Biostatistics Commons^™

The Effects Of 24 Weeks Of Resistance Training With Simultaneous Elastic And Free Weight Loading On Muscular Performance Of Novice Lifters, Todd C. Shoepe, David A. Ramirez, Robert J. Rovetti, David R. Kohler, Hawley C. Almstedt

Hawley Almstedt

The purpose of this investigation was to assess the effectiveness of variable resistance as provided through elastic plus free weight techniques in college aged males and females. Twenty novice lifters were randomly assigned to a traditional free weight only (6 males and 5 females) or elastic band plus free weight group (5 males and 5 females) and 9 more normally active controls (5 males and 4 females), were recruited to maintain normal activity for the duration of the study. No differences existed between control, free weight and elastic band at baseline for age, body height, body mass, body mass index, …

The Effects Of 24 Weeks Of Resistance Training With Simultaneous Elastic And Free Weight Loading On Muscular Performance Of Novice Lifters, Todd C. Shoepe, David A. Ramirez, Robert J. Rovetti, David R. Kohler, Hawley C. Almstedt

Todd Shoepe

The purpose of this investigation was to assess the effectiveness of variable resistance as provided through elastic plus free weight techniques in college aged males and females. Twenty novice lifters were randomly assigned to a traditional free weight only (6 males and 5 females) or elastic band plus free weight group (5 males and 5 females) and 9 more normally active controls (5 males and 4 females), were recruited to maintain normal activity for the duration of the study. No differences existed between control, free weight and elastic band at baseline for age, body height, body mass, body mass index, …

Movelets: A Dictionary Of Movement, Jiawei Bai, Jeff Goldsmith, Brian Caffo, Thomas A. Glass, Ciprian M. Crainiceanu

Johns Hopkins University, Dept. of Biostatistics Working Papers

Recent technological advances provide researchers a way of gathering real-time information on an individual’s movement through the use of wearable devices that record acceleration. In this paper, we propose a method for identifying activity types, like walking, standing, and resting, from acceleration data. Our approach decomposes movements into short components called “movelets”, and builds a reference for each activity type. Unknown activities are predicted by matching new movelets to the reference. We apply our method to data collected from a single, three-axis accelerometer and focus on activities of interest in studying physical function in elderly populations. An important technical advantage …

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

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 …

Effectively Selecting A Target Population For A Future Comparative Study, Lihui Zhao, Lu Tian, Tianxi Cai, Brian Claggett, L. J. Wei

Harvard University Biostatistics Working Paper Series

When comparing a new treatment with a control in a randomized clinical study, the treatment effect is generally assessed by evaluating a summary measure over a specific study population. The success of the trial heavily depends on the choice of such a population. In this paper, we show a systematic, effective way to identify a promising population, for which the new treatment is expected to have a desired benefit, using the data from a current study involving similar comparator treatments. Specifically, with the existing data we first create a parametric scoring system using multiple covariates to estimate subject-specific treatment differences. …

Targeted Minimum Loss Based Estimation Of An Intervention Specific Mean Outcome, Mark J. Van Der Laan, Susan Gruber

U.C. Berkeley Division of Biostatistics Working Paper Series

Targeted minimum loss based estimation (TMLE) provides a template for the construction of semiparametric locally efficient double robust substitution estimators of the target parameter of the data generating distribution in a semiparametric censored data or causal inference model based on a sample of independent and identically distributed copies from this data generating distribution (van der Laan and Rubin (2006), van der Laan (2008), van der Laan and Rose (2011)). TMLE requires 1) writing the target parameter as a particular mapping from a typically infinite dimensional parameter of the probability distribution of the unit data structure into the parameter space, 2) …

Population Intervention Causal Effects Based On Stochastic Interventions, Ivan Diaz Munoz, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

Estimating the causal effect of an intervention on a population typically involves defining parameters in a nonparametric structural equation model (Pearl, 2000, NPSEM) in which the treatment or exposure is deter- ministically assigned in a static or dynamic way. We define a new causal parameter that takes into account the fact that intervention policies can result in stochastically assigned exposures. The statistical parameter that identifies the causal parameter of interest is established. Inverse probability of treatment weighting (IPTW), augmented IPTW (A-IPTW), and targeted maximum likelihood estimators (TMLE) are developed. A simulation study is performed to demonstrate the properties of these …

