Numeric variables, categorizing

Clinical trial data come to the statistical programmer in two basic forms numeric variables and character string (text) variables. With this in mind, there are two considerations for all numeric and text variables. All data should be cleaned if they are needed for analyses, and any data entered asfree-text variables should be coded or categorized if they are needed for analyses. 

Clinical trial data come in two basic forms numeric variables and text variables. Numeric variables are easy for the statistical programmer to handle. Numbers can be analyzed with SAS in a continuous or categorical fashion without much effort. If a numeric variable needs categorization, it is easy enough to categorize the data within SAS. For example, if you had to classify patient age, a simple DATA step such as the following might serve well. 

Input variables are controllable, uncontrollable and disturbance variables. Controllable variables or factors X1 X2,..., X are variables, that can be directed or that can affect the research subject in order to change the response. They can be numerical (example temperature) or categorical (example raw material supplier). Uncontrollable variables Z1 Z2,..., Zp are measured and controlled during the experiment but they cannot be changed at our wish. They can be a major cause for variability in the responses. Other sources of variability are deviations around the set points of the controllable factors, plus sampling and measurement error. Furthermore, the system itself may be composed of parts that also exhibit variability. Disturbance, non controlled variables Wi, W2,..., Wq are immeasurable and their values are randomly changed in time. 

Before discussing how clinical data are described and analyzed, it is helpful to introduce several categories of data. Data are numerical representations of information, and different forms of numerical information have different characteristics that permit (or do not permit) certain analyses to be conducted on them. In clinical research, the term variable is often used when describing data for a particular characteristic of interest, since values for participants in a clinical trial will vary from one individual to another. Clinical data can fall within several categories, including numerical (continuous and discrete) data and categorical (ordinal and nominal) data. 

Ptak, T. (1997) Evaluation of reactive transport processes in a heterogeneous porous aquifer within a non-parametric numerical stochastic transport modelling framework based on sequential indicator simulation of categorical variables. In Soares, A. et al. (Eds.) geoENV I - Geostatistics for Environmental Applications, BHuwer, 153-164. 

Factor is a controllable variable of interest. The factor can be either quantitative or qualitative. A quantitative factor can be measured on a numerical scale. Some examples of qualitative factors include the temperature of a furnace, amount of a chemical, ratio of a material portion, weight of a snbstrate, etc. A qualitative factor can be categorized into a group. Examples include type of material, suppliers, operators, etc. 

All the preceding discussion has concerned continuous numerical data. Not all data is of this form, and we now discuss the encoding of nonnumerical data. Logical variables have one of two values that are encoded as zero and one. An example is the presence or absence (a yes or no) of a feature. Typically the presence (yes) is encoded as a 1 and the absence (no) as a 0. Categorical data should be encoded in some variation of a one-of-N code (N equals the number of categories) or a thermometer code. 

The type of variable under investigation will determine whether you display the data collected in a table as a line graph or as a bar chart To decide which type of graph to draw, you need to know the difference between continuous variables - which are measured so can have any numerical value within the range of results - and categoric... 

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