Homeolog Specific Expression Bias, 2016 College of William and Mary

#### Homeolog Specific Expression Bias, Ronald D. Smith

*Biology and Medicine Through Mathematics Conference*

No abstract provided.

Heterogeneous Responses To Viral Infection: Insights From Mathematical Modeling Of Yellow Fever Vaccine, 2016 Emory University

#### Heterogeneous Responses To Viral Infection: Insights From Mathematical Modeling Of Yellow Fever Vaccine, James R. Moore

*Biology and Medicine Through Mathematics Conference*

No abstract provided.

Facets: Allele-Specific Copy Number And Clonal Heterogeneity Analysis Tool Estimates For High-Throughput Dna Sequencing, 2016 Memorial Sloan-Kettering Cancer Center

#### Facets: Allele-Specific Copy Number And Clonal Heterogeneity Analysis Tool Estimates For High-Throughput Dna Sequencing, Ronglai Shen, Venkatraman Seshan

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

Allele-specific copy number analysis (ASCN) from next generation sequenc- ing (NGS) data can greatly extend the utility of NGS beyond the iden- tification of mutations to precisely annotate the genome for the detection of homozygous/heterozygous deletions, copy-neutral loss-of-heterozygosity (LOH), allele-specific gains/amplifications. In addition, as targeted gene panels are increasingly used in clinical sequencing studies for the detection of “actionable” mutations and copy number alterations to guide treatment decisions, accurate, tumor purity-, ploidy-, and clonal heterogeneity-adjusted integer copy number calls are greatly needed to more reliably interpret NGS- based cancer gene copy number data in the context of clinical ...

Interpretable High-Dimensional Inference Via Score Maximization With An Application In Neuroimaging, 2016 University of Pennsylvania

#### Interpretable High-Dimensional Inference Via Score Maximization With An Application In Neuroimaging, Simon N. Vandekar, Philip T. Reiss, Russell T. Shinohara

*UPenn Biostatistics Working Papers*

In the fields of neuroimaging and genetics a key goal is testing the association of a single outcome with a very high-dimensional imaging or genetic variable. Oftentimes summary measures of the high-dimensional variable are created to sequentially test and localize the association with the outcome. In some cases, the results for summary measures are significant, but subsequent tests used to localize differences are underpowered and do not identify regions associated with the outcome. We propose a generalization of Rao's score test based on maximizing the score statistic in a linear subspace of the parameter space. If the test rejects ...

A Link Between Paediatric Asthma And Obesity: Are They Caused By The Same Environmental Conditions?, 2016 The University of Western Ontario

#### A Link Between Paediatric Asthma And Obesity: Are They Caused By The Same Environmental Conditions?, Phylicia Gonsalves

*Electronic Thesis and Dissertation Repository*

The highly associated paediatric conditions of asthma and overweight have seen dramatic increases over the past few decades. This thesis explored air pollution exposure as a potential underlying mechanism of co-morbid asthma and overweight among adolescents aged 12 to 18 years. Data from the Canadian Community Health Survey were merged with a database containing estimates of air pollution as assessed by particulate matter ≤ 2.5 microns (PM_{2.5}) concentrations at the postal code centroid in southwestern Ontario. Logistic regression was used to conduct the analysis. Adolescents were more likely to be overweight as PM_{2.5 }concentrations increased. There ...

Walking To Recovery - The Effects Of Postsurgical Ambulation On Patient Recovery Times, 2016 University of Tennessee, Knoxville

#### Walking To Recovery - The Effects Of Postsurgical Ambulation On Patient Recovery Times, Trent William Stethen

*University of Tennessee Honors Thesis Projects*

No abstract provided.

Methods For Dealing With Death And Missing Data, And For Standardizing Different Health Variables In Longitudinal Datasets: The Cardiovascular Health Study, 2016 University of Washington

#### Methods For Dealing With Death And Missing Data, And For Standardizing Different Health Variables In Longitudinal Datasets: The Cardiovascular Health Study, Paula Diehr

*UW Biostatistics Working Paper Series*

Longitudinal studies of older adults usually need to account for deaths and missing data. The study databases often include multiple health-related variables, whose trends over time are hard to compare because they were measured on different scales. Here we present a unified approach to these three problems that was developed and used in the Cardiovascular Health Study. Data were first transformed to a new scale that had integer/ratio properties, and on which “dead” logically takes the value zero. Missing data were then imputed on this new scale, using each person’s own data over time. Imputation could thus be ...

Stochastic Optimization Of Adaptive Enrichment Designs For Two Subpopulations, 2016 Johns Hopkins University Bloomberg School of Public Health

#### Stochastic Optimization Of Adaptive Enrichment Designs For Two Subpopulations, Aaron Fisher, Michael Rosenblum

*Johns Hopkins University, Dept. of Biostatistics Working Papers*

An adaptive enrichment design is a randomized trial that allows enrollment criteria to be modified at interim analyses, based on preset decision rules. When there is prior uncertainty regarding treatment effect heterogeneity, these trials can provide improved power for detecting treatment effects in subpopulations. An obstacle to using these designs is that there is no general approach to determine what decision rules and other design parameters will lead to good performance for a given research problem. To address this, we present a simulated annealing approach for optimizing the parameters of an adaptive enrichment design for a given scientific application. Optimization ...

