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- Adaptive designs; Clinical trials; Efficiency; Group sequential tests; Sample size modification; Sufficiency (1)
- Alzheimer's disease; covariate-specific ROC curve; ignorable missingness; verification bias; weighted estimating equations (1)
- Biomarker; restricted survival time; selection impact curve; statistical interaction (1)
- Diagnosis; Marker combination; Prognosis; ROC curve; Sensitivity; Specificity (1)
- Distribution-free (1)
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- Doubly robust; estimating equation; missing at random; missing covariate; missing response (1)
- Efficiency (1)
- Efficiency; gene expression analysis; microarray; t-test (1)
- Likelihood; missing at random; missing not at random; Markov model; non-homogeneous; transition intensity (1)
- Nonparametric (1)
- Predictive biomarker; cutoff points; interaction; BATE curve; time-to-event outcome; varying-coefficient (1)
- Semiparametric (1)
- Spatio-temporal modeling; Air pollution; NOx; Los Angeles; Land use covariates; Profile likelihood (1)
Articles 1 - 10 of 10
Full-Text Articles in Physical Sciences and Mathematics
Some Observations On The Wilcoxon Rank Sum Test, Scott S. Emerson
Some Observations On The Wilcoxon Rank Sum Test, Scott S. Emerson
UW Biostatistics Working Paper Series
This manuscript presents some general comments about the Wilcoxon rank sum test. Even the most casual reader will gather that I am not too impressed with the scientific usefulness of the Wilcoxon test. However, the actual motivation is more to illustrate differences between parametric, semiparametric, and nonparametric (distribution-free) inference, and to use this example to illustrate how many misconceptions have been propagated through a focus on (semi)parametric probability models as the basis for evaluating commonly used statistical analysis models. The document itself arose as a teaching tool for courses aimed at graduate students in biostatistics and statistics, with parts of …
The Importance Of Statistical Theory In Outlier Detection, Sarah C. Emerson, Scott S. Emerson
The Importance Of Statistical Theory In Outlier Detection, Sarah C. Emerson, Scott S. Emerson
UW Biostatistics Working Paper Series
We explore the performance of the outlier-sum statistic (Tibshirani and Hastie, Biostatistics 2007 8:2--8), a proposed method for identifying genes for which only a subset of a group of samples or patients exhibits differential expression levels. Our discussion focuses on this method as an example of how inattention to standard statistical theory can lead to approaches that exhibit some serious drawbacks. In contrast to the results presented by those authors, when comparing this method to several variations of the $t$-test, we find that the proposed method offers little benefit even in the most idealized scenarios, and suffers from a number …
When Does Combining Markers Improve Classification Performance And What Are Implications For Practice?, Aasthaa Bansal, Margaret Sullivan Pepe
When Does Combining Markers Improve Classification Performance And What Are Implications For Practice?, Aasthaa Bansal, Margaret Sullivan Pepe
UW Biostatistics Working Paper Series
When an existing standard marker does not have sufficient classification accuracy on its own, new markers are sought with the goal of yielding a combination with better performance. The primary criterion for selecting new markers is that they have good performance on their own and preferably be uncorrelated with the standard. Most often linear combinations are considered. In this paper we investigate the increment in performance that is possible by combining a novel continuous marker with a moderately performing standard continuous marker under a variety of biologically motivated models for their joint distribution. We find that an uncorrelated continuous marker …
Adaptive Clinical Trial Designs With Pre-Specified Rules For Modifying The Sample Size: Understanding Efficient Types Of Adaptation, Gregory P. Levin, Sarah C. Emerson, Scott S. Emerson
Adaptive Clinical Trial Designs With Pre-Specified Rules For Modifying The Sample Size: Understanding Efficient Types Of Adaptation, Gregory P. Levin, Sarah C. Emerson, Scott S. Emerson
UW Biostatistics Working Paper Series
Methods allowing unplanned adaptations to the sample size based on the interim estimate of treatment effect do not base inference on the minimal sufficient statistic and suffer losses in efficiency when compared to group sequential designs [1, 2, 3]. However, when adaptive sampling plans are completely pre-specified at the design stage of the trial, investigators can proceed with frequentist inference based on the minimal sufficient statistic at the analysis stage. In the context of two general settings where different optimality criteria govern the choice of clinical trial design, we quantify the relative costs and benefits of a variety of fixed …
Bate Curve In Assessment Of Clinical Utility Of Predictive Biomarkers, Xiao-Hua Zhou, Yunbei Ma
Bate Curve In Assessment Of Clinical Utility Of Predictive Biomarkers, Xiao-Hua Zhou, Yunbei Ma
UW Biostatistics Working Paper Series
