• Communications for Statistical Applications and Methods
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• v.29 no.5
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• pp.547-559
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• 2022

For a chi-squared test, which is a statistical method used to test the independence of a contingency table of two factors, the expected frequency of each cell must be greater than 5. The percentage of cells with an expected frequency below 5 must be less than 20% of all cells. However, there are many cases in which the regional expected frequency is below 5 in general small area studies. Even in large-scale surveys, it is difficult to forecast the expected frequency to be greater than 5 when there is small area estimation with subgroup analysis. Another statistical method to test independence is to use the Bayes factor, but since there is a high ratio of data dependency due to the nature of the Bayesian approach, the low expected frequency tends to decrease the precision of the test results. To overcome these limitations, we will borrow information from areas with similar characteristics and pool the data statistically to propose a pooled Bayes test of independence in target areas. Jo et al. (2021) suggested hierarchical Bayesian pooling models for small area estimation of categorical data, and we will introduce the pooled Bayes factors calculated by expanding their restricted pooling model. We applied the pooled Bayes factors using bone mineral density and body mass index data from the Third National Health and Nutrition Examination Survey conducted in the United States and compared them with chi-squared tests often used in tests of independence.

• Communications for Statistical Applications and Methods
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• v.28 no.5
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• pp.553-562
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• 2021

Directional regression (DR; Li and Wang, 2007) is well-known as an exhaustive sufficient dimension reduction method, and performs well in complex regression models to have linear and nonlinear trends. However, the extension of DR is not well-done upto date, so we will extend DR to accommodate multivariate regression and large p-small n regression. We propose three versions of DR for multivariate regression and discuss how DR is applicable for the latter regression case. Numerical studies confirm that DR is robust to the number of clusters and the choice of hierarchical-clustering or pooled DR.

• Journal of Pharmaceutical Investigation
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• v.30 no.3
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• pp.191-199
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• 2000

Genomics is providing targets faster than we can validate them and combinatorial chemistry is providing new chemical entities faster than we can screen them. Historically, the drug discovery cascade has been established as a sequential process initiated with a potency screening against a selected biological target. In this sequential process, pharmacokinetics was often regarded as a low-throughput activity. Typically, limited pharmacokinetics studies would be conducted prior to acceptance of a compound for safety evaluation and, as a result, compounds often failed to reach a clinical testing due to unfavorable pharmacokinetic characteristics. A new paradigm in drug discovery has emerged in which the entire sample collection is rapidly screened using robotized high-throughput assays at the outset of the program. Higher-throughput pharmacokinetics (HTPK) is being achieved through introduction of new techniques, including automation for sample preparation and new experimental approaches. A number of in vitro and in vivo methods are being developed for the HTPK. In vitro studies, in which many cell lines are used to screen absorption and metabolism, are generally faster than in vivo screening, and, in this sense, in vitro screening is often considered as a real HTPK. Despite the elegance of the in vitro models, however, in vivo screenings are always essential for the final confirmation. Among these in vivo methods, cassette dosing technique, is believed the methods that is applicable in the screening of pharmacokinetics of many compounds at a time. The widespread use of liquid chromatography (LC) interfaced to mass spectrometry (MS) or tandem mass spectrometry (MS/MS) allowed the feasibility of the cassette dosing technique. Another approach to increase the throughput of in vivo screening of pharmacokinetics is to reduce the number of sample analysis. Two common approaches are used for this purpose. First, samples from identical study designs but that contain different drug candidate can be pooled to produce single set of samples, thus, reducing sample to be analyzed. Second, for a single test compound, serial plasma samples can be pooled to produce a single composite sample for analysis. In this review, we validated the issue whether the second method can be applied to practical screening of in vivo pharmacokinetics using data from seven of our previous bioequivalence studies. For a given drug, equally spaced serial plasma samples were pooled to achieve a 'Pooled Concentration' for the drug. An area under the plasma drug concentration-time curve (AUC) was then calculated theoretically using the pooled concentration and the predicted AUC value was statistically compared with the traditionally calculated AUC value. The comparison revealed that the sample pooling method generated reasonably accurate AUC values when compared with those obtained by the traditional approach. It is especially noteworthy that the accuracy was obtained by the analysis of only one sample instead of analyses of a number of samples that necessitates a significant man-power and time. Thus, we propose the sample pooling method as an alternative to in vivo pharmacokinetic approach in the selection potential lead(s) from combinatorial libraries.