Statistical Interpretation Including The Appropriate Statistical Tests, 2016 University of Kentucky

#### Statistical Interpretation Including The Appropriate Statistical Tests, Olga A. Vsevolozhskaya

*Olga A. Vsevolozhskaya*

Outline:

- Evaluation of treatment’s therapeutic potential after experimental stroke.
- Post-stroke behavioral testing and functional recovery.

Data-Adaptive Inference Of The Optimal Treatment Rule And Its Mean Reward. The Masked Bandit, 2016 Université Paris Ouest Nanterre

#### Data-Adaptive Inference Of The Optimal Treatment Rule And Its Mean Reward. The Masked Bandit, Antoine Chambaz, Wenjing Zheng, Mark J. Van Der Laan

*U.C. Berkeley Division of Biostatistics Working Paper Series*

This article studies the data-adaptive inference of an optimal treatment rule. A treatment rule is an individualized treatment strategy in which treatment assignment for a patient is based on her measured baseline covariates. Eventually, a reward is measured on the patient. We also infer the mean reward under the optimal treatment rule. We do so in the so called non-exceptional case, i.e., assuming that there is no stratum of the baseline covariates where treatment is neither beneficial nor harmful, and under a companion margin assumption.

Our pivotal estimator, whose definition hinges on the targeted minimum loss estimation (TMLE) principle ...

Recommendation To Use Exact P-Values In Biomarker Discovery Research, 2016 Fred Hutchinson Cancer Rsrch Center

#### Recommendation To Use Exact P-Values In Biomarker Discovery Research, Margaret Sullivan Pepe, Matthew F. Buas, Christopher I. Li, Garnet L. Anderson

*UW Biostatistics Working Paper Series*

Background: In biomarker discovery studies, markers are ranked for validation using *P*-values. Standard *P*-value calculations use normal approximations that may not be valid for small *P*-values and small sample sizes common in discovery research.

Methods: We compared exact *P*-values, valid by definition, with normal and logit-normal approximations in a simulated study of 40 cases and 160 controls. The key measure of biomarker performance was sensitivity at 90% specificity. Data for 3000 uninformative markers and 30 true markers were generated randomly, with 10 replications of the simulation. We also analyzed real data on 2371 antibody array markers ...

One-Step Targeted Minimum Loss-Based Estimation Based On Universal Least Favorable One-Dimensional Submodels, 2016 University of California, Berkeley, Division of Biostatistics

#### One-Step Targeted Minimum Loss-Based Estimation Based On Universal Least Favorable One-Dimensional Submodels, Mark J. Van Der Laan, Susan Gruber

*U.C. Berkeley Division of Biostatistics Working Paper Series*

Consider a study in which one observes n independent and identically distributed random variables whose probability distribution is known to be an element of a particular statistical model, and one is concerned with estimation of a particular real valued pathwise differentiable target parameter of this data probability distribution. The targeted maximum likelihood estimator (TMLE) is an asymptotically efficient substitution estimator obtained by constructing a so called least favorable parametric submodel through an initial estimator with score, at zero fluctuation of the initial estimator, that spans the efficient influence curve, and iteratively maximizing the corresponding parametric likelihood till no more updates ...

Cross-Validated Targeted Minimum-Loss-Based Estimation, 2016 University of California - Berkeley

#### Cross-Validated Targeted Minimum-Loss-Based Estimation, Wenjing Zheng, Mark Van Der Laan

*Wenjing Zheng*

No abstract provided.

Targeted Covariate-Adjusted Response-Adaptive Lasso-Based Randomized Controlled Trials, 2016 Université Paris Ouest Nanterre

#### Targeted Covariate-Adjusted Response-Adaptive Lasso-Based Randomized Controlled Trials, Antoine Chambaz, Wenjing Zheng, Mark Van Der Laan

*Wenjing Zheng*

We present a new covariate-adjusted response-adaptive randomized controlled trial design and inferential procedure built on top of it. The procedure is targeted in the sense that (i) the sequence of randomization schemes is group-sequentially determined by targeting a user-specified optimal randomization design based on accruing data and, (ii) our estimator of the user-specified parameter of interest, seen as the value of a functional evaluated at the true, unknown distribution of the data, is targeted toward it by following the paradigm of targeted minimum loss estimation. We focus for clarity on the case that the parameter of interest is the marginal ...

