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Accuracy Of The Newtom 3g™ In Measuring The Angle Of The Articular Eminence, Rehana Khan Dec 2005

Accuracy Of The Newtom 3g™ In Measuring The Angle Of The Articular Eminence, Rehana Khan

Loma Linda University Electronic Theses, Dissertations & Projects

The purpose of this study was to determine the accuracy of the Newtom 3G™ in determining the angulation of the articular eminence. The benefits of conducting this study were to provide additional uses for the standard records that are taken for the purposes of orthodontic treatment, as well as evaluate the Newtom 3G™ for accuracy in measuring the anatomy of the glenoid fossa. This study required 20 participants that volunteered to allow their records to be used. Records evaluated were the Newtom 3G™, impressions, and wax check bite registrations. The wax record was taken using the 'forced bite' technique to …

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Quantile-Function Based Null Distribution In Resampling Based Multiple Testing, Mark J. Van Der Laan, Alan E. Hubbard Nov 2005

Quantile-Function Based Null Distribution In Resampling Based Multiple Testing, Mark J. Van Der Laan, Alan E. Hubbard

U.C. Berkeley Division of Biostatistics Working Paper Series

Simultaneously testing a collection of null hypotheses about a data generating distribution based on a sample of independent and identically distributed observations is a fundamental and important statistical problem involving many applications. Methods based on marginal null distributions (i.e., marginal p-values) are attractive since the marginal p-values can be based on a user supplied choice of marginal null distributions and they are computationally trivial, but they, by necessity, are known to either be conservative or to rely on assumptions about the dependence structure between the test-statistics. Resampling based multiple testing (Westfall and Young, 1993) involves sampling from a joint null …

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Testing Primitive Polynomials For Generalized Feedback Shift Register Random Number Generators, Guinan Lian Nov 2005

Testing Primitive Polynomials For Generalized Feedback Shift Register Random Number Generators, Guinan Lian

Theses and Dissertations

The class of generalized feedback shift register (GFSR) random number generators was a promising method for random number generation in the 1980's, but was abandoned because of some flaws such as poor performance on certain tests for randomness. The poor performance may be due to the choice of primitive polynomials used in the generators, rather than inherent flaws in the method. The original GFSR generators were all based on primitive trinomials. This project examines several alternative choices of primitive polynomials with more than one "interior" term to address this problem and hopefully provide access to good random number generators.

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Data Adaptive Pathway Testing, Merrill D. Birkner, Alan E. Hubbard, Mark J. Van Der Laan Nov 2005

Data Adaptive Pathway Testing, Merrill D. Birkner, Alan E. Hubbard, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

A majority of diseases are caused by a combination of factors, for example, composite genetic mutation profiles have been found in many cases to predict a deleterious outcome. There are several statistical techniques that have been used to analyze these types of biological data. This article implements a general strategy which uses data adaptive regression methods to build a specific pathway model, thus predicting a disease outcome by a combination of biological factors and assesses the significance of this model, or pathway, by using a permutation based null distribution. We also provide several simulation comparisons with other techniques. In addition, …

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Optimal Feature Selection For Nearest Centroid Classifiers, With Applications To Gene Expression Microarrays, Alan R. Dabney, John D. Storey Nov 2005

Optimal Feature Selection For Nearest Centroid Classifiers, With Applications To Gene Expression Microarrays, Alan R. Dabney, John D. Storey

UW Biostatistics Working Paper Series

Nearest centroid classifiers have recently been successfully employed in high-dimensional applications. A necessary step when building a classifier for high-dimensional data is feature selection. Feature selection is typically carried out by computing univariate statistics for each feature individually, without consideration for how a subset of features performs as a whole. For subsets of a given size, we characterize the optimal choice of features, corresponding to those yielding the smallest misclassification rate. Furthermore, we propose an algorithm for estimating this optimal subset in practice. Finally, we investigate the applicability of shrinkage ideas to nearest centroid classifiers. We use gene-expression microarrays for …

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A New Approach To Intensity-Dependent Normalization Of Two-Channel Microarrays, Alan R. Dabney, John D. Storey Nov 2005

A New Approach To Intensity-Dependent Normalization Of Two-Channel Microarrays, Alan R. Dabney, John D. Storey