• Agribusiness and Information Management
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• v.8 no.2
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• pp.1-8
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• 2016

The present study has aimed at analyzing the technical and scale efficiencies of credit utilization by the farmer-borrowers in Chittoor district of Andhra Pradesh, India. DEA approach was followed to analyze the credit utilization efficiency and to analyze the factors influencing the credit utilization efficiency, log-linear regression analysis was attempted. DEA analysis revealed that, the number of farmers operating at CRS are more in number in marginal farms (40%) followed by other (35%) and small (17.5%) farms. Regarding the number of farmers operating at VRS, small farmers dominate the scenario with 72.5 per cent followed by other (67.5%) and marginal (42.5%) farmers. With reference to scale efficiency, marginal farmers are in majority (52.5%) followed by other (47.5%) and small (25%) farmers. At the pooled level, 26.7 per cent of the farmers are being operated at CRS, 63 per cent at VRS and 32.5 per cent of the farmers are either performed at the optimum scale or were close to the optimum scale (farms having scale efficiency values equal to or more than 0.90). Nearly 58, 15 and 28 percents of the farmers in the marginal farms category were found operating in the region of increasing, decreasing and constant returns respectively. Compared to marginal farmers category, there are less number of farmers operating at CRS both in small farmers category (15%) and other farmers category (22.5%). At the pooled level, only 5 per cent of the farmers are operating at DRS, majority of the farmers (73%) are operating at IRS and only 22 per cent of the farmers are operating at CRS indicating efficient utilization of credit. The log-linear regression model fitted to analyze the major determinants of credit utilization (technical) efficiency of farmer-borrowers revealed that, the three variables viz., cost of cultivation and family expenditure (both negatively influencing at 1% significant level) and family income (positively influencing at 1% significant level) are the major determinants of credit utilization efficiency across all the selected farmers categories and at pooled level. The analysis further indicate that, escalation in the cost of cultivation of crop enterprises in the region, rise in family expenditure and prior indebtedness of the farmers are showing adverse influence on the credit utilization efficiency of the farmer-borrowers.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.1147-1151
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• 2009

In this study, we address the problem of producing probability forecasts of summer seasonal rainfall, on the basis of Hindcast experiments from a ensemble of GCMs(cwb, gcps, gdaps, metri, msc_gem, msc_gm2, msc_gm3, msc_sef and ncep). An advanced Hierarchical Bayesian weighting scheme is developed and used to combine nine GCMs seasonal hindcast ensembles. Hindcast period is 23 years from 1981 to 2003. The simplest approach for combining GCM forecasts is to weight each model equally, and this approach is referred to as pooled ensemble. This study proposes a more complex approach which weights the models spatially and seasonally based on past model performance for rainfall. The Bayesian approach to multi-model combination of GCMs determines the relative weights of each GCM with climatology as the prior. The weights are chosen to maximize the likelihood score of the posterior probabilities. The individual GCM ensembles, simple poolings of three and six models, and the optimally combined multimodel ensemble are compared.

• Journal of Distribution Science
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• v.22 no.6
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• pp.1-10
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• 2024

Purpose: This study's objective is to examine the impact of firm-specific and macroeconomic factors on the business performance of non-cyclical and cyclical sectors in Indonesian listed firms. The evaluation of business performance holds paramount importance for the achievement and long-term viability of a company. Research Design Data and Methodology: The data for 61 non-cyclicals sector companies and 57 cyclicals sector companies was gathered over a 4-year period from 2018-2021. The model integrates firm size, leverage, and sales growth as firm-specific factors, with real GDP growth and inflation rate as macroeconomic variables. ROA and ROE are indicators of a firm's business performance. The regression models are estimated using the distribution of a dynamic approach with Arellano-Bond Panel Generalized Method of Moments (GMM) estimation. Results: The results of the pooled sample indicate that the historical ROA and ROE have a positive relationship with the business performance of all sectors, including both non-cyclical and cyclical industries. The ROE of non-cyclical enterprises is primarily influenced by firm-specific characteristics and macroeconomic influences. Conclusion: To ensure the successful implementation of the distribution of a dynamic approach towards enhancing corporate business performance, organizations need to take into account a combination of firm-specific factors and macroeconomic factors.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.11
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• pp.5375-5379
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• 2012

Introduction: Published studies on the association between Nijmegen breakage syndrome 1(NBS1) gene polymorphisms and breast cancer risk have been inconclusive, and a meta-analysis was therefore performed for clarification. Methods: Eligible articles were identified by a search of MEDLINE and EMBASE bibliographic databases for the period up to March 2012. The presence of between-study heterogeneity was investigated using the chi-square-based Cochran's Q statistic test. When there was statistical heterogeneity, the random effects model was chosen; otherwise, fixed effects estimates were reported as an alternative approach. Results: A total of 11 eligible articles (14 case-control studies) were identified, nine case-control studies were for the 657del5 mutation (7,534 breast cancer cases, 14,034 controls) and five case-control studies were for the I171V mutation (3,273 breast cancer cases, 4,004 controls). Our analysis results indicated that the 657del5 mutation was associated with breast cancer risk (carriers vs. non-carriers: pooled OR =2.63, 95% CI: 1.76-3.93), whereas the I171V mutation was not (carriers vs. non-carriers: pooled OR =1.52, 95% CI: 0.70-3.28). Conclusion: The present meta-analysis suggests that the 657del5 gene mutation in the NBS1 gene plays a role in breast cancer risk, while the I171V mutation does not exert a significant influence.

