scholarly journals Augmenting disease maps: a Bayesian meta-analysis approach

2020 ◽  
Vol 7 (8) ◽  
pp. 192151 ◽  
Author(s):  
Farzana Jahan ◽  
Earl W. Duncan ◽  
Susanna M. Cramb ◽  
Peter D. Baade ◽  
Kerrie L. Mengersen
Keyword(s):  
Cancer Incidence ◽  
Meta Analysis ◽  
Analysis Model ◽  
Small Areas ◽  
Unit Level ◽  
Level Data ◽  
Proposed Model ◽  
Small Area Level ◽  
Urban Rural ◽  
Analysis Models

Analysis of spatial patterns of disease is a significant field of research. However, access to unit-level disease data can be difficult for privacy and other reasons. As a consequence, estimates of interest are often published at the small area level as disease maps. This motivates the development of methods for analysis of these ecological estimates directly. Such analyses can widen the scope of research by drawing more insights from published disease maps or atlases. The present study proposes a hierarchical Bayesian meta-analysis model that analyses the point and interval estimates from an online atlas. The proposed model is illustrated by modelling the published cancer incidence estimates available as part of the online Australian Cancer Atlas (ACA). The proposed model aims to reveal patterns of cancer incidence for the 20 cancers included in ACA in major cities, regional and remote areas. The model results are validated using the observed areal data created from unit-level data on cancer incidence in each of 2148 small areas. It is found that the meta-analysis models can generate similar patterns of cancer incidence based on urban/rural status of small areas compared with those already known or revealed by the analysis of observed data. The proposed approach can be generalized to other online disease maps and atlases.

scholarly journals Multivariate Bayesian meta-analysis: joint modelling of multiple cancer types using summary statistics

2020 ◽  
Author(s):  
Farzana Jahan ◽  
Earl W Duncan ◽  
Susanna M Cramb ◽  
Peter D Baade ◽  
Kerrie Lee Mengersen
Keyword(s):  
Cancer Incidence ◽  
Meta Analysis ◽  
Analysis Model ◽  
Multiple Cancer ◽  
Small Areas ◽  
Multiple Cancers ◽  
Record Data ◽  
Cancer Types ◽  
Privacy And Confidentiality ◽  
Analysis Models

Abstract Background: Cancer atlases often provide estimates of cancer incidence, mortality or survival across small areas of a region or country. A recent example of a cancer atlas is the Australian cancer atlas (ACA), that provides interactive maps to visualise spatially smoothed estimates of cancer incidence and survival for 20 different cancers over 2148 small areas across Australia. Methods: The present study proposes a multivariate Bayesian meta-analysis model, which can model multiple cancers jointly using summary measures without requiring access to the unit record data. This new approach is illustrated by modelling the publicly available spatially smoothed standard incidence ratios (SIR) for multiple cancers in the ACA divided into three groups: common, rare/less common and smoking-related. The multivariate Bayesian meta-analysis models are fitted to each group in order to explore any possible association between the cancers in three remoteness regions: major cities, regional and remote areas across Australia. Results: Substantive correlation was observed among some cancer types. There was evidence that the magnitude of this correlation varied according to remoteness of a region. High risk areas for specific combinations of cancer types were identified and visualised from the proposed model. Conclusions: Publicly available spatially smoothed disease estimates can be used to explore additional research questions by modelling multiple cancer types jointly. These proposed multivariate meta-analysis models could be useful when unit record data are unavailable because of privacy and confidentiality requirements.

scholarly journals Multivariate Bayesian meta-analysis: joint modelling of multiple cancer types using summary statistics

2020 ◽  
Vol 19 (1) ◽  
Author(s):  
Farzana Jahan ◽  
Earl W. Duncan ◽  
Susana M. Cramb ◽  
Peter D. Baade ◽  
Kerrie L. Mengersen
Keyword(s):  
Cancer Incidence ◽  
Meta Analysis ◽  
Multiple Cancer ◽  
Correlation Matrices ◽  
Small Areas ◽  
Remote Areas ◽  
Multiple Cancers ◽  
Record Data ◽  
Cancer Types ◽  
Analysis Models

