Modeling and simulation in the drug approval process

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<ul><li><p>Abstracts 65S </p><p>in digitized form for a clinical trial examining the efficacy of MRI in diagnosis of Breast Cancer. On-line computer editing will identify missing information and perform range cheeks for inconsistencies. Data manager review will be performed on the electronic data submitted. Diagnostic MRI images will be electronically distributed for review by "expert" radiologists on their local PC's with results returned via electronic forms. The fmal data will be sent to the Statistical Office for analysis. Besides managing individual data forms, the study also will determine the mechanisms essential in accomplishing efficient transfer of information between the investigative institutions, Data Center, Statistical Office, Chairman's Office and the National Cancer Institute. This approach may aid in increasing patient enrollment in cooperative group trials by providing flexibility in data management requirements. RTOG trials will further test this process. </p><p>51 A PASSIVE INTRANET FOR VIEWING IMAGES, DATA, </p><p>AND DOCUMENTS AT A MULTICENTER TRIAL COORDINATING CENTER </p><p>Stephen C. Grubb The Johns Hopkins University </p><p>Baltimore, Maryland </p><p>An intranet is an inlbrmation system that is accessed like the World Wide Web (by using a web browser) but is appropriate for internal delivery of sensitive information because it is not accessible from the Internet or the outside world. </p><p>An intranet has been developed for use at the coordinating center of a muiticenter clinical trial. Because this trial collects data on forms that are directly faxed from clinical centers to a computer for electronic processing and storage, paper forms are not stored. This system serves as the primary means for data forms to be viewed at the coordinating center, and also provides access to data displays and documents, and facilitates image workflow (the processing and organizing of images). Staff members use standard web browser software, in conjunction with an auxiliary "helper" program that displays fax images, captures user input, and accesses the study database. </p><p>The technical development and administration of such an intranet may be simplified if all content is "passive", that is, made up of files located on the same computer system as the users. In situations where this may be done, a passive scheme eliminates the need for a web server program, and reduces concerns related to security and data access control. </p><p>This presentation includes an outline of the current content of the Submacular Surgery Trials (SST) Coordinating Center intranet, and screen snapshots of the software application. Also discussed are the intranet's use with regard to forms image workflow and data edit processing, and its use as an archive for documents such as the study protocol manual, manuscripts, clinic notices, and internal documentation. </p><p>52 MODELING AND SIMULATION IN THE DRUG </p><p>APPROVAL PROCESS </p><p>Donald B. Rubln Harvard University </p><p>Cambridge, Massachusetts </p><p>The focus on frequency operating characteristics of procedures is the hallmark of traditional statistics, and standard methods in the drug approval process are typically designed to be very simple (e.g., a t statistic) so that their operating characteristics can be analytically derived. These standard methods, however, can be inefficient, and more importantly, cannot adequately address complications such as noncompliance or drop out. When faced with such </p></li><li><p>66S Abstracts </p><p>complications, standard approaches typically use ad hoc patch-ups, such as "intention-to-treat on those with complete data" to create a data set that can be analyzed by a standard simple procedure. However, even though the original procedure had valid operating characteristics in the absence of complications, the ad hoe patched-up version of it can easily be invalid (e.g., be badly biased, 95% intervals can have 85% coverage, 5% tests can reject a true null hypothesis 50% of the time). In contrast, even with these complications, procedures created using some explicit modeling assumptions can have valid, much superior frequentist operating characteristics even when the assumptions underlying their creation are not really correct. Two problems, however, immediately occur: (1) many of these new procedures do not have analytic forms, and (2) evaluating even simple procedures in the presence of realistic complications is typically analytically intractable. Consequently, the only way to calculate the better procedures on one data set or to evaluate realistic frequency operating characteristics of procedures across many data sets is to use simulation methods. Fortunately, such simulation methods are currently under development for the drug approval process, at least in some settings. </p><p>53 ANALYZING A CONTINUOUS EFFICACY RESPONSE </p><p>IN A LONG-TERM TRIAL WITH HIGH DROPOUT RATE </p><p>Daniel Z. Wang and Thomas J. Cook Merck Research Laboratories </p><p>Rahway, New Jersey </p><p>Two common characteristics of a long-term clinical trial are high dropout rate and repeated measurement of efficacy response. Generally dropouts are due to adverse experience, insufficient response or treatment failure, or neutral reasons which are not necessarily treatment-related, Missing data because of dropouts present difficulties in applying conventional methodology. For instance, results of a complete-case analysis may be biased and inefficient; results of a last-observation-carried-forward analysis may also be biased and imputing missing data at the end of trial by observations at earlier time points may not be readily accepted. Clearly any meaningful analysis must incorporate information on dropouts such as dropout time and reason as well as available response data for the dropouts. We proposed to analyze efficacy response data in such long-term trial using three established methods with some modification. The goal is to provide criteria for efficacy, to fully explore the treatment effect from different perspectives, and to reach a robust conclusion on efficacy. The three methods are: a) stratified analysis of subjects; summary measure of efficacy based on their available data with the strata defined by dropout time; b) rank analysis in which dropouts due to adverse experience and treatment failure are considered to have the worst response; within the pool of the dropouts due to adverse experience or treatment failure, ranks are based on dropout time; subjects who completed study or dropouts due to neutral reasons are ranked by their response observations; c) linear and spline modeling using SAS PROC MIXED procedures based on available longitudinal response data to examine treatment effect over time. Application of the above approach to a recently completed trial (Proscar Long- term Efficacy and Safety Study (PLESS)) will be discussed. </p><p>54 A METHOD TO IDENTIFY THE SUBGROUP </p><p>FOR WHOM TREATMENT IS MOST EFFECTIVE </p><p>Henry Lynn Rho, Inc. </p><p>Chapel Hill, North Carolina </p><p>Although there is much controversy and misuse of subgroup analyses, the examination of the treatment effect in individual subgroups can provide valuable insights into the data and help design future studies. In randomized trials, the initial objective is to assess the </p></li></ul>