Abstract: Ever wondered where a data point came from? Ever been looking through some Tables, Figures and Listings (TFLs) and thought 'I wonder how this number came to be here?'. Chances are you are wondering about the traceability through your data collection, management, and reporting process. Where do you start tracing the data? Where do you finish? How am I going to trace this data effectively? This paper looks into the 'breadcrumb trail' through the data management and reporting cycle of clinical trials. Is there a simple solution out there to manage the traceability of the data in your trial? What will you do when the FDA come knocking and want to see the links through the steps in your process? These questions will soon be answered.

Abstract: Competing risks extends survival data by also observing a cause of failure. Once a subject fails, it is impossible for him to subsequently fail from any other cause. A well-established tool for summarising competing risks data is the cumulative incidence estimate, which estimates the probability of a subject failing from a specific cause of interest before a given time. Comparisons of the cumulative incidence estimates between groups of subjects can be made using Gray's test. However, there is no commonly available SAS code to perform such analyses, which helps to perpetuate the mistaken use of Kaplan–Meier estimates and log-rank tests in the analysis of competing risks data. This paper presents SAS code to provide cumulative incidence estimates and Gray's tests.

Abstract: One of the most common analyses in pharmaceutical research is bioequivalence test of two drugs. The FDA has endorsed the usage of Schuirmann's two one-sided hypotheses for the analyses in such studies. Generally, however, several measures on the drugs are taken simultaneously such that the data are multivariate. Nandakumar and McKean generalized Schuirmann's procedure to this multivariate setting. For bivariate data, the results can be summarized in a graphical display. These procedures are least-squares-type procedures and hence, are quite sensitive to mild outliers. To counter this sensitivity, Nandakumar and McKean also developed a simple highly efficient and robust analogue to their multivariate least-squares procedure. The robust results can also be displayed graphically, overlaid with the least-squares graphical results. In this paper, a SAS algorithm is presented, which implements these least-squares and robust multivariate tests for bioequivalence, including their graphical summaries.

Abstract: This paper is a follow-up to a paper in issue 3.1 of the Pharmaceutical Programming Journal where we provided an overview of the pharmacokinetics and pharmacodynamics (PK/PD) component of clinical trials as well as an initiative taken by the Biostatistics and Drug Metabolism and Pharmacokinetics (DMPK) departments at Biogen IDEC to develop more efficient processes for the handling and flow of PK/PD data in clinical trials. In this paper we describe the implementation of procedures developed in the initiative and the impact they have had on several clinical programs at Biogen. We have concluded that having a formal PK/PD data flow process has given the DMPK Scientists a place at the table when designing and conducting clinical trials as well as creating efficiencies for all involved functions.

Abstract: Confidence in software is good, but certainty is better. The test strategy at the heart of a validation plan determines the level of certainty a software project achieves. Ad hoc testing may momentarily convince a developer that a program is complete, but is inappropriate for programs and results intended for others. Perfect testing, on the opposite end of the spectrum, is rarely achievable. Testing is an exercise in both discipline and balance. Learning, adopting, and adapting testing practices refined by software professionals can strengthen a programming team even more than pursuing subtle new programming techniques. The benefits to a development project can be dramatic. Unit testing has proven invaluable as a cornerstone of the validation plan for an ongoing SAS® development project, rewarding the team with efficiency and confidence just short of certainty. The following discussion summarizes the awareness and advantages we have gained from these recent experiences, and encourages the reader t...

Abstract: A lot of aspects have to be considered for the software development and maintenance. This article will give an overview of some of them when dealing with the creation or maintenance of standard systems especially within the clinical environment. These aspects are estimations regarding effort, required group sizes, programming principles, ways to motivate the end users, and other aspects.

Abstract: Twice a year, most areas of North America and Europe observe a time shift. In spring, a positive shift occurs when clocks are reset from 02:00 to 03:00 hours, generally on a Sunday morning. In late autumn, a negative shift occurs when clocks are reset from 03:00 to 02:00 hours, generally on a Sunday morning. These time shifts can have an impact on the data quality if they are not considered appropriately. Especially for pharmacokinetic studies, this can lead to issues and wrong results in the calculation of time-dependent variables like area under the curve (AUC) or terminal half-life (t 1/2). It also affects recording of safety data like adverse events or serial ECGs. This paper describes possible pitfalls in the study set-up and possible impact on data quality and analysis. It also shows traceable data handling options in case a solution in the data collection cannot be achieved.

Abstract: PROC MIXED is commonly being used to compare treatment or other differences in phase 1 crossover trials. In such trials there is variation between subjects and also variation within subjects — these two sources of variation can be described by random effects. PROC MIXED is used because it can accommodate for random effects and as opposed to other SAS regression procedures, subjects with missing observations can be handled without removing all of their data from the analysis. A fixed effect is a parameter which is modelled in the same way as in PROC GLM — there are pre-specified levels of that effect, e.g. treatment group which is pre-defined in a trial because the aim is to compare responses among the fixed groups. In contrast a random effect is a parameter whose values cause a random variability within a trial and whose values are not known pre-trial, e.g. the subjects' responses in a trial. So commonly, subject is declared as a 'random' parameter in PROC MIXED to account for this random variatio...

Abstract: Numeric precision and representation issues are well-known topics in computer science. Non computer scientists sometimes are not aware of the problems that occur with these issues. This paper provides a brief introduction to problems based on numeric precision and the presentation of numbers on computerized systems. Numbers are stored as binary numbers in computerized systems. However, not all floating-point numbers can be represented properly in the binary system. When calculating with numbers that cannot be stored exactly, the result may not be as expected. In our case, 4.8 and 4.6 can not be stored exactly and therefore, the result of 4.8 minus 4.6 will not be exactly 0.2. The conversion of floating-point numbers to a storable IEEE-754 binary number is illustrated. Finally, some SAS functions and options are introduced that may help to deal with the issue.

Abstract: The SAS macro presented here allows programmers to check the compliance of SDTM domains with controlled terminology. The macro is based on two input metadata Excel files: one file contains variables/variable groups (e.g. --ACN) together with their corresponding 'codelist' term (e.g. ACN). The other is a reference list of all 'codelist' terms and their controlled terminology values (e.g. DOSE INCREASED, DOSE NOT CHANGED, etc.). Both files are based on the OpenCDISC standard checks for controlled terminology but could be customized to the sponsor's needs. The primary output consists of a list of all values that could not be found in the controlled terminology. Furthermore, the corresponding entries that have not been successfully mapped are also provided. Multiple studies and domains can be checked simultaneously. Therefore, programmers can use the macro at various stages, e.g. during the SDTM development process of a single domain, or when preparing multiple studies for pooling.