Clinical data analysis descriptive statistics

Descriptive statistics were used to profile and characterize the articles within each data field abstracted by the reviewers, including the type of clinical service performed, the site of the study or evaluation, and the type of analysis performed. 

It is common practice in analyses of clinical data to inspect the distributions of baseline characteristics - for example, demographics and measures of disease severity - through the use of descriptive summary statistics. This is an important analysis because it helps to describe the sample representing the target population of interest. If the sample is representative of the target population the inferences drawn from the study will be considered relevant. 

Rarely, however, will the data be in a format suitable for analysis. The more complex the data, the more pre-processing will be needed to put the data in a format suitable for analysis. The next step then is to look at the data and clean as needed. Check the quality of the data. Have the data been entered to suitable precision, for instance, two places behind the decimal Perform descriptive statistics and look at histograms to examine for discordant results. It is not uncommon in large clinical multi-national clinical trials for clinical chemistry data to be of different units between the United States... 

Analysis of most (perhaps 65%) pharmacokinetic data from clinical trials starts and stops with noncompartmental analysis (NCA). NCA usually includes calculating the area under the curve (AUC) of concentration versus time, or under the first-moment curve (AUMC, from a graph of concentration multiplied by time versus time). Calculation of AUC and AUMC facilitates simple calculations for some standard pharmacokinetic parameters and collapses measurements made at several sampling times into a single number representing exposure. The approach makes few assumptions, has few parameters, and allows fairly rigorous statistical description of exposure and how it is affected by dose. An exposure response model may be created. With respect to descriptive dimensions these dose-exposure and exposure-response models... 

A psychologist-led team took over after we completed the clinical studies in 1980 and 1981. They did a final analysis of the data and published the final rqrort. I was sorry to see that it was a dry, statistically technical document, virtually devoid of clinical descriptions. It was never widely distributed or publicized. 

The FDA s statisticians expect that each clinical protocol submitted will include a description of any planned interim analyses of the data, including the anticipated monitoring procedures, the variables to be analyzed, and the statistical methods to be applied. The latter includes the choice of significance level for each interim analysis, the frequency of analysis, and a declaration of the circumstances under which the study would be terminated before the planned number of patients have been entered into the study. 

Whitehead (W6) described an IBM-870 system for the recording of laboratory data on punch cards based on an earlier description by Peacock et al. (PI). The primary purpose of this system was to improve the presentation of individual laboratory reports, but the cards were subsequently sorted and the data on them subjected to statistical analysis, some of which revealed hitherto unsuspected sources of error as a result of variation in the technical performance of individual members of staff. Whitehead was among the first to apply cusum techniques to clinical chemistry laboratory data for control purposes, but his off-line system was not able to provide quality control information of immediate value to the laboratory. 

The term "statistical analysis" has two meanings. Statistical analysis, used in a general sense, can be regarded as a global description or plan of how data collected will be analyzed. The term "a statistical analysis" refers to an individual analytical technique that is used to describe and analyze numerical information. This book teaches you how to conduct a collection of statistical analyses that are appropriate for use in analyzing the results of clinical trials. 

---