Quality-of-Life Data

Sometimes you may also see quality-of-life (QOL) data collected for your clinical trial. Quality-of-life data are collected to measure the overall physical and mental well-being of a patient. These data are usually collected with a multiple-question patient questionnaire and may be summed up into an aggregate patient score for analysis. Some commonly used quality-of-life questionnaires are the SF-36 and SF-12 Health Survey, but there are quite a few disease-specific QOL questionnaires available to clinical researchers. 

Importing Relational Databases and Clinical Data Management Systems 42 SAS/ACCESS SQL Pass-Through Facility 42 SAS/ACCESS LI BN AM E Statement 43 Importing ASCII Text 44 

PROC IMPORT and the Import Wizard 44 SAS DATA Step 52 SAS Enterprise Guide 53 Importing Microsoft Office Files 56 LIBNAME Statement 58 Import Wizard and PROC IMPORT 59 SAS/ACCESS SQL Pass-Through Facility 64 SAS Enterprise Guide 65 Importing XML 68 

Importing Files in Other Proprietary Data Formats 79 

In most cases, the data that you use for clinical trial analyses are found in some kind of computer file external to the SAS System. The data you need may be found in a permanent SAS data set, a relational database table found in Oracle or Microsoft SQL Server, a Microsoft Access or Excel file, a simple delimited ASCII text file, or even an XML file. In any case SAS provides a wide array of ways in which to import data files into SAS. We explore these tools and the advantages and disadvantages of each in this chapter. 

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Demographics and Trial-Specific Baseline Data 27 Concomitant or Prior Medication Data 27 Medical History Data 29 Investigational Therapy Drug Log 30 Laboratory Data 31 Adverse Event Data 32 Endpoint/Event Assessment Data 35 Clinical Endpoint Committee (CEC) Data 36 Study Termination Data 37 Treatment Randomization Data 38 Quality-of-Life Data 40... 

The objective of the study must be to obtain real clinical evaluation of the use of the product studied. The information collected must include clinical data, safety data and/or quality-of life data to describe clinical experience sufficiently. 

Badia X, Herdman M. The importance of health-related quality-of-life data in determining the value of drug therapy. Clin Ther 2001 23 168-175. 

Quality of life data should come from the patient. In some cases (very young, very old, mentally unstable) patient proxies are used, but the patient should be considered the optimal choice... 

There are a variety of ways to express absolute QRA results. Absolute frequency results are estimates of the statistical likelihood of an accident occurring. Table 3 contains examples of typical statements of absolute frequency estimates. These estimates for complex system failures are usually synthesized using basic equipment failure and operator error data. Depending upon the availability, specificity, and quality of failure data, the estimates may have considerable statistical uncertainty (e.g., factors of 10 or more because of uncertainties in the input data alone). When reporting single-point estimates or best estimates of the expected frequency of rare events (i.e., events not expected to occur within the operating life of a plant), analysts sometimes provide a measure of the sensitivity of the results arising from data uncertainties. 

Studies on the particulate distributions from compressed natural gas (CNG) or diesel-fuelled engines with diesel oxidation catalyst (DOC) or partial diesel particle filter (pDPF) have also been performed. The results obtained are used as data for the model, to study the particle penetration into the human respiratory tracts. As a result, the number distribution of particles in different parts of lungs can be modeled [99-101]. Understanding the particle formation and their effects and finding the methods to ehminate the formed particulates from exhaust gas contribute to a cleaner urban environment and thus to a better quality of life. 

Only two randomized, controlled trials have been completed, and neither provides anything like compelling data (Table 2.6). Chouinard and Albright (1997) conducted a unique evaluation of a subset of patients from a previously conducted clinical trial. Subjects were categorized and profiled at baseline and end point according to clinical severity, and a group of psychiatric nurses were asked to rate various aspects of likely outcome and quality of life to each profile (mild, moderate or severe symptoms). Health state utilities were then calculated risperidone was found to provide more than double the number of quality-adjusted life years compared with haloperidol. Csernansky and Okamoto (1999) conducted a rather more conventional trial, but included no economic analyses. However, they did find that the use of risperidone substantially reduced relapse rates compared with haloperidol—an outcome likely to have a positive impact on cost-effectiveness. 

Economic studies should cover the full period over which the interventions could be expected to have an effect on resource use, survival and health-related quality of life. However, the economic evaluations of donepezil were based on effectiveness data from a limited number of trials, which were short in duration. This has a number of implications. First, the analysis can be limited to the effect of the drug during the period for which effectiveness data were available. In this case, it may be assumed that the treatment effect ceased after 6 months (Stewart et al, 1998). This assumption would only be valid if the donepezil were also discontinued at 6 months. If this is not the case, then the overall costs of the dmg may be underestimated and the benefits overestimated. 

The developments in the treatment of RA are tempered by the lack of evidence describing the long-term safety and efficacy of the BRMs. In addition, the cost associated with the medications can be a deterrent to use. Long-term data are needed to determine if patients receiving BRM therapy early in the course of disease have reduced disease activity, reduced joint deformities and disability, improved quality of life, and continued function as productive members of society. Cost analyses of long-term data may indicate that the increased expenses associated with BRMs are offset by the costs avoided for the treatment of advanced RA. 

When pelvic pain is the characterizing symptom of the disease, medical treatment could have a significant role. Several medical treatments have been proposed to treat secondary chronic pelvic pain due to endometriosis (Stones et al. 2004). Moreover, few data are available regarding the effectiveness of the treatments for endometriosis on the quality of life of these patients that seems to be deeply impaired (Carter 1998). 

In summary, there are five types of analysis that can be used to assess the incremental cost-effectiveness of a drug or service. The type performed is generally predicated by the therapeutic area being evaluated, the research question being addressed and the clinical data available. For example, whereas a CBA (which converts clinical effect into monetary terms) may not be considered (for ethical reasons) to be the best choice for oncology or HIV-related evaluations, a CUA (which takes into account both quality of life and survival duration) may be considered appropriate. 

In a 12-week trial involving nearly 200 adult outpatients with PTSD, preliminary data showed a significantly larger decrease in the Clinician-Administered PTSD Scale (CAPS) total score for sertraline (43%) than for placebo (31%). Sertraline also was shown to have positive effects on quality of life ( 274). In addition, sertraline has been shown to be effective in the treatment of PTSD with co-morbid alcoholism (275). 

Presently, some countries including the United States and a few Member States of the European Union use statistical methods to establish withdrawal periods. However, most countries employ a simple method the withdrawal period is set at the time point when residues in all tissues in all the animals have depleted to below the respective MRL values. When one has determined that time point, the estimation of a safety span also has to be considered in order to compensate for uncertainties of the biological variability. The dimensions of a safety span depend on various, not easy to specify, factors determined by the study design, the quality of the data, and the pharmacokinetic properties of the drug. Hence, an overall recommendation on the estimation of the safety span cannot be provided. An approximate guide for the safety span is likely to be a value of 10-30% of the time period when all observations are below the MRL. As an alternative, the safety span might be calculated from the tissue depletion curve as a value of possibly one to three times the half-life. 

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