Estimation And Hypothesis Testing For Four Types Of Change Point Regression Models With Non-Thresholded Covariates, 2016 Fred Hutchinson Cancer Research Institute

#### Estimation And Hypothesis Testing For Four Types Of Change Point Regression Models With Non-Thresholded Covariates, Youyi Fong, Ying Huang, Peter Gilbert, Sallie Permar

*UW Biostatistics Working Paper Series*

Change point models are a diverse set of non-regular models that all depend on change points or thresholds. Many software implementations exist for change point models that are aimed at detecting structural changes in a time series. Motivated by non-time series biometrical applications, the R package \textit{chngpt} provides estimation and hypothesis testing functionalities for four variants of change point regression models that allow covariates not subject to thresholding. We illustrate its usage with a real example from the study of immune responses associated with reduction in mother-to-child-transmission of HIV-1 viruses.

Maximum Likelihood Based Analysis Of Equally Spaced Longitudinal Count Data With Specified Marginal Means, First-Order Antedependence, And Linear Conditional Expectations, 2016 Division of Biostatistics, University of Pennsylvania Perelman School of Medicine; Boehringer-Ingelheim Pharmaceuticals, Inc.

#### Maximum Likelihood Based Analysis Of Equally Spaced Longitudinal Count Data With Specified Marginal Means, First-Order Antedependence, And Linear Conditional Expectations, Victoria Gamerman, Matthew Guerra, Justine Shults

*UPenn Biostatistics Working Papers*

This manuscript implements a maximum likelihood based approach that is appropriate for equally spaced longitudinal count data with over-dispersion, so that the variance of the outcome variable is larger than expected for the assumed Poisson distribution. We implement the proposed method in the analysis of two data sets and make comparisons with the semi-parametric generalized estimating equations (GEE) approach that incorrectly ignores the over-dispersion. The simulations demonstrate that the proposed method has better small sample efficiency than GEE. We also provide code in R that can be used to recreate the analysis results that we provide in this manuscript.

Conditional Screening For Ultra-High Dimensional Covariates With Survival Outcomes, 2016 Michigan State University

#### Conditional Screening For Ultra-High Dimensional Covariates With Survival Outcomes, Hyokyoung Grace Hong, Jian Kang, Yi Li

*The University of Michigan Department of Biostatistics Working Paper Series*

Identifying important biomarkers that are predictive for cancer patients' prognosis is key in gaining better insights into the biological influences on the disease and has become a critical component of precision medicine. The emergence of large-scale biomedical survival studies, which typically involve excessive number of biomarkers, has brought high demand in designing efficient screening tools for selecting predictive biomarkers. The vast amount of biomarkers defies any existing variable selection methods via regularization. The recently developed variable screening methods, though powerful in many practical setting, fail to incorporate prior information on the importance of each biomarker and are less powerful in ...

Marginal Structural Models With Counterfactual Effect Modifiers, 2016 University of California, Berkeley, Division of Biostatistics

#### Marginal Structural Models With Counterfactual Effect Modifiers, Wenjing Zheng, Zhehui Luo, Mark J. Van Der Laan

*U.C. Berkeley Division of Biostatistics Working Paper Series*

In health and social sciences, research questions often involve systematic assessment of the modification of treatment causal effect by patient characteristics, in longitudinal settings with time-varying or post-intervention effect modifiers of interest. In this work, we investigate the robust and efficient estimation of the so-called Counterfactual-History-Adjusted Marginal Structural Model (van der Laan and Petersen (2007)), which models the conditional intervention-specific mean outcome given modifier history in an ideal experiment where, possible contrary to fact, the subject was assigned the intervention of interest, including the treatment sequence in the conditioning history. We establish the semiparametric efficiency theory for these models, and ...

Simulating Longer Vectors Of Correlated Binary Random Variables Via Multinomial Sampling, 2016 Division of Biostatistics, Univeristy of Pennsylvania Perelman School of Medicine

#### Simulating Longer Vectors Of Correlated Binary Random Variables Via Multinomial Sampling, Justine Shults

*UPenn Biostatistics Working Papers*

The ability to simulate correlated binary data is important for sample size calculation and comparison of methods for analysis of clustered and longitudinal data with dichotomous outcomes. One available approach for simulating length n vectors of dichotomous random variables is to sample from the multinomial distribution of all possible length n permutations of zeros and ones. However, the multinomial sampling method has only been implemented in general form (without ﬁrst making restrictive assumptions) for vectors of length 2 and 3, because specifying the multinomial distribution is very challenging for longer vectors. I overcome this diﬃculty by presenting an algorithm for ...

Strengthening Instrumental Variables Through Weighting, 2016 The University Of Michigan

#### Strengthening Instrumental Variables Through Weighting, Douglas Lehmann, Yun Li, Rajiv Saran, Yi Li

*The University of Michigan Department of Biostatistics Working Paper Series*

Instrumental variable (IV) methods are widely used to deal with the issue of unmeasured confounding and are becoming popular in health and medical research. IV models are able to obtain consistent estimates in the presence of unmeasured confounding, but rely on assumptions that are hard to verify and often criticized. An instrument is a variable that influences or encourages individuals toward a particular treatment without directly affecting the outcome. Estimates obtained using instruments with a weak influence over the treatment are known to have larger small-sample bias and to be less robust to the critical IV assumption that the instrument ...