Biostatistics Commons^™

A Maximum Pseudo-Likelihood Approach For Estimating Species Trees Under The Coalescent Model, Liang Liu, Lili Yu, Scott V. Edwards

Biostatistics: Faculty Publications

Background

Several phylogenetic approaches have been developed to estimate species trees from collections of gene trees. However, maximum likelihood approaches for estimating species trees under the coalescent model are limited. Although the likelihood of a species tree under the multispecies coalescent model has already been derived by Rannala and Yang, it can be shown that the maximum likelihood estimate (MLE) of the species tree (topology, branch lengths, and population sizes) from gene trees under this formula does not exist. In this paper, we develop a pseudo-likelihood function of the species tree to obtain maximum pseudo-likelihood estimates (MPE) of species trees, …

Population Value Decomposition, A Framework For The Analysis Of Image Populations, Ciprian M. Crainiceanu, Brian S. Caffo, Sheng Luo, Vadim Zipunnikov

Johns Hopkins University, Dept. of Biostatistics Working Papers

Images, often stored in multidimensional arrays are fast becoming ubiquitous in medical and public health research. Analyzing populations of images is a statistical problem that raises a host of daunting challenges. The most severe challenge is that data sets incorporating images recorded for hundreds or thousands of subjects at multiple visits are massive. We introduce the population value decomposition (PVD), a general method for simultaneous dimensionality reduction of large populations of massive images. We show how PVD can seamlessly be incorporated into statistical modeling and lead to a new, transparent and fast inferential framework. Our methodology was motivated by and …

Targeted Bayesian Learning, Ivan Diaz Munoz, Alan E. Hubbard, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

Targeted maximum likelihood estimation (van der Laan & Rubin 2006) is a loss-based semi-parametric estimation method that yields a substitution estimator of a target parameter of the probability distribution of the data that solves the efficient influence curve estimating equation, and thereby yields a double robust locally efficient estimator of the parameter of interest, under regularity conditions. The Bayesian paradigm is concerned with including the researcher’s prior uncertainty about the parameter through a prior distribution, which combined with the likelihood yields a posterior distribution for the parameter that reflects the researcher’s posterior uncertainty. In this paper, we present a way …

The Pathways To Mental Health Care Of First-Episode Psychosis Patients: A Systematic Review., Kelly K. Anderson, Rebecca Fuhrer, Ashok K. Malla

Epidemiology and Biostatistics Publications

BACKGROUND: Although there is agreement on the association between delay in treatment of psychosis and outcome, less is known regarding the pathways to care of patients suffering from a first psychotic episode. Pathways are complex, involve a diverse range of contacts, and are likely to influence delay in treatment. We conducted a systematic review on the nature and determinants of the pathway to care of patients experiencing a first psychotic episode.

METHOD: We searched four databases (Medline, HealthStar, EMBASE, PsycINFO) to identify articles published between 1985 and 2009. We manually searched reference lists and relevant journals and used forward citation …

Landmark Prediction Of Survival, Layla Parast, Tianxi Cai

Harvard University Biostatistics Working Paper Series

No abstract provided.

Diagnosing And Responding To Violations In The Positivity Assumption, Maya L. Petersen, Kristin Porter, Susan Gruber, Yue Wang, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

The assumption of positivity or experimental treatment assignment requires that observed treatment levels vary within confounder strata. This article discusses the positivity assumption in the context of assessing model and parameter-specific identifiability of causal effects. Positivity violations occur when certain subgroups in a sample rarely or never receive some treatments of interest. The resulting sparsity in the data may increase bias with or without an increase in variance and can threaten valid inference. The parametric bootstrap is presented as a tool to assess the severity of such threats and its utility as a diagnostic is explored using simulated data. Several …

Stratifying Subjects For Treatment Selection With Censored Event Time Data From A Comparative Study, Lihui Zhao, Tianxi Cai, Lu Tian, Hajime Uno, Scott D. Solomon, L. J. Wei

Harvard University Biostatistics Working Paper Series

No abstract provided.

