Exploratory statistics

Massy, W. F. Principal components regression in exploratory statistical research. J. Am. Stat. Assoc. 1965, 60, 234-246. 

Because the number of data points is low, many of the statistical techniques that are today being discussed in the literature caimot be used. While this is true for the vast majority of control work that is being done in industrial labs, where acceptability and ruggedness of an evaluation scheme are major concerns, this need not be so in R D situations or exploratory or optimization work, where statisticians could well be involved. For products going to clinical trials or the market, the liability question automatically enforces the tried-and-true sort of solution that can at least be made palatable to lawyers on account of the reams of precedents, even if they do not understand the math involved. 

The significance level relates to the risk of designating a chance occurrence as statistically significant. Usually a 5% level is utilized for testing treatment effects. If a p-value of 0.04 is reported for a treatment effect, this means that there is only a 4% chance that the difference in response between the active and control treatments occurred due to chance. Keep in mind, however, that if many tests are run in a trial, it is entirely possible that one or two might be significant due to chance. As an extreme example, consider a study in which 100 statistical tests are run. We would expect five of those tests to show significance with a p-value of 0.05 or less due to chance. Therefore, it is essential to specify the main tests to be run in the protocol. Any tests that are conducted after the trial has been completed should be clearly labeled as post hoc exploratory analyses. 

Three statistical approaches described so far (CCK, LCA, and MA) rely on statistical models. They make certain assumptions about the latent structure, which allows them to define models and then evaluate these models with the data. CA is radically different it goes bottom-up from the data and does not make any structural assumptions whatsoever. CA is inductive and exploratory in nature. Is this advantageous We will come back to this question, but first let us get better acquainted with the methodology. 

Exploratory data analysis (3 ) is performed on the data base using multivariate statistical techniques. The objectives of... 

ICH E9 makes a very clear distinction between confirmatory and exploratory trials. From a statistical perspective this is an important distinction as certain aspects of the design and analysis of data depend upon this confirmatory/exploratory distinction. 

Besides the examples listed above, there are numerous exploratory association studies that have identified many potential polymorphism biomarkers for treatment response in membrane transporter, drug-metabolizing enzyme, and drug target genes. The methodology and statistical analysis for the candidate gene approach are simple the results are easy to interpret. 

From an analytical viewpoint, statistical approaches can be subdivided into two types Exploratory Data Analysis (EDA) and Confirmatory Data Analysis (CDA). Exploratory data analysis is concerned with pictorial methods for visualising data shape and for looking for patterns in multivariate data. It should always be used as a precursor for selection of appropriate statistical tools to confirm or quantify, which is the province of confirmatory data analysis. CDA is about applying specific tools to a problem, quantifying underlying effects and data modelling. This is the more familiar area of statistics to the analytical community. 

The committee recommends that biomarker researchers, public or private, should adhere to appropriate statistical principles when sampling populations for biomonitoring. Editors of peer-reviewed journals as well as agency administrators and reviewers should insist on explicit attention to such information to minimize the possibility of incorrect inferences—even more while the biomonitoring field remains exploratory and public understanding remains incomplete. 

In addition to having an acceptable safety profile, an investigational drug needs to display beneficial therapeutic effects. This takes us into the realm of therapeutic exploratory and therapeutic confirmatory trials. The statistical approaches discussed in this chapter are characteristic of those employed in these trials. 

Exploratory data analysis (EDA). This analysis, also called pretreatment of data , is essential to avoid wrong or obvious conclusions. The EDA objective is to obtain the maximum useful information from each piece of chemico-physical data because the perception and experience of a researcher cannot be sufficient to single out all the significant information. This step comprises descriptive univariate statistical algorithms (e.g. mean, normality assumption, skewness, kurtosis, variance, coefficient of variation), detection of outliers, cleansing of data matrix, measures of the analytical method quality (e.g. precision, sensibility, robustness, uncertainty, traceability) (Eurachem, 1998) and the use of basic algorithms such as box-and-whisker, stem-and-leaf, etc. 

The main goal of this section is to provide a summary of several of the most widely used multivariate procedures in food authentication out of the vast array currently available. These are included in well-known computer packages such as BMDP, IMSL, MATLAB, NAG, SAS, SPSS and STATISTIC A. The first three subsections describe unsupervised procedures, also called exploratory data analysis, that can reveal hidden patterns in complex data by reducing data to more interpretable information, to emphasize the natural grouping in the data and show which variables most strongly influence these patterns. The fourth and fifth subsections are focused on the supervised procedures of discriminant analysis and regression. The former produces good information when applied under the strictness of certain tests, whereas the latter is mainly used when the objective is calibration. 

---