Abstract Background Cancer atlases often provide estimates of cancer incidence, mortality or survival across small areas of a region or country. A recent example of a cancer atlas is the Australian cancer atlas (ACA), that provides interactive maps to visualise spatially smoothed estimates of cancer incidence and survival for 20 different cancer types over 2148 small areas across Australia. Methods The present study proposes a multivariate Bayesian meta-analysis model, which can model multiple cancers jointly using summary measures without requiring access to the unit record data. This new approach is illustrated by modelling the publicly available spatially smoothed standardised incidence ratios for multiple cancers in the ACA divided into three groups: common, rare/less common and smoking-related. The multivariate Bayesian meta-analysis models are fitted to each group in order to explore any possible association between the cancers in three remoteness regions: major cities, regional and remote areas across Australia. The correlation between the pairs of cancers included in each multivariate model for a group was examined by computing the posterior correlation matrix for each cancer group in each region. The posterior correlation matrices in different remoteness regions were compared using Jennrich’s test of equality of correlation matrices (Jennrich in J Am Stat Assoc. 1970;65(330):904–12. 10.1080/01621459.1970.10481133). Results Substantive correlation was observed among some cancer types. There was evidence that the magnitude of this correlation varied according to remoteness of a region. For example, there has been significant negative correlation between prostate and lung cancer in major cities, but zero correlation found in regional and remote areas for the same pair of cancer types. High risk areas for specific combinations of cancer types were identified and visualised from the proposed model. Conclusions Publicly available spatially smoothed disease estimates can be used to explore additional research questions by modelling multiple cancer types jointly. These proposed multivariate meta-analysis models could be useful when unit record data are unavailable because of privacy and confidentiality requirements.

scholarly journals Diabetes mellitus is not a risk factor for osteoarthritis

RMD Open ◽  
2020 ◽  
Vol 6 (1) ◽  
pp. e001030 ◽  
Author(s):  
Andrew Khor ◽  
Cheryl-Ann Ma ◽  
Cassandra Hong ◽  
Laura Li-Yao Hui ◽  
Ying Ying Leung
Keyword(s):  
Diabetes Mellitus ◽  
Risk Factor ◽  
Web Of Science ◽  
Confounding Factor ◽  
Meta Analysis ◽  
Analysis Model ◽  
Cross Sectional ◽  
Increased Risk ◽  
Analysis Models ◽  
Important Confounding Factor

BackgroundAssociation between diabetes mellitus (DM) and risk of osteoarthritis (OA) can be confounded by body mass index (BMI), a strong risk factor for both conditions. We evaluate the association between DM or hyperglycaemia with OA using systematic review and meta-analysis.MethodsWe searched PubMed and Web of Science databases in English for studies that gave information on the association between DM and OA. Two meta-analysis models were conducted to address: (1) risk of DM comparing subjects with and without OA and (2) risk of OA comparing subjects with and without DM. As far as available, risk estimates that adjusted for BMI were used.Results31 studies with a pooled population size of 295 100 subjects were reviewed. 16 and 15 studies reported positive associations and null/ negative associations between DM and OA. 68.8% of positive studies had adjusted for BMI, compared with 93.3% of null/negative studies. In meta-analysis model 1, there was an increase prevalence of DM in subjects with OA compared with those without (OR 1.56, 95% CI 1.28 to 1.89). In meta-analysis model 2, there was no increased risk of OA (OR 1.14, 95% CI 0.98 to 1.33) in subjects with DM compared with those without, regardless of gender and OA sites. Comparing subjects with DM to those without, an increased risk of OA was noted in cross-sectional studies, but not in case-control and prospective cohort studies.ConclusionsThis meta-analysis does not support DM as an independent risk factor for OA. BMI was probably the most important confounding factor.

