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- Keyword
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- Propensity score (3)
- Propensity score methods (3)
- Statistical Methodology (3)
- Inverse probability of treatment weighting (2)
- Kappa statistic (2)
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- Monte Carlo simulations (2)
- Observational study (2)
- Propensity score matching (2)
- Survival analysis (2)
- Clinical Trials (1)
- Clustered Matched-pair Data (1)
- Computer algorithms (1)
- Confounding (1)
- DCA (1)
- Event history analysis (1)
- Gaussian process (1)
- Genomewide association studies; Distance covariance/correlation; Empirical null estimation; Local false discovery rate; Multiple comparisons; Positive false discovery rate (1)
- Genomics (1)
- Immune Correlates (1)
- Likelihood ratio test (1)
- Marginal effects (1)
- Matching (1)
- Multiple Comparisons (1)
- Multivariate Analysis (1)
- Nonidentifiability (1)
- Optimal matching (1)
- Principal component analysis (1)
- SAUC (1)
- Survival Analysis (1)
- Time-to-event outcomes (1)
- Publication
Articles 1 - 14 of 14
Full-Text Articles in Physical Sciences and Mathematics
Bayesian Joint Selection Of Genes And Pathways: Applications In Multiple Myeloma Genomics, Lin Zhang, Jeffrey S. Morris, Jiexin Zhang, Robert Orlowski, Veerabhadran Baladandayuthapani
Bayesian Joint Selection Of Genes And Pathways: Applications In Multiple Myeloma Genomics, Lin Zhang, Jeffrey S. Morris, Jiexin Zhang, Robert Orlowski, Veerabhadran Baladandayuthapani
Jeffrey S. Morris
It is well-established that the development of a disease, especially cancer, is a complex process that results from the joint effects of multiple genes involved in various molecular signaling pathways. In this article, we propose methods to discover genes and molecular pathways significantly associ- ated with clinical outcomes in cancer samples. We exploit the natural hierarchal structure of genes related to a given pathway as a group of interacting genes to conduct selection of both pathways and genes. We posit the problem in a hierarchical structured variable selection (HSVS) framework to analyze the corresponding gene expression data. HSVS methods conduct …
Sas Macro: Testing Marginal Homogeneity In Clustered Matched-Pair Data, Zhao Yang
Sas Macro: Testing Marginal Homogeneity In Clustered Matched-Pair Data, Zhao Yang
Zhao (Tony) Yang, Ph.D.
The SAS Macro and simulated data example are used to demonstrate the application of tests for marginal homogeneity in clustered matched-pair data.
Sas Macro: Weighted Kappa Statistic For Clustered Matched-Pair Ordinal Data, Zhao Yang
Sas Macro: Weighted Kappa Statistic For Clustered Matched-Pair Ordinal Data, Zhao Yang
Zhao (Tony) Yang, Ph.D.
This SAS macro calculate the weighted kappa statistic and its corresponding non-parametric variance estimator for the clustered matched-pair ordinal data.
Sas Macro: Kappa Statistic For Clustered Physician-Patients Polytomous Data, Zhao Yang
Sas Macro: Kappa Statistic For Clustered Physician-Patients Polytomous Data, Zhao Yang
Zhao (Tony) Yang, Ph.D.
This SAS macro calculate the kappa statistic and its semi-parametric variance estimator for the clustered physician-patients polytomous data. The proposed method depends on the assumption of conditional independence for the clustered physician-patients data structure.
Combining Biomarkers Linearly And Nonlinearly For Classification Using The Area Under The Roc Curve, Youyi Fong, Shuxin Yin, Ying Huang
Combining Biomarkers Linearly And Nonlinearly For Classification Using The Area Under The Roc Curve, Youyi Fong, Shuxin Yin, Ying Huang
Youyi Fong
In biomedical studies, it is often of interest to classify/predict a subject's disease status based on some biomarker measurements. Two approaches have received a lot of attention in the biostatistical literature for finding optimal biomarker combinations using a training data. The likelihood approach maximizes logistic regression model likelihood, while the AUC (area under the receiver operating characteristic curve) approach maximizes the empirical AUC based on biomarker combination. The two approaches are complementary to each other in practice. Existing methods in the AUC approach either approximate the empirical AUC by a smooth function or replace it with a convex upper bound. …
Causal Models And Learning From Data: Integrating Causal Modeling And Statistical Estimation, Maya Petersen, M J. Van Der Laan
Causal Models And Learning From Data: Integrating Causal Modeling And Statistical Estimation, Maya Petersen, M J. Van Der Laan
Maya Petersen
No abstract provided.
Targeted Maximum Likelihood Estimation For Dynamic And Static Longitudinal Marginal Structural Working Models, Maya Petersen, J Schwab, S Gruber, N Blaser, M Schomaker, M J. Van Der Laan
Targeted Maximum Likelihood Estimation For Dynamic And Static Longitudinal Marginal Structural Working Models, Maya Petersen, J Schwab, S Gruber, N Blaser, M Schomaker, M J. Van Der Laan
Maya Petersen
No abstract provided.
