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Summer Institute in Statistics for Clinical Research: Module Descriptions

This is a tentative list of modules offered for the Summer Institute in Statistics for Clinical Research 2014. It is subject to change without notice. Please refer to the Calendar for dates of instruction, which is also subject to change without notice.

Module 1: Introduction to Clinical Trials – Day 1, Design
Instructors: Dan Gillen and Susanne May
Duration: 1 day
This module provides an overview of the primary design issues that must be considered by the many disciplines that collaborate on a clinical trial. In this module, we consider the clinical, scientific, and regulatory setting of clinical trials, describing the phased approach to “treatment discovery” in which the safety, efficacy, and effectiveness of candidate treatments are investigated. This one day module focuses on the issues surrounding disease area and identification of the target population, definition of the treatment, choice of clinical outcomes, and choice of comparators. Throughout the module emphasis is placed on pre-specification of these key elements in a well-defined study protocol.

Module 2: Introduction to Survival Analysis
Instructor: Barbara McKnight
Duration: 1 day
This one-day module provides an introduction to survival data and survival analysis methods. We will introduce important functions and quantities for summarizing a survival distribution including the survival function, the hazard function, and the median survival time. We will also describe how censoring occurs in data and how it can be accommodated in estimates of functions like the Kaplan-Meier curve, and in the comparison of two groups using the logrank test. The module will explain key concepts such as risk sets, informative censoring and competing risks. We will provide examples of studies where survival analysis is used and where it should not be used. The course will focus on application and understanding the concepts; mathematical details will be kept to a minimum.

Module 3: Introduction to Longitudinal Data Analysis
Instructors: Ben French and Colleen Sitlani
Duration: 1 day
Longitudinal studies follow individual subjects over time and repeatedly measure health status. This module will introduce and illustrate methods of analysis for longitudinal data. An introduction to pre-post data will be followed by an introduction to regression-based methods such as linear mixed-effects models and generalized estimating equations. Examples from randomized clinical trials and observational studies will be used to illustrate key approaches. Computing examples will be given in R and STATA.

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Module 4: Introduction to Clinical Trials – Day 2, Conduct and Implementation
Instructor: Dan Gillen
Duration: 1 day
This module continues the overview of the issues that must be considered by the many disciplines that collaborate on a clinical trial. As such, this module presumes the material of Module 1. In this module, we focus primarily on the issues related to the implementation of a clinical trial design and the conduct of the actual clinical trial. We provide an introduction to randomization, data management, planning for the prevention of missing data, and monitoring of the clinical trial by a data monitoring committee (DMC). Also covered is the planning for the eventual reporting of trial results.

Module 5: Survival Analysis in Clinical Trials
Instructors: Barbara McKnight and Susanne May
Duration: 1 day
This one-day module will cover more advanced topics in survival analysis, with an emphasis on applications in clinical trials. Knowledge of material in module 1 will be assumed. We will introduce methods suitable for testing hypotheses about a variety of quantitative summaries of the discrepancy between treatment groups’ survival. The choice of outcome event for the primary analysis will be considered. We will also discuss adjustment for prognostic variables using stratification and regression methods, and estimation of power and sample size. The course will focus on application and understanding the concepts; mathematical details will be kept to a minimum.

Module 6: Regression Models for Longitudinal Data Analysis
Instructors: Ben French and Colleen Sitlani
Duration: 1 day
Longitudinal studies follow individual subjects over time and repeatedly measure health status. This module will focus on regression-based methods such as linear mixed models and generalized estimating equations for continuous and binary outcomes. Advanced topics such as missing data and time-dependent exposures will also be discussed. Examples from randomized clinical trials and observational studies will be used to illustrate key approaches. Computing examples will be given in R and STATA.

Module 7: Development and Evaluation of Risk Prediction Models
Module 7 is now at full enrollment. If you want to be placed on its waiting list, please send an email to siscr@uw.edu.
Instructors: Katie Kerr and Holly Janes
Duration: 1 day
This short course will discuss the motivations for risk prediction models and the basic concepts underlying risk prediction for binary outcomes. Tools and metrics for evaluating risk prediction models will be covered, including ROC curves, calibration plots, and expected benefit. The course will also discuss methods to compare risk prediction models, paying particular attention to the case of two nested models and assessing the prediction increment of a new biomarker. Recent proposals for metrics that rely on the concept of reclassification to evaluate new biomarkers will be considered. Important concepts in using risk prediction models or biomarkers to guide treatment selection in the context of personalized medicine will be introduced.