Prediction Of Dna Methylation Based On Genomic Architecture And Applications Of Positional Weight Matrices, Juan Gallegos

Dissertations & Theses (Open Access)

Gene silencing due to epigenetic mechanisms shows evidence of significant contributions to cancer development. We hypothesis that the genetic architecture based on retrotransposon elements surrounding the transcription start site, plays an important role in the suppression and promotion of DNA methylation. In our investigation we found a high rate of SINE and LINEs retrotransposon elements near the transcription start site of unmethylated genes when compared to methylated genes. The presence of these elements were positively associated with promoter methylation, contrary to logical expectations, due to the malicious effects of retrotransposon elements which insert themselves randomly into the genome causing possible …

Bayesian Phase I Dose Finding In Cancer Trials, Lin Yang

Dissertations & Theses (Open Access)

This dissertation explores phase I dose-finding designs in cancer trials from three perspectives: the alternative Bayesian dose-escalation rules, a design based on a time-to-dose-limiting toxicity (DLT) model, and a design based on a discrete-time multi-state (DTMS) model.

We list alternative Bayesian dose-escalation rules and perform a simulation study for the intra-rule and inter-rule comparisons based on two statistical models to identify the most appropriate rule under certain scenarios. We provide evidence that all the Bayesian rules outperform the traditional ``3+3'' design in the allocation of patients and selection of the maximum tolerated dose.

The design based on a time-to-DLT model …

Gene By Bmi Interactions Influencing C-Reactive Protein Levels In European-Americans, Sarah Tudor

Dissertations & Theses (Open Access)

C-Reactive Protein (CRP) is a biomarker indicating tissue damage, inflammation, and infection. High-sensitivity CRP (hsCRP) is an emerging biomarker often used to estimate an individual’s risk for future coronary heart disease (CHD). hsCRP levels falling below 1.00 mg/l indicate a low risk for developing CHD, levels ranging between 1.00 mg/l and 3.00 mg/l indicate an elevated risk, and levels exceeding 3.00 mg/l indicate high risk. Multiple Genome-Wide Association Studies (GWAS) have identified a number of genetic polymorphisms which influence CRP levels. SNPs implicated in such studies have been found in or near genes of interest including: CRP, APOE, APOC, IL-6, …

A Comparison Of Spatio-Temporal Prediction Methods Of Cancer Incidence In The U.S, Michelle Hamlyn

UNLV Theses, Dissertations, Professional Papers, and Capstones

Cancer is the cause of one out of four deaths in the United States, and in 2009, researchers expected over 1.5 million new patients to be diagnosed with some form of cancer. People diagnosed with cancer, whether a common or rare type, need to undergo treatments, the amount and kind of which will depend on the severity of the cancer. So how do healthcare providers know how much funding is needed for treatment? What would better enable a pharmaceutical company to determine how much to allocate for research and development of drugs, the amount of each drug to manufacture, or …

Who Is The Research Subject In Cluster Randomized Trials In Health Research?, Andrew Mcrae, Charles Weijer, Ariella Binik, Angela White, Jeremy Grimshaw, Robert Boruch, Jamie Brehaut, Allan Donner, Martin Eccles, Raphael Saginur, Merrick Zwarenstein, Monica Taljaard

Charles Weijer

This article is part of a series of papers examining ethical issues in cluster randomized trials (CRTs) in health research. In the introductory paper in this series, we set out six areas of inquiry that must be addressed if the CRT is to be set on a firm ethical foundation. This paper addresses the first of the questions posed, namely, who is the research subject in a CRT in health research? The identification of human research subjects is logically prior to the application of protections as set out in research ethics and regulation. Aspects of CRT design, including the fact …

Asymptotic Theory For Cross-Validated Targeted Maximum Likelihood Estimation, Wenjing Zheng, Mark J. Van Der Laan

