Physical Sciences and Mathematics Commons^™

Using Multilevel Outcomes To Construct And Select Biomarker Combinations For Single-Level Prediction, Allison Meisner, Chirag R. Parikh, Kathleen F. Kerr

UW Biostatistics Working Paper Series

Biomarker studies may involve a multilevel outcome, such as no, mild, or severe disease. There is often interest in predicting one particular level of the outcome due to its clinical significance. The standard approach to constructing biomarker combinations in this context involves dichotomizing the outcome and using a binary logistic regression model. We assessed whether information can be usefully gained from instead using more sophisticated regression methods. Furthermore, it is often necessary to select among several candidate biomarker combinations. One strategy involves selecting a combination on the basis of its ability to predict the outcome level of interest. We propose …

Nonparametric Variable Importance Assessment Using Machine Learning Techniques, Brian D. Williamson, Peter B. Gilbert, Noah Simon, Marco Carone

UW Biostatistics Working Paper Series

In a regression setting, it is often of interest to quantify the importance of various features in predicting the response. Commonly, the variable importance measure used is determined by the regression technique employed. For this reason, practitioners often only resort to one of a few regression techniques for which a variable importance measure is naturally defined. Unfortunately, these regression techniques are often sub-optimal for predicting response. Additionally, because the variable importance measures native to different regression techniques generally have a different interpretation, comparisons across techniques can be difficult. In this work, we study a novel variable importance measure that can …

Combining Biomarkers By Maximizing The True Positive Rate For A Fixed False Positive Rate, Allison Meisner, Marco Carone, Margaret Pepe, Kathleen F. Kerr

UW Biostatistics Working Paper Series

Biomarkers abound in many areas of clinical research, and often investigators are interested in combining them for diagnosis, prognosis and screening. In many applications, the true positive rate for a biomarker combination at a prespecified, clinically acceptable false positive rate is the most relevant measure of predictive capacity. We propose a distribution-free method for constructing biomarker combinations by maximizing the true positive rate while constraining the false positive rate. Theoretical results demonstrate good operating characteristics for the resulting combination. In simulations, the biomarker combination provided by our method demonstrated improved operating characteristics in a variety of scenarios when compared with …

Developing Biomarker Combinations In Multicenter Studies Via Direct Maximization And Penalization, Allison Meisner, Chirag R. Parikh, Kathleen F. Kerr

UW Biostatistics Working Paper Series

When biomarker studies involve patients at multiple centers and the goal is to develop biomarker combinations for diagnosis, prognosis, or screening, we consider evaluating the predictive capacity of a given combination with the center-adjusted AUC (aAUC), a summary of conditional performance. Rather than using a general method to construct the biomarker combination, such as logistic regression, we propose estimating the combination by directly maximizing the aAUC. Furthermore, it may be desirable to have a biomarker combination with similar predictive capacity across centers. To that end, we allow for penalization of the variability in center-specific performance. We demonstrate good asymptotic properties …

Evaluation Of Multiple Interventions Using A Stepped Wedge Design, Vivian H. Lyons, Lingyu Li, James Hughes, Ali Rowhani-Rahbar

UW Biostatistics Working Paper Series

Background: Stepped wedge cluster randomized trials are a class of unidirectional crossover studies that have historically been limited to evaluating a single intervention. This design is especially suitable for pragmatic trials where the study feasibility can be improved with a phased introduction of the intervention. We examined variations of stepped wedge designs that would support evaluation of multiple interventions. Methods: We propose four different design variants for implementing a stepped wedge trial with two interventions: concurrent design, supplementation, replacement, and factorial designs. Analyses were conducted comparing the precision of the estimated intervention effects for the different designs. Results: Concurrent, …

Biomarker Combinations For Diagnosis And Prognosis In Multicenter Studies: Principles And Methods, Allison Meisner, Chirag R. Parikh, Kathleen F. Kerr

UW Biostatistics Working Paper Series

Many investigators are interested in combining biomarkers to predict an outcome of interest or detect underlying disease. This endeavor is complicated by the fact that many biomarker studies involve data from multiple centers. Depending upon the relationship between center, the biomarkers, and the target of prediction, care must be taken when constructing and evaluating combinations of biomarkers. We introduce a taxonomy to describe the role of center and consider how a biomarker combination should be constructed and evaluated. We show that ignoring center, which is frequently done by clinical researchers, is often not appropriate. The limited statistical literature proposes using …

Adaptive Non-Inferiority Margins Under Observable Non-Constancy, Brett S. Hanscom, Deborah J. Donnell, Brian D. Williamson, Jim Hughes

UW Biostatistics Working Paper Series

A central assumption in the design and conduct of non-inferiority trials is that the active-control therapy will have the same degree of effectiveness in the planned non-inferiority trial as it had in the prior placebo-controlled trials used to define the non-inferiority margin. This is referred to as the `constancy' assumption. If the constancy assumption fails, the chosen non-inferiority margin is not valid and the study runs the risk of approving an inferior product or failing to approve a beneficial product. The constancy assumption cannot be validated in a trial without a placebo arm, and it is unlikely ever to be …

Predicting Future Years Of Life, Health, And Functional Ability: A Healthy Life Calculator For Older Adults, Paula Diehr, Michael Diehr, Alice M. Arnold, Laura Yee, Michelle C. Odden, Calvin H. Hirsch, Stephen Thielke, Bruce Psaty, W Craig Johnson, Jorge Kizer, Anne B. Newman

UW Biostatistics Working Paper Series

Introduction

Planning for the future would be easier if we knew how long we will live and, more importantly, how many years we will be healthy and able to enjoy it. There are few well-documented aids for predicting our future health. We attempted to meet this need for persons 65 years of age and older.

Methods

Data came from the Cardiovascular Health Study, a large longitudinal study of older adults that began in 1990. Years of life (YOL) were defined by measuring time to death. Years of healthy life (YHL) were defined by an annual question about self-rated health, and …