Module 8: Data Monitoring Committees: Role of the Statistician
Instructor: Scott Emerson
Duration: 1 day
During the conduct of a randomized clinical trial, it is commonplace for the accruing data to be regularly monitored by a committee of independent researchers in order to assure patient safety and ethical conduct. Typically such committees are comprised of 3-5 members, including clinicians expert in the disease under study, the treatment, and/or any expected toxicities, ethicists, and a biostatistician. It is incumbent upon all members of the data monitoring committee (DMC) to collaborate fully with each other in their highly interdisciplinary task. In this short course, we will elaborate on the role of a DMC statistician in this process. We consider the review of the DMC Charter, the investigator's brochure, the protocol, and the statistical analysis plan from a DMC statistician's viewpoint. We then discuss the elements of a DMC report that will be commonly encountered, and the organized approach to the review of the data. We discuss our experience with commonly encountered problems that require particular input from the independent DMC statistician. The course is targeted to individuals who might serve on a DMC as well as those who might author the reports to a DMC.

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Module 9: Introduction to the Design and Evaluation of Adaptive Group Sequential Clinical Trials
Instructors: Daniel Gillen and John Kittelson
Duration: 1 day
Increasingly, clinical trials are conducted using group sequential methods in order to address the ethical and efficiency requirements for performing experiments with human volunteers. The design, conduct, and analysis of a sequential clinical trial is necessarily more involved than that for a clinical trial in which the data would only be analyzed at the end of the study. In this module we provide an introduction to methodology for the design and evaluation of group sequential trials from both a frequentist and Bayesian perspective. Methodology will presented using case studies and examples of clinical trials with continuous, binary, and survival endpoints. The sequential methods will be illustrated using RCTdesign, an R module for the design, monitoring, and analysis of clinical trials. Emphasis will be placed on both the scientific and statistical impact of group sequential trial design.

Module 10: Phase I and II Oncology Clinical Trials
*** Module 10 has been CANCELLED. If you have questions about alternative modules, please send an email to siscr@uw.edu. ***
Instructors: John Crowley and Antje Hoering
Duration: 1 day
Topics in early phases of oncology clinical trials (phase I-II) will be covered. Real world examples of recent SWOG and CRAB clinical trials will be given and trial designs will be demonstrated using our on-line calculators. The first half of the course will review basic concepts of oncology clinical trials including safety, efficacy and endpoint considerations. The most common phase I trial designs will be discussed. Single stage, two-stage phase II trial designs and randomized phase II trial designs, as well as pros and cons of these designs in various clinical settings, will be covered. The third edition of the Handbook of Statistics in Clinical Oncology edited by the instructors was published in the summer of 2012. The second half of the course will cover current topics in phase II oncology clinical trial designs based on this Handbook, including trial designs for cytostatic or targeted agents. Variations with targeted agents include dose exploration in seamless phase I/II trials; multiple strata phase II trials.

Module 11: Advanced Topics in Clinical Trials: Adaptive Randomization
Instructor: Scott Emerson
Duration: 1 day
Recently much attention has been devoted to the re-design of ongoing randomized clinical trials based on interim results. One such area of interest is the use of interim trial results to determine the randomization of future subjects. In this short course we will review such methods as “covariate adaptive” randomization and “play-the-winner” randomization schemes. We will focus on the ability of these methods to better address the efficiency and ethical issues inherent in randomized clinical trials. We will also discuss the special considerations that must be made at the time of study design, during conduct of the clinical trial, and when reporting results, and how those special considerations might impinge on gaining regulatory approval for a new drug, biologic, or device.

Module 12: Prognostic Biomarker Evaluation
Instructor: Patrick Heagerty
Duration: 1 day
Longitudinal studies allow investigators to correlate changes in time-dependent exposures or biomarkers with subsequent health outcomes. The use of baseline or time-dependent markers to predict a subsequent change in clinical status such as transition to a diseased state requires the formulation of appropriate classification and prediction error concepts. Similarly, the evaluation of markers that could be used to guide treatment require specification of operating characteristics associated with use of the marker. The first part of this course will introduce predictive accuracy concepts that allow evaluation of time-dependent sensitivity and specificity for prognosis of a subsequent event time. We will overview options that are appropriate for both baseline markers and for longitudinal markers. Methods will be illustrated using examples from HIV and cancer research. The second part of this course will involve a technology workshop that will introduce the R packages (survivalROC, risksetROC, compriskROC) that are currently available for predictive accuracy of survival model. This segment will include hands-on training and demonstration of how to use these R packages for answering research questions. Several real-data examples for analysis will be provided and the instructors will discuss implementation and interpretation.

