Clinical data analysis

As a general rule, clinical data are required as evidence to support conformity with the requirements of the Active Implantable Medical Devices (AIMD) and the Medical Device (MD) directives with regards to safety and effectiveness under the normal conditions of use, evaluation of undesirable side effects, and the acceptability of the benefit/risk ratio. Risk analysis should be used to establish key objectives that need to be addressed by clinical data, or alternatively to justify why clinical data are not required (mainly for Class I devices). The risk analysis process should help the manufacturer to identify known (or reasonably foreseeable) hazards associated with the use of the device, and decide how best to investigate and estimate the risks associated with each hazard. The clinical data should then be used to establish the safety and effectiveness of the device under the intended use conditions, and to demonstrate that any of the residual risks are acceptable, when weighed against the benefits derived from use of the device. 

Owing to the personal interest and experience of the authors, the emphasis in this chapter is on using computers for drug discovery. But the use of computers in laboratory instruments and for analysis of experimental and clinical data is no less important. This chapter is written with young scientists in mind. We feel it is important that the new investigator have an appreciation of how the field evolved to its present circumstance, if for no other reason than to help steer toward a better future for those scientists using or planning to use computers in the pharmaceutical industry. 

The E9 discusses the statistical issues in the design and conduct of a clinical trial. It details trial design, trial conduct, and data analysis and reporting. Although most useful... 

If data will be summarized or analyzed as part of the protocol-defined statistical analysis, they should be cleaned first. Cleaned in this context means that erroneous data entered into a variable are repaired before data analysis. Under the direction of the statistics group, the data management group is responsible for cleaning the clinical data. 

Before the statistical programmer receives data that are ready for analysis, the clinical data management group cleans the data. This is done through a query process, which is built into the clinical data management system. The clinical data management query process usually looks like this ... 

In order to reduce unnecessary data queries, the statistics group should be consulted early in the clinical database development process to identify variables critical for data analysis. Optimally, the statistical analysis plan would already be written by the time of database development so that the queries could be designed based on the critical variables indicated in the analysis plan. However, at the database development stage, usually only the clinical protocol exists to guide the statistics and clinical data management departments in developing the query or data management plan. 

There is only one good solution to handling free-text variables that are needed for statistical analysis. The free-text variables need to be coded by clinical data management in the clinical database. If the adverse events were coded with a dictionary such as MedDRA, the previous example might look like Program 2.3. 

Laboratory data may consist of many different collections of tests, such as ECG laboratory tests, microbiologic laboratory tests, and other therapeutic-indication-specific clinical lab tests. However, laboratory data traditionally consist of results from urinalysis, hematology, and blood chemistry tests. Traditional laboratory data can come from what are called local laboratories, which are labs at the clinical site, or from central laboratories where the clinical sites send their samples for analysis. Often when the laboratory data come from a central laboratory, there is no physical CRF page for the data and they are loaded into the clinical data management system directly from an electronic file. Local laboratory data may be represented with a CRF page such as this ... 

Once the raw clinical data have been imported into SAS, the next step is to transform those raw data into more useful analysis-ready data. Raw data here mean data that have been imported without manipulation into SAS from another data source. That data source is likely to be a clinical data management system, but it could also be external laboratory data, IVRS data, data found in Microsoft Office files, or CDISC model data serving as the raw data. These raw data as they exist are often not ready for analysis. There may be additional variables that need to be defined, and the data may not be structured in a way that is required for a particular SAS analysis procedure. So once the raw data have been brought into SAS, they usually require some kind of transformation into analysis-ready files, which this chapter will discuss. 

Typically, clinical data come to you in a shape that is dictated by the underlying CRF design and the clinical data management system. Most clinical data management systems use a relational data structure that is normalized and optimized for data management. Much of the time these normalized data are in a structure that is perfectly acceptable for analysis in SAS. However, sometimes the data need to be denormalized for proper analysis in SAS. 

Medical dictionaries often need to be referenced when creating various analysis data sets For instance, perhaps the raw adverse event database in your clinical data management system contains only the MedDRA code. The code is worth having, but you would need the adverse event body system and preferred medical term to provide a useful summary of events. 

SAS has always had and will maintain a central role in the data management, analysis, and reporting of clinical trial data. Because of the strong suite of SAS statistical procedures and the power of Base SAS programming, SAS remains a favorite of statisticians for the analysis of clinical trial data. Several companies have built their clinical trial data management and statistical analysis systems entirely with SAS software. More recently, SAS has offered SAS Drug Development as an industry solution that provides a comprehensive clinical trial analysis and reporting environment compliant with 21 CRF-Part 11. 

Analysis of biological data has now become far more complex, and there is a drive to develop software to allow disparate data sets, such as sequence, literature, clinical data and expression analyses, to all be accessible and interlinked. This allows movement between information systems and provides more complex meta-ana-lyses of these data sets, allowing a holistic view of biological research, in place of the current fragmented view we have available to us. This will ultimately lead to the blurring of boundaries between different disciplines, such as the areas of che-... 

Sodium bicarbonate administration for cardiac arrest is controversial because there are few clinical data supporting its use, and it may have some detrimental effects. Sodium bicarbonate can be used in special circumstances (i.e., underlying metabolic acidosis, hyperkalemia, salicylate overdose, or tricyclic antidepressant overdose). The dosage should be guided by laboratory analysis if possible. 

Amenta F, Mignini F, Rabbia F et al. (2002) Protective effect of anti-hypertensive treatment on cognitive function in essential hypertension analysis of published clinical data. J Neurol Sci 203-204 147-151... 

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