Personalized Evaluation Of Biomarker Value: A Cost-Benefit Perspective,
2014
Fred Hutchinson Cancer Research Center
Personalized Evaluation Of Biomarker Value: A Cost-Benefit Perspective, Ying Huang, Eric Laber
UW Biostatistics Working Paper Series
For a patient who is facing a treatment decision, the added value of information provided by a biomarker depends on the individual patient’s expected response to treatment with and without the biomarker, as well as his/her tolerance of disease and treatment harm. However, individualized estimators of the value of a biomarker are lacking. We propose a new graphical tool named the subject-specific expected benefit curve for quantifying the personalized value of a biomarker in aiding a treatment decision. We develop semiparametric estimators for two general settings: i) when biomarker data are available from a randomized trial; and ii) when biomarker …
Identifying Genetic Variants For Heart Rate Variability In The Acetylcholine Pathway,
2014
University of Groningen, Netherlands
Identifying Genetic Variants For Heart Rate Variability In The Acetylcholine Pathway, Harriëtte Riese, Loretto M. Muñoz, Catharina A. Hartman, Xiuhua Ding, Shaoyong Su, Albertine J. Oldehinkel, Arie M. Van Roon, Peter J. Van Der Most, Joop Lefrandt, Ron T. Gansevoort, Pim Van Der Harst, Niek Verweij, Carmilla M. M. Licht, Dorret I. Boomsma, Jouke-Jan Hottenga, Gonneke Willemsen, Brenda W. J. H. Penninx, Ilja M. Nolte, Eco J. C. De Geus, Xiaoling Wang, Harold Snieder
Biostatistics Faculty Publications
Heart rate variability is an important risk factor for cardiovascular disease and all-cause mortality. The acetylcholine pathway plays a key role in explaining heart rate variability in humans. We assessed whether 443 genotyped and imputed common genetic variants in eight key genes (CHAT, SLC18A3, SLC5A7, CHRNB4, CHRNA3, CHRNA, CHRM2 and ACHE) of the acetylcholine pathway were associated with variation in an established measure of heart rate variability reflecting parasympathetic control of the heart rhythm, the root mean square of successive differences (RMSSD) of normal RR intervals. The association was studied in a …
Oscillation Of Second-Order Emden–Fowler Neutral Delay Differential Equations,
2014
Missouri University of Science and Technology
Oscillation Of Second-Order Emden–Fowler Neutral Delay Differential Equations, Ravi P. Agarwal, Martin Bohner, Tongxing Li, Chenghui Zhang
Mathematics and Statistics Faculty Research & Creative Works
We establish some new criteria for the oscillation of second-order Emden–Fowler neutral delay differential equations. We study the case of super linear and the case of sublinear equations subject to various conditions. The results obtained show that the presence of a neutral term in a differential equation can cause or destroy oscillatory properties. Several examples are provided to illustrate the relevance of new theorems.
Cross-Design Synthesis For Extending The Applicability Of Trial Evidence When Treatment Effect Is Heterogeneous-I. Methodology,
2014
Division of Biostatistics and Bioinformatics, Department of Oncology, Johns Hopkins University
Cross-Design Synthesis For Extending The Applicability Of Trial Evidence When Treatment Effect Is Heterogeneous-I. Methodology, Ravi Varadhan, Carlos Weiss
Johns Hopkins University, Dept. of Biostatistics Working Papers
Randomized controlled trials (RCTs) provide reliable evidence for approval of new treatments, informing clinical practice, and coverage decisions. The participants in RCTs are often not a representative sample of the larger at-risk population. Hence it is argued that the average treatment effect from the trial is not generalizable to the larger at-risk population. An essential premise of this argument is that there is significant heterogeneity in the treatment effect (HTE). We present a new method to extrapolate the treatment effect from a trial to a target group that is inadequately represented in the trial, when HTE is present. Our method …
Cross-Design Synthesis For Extending The Applicability Of Trial Evidence When Treatment Effect Is Heterogeneous. Part Ii. Application And External Validation,
2014
Department of Family Medicine, Michigan State University
Cross-Design Synthesis For Extending The Applicability Of Trial Evidence When Treatment Effect Is Heterogeneous. Part Ii. Application And External Validation, Carlos Weiss, Ravi Varadhan
Johns Hopkins University, Dept. of Biostatistics Working Papers
Randomized controlled trials (RCTs) generally provide the most reliable evidence. When participants in RCTs are selected with respect to characteristics that are potential treatment effect modifiers, the average treatment effect from the trials may not be applicable to a specific target population. We present a new method to project the treatment effect from a RCT to a target group that is inadequately represented in the trial when there is heterogeneity in the treatment effect (HTE). The method integrates RCT and observational data through cross-design synthesis. An essential component is to identify HTE and a calibration factor for unmeasured confounding for …
Retained-Components Factor Transformation: Factor Loadings And Factor Score Predictors In The Column Space Of Retained Components,
2014
University of Bonn, Bonn, Germany
Retained-Components Factor Transformation: Factor Loadings And Factor Score Predictors In The Column Space Of Retained Components, André Beauducel, Frank Spohn
Journal of Modern Applied Statistical Methods
Factor loadings optimally account for the non-diagonal elements of the covariance matrix of observed variables. Principal component analysis leads to components accounting for a maximum of the variance of the observed variables. Retained-components factor transformation is proposed in order to combine the advantages of factor analysis and principal component analysis.