Module 13: Propensity Score Methods, Models and Adjustment
Module 13 is now at full enrollment. If you want to be placed on its waiting list, please send an email to siscr@uw.edu.
Instructors: Dave Stephens
Duration: 1.5 day **This course begins on Wednesday, June 25th (all day) and ends on Thursday, June 26th, 2014 (half day, morning only)
The propensity score is a key component of many causal inference procedures. After establishing the basic causal inference framework, we will outline the key methods of construction of propensity score functions, and study their core mathematical properties. We will detail the use of the propensity score in matching, inverse weighting and regression adjustments that allow the unconfounded effect of an exposure or treatment of interest to be estimated consistently. Using the framework of semiparametric inference, we will contrast the statistical properties of estimators derived using each method. We will investigate issues of model selection for the propensity score, and demonstrate the utility of judicious choice of predictors that enter into the propensity function: this will be illustrated in standard problems and also in the case of high-dimensional predictors. Longitudinal data will also be studied in the causal setting. Finally, we will develop the Bayesian framework for handling causal inference, and investigate how propensity function construction and usage translates to the new setting. All methods will be illustrated using examples from biostatistics, health research and econometrics. Computation will be performed in R.

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Module 14: Developing Prognostic and Predictive Biomarkers with High Dimensional Data
Instructors: Noah Simon and Richard Simon
Duration: 1 day
Modern medicine has graduated from broad spectrum treatments to targeted therapeutics. With new advances in high throughput biotechnology, it is inexpensive to run whole genome assays. Genetic features from these assays can be developed into biomarkers used to target therapies. A major difficulty here lies in the fact that the number of potential features is generally much greater than the number of patients. The goal of this module will be to introduce ideas in high dimensional predictive modeling, to discuss model validation and testing, and to give hands-on experience with these tools to build predictive biomarkers on real, high-dimensional datasets. Through the course, participants will become familiar with various methods in penalized regression, applications of cross-validation, as well as ideas in multiplicity and selection bias. Participants will gain experience applying these methods in R.

Module 15: Advanced Topics in Adaptive Group Sequential Clinical Trials
Instructors: Daniel Gillen and John Kittelson
Duration: 1 day
This course considers advanced topics in the design, monitoring, and analysis of adaptive group sequential clinical trials. The module will present and illustrate methods for adaptive trial monitoring that maintain pre-specified trial operating characteristics, the design of sequential non-inferiority trials, adjusted inference following the conclusion of a sequential trial, and methods for evaluating and monitoring survival endpoints in the presence of a time-varying treatment effect. The sequential methods will be illustrated using RCTdesign, an R module for the design, monitoring, and analysis of clinical trials. Many of the topics considered in this course will build on methodology presented in the module “An Introduction to the Design and Evaluation of Adaptive Group Sequential Clinical Trials” module and it is strongly recommended that students first take the introductory design course.

Module 16: Phase III Oncology Clinical Trials
*** Module 16 has been CANCELLED. If you have questions about alternative modules, please send an email to siscr@uw.edu. ***
Instructors: John Crowley and Antje Hoering
Duration: 1 day
Topics in phase III oncology clinical trials will be covered. Superiority and non-inferiority oncology phase III trials as well as DSMC issues will be reviewed. Real world examples of recent SWOG and CRAB clinical trials will be given and trial designs will be demonstrated using our on-line calculators. The third edition of the Handbook of Statistics in Clinical Oncology edited by the instructors was published in the summer of 2012. The second half of the course will cover current topics in phase III oncology clinical trial designs based on this Handbook, including trial designs for cytostatic or targeted agents. Variations with targeted agents include phase II/III trials; and phase III designs for joint biomarker and drug development.

Module 17: Adaptive Sample Size Re-estimation: Design and Inference
Instructors: Sarah Emerson and Greg Levin
Duration: 1 day
Adaptive clinical trial designs have been proposed as a promising new approach that may help improve the drug discovery process. Recent statistical papers have introduced a variety of methods to allow unplanned interim modifications to the study design while preserving the type I error rate of the clinical trial. In particular, there is a large body of literature exploring designs with unplanned modifications to the sample size based on interim estimates of the treatment effect falling into a “promising zone”. In this module we present the impact such designs can have on the power of randomized clinical trials. We provide guidance on the adaptive rules that lead to the greatest benefit to study precision, and we present and compare methods for full statistical inference following the use of such designs.