In this paper, for time-to-event data, we propose a new statistical framework for casual inference in evaluating clinical utility of predictive biomarkers and in selecting an optimal treatment for a particular patient. This new casual framework is based on a new concept, called Biomarker Adjusted Treatment Effect (BATE) curve, which can be used to represent the clinical utility of a predictive biomarker and select an optimal treatment for one particular patient. We then propose semi-parametric methods for estimating the BATE curves of biomarkers and establish asymptotic results of the proposed estimators for the BATE curves. We also conduct extensive simulation …
Non-Homogeneous Markov Process Models With Incomplete Observations: Application To A Dementia Disease Study, Xiao-Hua Zhou, Baojiang Chen
Non-Homogeneous Markov Process Models With Incomplete Observations: Application To A Dementia Disease Study, Xiao-Hua Zhou, Baojiang Chen
UW Biostatistics Working Paper Series
Identifying risk factors for transition rates among normal cognition, mildly cognitive impairment, dementia and death in an Alzheimer's disease study is very important. It is known that transition rates among these states are strongly time dependent. While Markov process models are often used to describe these disease progressions, the literature mainly focuses on time homogeneous processes, and limited tools are available for dealing with non-homogeneity. Further, patients may choose when they want to visit the clinics, which creates informative observations. In this paper, we develop methods to deal with non-homogeneous Markov processes through time scale transformation when observation times are …
Doubly Robust Estimates For Binary Longitudinal Data Analysis With Missing Response And Missing Covariates, Baojiang Chen, Xiao-Hua Zhou
Doubly Robust Estimates For Binary Longitudinal Data Analysis With Missing Response And Missing Covariates, Baojiang Chen, Xiao-Hua Zhou
UW Biostatistics Working Paper Series
Longitudinal studies often feature incomplete response and covariate data. Likelihood-based methods such as the EM algorithm give consistent estimators for model parameters when data are missing at random provided that the response model and the missing covariate model are correctly specified; but we do not need to specify the missing data mechanism. An alternative method is the weighted estimating equation which gives consistent estimators if the missing data and response models are correctly specified; but we do not need to specify the distribution of the covariates that have missing values. In this paper we develop a doubly robust estimation method …
Semiparametric Estimation Of The Covariate-Specific Roc Curve In Presence Of Ignorable Verification Bias, Danping Liu, Xiao-Hua Zhou
Semiparametric Estimation Of The Covariate-Specific Roc Curve In Presence Of Ignorable Verification Bias, Danping Liu, Xiao-Hua Zhou
UW Biostatistics Working Paper Series
Covariate-specific ROC curves are often used to evaluate the classification accuracy of a medical diagnostic test or a biomarker, when the accuracy of the test is associated with certain covariates. In many large-scale screening tests, the gold standard is subject to missingness due to high cost or harmfulness to the patient. In this paper, we propose a semiparametric estimation method for the covariate-specific ROC curves with a partial missing gold standard. A location-scale model is constructed for the test result to model the covariates' effect, but the residual distributions are left unspecified. Thus the baseline and link functions of the …
Evaluating Markers For Treatment Selection Based On Survival Time, Xiao Song, Xiao-Hua Zhou
Evaluating Markers For Treatment Selection Based On Survival Time, Xiao Song, Xiao-Hua Zhou
UW Biostatistics Working Paper Series
For many medical conditions there are several treatment options available to patients. We consider evaluating markers based on a simple treatment selection policy that incorporates information on the patient's marker value exceeding a threshold. Although traditional regression methods may assess the effect of the marker and treatment on outcomes, it is appealing to quantify more directly the potential impact on the population of using the marker to select treatment. A useful tool is the selection impact (SI) curve proposed by Song and Pepe (2004, \textit{Biometrics} \textbf{60}, 874--883) for binary outcomes. However, this approach does not deal with continuous outcomes, nor …
A Flexible Spatio-Temporal Model For Air Pollution: Allowing For Spatio-Temporal Covariates, Johan Lindstrom, Adam A. Szpiro, Paul D. Sampson, Lianne Sheppard, Assaf Oron, Mark Richards, Tim Larson
A Flexible Spatio-Temporal Model For Air Pollution: Allowing For Spatio-Temporal Covariates, Johan Lindstrom, Adam A. Szpiro, Paul D. Sampson, Lianne Sheppard, Assaf Oron, Mark Richards, Tim Larson
UW Biostatistics Working Paper Series
Given the increasing interest in the association between exposure to air pollution and adverse health outcomes, the development of models that provide accurate spatio-temporal predictions of air pollution concentrations at small spatial scales is of great importance when assessing potential health effects of air pollution. The methodology presented here has been developed as part of the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air), a prospective cohort study funded by the US EPA to investigate the relationship between chronic exposure to air pollution and cardiovascular disease. We present a spatio-temporal framework that models and predicts ambient air pollution by …