UW Biostatistics Working Paper Series

A two-channel microarray measures the relative expression levels of thousands of genes from a pair of biological samples. In order to reliably compare gene expression levels between and within arrays, it is necessary to remove systematic errors that distort the biological signal of interest. The standard for accomplishing this is smoothing "MA-plots" to remove intensity-dependent dye bias and array-specific effects. However, MA methods require strong assumptions. We review these assumptions and derive several practical scenarios in which they fail. The "dye-swap" normalization method has been much less frequently used because it requires two arrays per pair of samples. We show …

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Principal Component Analysis For Predicting Transcription-Factor Binding Motifs From Array-Derived Data, Yunlong Liu, Matthew P Vincenti, Hiroki Yokota Nov 2005

Principal Component Analysis For Predicting Transcription-Factor Binding Motifs From Array-Derived Data, Yunlong Liu, Matthew P Vincenti, Hiroki Yokota

Dartmouth Scholarship

The responses to interleukin 1 (IL-1) in human chondrocytes constitute a complex regulatory mechanism, where multiple transcription factors interact combinatorially to transcription-factor binding motifs (TFBMs). In order to select a critical set of TFBMs from genomic DNA information and an array-derived data, an efficient algorithm to solve a combinatorial optimization problem is required. Although computational approaches based on evolutionary algorithms are commonly employed, an analytical algorithm would be useful to predict TFBMs at nearly no computational cost and evaluate varying modelling conditions. Singular value decomposition (SVD) is a powerful method to derive primary components of a given matrix. Applying SVD …

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Nonparametric Estimation Of Bivariate Failure Time Associations In The Presence Of A Competing Risk, Karen Bandeen-Roche, Jing Ning Nov 2005

Nonparametric Estimation Of Bivariate Failure Time Associations In The Presence Of A Competing Risk, Karen Bandeen-Roche, Jing Ning

Johns Hopkins University, Dept. of Biostatistics Working Papers

There has been much research on the study of associations among paired failure times. Most has either assumed time invariance of association or been based on complex measures or estimators. Little has accommodated failures arising amid competing risks. This paper targets the conditional cause specific hazard ratio, a recent modification of the conditional hazard ratio to accommodate competing risks data. Estimation is accomplished by an intuitive, nonparametric method that localizes Kendall’s tau. Time variance is accommodated through a partitioning of space into “bins” between which the strength of association may differ. Inferential procedures are researched, small sample performance evaluated, and …

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Correspondences Between Regression Models For Complex Binary Outcomes And Those For Structured Multivariate Survival Analyses, Nicholas P. Jewell Nov 2005

Correspondences Between Regression Models For Complex Binary Outcomes And Those For Structured Multivariate Survival Analyses, Nicholas P. Jewell

U.C. Berkeley Division of Biostatistics Working Paper Series

Doksum and Gasko [5] described a one-to-one correspondence between regression models for binary outcomes and those for continuous time survival analyses. This correspondence has been exploited heavily in the analysis of current status data (Jewell and van der Laan [11], Shiboski [18]). Here, we explore similar correspondences for complex survival models and categorical regression models for polytomous data. We include discussion of competing risks and progressive multi-state survival random variables.

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Application Of A Variable Importance Measure Method To Hiv-1 Sequence Data, Merrill D. Birkner, Mark J. Van Der Laan Nov 2005

Application Of A Variable Importance Measure Method To Hiv-1 Sequence Data, Merrill D. Birkner, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

van der Laan (2005) proposed a method to construct variable importance measures and provided the respective statistical inference. This technique involves determining the importance of a variable in predicting an outcome. This method can be applied as an inverse probability of treatment weighted (IPTW) or double robust inverse probability of treatment weighted (DR-IPTW) estimator. A respective significance of the estimator is determined by estimating the influence curve and hence determining the corresponding variance and p-value. This article applies the van der Laan (2005) variable importance measures and corresponding inference to HIV-1 sequence data. In this data application, protease and reverse …

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Casual Mediation Analyses With Structural Mean Models, Thomas R. Tenhave, Marshall Joffe, Kevin Lynch, Greg Brown, Stephen Maisto Nov 2005

Casual Mediation Analyses With Structural Mean Models, Thomas R. Tenhave, Marshall Joffe, Kevin Lynch, Greg Brown, Stephen Maisto

