluster size (CS: 10, 20 and 50) to be estimated with five different estimator: ML, MLF, MLR, WLSMV and BAYES. Factors were interegated into 300 conditions (4 NG  3 CS  5 ICC  5 Estimator). For each condition, replications with convergence problems were exclude until at least 1.000 replications were generated and analyzed using Mplus 7.4, we also consider absolute percent bias <10% to represent an acceptable level of bias. We find that the degree of bias depends on sample size and ICC. We also show that WLSMV and BAYES estimator performed better than ML-based estimator across varying sample sizes and ICC’s conditions.Keywords: multilevel latent variable modeling, intraclass correlation coefficients, Markov Chain Monte Carlo method

scholarly journals Bayesian Classification of Proteomics Biomarkers from Selected Reaction Monitoring Data using an Approximate Bayesian Computation-Markov Chain Monte Carlo Approach

2018 ◽  
Vol 17 ◽  
pp. 117693511878692
Author(s):  
Kashyap Nagaraja ◽  
Ulisses Braga-Neto
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Sample Size ◽  
Approximate Bayesian Computation ◽  
Selected Reaction Monitoring ◽  
Bayesian Computation ◽  
Reaction Monitoring ◽  
Approximate Bayesian

Selected reaction monitoring (SRM) has become one of the main methods for low-mass-range–targeted proteomics by mass spectrometry (MS). However, in most SRM-MS biomarker validation studies, the sample size is very small, and in particular smaller than the number of proteins measured in the experiment. Moreover, the data can be noisy due to a low number of ions detected per peptide by the instrument. In this article, those issues are addressed by a model-based Bayesian method for classification of SRM-MS data. The methodology is likelihood-free, using approximate Bayesian computation implemented via a Markov chain Monte Carlo procedure and a kernel-based Optimal Bayesian Classifier. Extensive experimental results demonstrate that the proposed method outperforms classical methods such as linear discriminant analysis and 3NN, when sample size is small, dimensionality is large, the data are noisy, or a combination of these.

scholarly journals Estimation and Properties of a Time-Varying GQARCH(1,1)-M Model

2011 ◽  
Vol 2011 ◽  
pp. 1-39 ◽  
Author(s):  
Sofia Anyfantaki ◽  
Antonis Demos
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Em Algorithm ◽  
Sample Size ◽  
Dynamic Properties ◽  
Conditional Variance ◽  
Monte Carlo Algorithm ◽  
Time Varying ◽  
Likelihood Analysis

Time-varying GARCH-M models are commonly used in econometrics and financial economics. Yet the recursive nature of the conditional variance makes exact likelihood analysis of these models computationally infeasible. This paper outlines the issues and suggests to employ a Markov chain Monte Carlo algorithm which allows the calculation of a classical estimator via the simulated EM algorithm or a simulated Bayesian solution in only computational operations, where is the sample size. Furthermore, the theoretical dynamic properties of a time-varying GQARCH(1,1)-M are derived. We discuss them and apply the suggested Bayesian estimation to three major stock markets.

scholarly journals Deaminase associated single nucleotide variants in blood and saliva-derived exomes from healthy subjects

2019 ◽  
Author(s):  
Nathan E. Hall ◽  
Jared Mamrot ◽  
Christopher M.A. Frampton ◽  
Prue Read ◽  
Edward J. Steele ◽  
...  
Keyword(s):  
Exome Sequencing ◽  
Whole Exome Sequencing ◽  
Healthy Subjects ◽  
Intraclass Correlation ◽  
Correlation Coefficients ◽  
Healthy Individuals ◽  
Intraclass Correlation Coefficients ◽  
Whole Exome ◽  
The Impact ◽  
Group 1

