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- 0.632 resampling; Bootstrap; K-fold cross-validation; Model and variable selections; Perturbation-resampling; Prediction (1)
- ANCOVA; cross validation; efficiency augmentation; Mayo PBC data; semi-parametric efficiency (1)
- Asymptotics; Augmented kernel estimating equations; Double robustness; Efficiency; Inverse probability weighted kernel estimating equations; Kernel smoothing (1)
- Attributable Proportion (1)
- BLUPs; Kernel function; Model/variable selection; Nonparametric regression; Penalized likelihood; REML; Score test; Smoothing parameter; Support vector machines (1)
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- Backfitting algorithm; CAR model; collapsibility; epidemiology; Gauss-Seidel algorithm; iterative weighted least squares algorithm (1)
- Cardiovascular diseases; Cox's model; nonparametric functional estimation; risk index; ROC analysis; survival analysis (1)
- Coronary heart disease; nonparametric functional estimation; risk factors/markers; pointwise and simultaneous confidence interval; subgroup analysis (1)
- Cox's model; Nonparametric function estimation; Personalized medicine; Perturbation-resampling method; Stratified medicine; Subgroup analysis; Survival analysis (1)
- Cross-training-evaluation; Personalized medicine; Prediction; Stratified medicine; Subgroup analysis; Variable selection. (1)
- Cross-validation; HIV-infection; Nonparametric function estimation; Personalized medicine; Subgroup analysis (1)
- Cumulative Residual (1)
- Diagnostic Accuracy (1)
- Direct Effects (1)
- Effect Modification (1)
- Estimating equation; proportional hazards model; proportional odds model; right censoring; transformation model (1)
- Generalized Linear Model (1)
- Genetics (1)
- Heterogeneous regression; K-fold cross validation; misspecified regression model; optimal prediction region; prediction error rate (1)
- Interaction (1)
- MCMC; air pollution; spatio-temporal models; predictions; penalised splines (1)
- Model Checking (1)
- Principled sure independence screening; Multiple myeloma; Variable selection; Sure independence screening; Cox model; Ultra-high-dimensional covariates (1)
- ROC Regression (1)
Articles 1 - 18 of 18
Full-Text Articles in Statistical Models
Attributing Effects To Interactions, Tyler J. Vanderweele, Eric J. Tchetgen Tchetgen
Attributing Effects To Interactions, Tyler J. Vanderweele, Eric J. Tchetgen Tchetgen
Harvard University Biostatistics Working Paper Series
A framework is presented which allows an investigator to estimate the portion of the effect of one exposure that is attributable to an interaction with a second exposure. We show that when the two exposures are independent, the total effect of one exposure can be decomposed into a conditional effect of that exposure and a component due to interaction. The decomposition applies on difference or ratio scales. We discuss how the components can be estimated using standard regression models, and how these components can be used to evaluate the proportion of the total effect of the primary exposure attributable to …
Effectively Selecting A Target Population For A Future Comparative Study, Lihui Zhao, Lu Tian, Tianxi Cai, Brian Claggett, L. J. Wei
Effectively Selecting A Target Population For A Future Comparative Study, Lihui Zhao, Lu Tian, Tianxi Cai, Brian Claggett, L. J. Wei
Harvard University Biostatistics Working Paper Series
When comparing a new treatment with a control in a randomized clinical study, the treatment effect is generally assessed by evaluating a summary measure over a specific study population. The success of the trial heavily depends on the choice of such a population. In this paper, we show a systematic, effective way to identify a promising population, for which the new treatment is expected to have a desired benefit, using the data from a current study involving similar comparator treatments. Specifically, with the existing data we first create a parametric scoring system using multiple covariates to estimate subject-specific treatment differences. …
On The Covariate-Adjusted Estimation For An Overall Treatment Difference With Data From A Randomized Comparative Clinical Trial, Lu Tian, Tianxi Cai, Lihui Zhao, L. J. Wei
On The Covariate-Adjusted Estimation For An Overall Treatment Difference With Data From A Randomized Comparative Clinical Trial, Lu Tian, Tianxi Cai, Lihui Zhao, L. J. Wei
Harvard University Biostatistics Working Paper Series
No abstract provided.
