Clinical Trials Commons^™

Volunteer Studies In Pain Research — Opportunities And Challenges To Replace Animal Experiments: The Report And Recommendations Of A Focus On Alternatives Workshop, C. K. Langley, Q. Aziz, C. Bountra, N. Gordon, P. Hawkins, A. Jones, G. Langley, T. Nurmikko, I. Tracey

Experimentation Collection

Despite considerable research, effective and safe treatments for human pain disorders remain elusive. Understanding the biology of different human pain conditions and researching effective treatments continue to be dominated by animal models, some of which are of limited value. British and European legislation demands that non-animal approaches should be considered before embarking on research using experimental animals. Recent scientific and technical developments, particularly in human neuroimaging, offer the potential to replace some animal procedures in the study of human pain. A group of pain research experts from academia and industry met with the aim of exploring creatively the tools, strategies …

The Calculation Of The 97.5% Upper Confidence Bound: Application To Clustered Binary Data In A Binomial Non-Inferiority Two-Sample Trial., William F. Mccarthy

COBRA Preprint Series

This paper will discuss the analysis of a cluster randomized binomial non-inferiority two-sample trial. The determination of the intra-cluster correlation coefficient (ICC) and its use in the calculation of the 97.5% upper confidence bound for delta, the true difference in binomial proportions between the active control and the experimental treatment groups, will be outlined.

The Design And Sample Size Requirement For A Cluster Randomized Non-Inferiority Trial With Two Binary Co-Primary Outcomes., William F. Mccarthy

COBRA Preprint Series

This paper will discuss the design and sample size requirement for a cluster randomized non-inferiority trial with two binary co-primary outcomes. A hypothetical study (the EXAMPLE Trial) will be considered.

Lets assume the EXAMPLE Trial will consist of two separate binomial non-inferiority two-sample trials. Trial 1: the Coronary Artery Disease known population (co-primary 1) and Trial 2: the Coronary Artery Disease unknown population (co-primary 2). A physician-month cluster randomization scheme will be used. That is, for each trial (trial 1 and trial 2) every month for a 12-month period, each physician participating in the EXAMPLE Trial will be allocated a …

A Comparison Of Methods For Estimating The Causal Effect Of A Treatment In Randomized Clinical Trials Subject To Noncompliance, Rod Little, Qi Long, Xihong Lin

Harvard University Biostatistics Working Paper Series

No abstract provided.

Analysis Of Subgroup Effects In Randomized Trials When Subgroup Membership Is Informatively Missing: Application To The Madit Ii Study, Daniel O. Scharfstein, Georgiana Onicescu, Steven Goodman

Johns Hopkins University, Dept. of Biostatistics Working Papers

In this paper, we develop and implement a general sensitivity analysis methodology for drawing inference about subgroup effects in a two-arm randomized trial when subgroup status is only known for a non-random sample in one of the trial arms. The methodology is developed in the context of the MADIT II study, a randomized trial designed to evaluate the effectiveness of implantable defibrillators on survival.

Causal Inference In Observational Studies With Outcome-Dependent Sampling, Weiwei Wang, Daniel Scharfstein, Zhiqiang Tan, Ellen J. Mackenzie

Johns Hopkins University, Dept. of Biostatistics Working Papers

In this paper, we consider estimation of the causal effect of a treatment on an outcome from observational data collected in two phases. In the first phase, a simple random sample of individuals are drawn from a population. On these individuals, information is obtained on treatment, outcome, and a few low-dimensional confounders. These individuals are then stratified according to these factors. In the second phase, a random sub-sample of individuals are drawn from each stratum, with known, stratum-specific selection probabilities. On these individuals, a rich set of confounding factors are collected. In this setting, we introduce four estimators: (1) simple …

Nonparametric Inference Procedure For Percentiles Of The Random Effect Distribution In Meta Analysis, Rui Wang, Lu Tian, Tianxi Cai, L. J. Wei

Harvard University Biostatistics Working Paper Series

No abstract provided.

Parametric Non-Mixture Cure Models For Schedule-Finding Of Therapeutic Agents, Thomas M. Braun, Changying A. Liu

The University of Michigan Department of Biostatistics Working Paper Series

We propose a Phase I clinical trial design that seeks to determine the cumulative safety of a series of administrations of a fixed dose of an investigational agent. In contrast to traditional Phase I trials that are designed to solely find the maximum tolerated dose (MTD) of the agent, our design instead identifies a maximum tolerated schedule (MTS) that includes an MTD as well as a vector of recommended administration times. Our model is based upon a non-mixture cure model that constrains the probability of toxicity for all subjects to monotonically increase with both dose and the number of administrations …

Abstracts In High Profile Journals Often Fail To Report Harm, Enrique Bernal-Delgado, Elliot S. Fisher

Dartmouth Scholarship

To describe how frequently harm is reported in the abstract of high impact factor medical journals. We carried out a blinded structured review of a random sample of 363 Randomised Controlled Trials (RCTs) carried out on human beings, and published in high impact factor medical journals in 2003. Main endpoint: 1) Proportion of articles reporting harm in the abstract; and 2) Proportion of articles that reported harm in the abstract when harm was reported in the main body of the article. Analysis: Corrected Prevalence Ratio (cPR) and its exact confidence interval were calculated. Non-conditional logistic regression was used.

Targeted Methods For Biomarker Discovery, The Search For A Standard, Catherine Tuglus, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

More often than not biomarker studies analyze large quantities of variables with complicated and generally unknown correlation structure. There are numerous statistical methods which attempt to unravel these variables and determine the underlying mechanism through identification of causally related biomarkers. Results from these methods are generally difficult to interpret and nearly impossible to compare across studies. The FDA has currently called for a standardization of methods and protocol for biomarker detection. In response, we propose targeted variable importance (tVIM) as a standardized method for biomarker discovery. Through the use of targeted Maximum Likelihood, tVIM provides double robust estimates of variable …

Covariate Adjustment For The Intention-To-Treat Parameter With Empirical Efficiency Maximization, Daniel B. Rubin, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

In randomized experiments, the intention-to-treat parameter is defined as the difference in expected outcomes between groups assigned to treatment and control arms. There is a large literature focusing on how (possibly misspecified) working models can sometimes exploit baseline covariate measurements to gain precision, although covariate adjustment is not strictly necessary. In Rubin and van der Laan (2008), we proposed the technique of empirical efficiency maximization for improving estimation by forming nonstandard fits of such working models. Considering a more realistic randomization scheme than in our original article, we suggest a new class of working models for utilizing covariate information, show …

Estimation Of Controlled Direct Effects, Sylvie Goetgeluk, Stijn Vansteelandt, Els Goetghebeur

Harvard University Biostatistics Working Paper Series

No abstract provided.