AbstractBackgroundDeaminases play an important role in shaping inherited and somatic variants. Disease related SNVs are associated with deaminase mutagenesis and genome instability. Here, we investigate the reproducibility and variance of whole exome SNV calls in blood and saliva of healthy subjects and analyze variants associated with AID, ADAR, APOBEC3G and APOBEC3B deaminase sequence motifs.MethodsSamples from twenty-four healthy Caucasian volunteers, allocated into two groups, underwent whole exome sequencing. Group 1 (n=12) analysis involved one blood and four saliva replicates. A single saliva sample was sequenced for Group 2 subjects (n=12). Overall, a total of 72 whole exome datasets were analyzed. Biological (Group 1 & 2) and technical (Group 1) variance of SNV calls and deaminase metrics were calculated and analyzed using intraclass correlation coefficients. Candidate somatic SNVs were identified and evaluated.ResultsWe report high blood-saliva concordance in germline SNVs from whole exome sequencing. Concordant SNVs, found in all subject replicates, accounted for 97% of SNVs located within the protein coding sequence of genes. Discordant SNVs have a 30% overlap with variants that fail gnomAD quality filters and are less likely to be found in dbSNP. SNV calls and deaminase-associated metrics were found to be reproducible and robust (intraclass correlation coefficients >0.95). No somatic SNVs were conclusively identified when comparing blood and saliva samples.ConclusionsSaliva and blood both provide high quality sources of DNA for whole exome sequencing, with no difference in ability to resolve SNVs and deaminase-associated metrics. We did not identify somatic SNVs when comparing blood and saliva of healthy individuals, and we conclude that more specialized investigative methods are required to comprehensively assess the impact of deaminase activity on genome stability in healthy individuals.

Clinical correlates and implications of the reliability of the frailty index in the Canadian Longitudinal Study on Aging

2021 ◽  
Author(s):  
Quoc Dinh Nguyen ◽  
Erica M Moodie ◽  
Mark R Keezer ◽  
Christina Wolfson
Keyword(s):  
Odds Ratio ◽  
Frailty Index ◽  
Intraclass Correlation ◽  
Correlation Coefficients ◽  
Community Dwelling ◽  
Deficit Accumulation ◽  
Intraclass Correlation Coefficients ◽  
Reliability Estimates ◽  
Predicted Probability ◽  
The Impact

Abstract Background Deficit-accumulation frailty indices (FIs) are widely used to characterize frailty. FIs vary in number and composition of items; the impact of this variation on reliability and clinical applicability is unknown. Methods We simulated 12,000 studies using a set of 70 candidate deficits in 12,080 community-dwelling participants 65 years and older. For each study, we varied the number (5, 10, 15, 25, 35, 45) and composition (random selection) of items defining the FI and calculated descriptive and predictive estimates: frailty score, prevalence, frailty cut-off, mortality odds ratio, predicted probability of mortality for FI=0.28 (prevalence threshold), and FI cut-off predicting 10% mortality over the follow-up. We summarized the estimates’ medians and spreads (0.025-0.975 quantiles) by number of items and calculated intraclass correlation coefficients (ICC). Results Medians of frailty scores were 0.11-0.12 with decreasing spreads from 0.04-0.24 to 0.10-0.14 for 5-item and 45-item FIs. The median cut-offs identifying 15% as frail was 0.19-0.20 and stable; the spreads decreased with more items. However, medians and spreads for the prevalence of frailty (medians: 11% to 3%), mortality odds ratio (medians:1.24 to 2.19), predicted probability of mortality (medians: 8% to 17%), and FI cut-off predicting 10% mortality (medians: 0.38 to 0.20) varied markedly. ICC increased from 0.19 (5-item FIs) to 0.84 (45-item FIs). Conclusions Variability in the number and composition of items of individual FIs strongly influences their reliability. Estimates using FIs may not be sufficiently stable for generalizing results or direct application. We propose avenues to improve the development, reporting, and interpretation of FIs.

scholarly journals Accessing the syndemic of COVID-19 and malaria intervention in Africa

2020 ◽  
Author(s):  
Benyun Shi ◽  
Jinxin Zheng ◽  
Shang Xia ◽  
Shan Lin ◽  
Xinyi Wang ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Malaria Transmission ◽  
Vectorial Capacity ◽  
Insecticide Treated Nets ◽  
Transmission Potential ◽  
The Impact ◽  
Pharmaceutical Interventions ◽  
Epidemiological Parameters