Stratifying Subjects For Treatment Selection With Censored Event Time Data From A Comparative Study, Lihui Zhao, Tianxi Cai, Lu Tian, Hajime Uno, Scott D. Solomon, L. J. Wei
Stratifying Subjects For Treatment Selection With Censored Event Time Data From A Comparative Study, Lihui Zhao, Tianxi Cai, Lu Tian, Hajime Uno, Scott D. Solomon, L. J. Wei
Harvard University Biostatistics Working Paper Series
No abstract provided.
Principled Sure Independence Screening For Cox Models With Ultra-High-Dimensional Covariates, Sihai Dave Zhao, Yi Li
Principled Sure Independence Screening For Cox Models With Ultra-High-Dimensional Covariates, Sihai Dave Zhao, Yi Li
Harvard University Biostatistics Working Paper Series
No abstract provided.
Nonparametric Regression With Missing Outcomes Using Weighted Kernel Estimating Equations, Lu Wang, Andrea Rotnitzky, Xihong Lin
Nonparametric Regression With Missing Outcomes Using Weighted Kernel Estimating Equations, Lu Wang, Andrea Rotnitzky, Xihong Lin
Harvard University Biostatistics Working Paper Series
No abstract provided.
A Class Of Semiparametric Mixture Cure Survival Models With Dependent Censoring, Megan Othus, Yi Li, Ram C. Tiwari
A Class Of Semiparametric Mixture Cure Survival Models With Dependent Censoring, Megan Othus, Yi Li, Ram C. Tiwari
Harvard University Biostatistics Working Paper Series
No abstract provided.
Analysis Of Randomized Comparative Clinical Trial Data For Personalized Treatment Selections, Tianxi Cai, Lu Tian, Peggy H. Wong, L. J. Wei
Analysis Of Randomized Comparative Clinical Trial Data For Personalized Treatment Selections, Tianxi Cai, Lu Tian, Peggy H. Wong, L. J. Wei
Harvard University Biostatistics Working Paper Series
No abstract provided.
Calibrating Parametric Subject-Specific Risk Estimation, Tianxi Cai, Lu Tian, Hajime Uno, Scott D. Solomon, L. J. Wei
Calibrating Parametric Subject-Specific Risk Estimation, Tianxi Cai, Lu Tian, Hajime Uno, Scott D. Solomon, L. J. Wei
Harvard University Biostatistics Working Paper Series
No abstract provided.
Evaluating Subject-Level Incremental Values Of New Markers For Risk Classification Rule, Tianxi Cai, Lu Tian, Donald M. Lloyd-Jones, L. J. Wei
Evaluating Subject-Level Incremental Values Of New Markers For Risk Classification Rule, Tianxi Cai, Lu Tian, Donald M. Lloyd-Jones, L. J. Wei
Harvard University Biostatistics Working Paper Series
No abstract provided.
Semiparametric Regression Of Multi-Dimensional Genetic Pathway Data: Least Squares Kernel Machines And Linear Mixed Models, Dawei Liu, Xihong Lin, Debashis Ghosh
Semiparametric Regression Of Multi-Dimensional Genetic Pathway Data: Least Squares Kernel Machines And Linear Mixed Models, Dawei Liu, Xihong Lin, Debashis Ghosh
Harvard University Biostatistics Working Paper Series
No abstract provided.
Predicting Future Responses Based On Possibly Misspecified Working Models, Tianxi Cai, Lu Tian, Scott D. Solomon, L.J. Wei
Predicting Future Responses Based On Possibly Misspecified Working Models, Tianxi Cai, Lu Tian, Scott D. Solomon, L.J. Wei
Harvard University Biostatistics Working Paper Series
No abstract provided.