Pairwise Comparison In Repeated Measures,
2014
Nnamdi Azikiwe University, Awka, Nigeria
Pairwise Comparison In Repeated Measures, I.C.A. Oyeka, C. C. Nnanatu
Journal of Modern Applied Statistical Methods
Sometimes a random sample of subjects or patients may be exposed to a battery of diagnostic tests or medication over time and interest is on determining whether there is progressive remission of condition, disease or symptom. Also perhaps early in a program or experiment, subjects or candidates may be required to significantly improve in their performance rates at the current trial relative to an immediately preceding trial, otherwise they may have to withdraw from or drop out. The research interest would then be to determine some critical minimum marginal success rate to guide the management in decision making as well …
A Comparison Of Methods For Group Prediction With High Dimensional Data,
2014
Ball State University
A Comparison Of Methods For Group Prediction With High Dimensional Data, Holmes Finch
Journal of Modern Applied Statistical Methods
High dimensional data is the situation in which the number of variables included in an analysis approaches or exceeds the sample size. In the context of group classification, researchers are typically interested in finding a model that can be used to correctly place an individual into their appropriate group; e.g. correctly diagnose individuals with depression. However, when the size of the training sample is small and the number of predictors used to differentiate the groups is larger, standard approaches such as discriminant analysis may not work well. In order to address this issue, statisticians have developed a number of tools …
Objective Priors For Estimation Of Extended Exponential Geometric Distribution,
2014
Universidade de São Paulo, São Paulo, Brazil
Objective Priors For Estimation Of Extended Exponential Geometric Distribution, Pedro L. Ramos, Fernando A. Moala, Jorge A. Achcar
Journal of Modern Applied Statistical Methods
A Bayesian analysis was developed with different noninformative prior distributions such as Jeffreys, Maximal Data Information, and Reference. The aim was to investigate the effects of each prior distribution on the posterior estimates of the parameters of the extended exponential geometric distribution, based on simulated data and a real application.
Some General Guidelines For Choosing Missing Data Handling Methods In Educational Research,
2014
University of Illinois at Urbana-Champaign
Some General Guidelines For Choosing Missing Data Handling Methods In Educational Research, Jehanzeb R. Cheema
Journal of Modern Applied Statistical Methods
The effect of a number of factors, such as the choice of analytical method, the handling method for missing data, sample size, and proportion of missing data, were examined to evaluate the effect of missing data treatment on accuracy of estimation. A methodological approach involving simulated data was adopted. One outcome of the statistical analyses undertaken in this study is the formulation of easy-to-implement guidelines for educational researchers that allows one to choose one of the following factors when all others are given: sample size, proportion of missing data in the sample, method of analysis, and missing data handling method.
Bayesian Inference For Volatility Of Stock Prices,
2014
Mangalore University, Mangalagangorthri, Karnataka, India
Bayesian Inference For Volatility Of Stock Prices, Juliet G. D'Cunha, K. A. Rao
Journal of Modern Applied Statistical Methods
Lognormal distribution is widely used in the analysis of failure time data and stock prices. Maximum likelihood and Bayes estimator of the coefficient of variation of lognormal distribution along with confidence/credible intervals are developed. The utility of Bayes procedure is illustrated by analyzing prices of selected stocks.
Local Bandwidths For Improving Performance Statistics Of Model-Robust Regression 2,
2014
University of Benin, Benin City, Nigeria
Local Bandwidths For Improving Performance Statistics Of Model-Robust Regression 2, Efosa Edionwe, Julian L. Mbegbu
Journal of Modern Applied Statistical Methods
Model-Robust Regression 2 (MRR2) method is a semi-parametric regression approach that combines parametric and nonparametric fits. The bandwidth controls the smoothness of the nonparametric portion. We present a methodology for deriving data-driven local bandwidth that enhances the performance of MRR2 method for fitting curves to data generated from designed experiments.
Contrast Of Bayesian And Classical Sample Size Determination,
2014
University of Dhaka, Dhaka, Bangladesh
Contrast Of Bayesian And Classical Sample Size Determination, Farhana Sadia, Syed S. Hossain
Journal of Modern Applied Statistical Methods
Sample size determination is a prerequisite for statistical surveys. A comprehensive overview of the Bayesian approach for computation of the sample size, and a comparison with classical approaches, is presented. Two surveys are taken as example to illustrate the accuracy and efficiency of each approach, and to make recommendations about which method is preferred. The Bayesian approach of sample size determination may require fewer subjects if proper prior information is available.