Abstract Background: The pandemic of the coronavirus disease 2019 (COVID-19) has caused substantial disruptions to health services in the low and middle-income countries with a high burden of other diseases, such as malaria in sub-Saharan Africa. The aim of this study is to assess the impact of COVID-19 pandemic on malaria transmission potential in malaria-endemic countries in Africa. Methods: We present a data-driven method to quantify the extent to which the COVID-19 pandemic, as well as various non-pharmaceutical interventions (NPIs), could lead to the change of malaria transmission potential in 2020. First, we adopt a particle Markov Chain Monte Carlo method to estimate epidemiological parameters in each country by fitting the time series of the cumulative number of reported COVID-19 cases. Then, we simulate the epidemic dynamics of COVID-19 under two groups of NPIs: (i) contact restriction and social distancing, and (ii) early identification and isolation of cases. Based on the simulated epidemic curves, we quantify the impact of COVID-19 epidemic and NPIs on the distribution of insecticide-treated nets (ITNs). Finally, by treating the total number of ITNs available in each country in 2020, we evaluate the negative effects of COVID-19 pandemic on malaria transmission potential based on the notion of vectorial capacity. Results: In this paper, we conduct case studies in four malaria-endemic countries, Ethiopia, Nigeria, Tanzania, and Zambia, in Africa. The epidemiological parameters (i.e., the basic reproduction number R_0 and the duration of infection D_I) of COVID-19 in each country are estimated as follows: Ethiopia (R_0=1.57, D_I=5.32), Nigeria (R_0=2.18, D_I=6.58), Tanzania (R_0=2.47, D_I=6.01), and Zambia (R_0=2.12, D_I=6.96). Based on the estimated epidemiological parameters, the epidemic curves simulated under various NPIs indicated that the earlier the interventions are implemented, the better the epidemic is controlled. Moreover, the effect of combined NPIs is better than contact restriction and social distancing only. By treating the total number of ITNs available in each country in 2020 as a baseline, our results show that even with stringent NPIs, malaria transmission potential will remain higher than expected in the second half of 2020. Conclusions: By quantifying the impact of various NPI response to the COVID-19 pandemic on malaria transmission potential, this study provides a way to jointly address the syndemic between COVID-19 and malaria in malaria-endemic countries in Africa. The results suggest that the early intervention of COVID-19 can effectively reduce the scale of the epidemic and mitigate its impact on malaria transmission potential. Keywords : COVID-19 pandemic; Non-pharmaceutical interventions; Particle Markov chain Monte Carlo; Insecticide-treated nets; Vectorial capacity; Malaria transmission potential

scholarly journals Assessment of a generalizable methodology to assess learning from manikin-based simulation technology*

2014 ◽  
Vol 28 (1) ◽  
pp. 16-20 ◽  
Author(s):  
Dominic A. Giuliano ◽  
Marion McGregor
Keyword(s):  
Learning Outcomes ◽  
Intraclass Correlation ◽  
Correlation Coefficients ◽  
Wisdom Of Crowds ◽  
Diverse Range ◽  
Intraclass Correlation Coefficients ◽  
Significant Difference ◽  
Assessment Procedures ◽  
The Impact

Objective This study combined a learning outcomes-based checklist and salient characteristics derived from wisdom-of-crowds theory to test whether differing groups of judges (diversity maximized versus expertise maximized) would be able to appropriately assess videotaped, manikin-based simulation scenarios. Methods Two groups of 3 judges scored 9 videos of interns managing a simulated cardiac event. The first group had a diverse range of knowledge of simulation procedures, while the second group was more homogeneous in their knowledge and had greater simulation expertise. All judges viewed 3 types of videos (predebriefing, postdebriefing, and 6 month follow-up) in a blinded fashion and provided their scores independently. Intraclass correlation coefficients (ICCs) were used to assess the reliability of judges as related to group membership. Scores from each group of judges were averaged to determine the impact of group on scores. Results Results revealed strong ICCs for both groups of judges (diverse, 0.89; expert, 0.97), with the diverse group of judges having a much wider 95% confidence interval for the ICC. Analysis of variance of the average checklist scores indicated no significant difference between the 2 groups of judges for any of the types of videotapes assessed (F = 0.72, p = .4094). There was, however, a statistically significant difference between the types of videos (F = 14.39, p = .0004), with higher scores at the postdebrief and 6-month follow-up time periods. Conclusions Results obtained in this study provide optimism for assessment procedures in simulation using learning outcomes-based checklists and a small panel of judges.

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