Semiparametric Latent Variable Regression Models For Spatio-Temporal Modeling Of Mobile Source Particles In The Greater Boston Area, Alexandros Gryparis, Brent A. Coull, Joel Schwartz, Helen H. Suh
Semiparametric Latent Variable Regression Models For Spatio-Temporal Modeling Of Mobile Source Particles In The Greater Boston Area, Alexandros Gryparis, Brent A. Coull, Joel Schwartz, Helen H. Suh
Harvard University Biostatistics Working Paper Series
Traffic particle concentrations show considerable spatial variability within a metropolitan area. We consider latent variable semiparametric regression models for modeling the spatial and temporal variability of black carbon and elemental carbon concentrations in the greater Boston area. Measurements of these pollutants, which are markers of traffic particles, were obtained from several individual exposure studies conducted at specific household locations as well as 15 ambient monitoring sites in the city. The models allow for both flexible, nonlinear effects of covariates and for unexplained spatial and temporal variability in exposure. In addition, the different individual exposure studies recorded different surrogates of traffic …
Model Checking For Roc Regression Analysis, Tianxi Cai, Yingye Zheng
Model Checking For Roc Regression Analysis, Tianxi Cai, Yingye Zheng
Harvard University Biostatistics Working Paper Series
The Receiver Operating Characteristic (ROC) curve is a prominent tool for characterizing the accuracy of continuous diagnostic test. To account for factors that might invluence the test accuracy, various ROC regression methods have been proposed. However, as in any regression analysis, when the assumed models do not fit the data well, these methods may render invalid and misleading results. To date practical model checking techniques suitable for validating existing ROC regression models are not yet available. In this paper, we develop cumulative residual based procedures to graphically and numerically assess the goodness-of-fit for some commonly used ROC regression models, and …
Model Evaluation Based On The Distribution Of Estimated Absolute Prediction Error, Lu Tian, Tianxi Cai, Els Goetghebeur, L. J. Wei
Model Evaluation Based On The Distribution Of Estimated Absolute Prediction Error, Lu Tian, Tianxi Cai, Els Goetghebeur, L. J. Wei
Harvard University Biostatistics Working Paper Series
The construction of a reliable, practically useful prediction rule for future response is heavily dependent on the "adequacy" of the fitted regression model. In this article, we consider the absolute prediction error, the expected value of the absolute difference between the future and predicted responses, as the model evaluation criterion. This prediction error is easier to interpret than the average squared error and is equivalent to the mis-classification error for the binary outcome. We show that the distributions of the apparent error and its cross-validation counterparts are approximately normal even under a misspecified fitted model. When the prediction rule is …
Gauss-Seidel Estimation Of Generalized Linear Mixed Models With Application To Poisson Modeling Of Spatially Varying Disease Rates, Subharup Guha, Louise Ryan
Gauss-Seidel Estimation Of Generalized Linear Mixed Models With Application To Poisson Modeling Of Spatially Varying Disease Rates, Subharup Guha, Louise Ryan
Harvard University Biostatistics Working Paper Series
Generalized linear mixed models (GLMMs) provide an elegant framework for the analysis of correlated data. Due to the non-closed form of the likelihood, GLMMs are often fit by computational procedures like penalized quasi-likelihood (PQL). Special cases of these models are generalized linear models (GLMs), which are often fit using algorithms like iterative weighted least squares (IWLS). High computational costs and memory space constraints often make it difficult to apply these iterative procedures to data sets with very large number of cases.
This paper proposes a computationally efficient strategy based on the Gauss-Seidel algorithm that iteratively fits sub-models of the GLMM …
A Pseudolikelihood Approach For Simultaneous Analysis Of Array Comparative Genomic Hybridizations (Acgh), David A. Engler, Gayatry Mohapatra, David N. Louis, Rebecca Betensky
A Pseudolikelihood Approach For Simultaneous Analysis Of Array Comparative Genomic Hybridizations (Acgh), David A. Engler, Gayatry Mohapatra, David N. Louis, Rebecca Betensky
Harvard University Biostatistics Working Paper Series
DNA sequence copy number has been shown to be associated with cancer development and progression. Array-based Comparative Genomic Hybridization (aCGH) is a recent development that seeks to identify the copy number ratio at large numbers of markers across the genome. Due to experimental and biological variations across chromosomes and across hybridizations, current methods are limited to analyses of single chromosomes. We propose a more powerful approach that borrows strength across chromosomes and across hybridizations. We assume a Gaussian mixture model, with a hidden Markov dependence structure, and with random effects to allow for intertumoral variation, as well as intratumoral clonal …
The Sensitivity And Specificity Of Markers For Event Times, Tianxi Cai, Margaret S. Pepe, Thomas Lumley, Yingye Zheng, Nancy Swords Jenny
The Sensitivity And Specificity Of Markers For Event Times, Tianxi Cai, Margaret S. Pepe, Thomas Lumley, Yingye Zheng, Nancy Swords Jenny
Harvard University Biostatistics Working Paper Series
No abstract provided.