Random or Fixed? An Empirical Examination of Meta-Analysis Model Choices

2018 ◽  
Vol 22 (3) ◽  
pp. 290-304 ◽  
Author(s):  
Kathleen L. Slaney ◽  
Donna Tafreshi ◽  
Richard Hohn
Keyword(s):  
Empirical Investigation ◽  
Meta Analysis ◽  
High Rate ◽  
Model Choice ◽  
Analysis Model ◽  
Specific Context ◽  
Empirical Examination ◽  
Two Samples ◽  
Meta Analyses ◽  
Analysis Models

When conducting meta-analyses, researchers must make decisions about which statistical model is most appropriate for the specific context and aims of the meta-analysis. Although there are several meta-analysis models, most researchers choose between two general models: fixed-effect (FE) and random-effects (RE). Yet, the basis on which these two general models are distinguished and of when it is appropriate to use one or the other varies in the methodological literature. Although model-to-inference inconsistencies have been previously noted, there has been little empirical investigation of whether, and to what extent, the varying conceptualizations of the distinctions between FE and RE models are reflected in published meta-analyses. The present study explores whether conceptualizations of model distinctions among psychological researchers are consistent with those in the methods literature. We also examine model choices and rationales given by psychological researchers in two samples of published meta-analyses in psychology-related journals. We identify four primary categories for distinguishing between FE and RE models, only two of which were predominant in our samples. Although model choice appears to be reported at a moderately high rate, many researchers continue not to provide explicit rationales for their model choices or do not clearly tie model choices to the specific research aims of the meta-analyses. Implications of these findings are discussed.

scholarly journals Applied Comparison of Meta-analysis Techniques

10.36469/9848 ◽  
2013 ◽  
Vol 1 (1) ◽  
pp. 14-22
Author(s):  
Li Wang ◽  
Colin Lewis-Beck ◽  
Elyse Fritschel ◽  
Erdem Baser ◽  
Onur Baser
Keyword(s):  
Random Effects ◽  
Fixed Effects ◽  
Meta Analysis ◽  
Fixed Effects Model ◽  
Analysis Techniques ◽  
Level Data ◽  
Study Results ◽  
Meta Regression ◽  
Treatment Comparisons ◽  
Analysis Models

Background: Meta-analysis is an approach that combines findings from similar studies. The aggregation of study level data can provide precise estimates for outcomes of interest, allow for unique treatment comparisons, and explain the differences arising from conflicting study results. Proper meta-analysis includes five basic steps: identify relevant studies; extract summary data from each paper; compute study effect sizes, perform statistical analysis; and interpret and report the results. Objectives: This study aims to review meta-analysis methods and their assumptions, apply various meta-techniques to empirical data, and compare the results from each method. Methods: Three different meta-analysis techniques were applied to a dataset looking at the effects of the bacille Calmette-Guerin (BCG) vaccine on tuberculosis (TB). First, a fixed-effects model was applied; then a random-effects model; and third meta-regression with study-level covariates were added to the model. Overall and stratified results, by geographic latitude were reported. Results: All three techniques showed a statistically significant effects from the vaccination. However, once covariates were added, efficacy diminished. Independent variables, such as the latitude of the location in which the study was performed, appeared to be partially driving the results. Conclusions: Meta-analysis is useful for drawing general conclusions from a variety of studies. However, proper study and model selection are important to ensure the correct interpretation of results. Basic meta-analysis models are fixed-effects, random-effects, and meta-regression.

scholarly journals A Weighted Additive Model for Comparative Analysis on Demand Factors among Multiple Seasonal Products: Nationwide Demand of Japanese Alcoholic Beverages

2020 ◽  
Vol 02 (01) ◽  
pp. 42-62
Author(s):  
Tsuyoshi Kurihara ◽  
Takaaki Kawanaka ◽  
Hiroshi Yamashita
Keyword(s):  
Comparative Analysis ◽  
Additive Model ◽  
Alcoholic Beverages ◽  
Analysis Model ◽  
Climate Fluctuations ◽  
Proposed Model ◽  
Public Data ◽  
Two Factors ◽  
Analysis Models ◽  
Seasonal Products

The climate has recently been fluctuating globally. Such climate, which affects the demand for seasonal products, varies depending on geographical conditions. However, analysis models of previous studies on nationwide demand for seasonal products have not considered differences in climate attributed to different regions in the country. Therefore, in this study, focusing on the relationship between climate and region, we extract common characteristics on the nationwide demand factors for a seasonal product group (Japanese alcoholic beverages). We propose a new demand analysis model that enables a comparative analysis among multiple items, more accurately, cross-sectionally, and concisely by using two independent factors: meteorological factors by item and regional weights (demand). We develop a new algorithm using alternating least squares for the estimation problem of inseparable parameters generated by expressing these two factors in the form of products. The validity and effectiveness of the proposed model and algorithm were confirmed by empirical analysis using public data. This makes it possible to theoretically consider regional differences (climate and demand) for nationwide demand of seasonal products. Consequently, the proposed model can be used to conduct multifaceted analysis to situation changes such as climate fluctuations to realize effective sales and operation planning of seasonal products.