Multiple Comparison Procedures For Neuroimaging Genomewide Association Studies, Wen-Yu Hua, Thomas E. Nichols, Debashis Ghosh
Multiple Comparison Procedures For Neuroimaging Genomewide Association Studies, Wen-Yu Hua, Thomas E. Nichols, Debashis Ghosh
Debashis Ghosh
Recent research in neuroimaging has been focusing on assessing associations between genetic variants measured on a genomewide scale and brain imaging phenotypes. Many publications in the area use massively univariate analyses on a genomewide basis for finding single nucleotide polymorphisms that influence brain structure. In this work, we propose using various dimensionalityreduction methods on both brain MRI scans and genomic data, motivated by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study. We also consider a new multiple testing adjustments inspired from the idea of local false discovery rate of Efron and others (2001). Our proposed procedure is able to find associations …
On Likelihood Ratio Tests When Nuisance Parameters Are Present Only Under The Alternative, Cz Di, K-Y Liang
On Likelihood Ratio Tests When Nuisance Parameters Are Present Only Under The Alternative, Cz Di, K-Y Liang
Chongzhi Di
In parametric models, when one or more parameters disappear under the null hypothesis, the likelihood ratio test statistic does not converge to chi-square distributions. Rather, its limiting distribution is shown to be equivalent to that of the supremum of a squared Gaussian process. However, the limiting distribution is analytically intractable for most of examples, and approximation or simulation based methods must be used to calculate the p values. In this article, we investigate conditions under which the asymptotic distributions have analytically tractable forms, based on the principal component decomposition of Gaussian processes. When these conditions are not satisfied, the principal …
Hypothesis Testing For An Extended Cox Model With Time-Varying Coefficients, Takumi Saegusa, Chongzhi Di, Ying Qing Chen
Hypothesis Testing For An Extended Cox Model With Time-Varying Coefficients, Takumi Saegusa, Chongzhi Di, Ying Qing Chen
Chongzhi Di
In many randomized clinical trials, the log-rank test has routinely been used to detect a treatment effect under the Cox proportional hazards model for censored time-to-event outcomes. However, it may lose power substantially when the proportional hazards assumption does not hold. There are approaches to testing the proportionality, such as the smoothing spline-based score test by Lin, Zhang and Davidian (2006). In this paper, we consider an extended Cox model assuming time-varying treatment effect. We then use smoothing splines to model the time-varying treatment effect, and we propose spline-based score tests for the overall treatment effect. Our proposed tests take …
Multilevel Sparse Functional Principal Component Analysis, Cz Di, C M. Crainiceanu, W Jank
Multilevel Sparse Functional Principal Component Analysis, Cz Di, C M. Crainiceanu, W Jank
Chongzhi Di
We consider analysis of sparsely sampled multilevel functional data, where the basic observational unit is a function and data have a natural hierarchy of basic units. An example is when functions are recorded at multiple visits for each subject. Multilevel functional principal component analysis (MFPCA; Di et al. 2009) was proposed for such data when functions are densely recorded. Here we consider the case when functions are sparsely sampled and may contain only a few observations per function. We exploit the multilevel structure of covariance operators and achieve data reduction by principal component decompositions at both between and within subject …
A Comparison Of 12 Algorithms For Matching On The Propensity Score, Peter C. Austin
A Comparison Of 12 Algorithms For Matching On The Propensity Score, Peter C. Austin
Peter Austin
Propensity-score matching is increasingly being used to reduce the confounding that can occur in observational studies examining the effects of treatments or interventions on outcomes. We used Monte Carlo simulations to examine the following algorithms for forming matched pairs of treated and untreated subjects: optimal matching, greedy nearest neighbor matching without replacement, and greedy nearest neighbor matching without replacement within specified caliper widths. For each of the latter two algorithms, we examined four different sub-algorithms defined by the order in which treated subjects were selected for matching to an untreated subject: lowest to highest propensity score, highest to lowest propensity …
The Use Of Propensity Score Methods With Survival Or Time-To-Event Outcomes: Reporting Measures Of Effect Similar To Those Used In Randomized Experiments, Peter C. Austin
The Use Of Propensity Score Methods With Survival Or Time-To-Event Outcomes: Reporting Measures Of Effect Similar To Those Used In Randomized Experiments, Peter C. Austin
Peter Austin
Propensity score methods are increasingly being used to estimate causal treatment effects in observational studies. In medical and epidemiological studies, outcomes are frequently time-to-event in nature. Propensity-score methods are often applied incorrectly when estimating the effect of treatment on time-to-event outcomes. This article describes how two different propensity score methods (matching and inverse probability of treatment weighting) can be used to estimate the measures of effect that are frequently reported in randomized controlled trials: (i) marginal survival curves, which describe survival in the population if all subjects were treated or if all subjects were untreated; and (ii) marginal hazard ratios. …
The Performance Of Different Propensity Score Methods For Estimating Absolute Effects Of Treatments On Survival Outcomes: A Simulation Study, Peter C. Austin
The Performance Of Different Propensity Score Methods For Estimating Absolute Effects Of Treatments On Survival Outcomes: A Simulation Study, Peter C. Austin
Peter Austin
Observational studies are increasingly being used to estimate the effect of treatments, interventions and exposures on outcomes that can occur over time. Historically, the hazard ratio, which is a relative measure of effect, has been reported. However, medical decision making is best informed when both relative and absolute measures of effect are reported. When outcomes are time-to-event in nature, the effect of treatment can also be quantified as the change in mean or median survival time due to treatment and the absolute reduction in the probability of the occurrence of an event within a specified duration of follow-up. We describe …