Data analysis plan

Phase IV studies can also take the form of retrospective pooled analyses which are designed to reflect the totality of clinical research experience with a new drug which is usually obtained via analysis of pooled clinical trial databases. Such data-mining efforts should not be considered inferior to data obtained from the conduct of an individual clinical trial. In fact, there are substantial benefits of such an approach, as the results of a given trial, especially if the end point is not prespecified to be primary because of power limitations, can be a function of chance. To assure that the results obtained from pooled analyses are not biased, a prespecified data analysis plan is often formulated as the first step, outlining the clear goals of the proposed analysis as well as its methodology. Key to this type of analysis is the definition of the outcome measure. Both efficacy and safety measures can be the focus of these types of analyses. 

A study protocol is often supplemented with another very important document called the statistical analysis plan (sometimes referred to by similar names such as a data analysis plan or reporting analysis plan). The statistical analysis plan often supplements a study protocol by providing a very detailed account of the analyses that will be conducted at the completion of data acquisition. The statistical analysis plan should be written in conjunction with (and at the same time as) the protocol, but in reality this does not always happen. At the very least it should be finalized before the statistical analysis and breaking of the blind. In many instances (for example, confirmatory trials) it may be helpful to submit the final statistical analysis plan to the appropriate regulatory authorities for their input. 

Table 11.1 lists some useful headings in a data analysis plan. The specific outline will vary from company to company and/or from project to project. Clearly, there is a great deal of information included in this type of document, so it is necessary to realize that writing it will require a considerable amount of time. On the other... 

TABLE 11.1 Examples of Useful Items in a Data Analysis Plan... 

From the perspective of the time required for modeling, it is apparent that a very important aspect of the data collection phase is ensuring that the pharmacometri-cian takes the time to prepare for the modeling. This preparatory work should include finalization of the data analysis plan, preparation of model building procedures, and construction of a template or templates for the report. In this way, the data collection phase can shorten the time required for modeling. 

Given the level of research activity devoted to identification of influential observations, considerably less effort has been devoted to what to do about them. Under guidelines (E9 Statistical Principles for Clinical Trials) developed by the International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use (1997), more commonly called ICH, several principles for dealing with outliers or influential observations are presented. First, data analysis should be defined prior to analyzing the data, preferable before data collection even begins. The data analysis plan should specify in detail how outliers or influential observations will be handled. Second, in the absence of a plan for handling outliers or influential observations, the analyst should do two analyses, one with and the other without the points in question, and the differences between the results should be presented in the discussion of the results. Lastly, identification of outliers should be based on statistical, as well as scientific rationale, and that the context of the data point should dictate how to deal with it. 

Today, good modeling practices dictate that a data analysis plan (DAP) be written prior to any modeling being conducted and prior to any unblinding of the data. The reason being that model credibility is increased to outside reviewers when there is impartiality in the model development process. The DAP essentially provides a blueprint for the analysis. It should provide details... 

Table 8.1 Sample table of contents of a data analysis plan.

To see what impact these observations might have on the parameter estimates, these observations were removed and the best model after backwards stepwise model development was refit. The results are shown in Table 9.15. Removal of these observations resulted in a decrease in the OFV, AIC, and condition number with little to no change in the parameter estimates. Although to be fair, direct comparison of the OFVs and AICs is not valid because of the unequal number of observations in the data sets. More importantly, although the distribution of weighted residuals was not normally distributed, the distribution was no longer skewed. Also, the standard error of the estimates all decreased. Whether to remove these observations from the data set is not immediately clear and if removal of observations was not specified a priori in the data analysis plan then their removal should probably not be made. In this case, it was decided to remove the observations in the data set. 

Population pharmacokinetic analysis proceeds in stages. The most successful efforts have the benefit of prospective smdy designs, clearly defined objectives, and data analysis plans that are defined prior to study initiation. Although some deviation in the order of the analysis plan is possible, there is a typical sequence that is followed ... 

Table 15.9 lists the common graphical representations with the typical usage and interpretation in a population approach setting. Some specific examples of representative plots are shown in Figures 15.2 through 15.5. Ette and Ludden have provided an informative example of how these and other diagnostic techniques are integrated into the data analysis plan of a population pharmacokinetic analysis. 

The handling and analysis of pharmacoeco-nomic data should be along the lines familiar to those observing good clinical practices (GCP) for other purposes. Data collection instruments need to be selected, or created and incorporated into case report forms, just as for any other end-point. Data analysis plans should be created prospectively. The statistical analysis plan should be prospective, and should help put the pharmacoe-conomic measures in the context of other properties of the test medication (Table 19.3). Are they... 

The data analysis plan describing how the data will be analyzed how often by whom how the results will be presented, discussed, and used for internal program decision making and what information will be shared with external groups... 

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