Wenjing Zheng

We consider a targeted maximum likelihood estimator of a path-wise differentiable parameter of the data generating distribution in a semi-parametric model based on observing n independent and identically distributed observations. The targeted maximum likelihood estimator (TMLE) uses V-fold sample splitting for the initial estimator in order to make the TMLE maximally robust in its bias reduction step. We prove a general theorem that states asymptotic efficiency (and thereby regularity) of the targeted maximum likelihood estimator when the initial estimator is consistent and a second order term converges to zero in probability at a rate faster than the square root of …

Targeted Maximum Likelihood Estimation Of Natural Direct Effect, Wenjing Zheng, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

In many causal inference problems, one is interested in the direct causal effect of an exposure on an outcome of interest that is not mediated by certain intermediate variables. Robins and Greenland (1992) and Pearl (2000) formalized the definition of two types of direct effects (natural and controlled) under the counterfactual framework. Since then, identifiability conditions for these effects have been studied extensively. By contrast, considerably fewer efforts have been invested in the estimation problem of the natural direct effect. In this article, we propose a semiparametric efficient, multiply robust estimator for the natural direct effect of a binary treatment …

The Use Of Imputed Values In The Meta-Analysis Of Genome-Wide Association Studies., Shuo Jiao, Li Hsu, Carolyn Hutter, Ulrike Peters

Shuo Jiao

In genome-wide association studies (GWAS), it is a common practice to impute the genotypes of untyped single nucleotide polymorphism (SNP) by exploiting the linkage disequilibrium structure among SNPs. The use of imputed genotypes improves genome coverage and makes it possible to perform meta-analysis combining results from studies genotyped on different platforms. A popular way of using imputed data is the "expectation-substitution" method, which treats the imputed dosage as if it were the true genotype. In current practice, the estimates given by the expectation-substitution method are usually combined using inverse variance weighting (IVM) scheme in meta-analysis. However, the IVM is not …

On The Covariate-Adjusted Estimation For An Overall Treatment Difference With Data From A Randomized Comparative Clinical Trial, Lu Tian, Tianxi Cai, Lihui Zhao, L. J. Wei

Harvard University Biostatistics Working Paper Series

No abstract provided.

Meta-Analysis Of New Genome-Wide Association Studies Of Colorectal Cancer Risk.

Shuo Jiao

Colorectal cancer is the second leading cause of cancer death in developed countries. Genome-wide association studies (GWAS) have successfully identified novel susceptibility loci for colorectal cancer. To follow up on these findings, and try to identify novel colorectal cancer susceptibility loci, we present results for GWAS of colorectal cancer (2,906 cases, 3,416 controls) that have not previously published main associations. Specifically, we calculated odds ratios and 95% confidence intervals using log-additive models for each study. In order to improve our power to detect novel colorectal cancer susceptibility loci, we performed a meta-analysis combining the results across studies. We selected the …

Targeted Minimum Loss Based Estimation Based On Directly Solving The Efficient Influence Curve Equation, Paul Chaffee, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

Applying targeted maximum likelihood estimation to longitudinal data can be computationally intensive. As the number of time points and/or number of intermediate factors grows, the computation resources consumed by these algorithms likewise increases. Different TMLE algorithms have different computational speeds and implementation challenges; there may also be efficiency differences of the corresponding estimators. The algorithm we describe here proceeds by solving the empirical efficient influence curve equation directly using numerical computation methods, rather than indirectly (by solving a score equation), which is the usual route. We believe that this estimator is the simplest of the TMLE procedures to implement in …

Targeted Methods For Finding Quantitative Trait Loci, Hui Wang, Sherri Rose, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

Conventional genetic mapping methods typically assume parametric models with Gaussian errors, and obtain parameter estimates through maximum likelihood estimation. We propose a general semiparametric model to map quantitative trait loci (QTL) in experimental crosses. In contrast with widely-used interval mapping (IM) derived methods, our model requires fewer assumptions and also accommodates various machine learning algorithms. Estimation using both targeted maximum likelihood and collaborative targeted maximum likelihood methods is compared to a composite interval mapping (CIM) approach. We demonstrate with simulations and real data analyses that, on average, our semiparametric targeted learning approach produces less biased QTL effect estimates than those …