Module 18: RCT Analysis in the Presence of Missing Data: Sensitivity Analyses
Instructor: Scott Emerson
Duration: 1 day
At the request of the U.S. Food and Drug Administration, the National Academy of Sciences convened the Panel on the Handling of Missing Data in Clinical Trials to prepare a report that would make recommendations that could be used to aid in the FDA’s eventual development of a Guidance for Industry on that topic. Chief among the findings and recommendations of the resultant report was the need to make all efforts to prevent the occurrence of missing data. However, when missing data is unavoidable, the report also stressed the need for the careful specification of data analysis models that would explicitly account for any missingness. Furthermore, because all such analysis methods are based on untestable assumptions, the report also stressed the need for carefully conducted sensitivity analyses that would quantify the robustness of trial results to departures from any assumed mechanism of missingness. In this module we explore an approach to defining sensitivity analyses that first defines a missing at random (MAR) mechanism, and then explores a spectrum of departures that encompass relevant missing not at random (MNAR) models.

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Module 19: Advanced topics in Design of Clinical Trials
Instructor: Tom Fleming
Duration: 1 day
This module provides detailed discussion of a number of topics that are currently receiving special emphasis in the community of clinical trialists. As such, this module presumes the material of Module 1. Included in this module are presentations on the important distinctions between confirmatory and exploratory studies; the use of biomarkers and surrogate endpoints in clinical trials; the handling of missing data in clinical trials and issues surrounding the conduct of noninferiority trials.

Module 20: Adaptive enrichment designs: methods and software
Instructor: Michael Rosenblum
Duration: 1 day
Adaptive enrichment designs involve preplanned rules for modifying enrollment criteria based on accrued data. They involve multiple populations of interest, e.g., defined in terms of a biomarker or risk score measured at baseline. This module will present methods and software for the design and analysis of adaptive enrichment designs. It will also present methods for improving precision of estimators of the average treatment effect, by leveraging information in baseline variables; these methods can be used in adaptive designs as well as non-adaptive trial designs.

Module 21: Using Electronic Healthcare Data for Comparative Safety and Effectiveness Research
Instructors: Jennifer Nelson and Andrea Cook
Duration: 0.5 day †(half day, morning only)
Healthcare delivery organizations like Group Health maintain large databases for administrative and clinical purposes, and the secondary use of these data to conduct comparative safety and effectiveness research is increasing. This module considers the design and analysis of large scale, multi-site epidemiological studies and pragmatic clinical trials in this database setting. In particular, this course will present an overview of the type of data that arise in large healthcare databases, the kinds of research questions currently being asked within several national database research initiatives, and the key statistical challenges that are faced. Major methodological topic areas will include 1) sequential monitoring of observational databases for safety surveillance and 2) design considerations for pragmatic clinical trials. Statistical challenges include selection bias, misclassification, confounding, and missing data. Real world examples from the CDC Vaccine Safety Datalink, FDA Mini-Sentinel Pilot for medical product safety monitoring, and the NIH Collaboratory will be used to illustrate concepts and issues.

Module 22: Personalized Medicine: Dynamic Treatment Regimes
Instructor: Erica Moodie
Duration: 0.5 day †(half day, morning only)
Effective treatment of chronic disorders such as mental illnesses, cancer, and HIV infection typically requires ongoing interventions where clinicians make repeated (sequential) therapeutic decisions, adapting the type, dosage and timing of treatment according to evolving patient characteristics. Dynamic treatment regimes (DTRs) operationalize this sequential decision-making process. Constructing data-driven DTRs from observational data or sequentially randomized trials comprise a cutting-edge area of biostatistical research. This course will provide an overview of the area, beginning with a discussion of relevant data sources for constructing DTRs and design of efficient studies to produce such data. We will then turn our attention to estimation using a method called Q-learning; continuous, discrete, time-to-event, and composite outcome types will be covered. Next, we will discuss inferential challenges and solutions. We will demonstrate estimation of optimal DTRs using Q-learning and associated inference using the R package qLearn. Prerequisites: thorough grasp of regression analysis and familiarity with elementary linear algebra and basic large-sample theory. Demonstrations will be performed using the R computing environment.

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