Open Access. Powered by Scholars. Published by Universities.®
- Discipline
- Keyword
-
- Bootstrap method (2)
- Diagnostic tests (2)
- Disease screening (2)
- Edgeworth expansion (2)
- Sensitivity (2)
-
- Skewness (2)
- BCa bootstrap (1)
- Binomial proportion (1)
- Bionomial proportion (1)
- Causal inference (1)
- Classification (1)
- Classifier performance (1)
- Confidence interval (1)
- Confidence intervals (1)
- Cost data (1)
- Diagnostic accuracy (1)
- Diagnostic test (1)
- Estimating equation (1)
- Generalized linear model (1)
- Observational study (1)
- Paired data (1)
- Prediction (1)
- Propensity score (1)
- Proportion (1)
- Sensitivity and specificity (1)
- Skewed distribution; censored date (1)
- Specificity (1)
- Stratification (1)
Articles 1 - 7 of 7
Full-Text Articles in Statistical Methodology
A New Confidence Interval For The Difference Between Two Binomial Proportions Of Paired Data, Xiao-Hua Zhou, Gengsheng Qin
A New Confidence Interval For The Difference Between Two Binomial Proportions Of Paired Data, Xiao-Hua Zhou, Gengsheng Qin
UW Biostatistics Working Paper Series
Motivated by a study on comparing sensitivities and specificities of two diagnostic tests in a paired design when the sample size is small, we first derived an Edgeworth expansion for the studentized difference between two binomial proportions of paired data. The Edgeworth expansion can help us understand why the usual Wald interval for the difference has poor coverage performance in the small sample size. Based on the Edgeworth expansion, we then derived a transformation based confidence interval for the difference. The new interval removes the skewness in the Edgeworth expansion; the new interval is easy to compute, and its coverage …
Improved Confidence Intervals For The Sensitivity At A Fixed Level Of Specificity Of A Continuous-Scale Diagnostic Test, Xiao-Hua Zhou, Gengsheng Qin
Improved Confidence Intervals For The Sensitivity At A Fixed Level Of Specificity Of A Continuous-Scale Diagnostic Test, Xiao-Hua Zhou, Gengsheng Qin
UW Biostatistics Working Paper Series
For a continuous-scale test, it is an interest to construct a confidence interval for the sensitivity of the diagnostic test at the cut-off that yields a predetermined level of its specificity (eg. 80%, 90%, or 95%). IN this paper we proposed two new intervals for the sensitivity of a continuous-scale diagnostic test at a fixed level of specificity. We then conducted simulation studies to compare the relative performance of these two intervals with the best existing BCa bootstrap interval, proposed by Platt et al. (2000). Our simulation results showed that the newly proposed intervals are better than the BCa bootstrap …
Bootstrap Confidence Intervals For Medical Costs With Censored Observations, Hongyu Jiang, Xiao-Hua Zhou
Bootstrap Confidence Intervals For Medical Costs With Censored Observations, Hongyu Jiang, Xiao-Hua Zhou
UW Biostatistics Working Paper Series
Medical costs data with administratively censored observations often arise in cost-effectiveness studies of treatments for life threatening diseases. Mean of medical costs incurred from the start of a treatment till death or certain timepoint after the implementation of treatment is frequently of interest. In many situations, due to the skewed nature of the cost distribution and non-uniform rate of cost accumulation over time, the currently available normal approximation confidence interval has poor coverage accuracy. In this paper, we proposed a bootstrap confidence interval for the mean of medical costs with censored observations. In simulation studies, we showed that the proposed …
New Intervals For The Difference Between Two Independent Binomial Proportions, Xiao-Hua Zhou, Min Tsao, Gengsheng Qin
New Intervals For The Difference Between Two Independent Binomial Proportions, Xiao-Hua Zhou, Min Tsao, Gengsheng Qin
UW Biostatistics Working Paper Series
In this paper we gave an Edgeworth expansion for the studentized difference of two binomial proportions. We then proposed two new intervals by correcting the skewness in the Edgeworth expansion in a direct and an indirect way. Such the bias-correct confidence intervals are easy to compute, and their coverage probabilities converge to the nominal level at a rate of O(n-½), where n is the size of the combined samples. Our simulation results suggest tat in finite samples the new interval based on the indirect method have the similar performance to the two best existing intervals in terms of coverage accuracy …
A Bootstrap Confidence Interval Procedure For The Treatment Effect Using Propensity Score Subclassification, Wanzhu Tu, Xiao-Hua Zhou
A Bootstrap Confidence Interval Procedure For The Treatment Effect Using Propensity Score Subclassification, Wanzhu Tu, Xiao-Hua Zhou
UW Biostatistics Working Paper Series
In the analysis of observational studies, propensity score subclassification has been shown to be a powerful method for adjusting unbalanced covariates for the purpose of causal inferences. One practical difficulty in carrying out such an analysis is to obtain a correct variance estimate for such inferences, while reducing bias in the estimate of the treatment effect due to an imbalance in the measured covariates. In this paper, we propose a bootstrap procedure for the inferences concerning the average treatment effect; our bootstrap method is based on an extension of Efron’s bias-corrected accelerated (BCa) bootstrap confidence interval to a two-sample problem. …
Semiparametric Receiver Operating Characteristic Analysis To Evaluate Biomarkers For Disease, Tianxi Cai, Margaret S. Pepe
Semiparametric Receiver Operating Characteristic Analysis To Evaluate Biomarkers For Disease, Tianxi Cai, Margaret S. Pepe
UW Biostatistics Working Paper Series
The receiver operating characteristic (ROC) curve is a popular method for characterizing the accuracy of diagnostic tests when test results are not binary. Various methodologies for estimating and comparing ROC curves have been developed. One approach, due to Pepe, uses a parametric regression model with the baseline function specified up to a finite-dimensional parameter. In this article we extend the regression models by allowing arbitrary nonparametric baseline functions. We also provide asymptotic distribution theory and procedures for making statistical inference. We illustrate our approach with dataset from a prostate cancer biomarker study. Simulation studies suggest that the extra flexibility inherent …
Semi-Parametric Regression For The Area Under The Receiver Operating Characteristic Curve, Lori E. Dodd, Margaret S. Pepe
Semi-Parametric Regression For The Area Under The Receiver Operating Characteristic Curve, Lori E. Dodd, Margaret S. Pepe
UW Biostatistics Working Paper Series
Medical advances continue to provide new and potentially better means for detecting disease. Such is true in cancer, for example, where biomarkers are sought for early detection and where improvements in imaging methods may pick up the initial functional and molecular changes associated with cancer development. In other binary classification tasks, computational algorithms such as Neural Networks, Support Vector Machines and Evolutionary Algorithms have been applied to areas as diverse as credit scoring, object recognition, and peptide-binding prediction. Before a classifier becomes an accepted technology, it must undergo rigorous evaluation to determine its ability to discriminate between states. Characterization of …