scholarly journals A GIS-Based Spatial Analysis Model Approach for Identification of Optimal Hydrotechnical Solutions for Gully Erosion Stabilization. Case Study

2021 ◽  
Vol 11 (11) ◽  
pp. 4847
Author(s):  
Ștefan Bilașco ◽  
Sanda Roșca ◽  
Iuliu Vescan ◽  
Ioan Fodorean ◽  
Vasile Dohotar ◽  
...  
Keyword(s):  
Spatial Analysis ◽  
Spatial Databases ◽  
Analysis Model ◽  
Agricultural Lands ◽  
Erosion Processes ◽  
Impact Processes ◽  
Proposed Model ◽  
Analysis Models ◽  
The Impact

The accentuated degradation of agricultural lands as a result of deep erosion processes is the main problem identified in abandoned agricultural lands under the rainfall intensities, increasing number of hot days, indirectly under the impact processes derived from them (soil erosion, vegetation drying, etc.), as well as inadequate or poor management policies implemented by local authorities. The present study aims to develop and present a methodology based on GIS spatial analysis to choose the best hydro-amelioration solution for the arrangement of a complex ravine that negatively affects the entire agroecological area in its immediate vicinity. The proposed model is developed on spatial databases obtained based on UAV flights, the simulation of flow rate values and the establishment of three hydraulic analysis models through the HEC-RAS software with the main purpose of evaluating the results and databases, in order to identify the best implementing model for the stabilization and reduction in erosion within the analysed area. The comparative analysis of the three analysed scenarios highlighted the fact that a dam-type structure with overflow represents the best hydro-ameliorative solution to be implemented in the present study. The accuracy of the obtained results highlights the usefulness of developing GIS models of transdisciplinary spatial analysis to identify optimal solutions that can be implemented in territories with similar characteristics.

ANALISIS MODEL KEBUTUHAN PERGERAKAN PENUMPANG DAN BARANG BANDARA RAHADI OESMAN KETAPANG

2012 ◽  
Vol 10 (1) ◽  
Author(s):  
. Elsa Trimukti
Keyword(s):  
Growth Rate ◽  
Trend Analysis ◽  
Polynomial Regression ◽  
Air Transport ◽  
Trip Generation ◽  
Analysis Model ◽  
Exponential Regression ◽  
Share Model ◽  
Strategic Role ◽  
Analysis Models

Airport of Rahadi Oesman in Kabupaten Ketapang Kalimantan Barat represent the main and important gate for air transport in Kabupaten Ketapang, where this airport own the strategic role in service activities of this transportation even for domestic transportation or regional. Activity in Airport of Rahadi Oesman in a few this the last year has growth so fast growth, so that felt the infrastructure and also available facility in this time have is not adequate again to support the growth rate of air traffic in this airport. In the plan development of facility of air side and also land side of the airport require to be conducted an analysis model of trip generation or attraction of passenger and goods. These models need for the prediction of mount the growth of passenger and goods/cargo and estimate the amount of passenger and aircraft movement in the future pursuant to aircraft characteristic that to be used. The models used for prediction of passenger and goods in this study are Trend Analysis Models consisted of linear regression trend method, exponential regression trend method, and polynomial regression trend method. Besides model of trend analysis, in this study also analyzed Market Share Model. Result from third model then compared to one another to obtain the most appropriate model. Pursuant to analyses result obtained that the best or most appropriate model is Model of Trend Analysis.Model for the attraction passenger is Y = 21,18X2+ 6181X + 5788 by R2= 0,922.Model for the generation passenger is Y = 128,3X2+ 7515X + 4965 by R2= 0,907.Model for the passenger of transit is Y = 795X2+ 561X + 3361 by R2= 1Model for the cargo movement is Y = 2468X2+ 41054X 28341 by R2= 0,918.

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