The Information Criterion,
2014
Department of Statistics, Payam Noor University, Iran
The Information Criterion, Masume Ghahramani
Journal of Modern Applied Statistical Methods
The Akaike information criterion, AIC, is widely used for model selection. Using the AIC as the estimator of asymptotic unbias for the second term Kullbake-Leibler risk considers the divergence between the true model and offered models. However, it is an inconsistent estimator. A proposed approach the problem is the use of A'IC, a consistently offered information criterion. Model selection of classic and linear models are considered by a Monte Carlo simulation.
Gumbel-Weibull Distribution: Properties And Applications,
2014
Marshall University
Gumbel-Weibull Distribution: Properties And Applications, Raid Al-Aqtash, Carl Lee, Felix Famoye
Journal of Modern Applied Statistical Methods
Some properties of the Gumbel-Weibull distribution including the mean deviations and modes are studied. A detailed discussion of regions of unimodality and bimodality is given. The method of maximum likelihood is proposed for estimating the distribution parameters and a simulation is conducted to study the performance of the method. Three tests are given for testing the significance of a distribution parameter. The applications of Gumbel-Weibull distribution are emphasized. Five data sets are used to illustrate the flexibility of the distribution in fitting unimodal and bimodal data sets.
Fitting Stereotype Logistic Regression Models For Ordinal Response Variables In Educational Research (Stata),
2014
Eastern Connecticut State University
Fitting Stereotype Logistic Regression Models For Ordinal Response Variables In Educational Research (Stata), Xing Liu
Journal of Modern Applied Statistical Methods
The stereotype logistic (SL) model is an alternative to the proportional odds (PO) model for ordinal response variables when the proportional odds assumption is violated. This model seems to be underutilized. One major reason is the constraint of current statistical software packages. Statistical Package for the Social Sciences (SPSS) cannot perform the SL regression analysis, and SAS does not have the procedure developed to directly estimate the model. The purpose of this article was to illustrate the stereotype logistic (SL) regression model, and apply it to estimate mathematics proficiency level of high school students using Stata. In addition, it compared …
Optimal Location Design For Prediction Of Spatial Correlated Environmental Functional Data,
2014
Shahid Chamran University, Iran
Optimal Location Design For Prediction Of Spatial Correlated Environmental Functional Data, Mahdi Rasekhi, B. Jamshidi, F. Rivaz
Journal of Modern Applied Statistical Methods
The optimal choice of sites to make spatial prediction is critical for a better understanding of really spatio-temporal data. It is important to obtain the essential spatio-temporal variability of the process in determining optimal design, because these data tend to exhibit both spatial and temporal variability. Two new methods of prediction for spatially correlated functional data are considered. The first method models spatial dependency by fitting variogram to empirical variogram, similar to ordinary kriging (univariate approach). The second method models spatial dependency by linear model co-regionalization (multivariate approach). The variance of prediction method was chosen as the optimization design criterion. …
Missing Data And The Statistical Modeling Of Adolescent Pregnancy,
2014
Texas A&M University
Missing Data And The Statistical Modeling Of Adolescent Pregnancy, Dudley L. Poston Dr., Eugenia Conde Dr.
Journal of Modern Applied Statistical Methods
Missing data is a pervasive problem in social science research. Many techniques have been developed to handle the problem. Different ways of handling missing data were shown to lead to different results in statistical models. A demonstration was given based on statistical modeling of the likelihood of a woman reporting having had an adolescent pregnancy by handling missing data with several different approaches. Results indicate that many of the independent variables in the model vary in whether they are, or are not, statistically significant in predicting the log odds of a woman having a teen pregnancy, and in the ranking …
Conover’S F Test As An Alternative To Durbin’S Test,
2014
University of Newcastle
Conover’S F Test As An Alternative To Durbin’S Test, Donald J. Best, John Charles Rayner
Journal of Modern Applied Statistical Methods
Data consisting of ranks within blocks are considered for balanced incomplete block designs. An F test statistic from ANOVA is better approximated by an F distribution than the Durbin statistic is approximated by a chi-squared distribution. Indicative powers demonstrate that the F test is generally superior to Durbin’s test.
A Bivariate Distribution With Conditional Gamma And Its Multivariate Form,
2014
Old Dominion University
A Bivariate Distribution With Conditional Gamma And Its Multivariate Form, Sumen Sen, Rajan Lamichhane, Norou Diawara
Journal of Modern Applied Statistical Methods
A bivariate distribution whose marginal are gamma and beta prime distribution is introduced. The distribution is derived and the generation of such bivariate sample is shown. Extension of the results are given in the multivariate case under a joint independent component analysis method. Simulated applications are given and they show consistency of our approach. Estimation procedures for the bivariate case are provided.
