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Articles 1 - 30 of 41
Full-Text Articles in Biostatistics
A General Approach To Goodness Of Fit For U Processes, Debashis Ghosh, Youngjoo Cho
A General Approach To Goodness Of Fit For U Processes, Debashis Ghosh, Youngjoo Cho
Debashis Ghosh
Goodness of fit procedures are essential tools for assessing model adequacy in statistics. In this work, we present a general theory and approach to goodness of fit techniques based on U-processes for the accelerated failure time (AFT) model. Many of the examples will focus on U-statistics of order 2. While many authors have proposed goodness of fit tests for U-statistics of order one, less has been developed for higher order U-statistics. In this paper, we propose goodness of fit tests for U-statistics of order 2 by using theoretical results from Nolan and Pollard (1987) and Nolan and Pollard (1988). We …
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 …
Integrative Analysis Of Prognosis Data On Multiple Cancer Subtypes, Shuangge Ma
Integrative Analysis Of Prognosis Data On Multiple Cancer Subtypes, Shuangge Ma
Shuangge Ma
In cancer research, profiling studies have been extensively conducted, searching for genes/SNPs associated with prognosis. Cancer is diverse. Examining similarity and difference in the genetic basis of multiple subtypes of the same cancer can lead to a better understanding of their connections and distinctions. Classic meta-analysis methods analyze each subtype separately and then compare analysis results across subtypes. Integrative analysis methods, in contrast, analyze the raw data on multiple subtypes simultaneously and can outperform meta-analysis methods. In this study, prognosis data on multiple subtypes of the same cancer are analyzed. An AFT (accelerated failure time) model is adopted to describe …
Incorporating Network Structure In Integrative Analysis Of Cancer Prognosis Data, Shuangge Ma
Incorporating Network Structure In Integrative Analysis Of Cancer Prognosis Data, Shuangge Ma
Shuangge Ma
In high-throughput cancer genomic studies, markers identified from the analysis of single datasets may have unsatisfactory properties because of low sample sizes. Integrative analysis pools and analyzes raw data from multiple studies, and can effectively increase sample size and lead to improved marker identification results. In this study, we consider the integrative analysis of multiple high-throughput cancer prognosis studies. In the existing integrative analysis studies, the interplay among genes, which can be described using the network structure, has not been effectively accounted for. In network analysis, tightly-connected nodes (genes) are more likely to have related biological functions and similar regression …
Risk Factors Of Follicular Lymphoma, Shuangge Ma
Health Insurance Coverage And Impact: A Survey In Three Cities In China, Shuangge Ma
Health Insurance Coverage And Impact: A Survey In Three Cities In China, Shuangge Ma
Shuangge Ma
No abstract provided.
Integrative Analysis Of Multiple Cancer Genomic Datasets Under The Heterogeneity Model, Shuangge Ma
Integrative Analysis Of Multiple Cancer Genomic Datasets Under The Heterogeneity Model, Shuangge Ma
Shuangge Ma
No abstract provided.
Health Insurance Coverage, Medical Expenditure And Coping Strategy: Evidence From Taiwan, Shuangge Ma
Health Insurance Coverage, Medical Expenditure And Coping Strategy: Evidence From Taiwan, Shuangge Ma
Shuangge Ma
No abstract provided.
Impact Of Illness And Medical Expenditure On Household Consumptions: A Survey In Western China, Shuangge Ma
Impact Of Illness And Medical Expenditure On Household Consumptions: A Survey In Western China, Shuangge Ma
Shuangge Ma
No abstract provided.
Identification Of Gene-Environment Interactions In Cancer Prognosis Studies Using Penalization, Shuangge Ma
Identification Of Gene-Environment Interactions In Cancer Prognosis Studies Using Penalization, Shuangge Ma
Shuangge Ma
High-throughput cancer studies have been extensively conducted, searching for genetic risk factors independently associated with prognosis beyond clinical and environmental risk factors. Many studies have shown that the gene-environment interactions may have important implications. Some of the existing methods, such as the commonly adopted single-marker analysis, may be limited in that they cannot accommodate the joint effects of a large number of genetic markers or use ineffective marker identification techniques. In this study, we analyze cancer prognosis studies, and adopt the AFT (accelerated failure time) model to describe survival. A weighted least squares approach, which has the lowest computational cost, …
Clustering With Exclusion Zones: Genomic Applications, Mark Segal, Yuanyuan Xiao, Fred Huffer
Clustering With Exclusion Zones: Genomic Applications, Mark Segal, Yuanyuan Xiao, Fred Huffer
Mark R Segal
Methods for formally evaluating the clustering of events in space or time, notably the scan statistic, have been richly developed and widely applied. In order to utilize the scan statistic and related approaches, it is necessary to know the extent of the spatial or temporal domains wherein the events arise. Implicit in their usage is that these domains have no “holes”—hereafter “exclusion zones”—regions in which events a priori cannot occur. However, in many contexts, this requirement is not met. When the exclusion zones are known, it is straightforward to correct the scan statistic for their occurrence by simply adjusting the …
Integrative Analysis Of Cancer Genomic Data, Shuangge Ma
Integrative Analysis Of Cancer Genomic Data, Shuangge Ma
Shuangge Ma
In the past decade, we have witnessed a period of unparallel development in the field of cancer genomics. To address the same or similar biomedical questions, multiple cancer genomic studies have been independently designed and conducted. Cancer gene signatures identified from analysis of individual datasets often have low reproducibility. A cost-effective way of improving reproducibility is to conduct integrative analysis of datasets from multiple studies with comparable designs. To properly integrate multiple studies and conduct integrative analysis, we need to access various public data warehouses, retrieve experiment protocols and raw data, evaluate individual studies and select those with comparable designs, …
Identification Of Cancer-Associated Gene Pathways From Analysis Of Expression Data, Shuangge Ma
Identification Of Cancer-Associated Gene Pathways From Analysis Of Expression Data, Shuangge Ma
Shuangge Ma
No abstract provided.
Lecture 5, Shuangge Ma
Final Project, Shuangge Ma
Lecture 4, Shuangge Ma
Lecture 4, Shuangge Ma
Computer Intensive Methods Lecture 13, Shuangge Ma
Final Project (Description), Shuangge Ma
Final Project (Data), Shuangge Ma
Lecture 3, Shuangge Ma
Lecture 2, Shuangge Ma
Reference: Multiple Imputation, Shuangge Ma
Reference: Weighted Bootstrap, Shuangge Ma
Computer Intensive Methods Lecture 9, Shuangge Ma
Computer Intensive Methods Lecture 8, Shuangge Ma
Reference: Counter Examples [Bootstrap], Shuangge Ma
Reference: Counter Examples [Bootstrap], Shuangge Ma
Shuangge Ma
No abstract provided.
Computer Intensive Methods Lecture 7 (Lab 2), Shuangge Ma
Computer Intensive Methods Lecture 7 (Lab 2), Shuangge Ma
Shuangge Ma
No abstract provided.
Computer Intensive Methods Lecture 6, Shuangge Ma