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Articles 1 - 7 of 7
Full-Text Articles in Statistical Methodology
Constructing A Confidence Interval For The Fraction Who Benefit From Treatment, Using Randomized Trial Data, Emily J. Huang, Ethan X. Fang, Daniel F. Hanley, Michael Rosenblum
Constructing A Confidence Interval For The Fraction Who Benefit From Treatment, Using Randomized Trial Data, Emily J. Huang, Ethan X. Fang, Daniel F. Hanley, Michael Rosenblum
Johns Hopkins University, Dept. of Biostatistics Working Papers
The fraction who benefit from treatment is the proportion of patients whose potential outcome under treatment is better than that under control. Inference on this parameter is challenging since it is only partially identifiable, even in our context of a randomized trial. We propose a new method for constructing a confidence interval for the fraction, when the outcome is ordinal or binary. Our confidence interval procedure is pointwise consistent. It does not require any assumptions about the joint distribution of the potential outcomes, although it has the flexibility to incorporate various user-defined assumptions. Unlike existing confidence interval methods for partially …
Comparison Of Adaptive Randomized Trial Designs For Time-To-Event Outcomes That Expand Versus Restrict Enrollment Criteria, To Test Non-Inferiority, Josh Betz, Jon Arni Steingrimsson, Tianchen Qian, Michael Rosenblum
Comparison Of Adaptive Randomized Trial Designs For Time-To-Event Outcomes That Expand Versus Restrict Enrollment Criteria, To Test Non-Inferiority, Josh Betz, Jon Arni Steingrimsson, Tianchen Qian, Michael Rosenblum
Johns Hopkins University, Dept. of Biostatistics Working Papers
Adaptive enrichment designs involve preplanned rules for modifying patient enrollment criteria based on data accrued in an ongoing trial. These designs may be useful when it is suspected that a subpopulation, e.g., defined by a biomarker or risk score measured at baseline, may benefit more from treatment than the complementary subpopulation. We compare two types of such designs, for the case of two subpopulations that partition the overall population. The first type starts by enrolling the subpopulation where it is suspected the new treatment is most likely to work, and then may expand inclusion criteria if there is early evidence …
Estimating Autoantibody Signatures To Detect Autoimmune Disease Patient Subsets, Zhenke Wu, Livia Casciola-Rosen, Ami A. Shah, Antony Rosen, Scott L. Zeger
Estimating Autoantibody Signatures To Detect Autoimmune Disease Patient Subsets, Zhenke Wu, Livia Casciola-Rosen, Ami A. Shah, Antony Rosen, Scott L. Zeger
Johns Hopkins University, Dept. of Biostatistics Working Papers
Autoimmune diseases are characterized by highly specific immune responses against molecules in self-tissues. Different autoimmune diseases are characterized by distinct immune responses, making autoantibodies useful for diagnosis and prediction. In many diseases, the targets of autoantibodies are incompletely defined. Although the technologies for autoantibody discovery have advanced dramatically over the past decade, each of these techniques generates hundreds of possibilities, which are onerous and expensive to validate. We set out to establish a method to greatly simplify autoantibody discovery, using a pre-filtering step to define subgroups with similar specificities based on migration of labeled, immunoprecipitated proteins on sodium dodecyl sulfate …
Evaluation Of Progress Towards The Unaids 90-90-90 Hiv Care Cascade: A Description Of Statistical Methods Used In An Interim Analysis Of The Intervention Communities In The Search Study, Laura Balzer, Joshua Schwab, Mark J. Van Der Laan, Maya L. Petersen
Evaluation Of Progress Towards The Unaids 90-90-90 Hiv Care Cascade: A Description Of Statistical Methods Used In An Interim Analysis Of The Intervention Communities In The Search Study, Laura Balzer, Joshua Schwab, Mark J. Van Der Laan, Maya L. Petersen
U.C. Berkeley Division of Biostatistics Working Paper Series
WHO guidelines call for universal antiretroviral treatment, and UNAIDS has set a global target to virally suppress most HIV-positive individuals. Accurate estimates of population-level coverage at each step of the HIV care cascade (testing, treatment, and viral suppression) are needed to assess the effectiveness of "test and treat" strategies implemented to achieve this goal. The data available to inform such estimates, however, are susceptible to informative missingness: the number of HIV-positive individuals in a population is unknown; individuals tested for HIV may not be representative of those whom a testing intervention fails to reach, and HIV-positive individuals with a viral …
It's All About Balance: Propensity Score Matching In The Context Of Complex Survey Data, David Lenis, Trang Q. ;Nguyen, Nian Dong, Elizabeth A. Stuart
It's All About Balance: Propensity Score Matching In The Context Of Complex Survey Data, David Lenis, Trang Q. ;Nguyen, Nian Dong, Elizabeth A. Stuart
Johns Hopkins University, Dept. of Biostatistics Working Papers
Many research studies aim to draw causal inferences using data from large, nationally representative survey samples, and many of these studies use propensity score matching to make those causal inferences as rigorous as possible given the non-experimental nature of the data. However, very few applied studies are careful about incorporating the survey design with the propensity score analysis, which may mean that the results don’t generate population inferences. This may be because few methodological studies examine how to best combine these methods. Furthermore, even fewer of the methodological studies incorporate different non-response mechanisms in their analysis. This study examines methods …
Variance Prior Specification For A Basket Trial Design Using Bayesian Hierarchical Modeling, Kristen Cunanan, Alexia Iasonos, Ronglai Shen, Mithat Gonen
Variance Prior Specification For A Basket Trial Design Using Bayesian Hierarchical Modeling, Kristen Cunanan, Alexia Iasonos, Ronglai Shen, Mithat Gonen
Memorial Sloan-Kettering Cancer Center, Dept. of Epidemiology & Biostatistics Working Paper Series
Background: In the era of targeted therapies, clinical trials in oncology are rapidly evolving, wherein patients from multiple diseases are now enrolled and treated according to their genomic mutation(s). In such trials, known as basket trials, the different disease cohorts form the different baskets for inference. Several approaches have been proposed in the literature to efficiently use information from all baskets while simultaneously screening to find individual baskets where the drug works. Most proposed methods are developed in a Bayesian paradigm that requires specifying a prior distribution for a variance parameter, which controls the degree to which information is shared …
Optimized Variable Selection Via Repeated Data Splitting, Marinela Capanu, Colin B. Begg, Mithat Gonen
Optimized Variable Selection Via Repeated Data Splitting, Marinela Capanu, Colin B. Begg, Mithat Gonen
Memorial Sloan-Kettering Cancer Center, Dept. of Epidemiology & Biostatistics Working Paper Series
We introduce a new variable selection procedure that repeatedly splits the data into two sets, one for estimation and one for validation, to obtain an empirically optimized threshold which is then used to screen for variables to include in the final model. Simulation results show that the proposed variable selection technique enjoys superior performance compared to candidate methods, being amongst those with the lowest inclusion of noisy predictors while having the highest power to detect the correct model and being unaffected by correlations among the predictors. We illustrate the methods by applying them to a cohort of patients undergoing hepatectomy …