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Articles 1 - 9 of 9
Full-Text Articles in Clinical Trials
A Modular Framework For Early-Phase Seamless Oncology Trials, Philip S. Boonstra, Thomas M. Braun, Elizabeth C. Chase
A Modular Framework For Early-Phase Seamless Oncology Trials, Philip S. Boonstra, Thomas M. Braun, Elizabeth C. Chase
The University of Michigan Department of Biostatistics Working Paper Series
Background: As our understanding of the etiology and mechanisms of cancer becomes more sophisticated and the number of therapeutic options increases, phase I oncology trials today have multiple primary objectives. Many such designs are now 'seamless', meaning that the trial estimates both the maximum tolerated dose and the efficacy at this dose level. Sponsors often proceed with further study only with this additional efficacy evidence. However, with this increasing complexity in trial design, it becomes challenging to articulate fundamental operating characteristics of these trials, such as (i) what is the probability that the design will identify an acceptable, i.e. safe …
C-Learning: A New Classification Framework To Estimate Optimal Dynamic Treatment Regimes, Baqun Zhang, Min Zhang
C-Learning: A New Classification Framework To Estimate Optimal Dynamic Treatment Regimes, Baqun Zhang, Min Zhang
The University of Michigan Department of Biostatistics Working Paper Series
Personalizing treatment to accommodate patient heterogeneity and the evolving nature of a disease over time has received considerable attention lately. A dynamic treatment regime is a set of decision rules, each corresponding to a decision point, that determine that next treatment based on each individual’s own available characteristics and treatment history up to that point. We show that identifying the optimal dynamic treatment regime can be recast as a sequential classification problem and is equivalent to sequentially minimizing a weighted expected misclassification error. This general classification perspective targets the exact goal of optimally individualizing treatments and is new and fundamentally …
Parametric Non-Mixture Cure Models For Schedule-Finding Of Therapeutic Agents, Thomas M. Braun, Changying A. Liu
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 …
Simultaneously Optimizing Dose And Schedule Of A New Cytotoxic Agent, Thomas M. Braun, Peter F. Thall, Hoang Nguyen, Marcos De Lima
Simultaneously Optimizing Dose And Schedule Of A New Cytotoxic Agent, Thomas M. Braun, Peter F. Thall, Hoang Nguyen, Marcos De Lima
The University of Michigan Department of Biostatistics Working Paper Series
Traditionally, phase I clinical trial designs determine a maximum tolerated dose of an experimental cytotoxic agent based on a fixed schedule, usually one course consisting of multiple administrations, while varying the dose per administration between patients. However, in actual medical practice patients often receive several courses of treatment, and some patients may receive one or more dose reductions due to low-grade (non-dose limiting) toxicity in previous courses. As a result, the overall risk of toxicity for each patient is a function of both the schedule and the dose used at each adminstration. We propose a new paradigm for Phase I …
New Estimating Methods For Surrogate Outcome Data, Bin Nan
New Estimating Methods For Surrogate Outcome Data, Bin Nan
The University of Michigan Department of Biostatistics Working Paper Series
Surrogate outcome data arise frequently in medical research. The true outcomes of interest are expensive or hard to ascertain, but measurements of surrogate outcomes (or more generally speaking, the correlates of the true outcomes) are usually available. In this paper we assume that the conditional expectation of the true outcome given covariates is known up to a finite dimensional parameter. When the true outcome is missing at random, the e±cient score function for the parameter in the conditional mean model has a simple form, which is similar to the generalized estimating functions. There is no integral equation involved as in …
Asymptotic Results For Simultaneous Group Sequential Analysis Of Rank-Based And Weighted Kaplan-Meier Tests With Paired Survival Data In The Presence Of Censoring. Technical Report, Adin-Cristian Andrei, Susan Murray
Asymptotic Results For Simultaneous Group Sequential Analysis Of Rank-Based And Weighted Kaplan-Meier Tests With Paired Survival Data In The Presence Of Censoring. Technical Report, Adin-Cristian Andrei, Susan Murray
The University of Michigan Department of Biostatistics Working Paper Series
This research sequentially monitors paired survival differences using a new class of non-parametric tests based on functionals of standardized paired weighted log-rank (PWLR) and standardized paired weighted Kaplan-Meier (PWKM) tests. During a trial these tests may alternately assume the role of the more extreme statistic. By monitoring PEMAX, the maximum between the absolute values of the standardized PWLR and PWKM, one combines advantages of rank-based and non rank-based paired testing paradigms. Simulations show that monitoring treatment differences using PEMAX maintains type I error and is nearly as powerful as using the more advantageous of the two tests, in proportional hazards …
Causal Inference In Hybrid Intervention Trials Involving Treatment Choice, Qi Long, Rod Little, Xihong Lin
Causal Inference In Hybrid Intervention Trials Involving Treatment Choice, Qi Long, Rod Little, Xihong Lin
The University of Michigan Department of Biostatistics Working Paper Series
Randomized allocation of treatments is a cornerstone of experimental design, but has drawbacks when a limited set of individuals are willing to be randomized, or the act of randomization undermines the success of the treatment. Choice-based experimental designs allow a subset of the participants to choose their treatments. We discuss here causal inferences for experimental designs where some participants are randomly allocated to treatments and others receive their treatment preference. This paper was motivated by the “Women Take Pride” (WTP) study (Janevic et al., 2001), a doubly randomized preference trail (DRPT) to assess behavioral interventions for women with heart disease. …
An Extended General Location Model For Causal Inference From Data Subject To Noncompliance And Missing Values, Yahong Peng, Rod Little, Trivellore E. Raghuanthan
An Extended General Location Model For Causal Inference From Data Subject To Noncompliance And Missing Values, Yahong Peng, Rod Little, Trivellore E. Raghuanthan
The University of Michigan Department of Biostatistics Working Paper Series
Noncompliance is a common problem in experiments involving randomized assignment of treatments, and standard analyses based on intention-to treat or treatment received have limitations. An attractive alternative is to estimate the Complier-Average Causal Effect (CACE), which is the average treatment effect for the subpopulation of subjects who would comply under either treatment (Angrist, Imbens and Rubin, 1996, henceforth AIR). We propose an Extended General Location Model to estimate the CACE from data with non-compliance and missing data in the outcome and in baseline covariates. Models for both continuous and categorical outcomes and ignorable and latent ignorable (Frangakis and Rubin, 1999) …
Mixtures Of Varying Coefficient Models For Longitudinal Data With Discrete Or Continuous Non-Ignorable Dropout, Joseph W. Hogan, Xihong Lin, Benjamin A. Herman
Mixtures Of Varying Coefficient Models For Longitudinal Data With Discrete Or Continuous Non-Ignorable Dropout, Joseph W. Hogan, Xihong Lin, Benjamin A. Herman
The University of Michigan Department of Biostatistics Working Paper Series
The analysis of longitudinal repeated measures data is frequently complicated by missing data due to informative dropout. We describe a mixture model for joint distribution for longitudinal repeated measures, where the dropout distribution may be continuous and the dependence between response and dropout is semiparametric. Specifically, we assume that responses follow a varying coefficient random effects model conditional on dropout time, where the regression coefficients depend on dropout time through unspecified nonparametric functions that are estimated using step functions when dropout time is discrete (e.g., for panel data) and using smoothing splines when dropout time is continuous. Inference under the …