• Asian-Australasian Journal of Animal Sciences
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• v.19 no.11
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• pp.1519-1523
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• 2006

Through a screening and selection approach method, decamer random markers were used in a technique called random amplified polymorphic DNA (RAPD) assay with 252 genomic DNAs isolated from four major Haimen chicken populations: Rugao (62), Jiangchun (62), Wan-Nan (63) and Cshiqishi (65). A total of 3-score decamer random primers (S241-S260, S1081-S1100 and S1341-S1360) were employed in the preliminary RAPD-polymerase chain reaction (RAPD-PCR) assay with 50 random template DNA samples from all the populations. Four (6.67%) of the primers that produced obvious polymorphic patterns, interpretable and reproducible bands were selected and used with both the individual DNAs from each population and with pooled DNA samples of the four populations in subsequent analyses. The selected primers produced a total of 131 fragments with molecular size ranging from 835 to 4,972 base pairs (bp) when used with the individual DNAs; 105 (80.15%) of these fragments were polymorphic. With the pooled DNAs, 47 stable and characteristic bands with molecular size ranging from 840 to 4,983 bp, of which 23 (48.94%) polymorphic, were also generated. The band-sharing coefficient (BSC) calculated for the individuals in the population and among populations of bulked samples was between 0.8247 (Rugao) and 0.9500 (Cshiqishi); for pairwise populations, it was between 0.7273 (Rugao vs. Wan-Nan) and 0.9367 (Jiangchun vs. Cshiqishi) chicken populations. Using the BSC for individual and pairwise populations, the Nei's standard genetic distances between the chicken populations were determined and ranged from 0.0043 (Jiangchun vs. Cshiqishi) to 0.1375 (Rugao vs. Cshiqishi). The reconstructed dendrogram linked the Jiangchun and Cshiqishi chickens as closely related populations, followed by Wan-Nan, while the Rugao was the most genetically distant among the populations.

• Clinical and Experimental Pediatrics
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• v.62 no.10
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• pp.374-379
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• 2019

Background: Attention deficit hyperactivity disorder (ADHD) symptoms have a major impact on individuals, families, and society. Therefore identification risk factors of ADHD are a public health priority. Purpose: This is meta-analysis evaluated the association between maternal prepregnancy body mass index and the risk of ADHD among the resulting offspring. Methods: The search identified studies published through December 2018 in the PubMed, Web of Science, and Scopus databases. The odds ratios (ORs) or hazard ratios (HRs) with 95% confidence intervals (CI) extracted from eligible studies were used as the common measure of association among studies. Results: A significant association was found between overweight women and the risk of ADHD among children with the pooled HR and OR estimates (HR, 1.27 and 95% CI, 1.17-1.37; OR, 1.28 and 95% CI, 1.15-1.40, respectively). This association was significant between obese women and the risk of ADHD among children and adolescents with the pooled estimates of HR and OR (HR, 1.65 and 95% CI, 1.55-1.76; OR, 1.42 and 95% CI, 1.23-1.61). Conclusion: The current epidemiological studies present sufficient evidence that prepregnancy overweight and obesity are significantly associated with an increased risk of ADHD among children and adolescents. These findings provide a new approach to preventing ADHD by controlling weight gain in the prenatal period, which should be considered by policymakers.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.17
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• pp.7187-7193
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• 2014

Background: The aim of this meta-analysis was to compare sensitivities and specificities of fine needle aspiration (FNA) and core needle biopsy (CNB) in the diagnosis of thyroid cancer. Materials and Methods: Articles were screened in Medline, the Cochrane Library, EMBASE and Google Scholar, and subsequently included and excluded based on the patient/problem-intervention-comparison-outcome (PICO) principle. Primary outcome was defined in terms of diagnostic values (sensitivity and specificity) of FNA and CNB for thyroid cancer. Secondary outcome was defined as the accuracy of diagnosis. Compiled FNA and CNB results from the final studies selected as appropriate for meta-analysis were compared with cases for which final pathology diagnoses were available. Statistical analyses were performed for FNA and CNB for all of the selected studies together, and for individual studies using the leave-one-out approach. Results: Article selection and screening yielded five studies for meta-analysis, two of which were prospective and the other three retrospective, for a total of 1,264 patients. Pooled diagnostic sensitivities of FNA and CNB methods were 0.68 and 0.83, respectively, with specificities of 0.93 and 0.94. The areas under the summary ROC curves were 0.905 (${\pm}0.030$) for FNA and 0.745 (${\pm}0.095$) for CNB, with no significant difference between the two. No one study had greater influence than any other on the pooled estimates for diagnostic sensitivity and specificity. Conclusions: FNA and CNB do not differ significantly in sensitivity and specificity for diagnosis of thyroid cancer.