UPenn Biostatistics Working Papers

We represent a linear structural mean model (SMM)approach for analyzing mediation of a randomized baseline intervention's effect on a univariate follow-up outcome. Unlike standard mediation analyses, our approach does not assume that the mediating factor is randomly assigned to individuals (i.e., sequential ignorability). Hence, a comparison of the results of the proposed and standard approaches in with respect to mediation offers a sensitivity analyses of the sequential ignorability assumption. The G-estimation procedure for the proposed SMM represents an extension of the work on direct effects of randomized treatment effects for survival outcomes by Robins and Greenland (1994) (Section 5.0 and …

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Development Of Commercial Applications For Recycled Plastics Using Finite Element Analysis, Nanjunda Narasimhamurthy Nov 2005

Development Of Commercial Applications For Recycled Plastics Using Finite Element Analysis, Nanjunda Narasimhamurthy

Theses and Dissertations

This thesis investigates the suitability of thermo-kinetically recycled plastics for use in commercial product applications using finite element analysis and statistics. Different recycled material blends were tested and evaluated for their use in commercial product applications. There are six different blends of thermo-kinetically recycled plastics used for testing and CATIA is used for finite element analysis. The different types of thermo-kinetically recycled plastics blends are: pop bottles made of PolyethyleneTeraphthalate (PET), milk jugs made of High-Density Polyethylene (HDPE), Vinyl seats made of Poly Vinyl Chloride (PVC) and small amount of Polypropylene (PP) and Urethane, electronic scrap made of engineering resins …

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A General Imputation Methodology For Nonparametric Regression With Censored Data, Dan Rubin, Mark J. Van Der Laan Nov 2005

A General Imputation Methodology For Nonparametric Regression With Censored Data, Dan Rubin, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

We consider the random design nonparametric regression problem when the response variable is subject to a general mode of missingness or censoring. A traditional approach to such problems is imputation, in which the missing or censored responses are replaced by well-chosen values, and then the resulting covariate/response data are plugged into algorithms designed for the uncensored setting. We present a general methodology for imputation with the property of double robustness, in that the method works well if either a parameter of the full data distribution (covariate and response distribution) or a parameter of the censoring mechanism is well approximated. These …

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Estimating A Treatment Effect With Repeated Measurements Accounting For Varying Effectiveness Duration, Ying Qing Chen, Jingrong Yang, Su-Chun Cheng Nov 2005

Estimating A Treatment Effect With Repeated Measurements Accounting For Varying Effectiveness Duration, Ying Qing Chen, Jingrong Yang, Su-Chun Cheng

UW Biostatistics Working Paper Series

To assess treatment efficacy in clinical trials, certain clinical outcomes are repeatedly measured for same subject over time. They can be regarded as function of time. The difference in their mean functions between the treatment arms usually characterises a treatment effect. Due to the potential existence of subject-specific treatment effectiveness lag and saturation times, erosion of treatment effect in the difference may occur during the observation period of time. Instead of using ad hoc parametric or purely nonparametric time-varying coefficients in statistical modeling, we first propose to model the treatment effectiveness durations, which are the varying time intervals between the …

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Modeling Differentiated Treatment Effects For Multiple Outcomes Data, Hongfei Guo, Karen Bandeen-Roche Nov 2005

Modeling Differentiated Treatment Effects For Multiple Outcomes Data, Hongfei Guo, Karen Bandeen-Roche

Johns Hopkins University, Dept. of Biostatistics Working Papers

Multiple outcomes data are commonly used to characterize treatment effects in medical research, for instance, multiple symptoms to characterize potential remission of a psychiatric disorder. Often either a global, i.e. symptom-invariant, treatment effect is evaluated. Such a treatment effect may over generalize the effect across the outcomes. On the other hand individual treatment effects, varying across all outcomes, are complicated to interpret, and their estimation may lose precision relative to a global summary. An effective compromise to summarize the treatment effect may be through patterns of the treatment effects, i.e. "differentiated effects." In this paper we propose a two-category model …

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Modeling Distributions Of Test Scores With Mixtures Of Beta Distributions, Jingyu Feng Nov 2005

Modeling Distributions Of Test Scores With Mixtures Of Beta Distributions, Jingyu Feng

Theses and Dissertations

Test score distributions are used to make important instructional decisions about students. The test scores usually do not follow a normal distribution. In some cases, the scores appear to follow a bimodal distribution that can be modeled with a mixture of beta distributions. This bimodality may be due different levels of students' ability. The purpose of this study was to develop and apply statistical techniques for fitting beta mixtures and detecting bimodality in test score distributions. Maximum likelihood and Bayesian methods were used to estimate the five parameters of the beta mixture distribution for scores in four quizzes in a …