Spousal Concordance In Academic Achievements And Intelligence And Family-Based Association Studies Identified Novel Loci Associated With Intelligence., Yue Pan

Electronic Theses and Dissertations

Assortative Mating, the tendency for mate selection to occur on the basis of similar traits, plays an essential role in understanding the genetic variation on academic achievements and intelligence (IQ). It is an important mechanism explaining spousal concordance. We used principal component analysis (PCA) for spousal correlation. There is a significant positive correlation between spouses by the new variable PC1 (correlation coefficient=0.515, p<0.0001). We further research the genetic factor that affects IQ by using the same data. We performed a low density genome-wide association (GWA) analysis with a family-based association test to identify genetic variants that associated with intelligence as measured by WAIS full-score IQ (FSIQ). NTM at 11q25 (rs411280, p=0.000764) and NR3C2 at 4q31.23 (rs3846329, p=0.000675) were 2 novel genes that haven't been associated with IQ from other studies. This study may serve as a resource for replication in other populations and a foundation for future investigations.

A Bayesian Approach To Dose-Response Assessment And Drug-Drug Interaction Analysis: Application To In Vitro Studies, Violeta G. Hennessey

Dissertations and Theses (Open Access)

The considerable search for synergistic agents in cancer research is motivated by the therapeutic benefits achieved by combining anti-cancer agents. Synergistic agents make it possible to reduce dosage while maintaining or enhancing a desired effect. Other favorable outcomes of synergistic agents include reduction in toxicity and minimizing or delaying drug resistance. Dose-response assessment and drug-drug interaction analysis play an important part in the drug discovery process, however analysis are often poorly done. This dissertation is an effort to notably improve dose-response assessment and drug-drug interaction analysis.

The most commonly used method in published analysis is the Median-Effect Principle/Combination Index method …

Principled Sure Independence Screening For Cox Models With Ultra-High-Dimensional Covariates, Sihai Dave Zhao, Yi Li

Harvard University Biostatistics Working Paper Series

No abstract provided.

An Inferential Framework For Network Hypothesis Tests: With Applications To Biological Networks, Phillip Yates

Theses and Dissertations

The analysis of weighted co-expression gene sets is gaining momentum in systems biology. In addition to substantial research directed toward inferring co-expression networks on the basis of microarray/high-throughput sequencing data, inferential methods are being developed to compare gene networks across one or more phenotypes. Common gene set hypothesis testing procedures are mostly confined to comparing average gene/node transcription levels between one or more groups and make limited use of additional network features, e.g., edges induced by significant partial correlations. Ignoring the gene set architecture disregards relevant network topological comparisons and can result in familiar n<

Estimation Of Causal Effects Of Community Based Interventions, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

Suppose one assigns two interventions to a small number K of different populations or communities, and one measures covariates and outcomes on a random sample of independent individuals from each of the K populations. We investigate the problem of identification and estimation of the causal effect of the choice of intervention assigned at the community level, and, if the intervention is time-dependent, the causal effect of the changes in the intervention at time t, on the outcome. The challenge one is confronted with is that different populations have different environmental factors and that the intervention and environment are assigned to …

Multi-State Life Tables, Equilibrium Prevalence, And Baseline Selection Bias, Paula Diehr, David Yanez

UW Biostatistics Working Paper Series

Consider a 3-state system with one absorbing state, such as Healthy, Sick, and Dead. If the system satisfies the 1-step Markov conditions, the prevalence of the Healthy state will converge to a value that is independent of the initial distribution. This equilibrium prevalence and its variance are known under the assumption of time homogeneity, and provided reasonable estimates in the time non-homogeneous systems studied. Here, we derived the equilibrium prevalence for a system with more than three states. Under time homogeneity, the equilibrium prevalence distribution was shown to be an eigenvector of a partition of the matrix of transition probabilities. …

