Joinpoint regression
Colorectal cancer mortality trends in Serbia during 1991–2010: an age-period-cohort analysis and a joinpoint regression analysis
... 2 Joinpoint regression analysis of colorectal cancer mortality in Serbia (by age) during the period 1991–2010, with data for the beginning and the end of the study ...
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Geographical, temporal and racial disparities in late-stage prostate cancer incidence across Florida: A multiscale joinpoint regression analysis
... the joinpoint corresponding to the first significant decline in proportion of late-stage diagnosis ...from joinpoint regression of raw rates and noise-filtered rates are relatively similar: the ...
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Joinpoint regression analysis with time-on-study as time-scale. Application to three Italian population-based cohort studies
... Joinpoint regression analysis may be used when the temporal trend of a given quantity, like incidence, prevalence and mortality ...begins). Joinpoint regression analysis can be therefore ...
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Temporal trends in motor vehicle fatalities in the United States, 1968 to 2010 - a joinpoint regression analysis
... is joinpoint regression. Joinpoint regression identifies the points (years) in which there were statisti- cally significant differences in the trend of a chosen out- come and quantifies the ...
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Comparing trends in mortality from cardiovascular disease and cancer in the United Kingdom, 1983–2013: joinpoint regression analysis
... men. Joinpoint regression analysis indicated that trends of decreasing rates of CVD have increased over time while decreasing trends in cancer mortality rates have slowed down since the ...
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Cancer mortality in Serbia, 1991–2015: an age-period-cohort and joinpoint regression analysis
... point regression analysis. The Joinpoint Regression Pro- gram (version ...[13]. Joinpoint analysis was used to identify the best-fit- ting point, where a statistically significant change ...
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A study of fertility trend in the urban and rural areas of Sabzevar during 2002 to 2014: Joinpoint Regression Analysis
... According to the results of this study, in the urban and rural areas of Sabzevar, fertility is higher than replacement level, but in urban and rural areas of Sabzevar, the highest inc[r] ...
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Airborne Endotoxin from Indoor and Outdoor Environments: Effects of Sample Dilution on the Kinetic Limulus Amebocyte Lysate (LAL) Assay.
... a joinpoint regression model, it was determined that a dilution factor of 50 to 100 was generally sufficient to eliminate the presence of enhancing ...
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The burden of cancers associated with HIV in the South African public health sector, 2004–2014: a record linkage study
... Introduction: The impact of South Africa ’ s high human immunodeficiency virus (HIV) burden on cancer risk is not fully understood, particularly in the context of antiretroviral treatment (ART) availability. We examined ...
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Trend analysis of “garbage codes” as the underlying cause of cancer death: Joinpoint Analysis
... Trend of the percentage of the “garbage codes” with corresponding 95% confidence intervals (CI) were calculated by performing Joinpoint regression analyses �20�. For regression analyses, ...
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Using Hashimoto thyroiditis as gold standard to determine the upper limit value of thyroid stimulating hormone in a Chinese cohort
... Whereas we expect the normal range as a tool to screen out abnormal individuals. We tried an alternative approach to exclude the influence to TSH value con- founded by individuals with HT. Based on the fact that TSH and ...
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Original Article Trends in the incidence and mortality of cutaneous melanoma in Hong Kong between 1983 and 2015
... Abstract: The rapid growth of cutaneous melanoma (CM) has been observed globally, especially in the White popu- lation. However, due to the low incidence and mortality rates, epidemiological analyses for the CM in the ...
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High quantile regression for extreme events
... quantile regression method, and give its good asymptotic properties for any uniformly bounded positive weight independent of response variable y, with conditional density as the ...three regression methods: ...
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A Machine Learning Approach to Forecast Bitcoin Prices
... Lasso Regression algorithm introduces the L1 regularization term that plays a vital role in feature ...Lasso Regression evaluation metrics and results are tabulated in Table ...
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Robust Regression Diagnostics of Influential Observations in Linear Regression Model
... From all the influential points identified by Cook’s D and DFFITS statistics respectively, DFBETAS based on OLS revealed that cases 15 and 16 affect the regression coefficients.. The rob[r] ...
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<p>Identification of Novel lncRNA Markers in Glioblastoma Multiforme and Their Clinical Significance: A Study Based on Multiple Sequencing Data</p>
... patient outcomes. We selected the top ten survival asso- ciated lncRNAs as candidates for our model. To exclude any lncRNAs that may not be independent factor in our risk predictor, lasso regression (Figure 4B) ...
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A Comparison Study of Ridge Regression and Principle Component Regression with Application
... The purpose of this paper is to discuss the multicollinearity problem in regression models and presents some typical ways of handling the collinearity problem. In Addition, the paper attempts to compare RR , and ...
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The CAPTAIN Toolbox for System Identification, Time Series Analysis, Forecasting and Control:Getting Started Guide
... dynamic regression, dar and darsp are instead useful for evaluating changing signal spectra and time-frequency analysis based on DAR models, since they provide the AR spectrum at each point in time based on the ...
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The CAPTAIN Toolbox for System Identification, Time Series Analysis, Forecasting and Control: Guide to TVPMOD: Time Variable Parameter Models
... Sequential Spectral Decomposition, reserved for a certain class of unobserved components models, i.e. Trend plus Auto-Regression (AR) also discussed in Chapter 3. This approach consists of decomposing the ...
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Predicting Movie Success Based On Imdb Data
... to regression problems [6]. SVM Regression tries to find a function f(x) that has at most deviation from the actually obtained targets y for all the training data, and at the same time is as flat as ...SVM ...
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