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Model Evaluation Based On The Distribution Of Estimated Absolute Prediction Error, Lu Tian, Tianxi Cai, Els Goetghebeur, L. J. Wei Nov 2005

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 …

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Efficacy Studies Of Malaria Treatments In Africa: Efficient Estimation With Missing Indicators Of Failure, Rhoderick N. Machekano, Grant Dorsey, Alan E. Hubbard Nov 2005

Efficacy Studies Of Malaria Treatments In Africa: Efficient Estimation With Missing Indicators Of Failure, Rhoderick N. Machekano, Grant Dorsey, Alan E. Hubbard

U.C. Berkeley Division of Biostatistics Working Paper Series

Efficacy studies of malaria treatments can be plagued by indeterminate outcomes for some patients. The study motivating this paper defines the outcome of interest (treatment failure) as recrudescence and for some subjects, it is unclear whether a recurrence of malaria is due to that or new infection. This results in a specific kind of missing data. The effect of missing data in causal inference problems is widely recognized. Methods that adjust for possible bias from missing data include a variety of imputation procedures (extreme case analysis, hot-deck, single and multiple imputation), inverse weighting methods, and likelihood based methods (data augmentation, …

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Comparison Of Statistical Tests In Logistic Regression: The Case Of Hypernatreamia, Stylianos Katsaragakis, Christos Koukouvinos, Stella Stylianou, Eleni-Maria Theodoraki, Eleni-Maria Theodoraki Nov 2005

Comparison Of Statistical Tests In Logistic Regression: The Case Of Hypernatreamia, Stylianos Katsaragakis, Christos Koukouvinos, Stella Stylianou, Eleni-Maria Theodoraki, Eleni-Maria Theodoraki

Journal of Modern Applied Statistical Methods

The logistic regression has become an integral component of any medical data analysis concerning binary responses. The main issue rising after the adaptation of the final model is its goodness-of-fit. The fit of the model is assessed via the overall measures and summary statistics and comparing them in the case of hypernateamia.

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An Estimator Of Intervention Effect On Disease Severity, David Siev Nov 2005

An Estimator Of Intervention Effect On Disease Severity, David Siev

Journal of Modern Applied Statistical Methods

When a medical intervention prevents a dichotomous outcome, the size of its effect is often estimated with the prevented fraction. Some interventions may reduce the severity of an outcome without entirely preventing it. To quantify the effect of a severity-moderating intervention, a measure termed the mitigated fraction (MF) is proposed. MF has broad applicability, because it measures the overlap of two empirical distributions based on their stochastic ordering. It is also useful in the specific context of medical interventions, because it shares certain structural and functional features with the prevented fraction. The two measures may be applied together …

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Bootstrap Intervals Of The Parameters Of Lognormal Distribution Using Power Rule Model And Accelerated Life Tests, Mohammed Al-Haj Ebrahem Nov 2005

Bootstrap Intervals Of The Parameters Of Lognormal Distribution Using Power Rule Model And Accelerated Life Tests, Mohammed Al-Haj Ebrahem

Journal of Modern Applied Statistical Methods

Assumed that the distribution of the lifetime of any unit follows a lognormal distribution with parameters μ and σ . Also, assume that the relationship between μ and the stress level V is given by the power rule model. Several types of bootstrap intervals of the parameters were studied and their performance was studied using simulations and compared in term of attainment of the nominal confidence level, symmetry of lower and upper error rates and the expected width. Conclusions and recommendations are given.

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Large Sample And Bootstrap Intervals For The Gamma Scale Parameter Based On Grouped Data, Ayman Baklizi, Amjad Al-Nasser Nov 2005

Large Sample And Bootstrap Intervals For The Gamma Scale Parameter Based On Grouped Data, Ayman Baklizi, Amjad Al-Nasser

Journal of Modern Applied Statistical Methods

Interval estimation of the scale parameter of the gamma distribution using grouped data is considered in this article. Exact intervals do not exist and approximate intervals are needed Recently, Chen and Mi (2001) proposed alternative approximate intervals. In this article, some bootstrap and jackknife type intervals are proposed. The performance of these intervals is investigated and compared. The results show that some of the suggested intervals have a satisfactory statistical performance in situations where the sample size is small with heavy proportion of censoring.