An Empirical Approach To Evaluating Sufficient Similarity: Utilization Of Euclidean Distance As A Similarity Measure, Scott Marshall

Theses and Dissertations

Individuals are exposed to chemical mixtures while carrying out everyday tasks, with unknown risk associated with exposure. Given the number of resulting mixtures it is not economically feasible to identify or characterize all possible mixtures. When complete dose-response data are not available on a (candidate) mixture of concern, EPA guidelines define a similar mixture based on chemical composition, component proportions and expert biological judgment (EPA, 1986, 2000). Current work in this literature is by Feder et al. (2009), evaluating sufficient similarity in exposure to disinfection by-products of water purification using multivariate statistical techniques and traditional hypothesis testing. The work of …

Cost And Accuracy Comparisons In Medical Testing Using Sequential Testing Strategies, Anwar Ahmed

Theses and Dissertations

The practice of sequential testing is followed by the evaluation of accuracy, but often not by the evaluation of cost. This research described and compared three sequential testing strategies: believe the negative (BN), believe the positive (BP) and believe the extreme (BE), the latter being a less-examined strategy. All three strategies were used to combine results of two medical tests to diagnose a disease or medical condition. Descriptions of these strategies were provided in terms of accuracy (using the maximum receiver operating curve or MROC) and cost of testing (defined as the proportion of subjects who need 2 tests to …

Powerful Snp Set Analysis For Case-Control Genome Wide Association Studies, Michael C. Wu, Peter Kraft, Michael P. Epstein, Deanne M. Taylor, Stephen J. Chanock, David J. Hunter, Xihong Lin

Harvard University Biostatistics Working Paper Series

No abstract provided.

Estimating Causal Effects In Trials Involving Multi-Treatment Arms Subject To Non-Compliance: A Bayesian Frame-Work, Qi Long, Roderick J. Little, Xihong Lin

Harvard University Biostatistics Working Paper Series

No abstract provided.

A Targeted Maximum Likelihood Estimator Of A Causal Effect On A Bounded Continuous Outcome, Susan Gruber, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

Targeted maximum likelihood estimation of a parameter of a data generating distribution, known to be an element of a semiparametric model, involves constructing a parametric model through an initial density estimator with parameter epsilon representing an amount of fluctuation of the initial density estimator, where the score of this fluctuation model at epsilon=0 equals the efficient influence curve/canonical gradient. The latter constraint can be satisfied by many parametric fluctuation models, since it represents only a local constraint of its behavior at zero fluctuation. However, it is very important that the fluctuations stay within the semiparametric model for the observed data …

Super Learner In Prediction, Eric C. Polley, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

Super learning is a general loss based learning method that has been proposed and analyzed theoretically in van der Laan et al. (2007). In this article we consider super learning for prediction. The super learner is a prediction method designed to find the optimal combination of a collection of prediction algorithms. The super learner algorithm finds the combination of algorithms minimizing the cross-validated risk. The super learner framework is built on the theory of cross-validation and allows for a general class of prediction algorithms to be considered for the ensemble. Due to the previously established oracle results for the cross-validation …

Assessing Noninferiority In A Three-Arm Trial Using The Bayesian Approach, Pulak Ghosh, Farouk S. Nathoo, Mithat Gonen, Ram C. Tiwari

Memorial Sloan-Kettering Cancer Center, Dept. of Epidemiology & Biostatistics Working Paper Series

Non-inferiority trials, which aim to demonstrate that a test product is not worse than a competitor by more than a pre-specified small amount, are of great importance to the pharmaceutical community. As a result, methodology for designing and analyzing such trials is required, and developing new methods for such analysis is an important area of statistical research. The three-arm clinical trial is usually recommended for non-inferiority trials by the Food and Drug Administration (FDA). The three-arm trial consists of a placebo, a reference, and an experimental treatment, and simultaneously tests the superiority of the reference over the placebo along with …