Abstracts 121s

P48 AUDITING AND VERIFICATION OF CLINICAL DATA:

A COORDINATING CENTER’S ROLE

A.M.Y. Randall, Knut Ra and Gene11 L. Knatterud Maryland Medical Research Institute

Baltimore, Maryland

Clinical trial management demands continual monitoring of the data to ensure consistency and reliability in order to yield valid study results. Some procedures can be implemented during the courseof the study; other methods can only be executed at study end.

The Post CABG Clinical Trial implemented various proceduresto improve the quality of the data. Demographic characteristics were colh&ed on multiple forms. At the end of baseline data collection, these items, such as gender, race and biih date, were compared for consistency, and errors were corrected. Clinical events were reported on follow-up forms as well as on specific event forms. During the course of the study, Clinical Centers were queried about inconsistencies. At the end of the study, ah inconsistencies in event reporting were itemized. Comprehensive chronological listings of all data collected for selected patients were generated and sent to the clinical centers for verification. Written narratives, including autopsy reports, were reviewed by a physician and compared to existing database records; discrepancies were resolved. For each verified event a completed event form was required; no other events were included in the analyses.

This poster presentation will describe the methods used to audit clinical data and the results of the verification process.

P49 COMBINING GRAPHICAL AND TABULAR DISPLAYS

IN DATA MONITORING REPORTS

Mark L. Van Natta, MIchele Donithan and James Tonascia The Johns Hopkins University

Baltimore, Maryland

In trials with measured responses, data monitoring often involves large numbers of comparisons among treatment groups (for treatment effects) and among clinics (for performance). Ideally, both graphical displays and tables of summary statistics should both be used to make informal comparisons. The combination of graphical and tabular material is often difticult due to limitations in the statistical software.

SAS macros (WINDOWS version 6.08) have been written to combine both the graphical (e.g., boxplot) and numerical (e.g., descriptive statistics) display of group comparisons. These macros create as output a hiih resolution graphics file that can be incorporated into most standard DOS/WINDOWS based word processing packages. The macros can loop over any number of variables and produce one fde per variable. Hardware fonts for a laser printer are embedded and an encapsulated Postscript graphic file (.EPS) is created which is smaller than other graphii fde types.

Advantages of the SAS macros include the elimination of transcription and typing errors, the high resolution of the text, and the ability to produce large numbers of standardized tables quickly.

122s

Examples of the application of this methodology to data monitoring reports will use data from the Childhood Asthma Management Program (CAMP) and the Studies of the Ocular Complications of Aids (SOCA).

P50 MINIMIZATION TECHNOLOGIES IN RANDOMIZED

OPEN INTERVENTION STUDIES

Gerrit J. Weverling and Martin H. Prins AMC, MATECKEB

Amsterdam, The Netherlands

Background. Miniiization is a well accepted statistical method to limit imbalance in relatively small randomized clinical trials in conditions with known important prognostic baseline characteristics. However the gain in balance is paid for by an increase in predictability, that is especially of concern in open studies performed in one or few centers.

Objective. To explore the relationship between imbalance and predictability in different minimization strategies (MSs) using simulation techniques.

Method. Populations were created with a 2 level prognostic factor (l:l, 1:4 and 1:9 ratio of levels) and a 3 level prognostic factor (l:l:l, 2:l:l and 8:l:l ratio of levels). Randomization to two treatments was performed using l Jircd-Pminhimtion (P = 0.8 for the treatment that causes least imbalance, P =0.5

in case of balance) l sh@ing-P minUn, in which the baseline P = 0.5 is shifted to the maximum of

P =0.8 based on the signed sum of the squared absolute imbalance. with normal randomization (P =0.5), and full minimization (P =l.O in case of imbalance) as a reference. Median and 5 to 95 percentiles of imbalance and predictability (average of excess of the P over P = 0.5) and imbalance according to Pocock (ref) were calculated from 200 simulated runs of 30, 50 and 100 patients each with resampling patients from the populations.

Rd. Using populations with equally distributed levels of prognostic factors predictability was lower with the use of “Shifting-P”, without important deterioration of imbalance. The foUowing table shows the median and 5 to 95 percentile ranges of imbalance and predictability in 30 patient trials with 2 prognostic factors:

Imbalance Iwdictabii

Normal 0.13 (0.00-0.40) 0.00 (0.00-0.00)

Full 0.00 (0.00-0.07) 0.25 (0.23-0.25)

Fixed-P 0.06 (0.00-0.10) 0.17 (0.15-0.21)

Shifting-P 0.07 (0.00-0.20) 0.07 (0.04-0.13)

For one factor simulation runs as well as for higher number of patients in a trial similar results were obtained. For all combinations the amount of imbalance increased in all MSs with unequal distributed levels of prognostic factors.