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A Comparison Of The Spearman-Brown And Flanagan-Rulon Formulas For Split Half Reliability Under Various Variance Parameter Conditions, David A. Walker Nov 2005

A Comparison Of The Spearman-Brown And Flanagan-Rulon Formulas For Split Half Reliability Under Various Variance Parameter Conditions, David A. Walker

Journal of Modern Applied Statistical Methods

Differences between the Spearman-Brown and Flanagan-Rulon formulas are examined when the variance parameters for two halves of a test had the following ratios: 1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0 and also had a correlation between the two halves of a test at 1.00, .95, .90, .80, .70, .60, .50, .40, .30, .20, .10, .05. It was found that use of the Spearman-Brown formula to estimate the population ρ when the ratio between the standard deviations on two halves of a test is disparate, or beyond .9 to 1.1, was not warranted. Applied and theoretical examples …

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Restricted Quasi-Independent Model Resolves Paradoxical Behaviors Of Cohen’S Kappa, Vicki Stover Hertzberg, Frank Xu, Michael Haber Nov 2005

Restricted Quasi-Independent Model Resolves Paradoxical Behaviors Of Cohen’S Kappa, Vicki Stover Hertzberg, Frank Xu, Michael Haber

Journal of Modern Applied Statistical Methods

Cohen’s kappa, an index of inter-rater agreement, behaves paradoxically in 2×2 tables. λA is derived, an index from the restricted quasi-independent model for 2×2 tables. Simulation studies are used to demonstrate λA has superior performance compared to Scott’s pi. Moreover, λA does not show paradoxical behavior for 2×2 tables.

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Maximum Tests Are Adaptive Permutation Tests, Markus Neuhäeuser, Ludwig A. Hothorn Nov 2005

Maximum Tests Are Adaptive Permutation Tests, Markus Neuhäeuser, Ludwig A. Hothorn

Journal of Modern Applied Statistical Methods

In some areas, e.g., statistical genetics, it is common to apply a maximum test, where the maximum of several competing test statistics is used as a new statistic, and the permutation distribution of the maximum is used for inference. Here, it is shown that maximum tests are special cases of adaptive permutation tests. The 30-year old idea of adaptive statistical tests is more flexible than previously thought when permutation tests are used, and the selector statistic is calculated for every permutation. Because the independence between the selector and the test statistics is no longer needed, the test statistics themselves can …

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Inferences About The Components Of A Generalized Additive Model, Rand R. Wilcox Nov 2005

Inferences About The Components Of A Generalized Additive Model, Rand R. Wilcox

Journal of Modern Applied Statistical Methods

A method for making inferences about the components of a generalized additive model is described. It is found that a variation of the method, based on means, performs well in simulations. Unlike many other inferential methods, switching from a mean to a 20% trimmed mean was found to offer little or no advantage in terms of both power and controlling the probability of a Type I error.

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The Individuals Control Chart In Case Of Non-Normality, BetüL Kan, Berna Yazici Nov 2005

The Individuals Control Chart In Case Of Non-Normality, BetüL Kan, Berna Yazici

Journal of Modern Applied Statistical Methods

This article examines the effects of non-normality as measured by skewness and provides an alternative method of designing individuals control chart with non-normal distributions. A skewness correction method for constructing the individuals control chart is provided. An example of thickness of biscuit process is presented to illustrate the individuals control chart limits.

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An Alternative To Warner’S Randomized Response Model, Sat Gupta, Javid Shabbir Nov 2005

An Alternative To Warner’S Randomized Response Model, Sat Gupta, Javid Shabbir

Journal of Modern Applied Statistical Methods

A modification to Warner’s (1965) Randomized Response Model is suggested. The suggested model is more efficient than the original model.

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Inference For P(Y, Vee Ming Ng Nov 2005

Inference For P(Y, Vee Ming Ng

Journal of Modern Applied Statistical Methods

Some tests and confidence bounds for the reliability parameter R=P(Y

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Determining Parallel Analysis Criteria, Marley W. Watkins Nov 2005

Determining Parallel Analysis Criteria, Marley W. Watkins

Journal of Modern Applied Statistical Methods

Determining the number of factors to extract is a critical decision in exploratory factor analysis. Simulation studies have found the Parallel Analysis criterion to be accurate, but it is computationally intensive. Two freeware programs that implement Parallel Analysis on Macintosh and Windows operating systems are presented.

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