Glossary

CDISC Analysis Data Model.

Significance level. Although alpha can be any value between 0 and 1, it is typically set at either 0.01, 0.05 or 0.10.

Statistical models and procedures that partition observed Variance in a Variable into components attributable to different variation sources. By analyzing comparisons of variance estimates, ANOVA can determine whether the Means of several groups are statistically equal.

A graphical representation of discrete or non-continuous data. It consists of rectangular bars whose lengths are proportional to the magnitude of the values that they represent. See Bar Chart.

A Variable that contains two discrete values (0 and 1, for example).

A group of Glossarys that work together to perform a task. Examples in humans include the digestive system, the nervous system, and the endocrine system.

A two-dimensional Scatterplot showing the relationship between two Variables over time. Each circle, or bubble, represents a single instance of an ID variable. See Bubble Plot.

An optional (in most reports) Variable specification whose values define groups of Observations, such as hour, month, or year. Specifying a BY variable enables you to animate an image so that you can see how response values change according to some grouping, like over time. Alternatively, BY variables can enable analyses to be performed separately on different groups as defined by a variable such as gender.

Direct representations of a data table, drawn as a rectangular array of cells with each cell corresponding to a data table entry. Colors are assigned to each cell based on the range and type of values found in the column

A Variable whose values can consist of alphabetic and special characters as well as numeric characters

An item in a dialog or that you can select without affecting any other items. You can deactivate a check box by selecting it again.

The Variable whose values define the groups for analysis. Class variables can have continuous values, but they typically have a few discrete values that define the classifications of the variable. Values can either be character or numeric.

The process of dividing a data set into mutually exclusive groups such that the Observations for each group are as close as possible to one another, and different groups are as far as possible from one another.

A Variable whose values are used to specify how the graphical output of an analysis is to be colored.

See Mosaic Plot.

A relationship between Variables in terms of dependence.

A measure of the relationship between two Variables. It equals the Correlation Coefficient between the two variables times the square roots of their Variances.

Comma-separated value format. This text format stores tabular data, with line breaks and commas used to delimit table rows and columns, respectively.

A Variable whose value is determined by the value of another variable or by the values of a set of variables. This variable lists the responses you measure. In a two-dimensional plot, the dependent variable is usually plotted on the y (horizontal) axis.

Graphics showing the number or proportion of events falling within a particular interval. JMP Clinical software presents these distributions as histograms or Parallel Plots. See Distribution.

To start at one level of a dimension hierarchy and to click through one or more lower levels until you reach the data that you are interested in.

For a given square matrix, a nonzero vector that changes length, but not direction, when multiplied by the matrix. The computation of principal components for a set of Variables uses the eigenvectors of the variables' Covariance or Correlation matrix.

Also referred to as an Independent Variable or predictor variable, a factor is a Variable included in a model to account for variation in a response. Factors are the variables whose values (levels) you set to study their relationship to a response. You often experiment with many potentially influential factors at the same time.

The probability of making one or more false discoveries (Type I Errors) among all hypotheses while performing multiple pairwise tests.

The effects that drive the variation that you are interested in assessing that have a fixed number of well-defined levels. They can also include nuisance variables that you need to consider in your model. Fixed effects include factors such as experimental treatment, disease status, age or developmental status of the test organisms, and gender. Variation due to fixed effects is the variation that you are interested in estimating and must be kept in the analysis.

The nth root of the product of the data. The statistic is helpful when the data contains a large value in a skewed distribution.

A heat map is a visual representation that shows the intensity of a phenomenon as color in two dimensions.

A method of cluster analysis that constructs a hierarchy of clustering. Strategies include the agglomerative approach, where each cluster initially contains only one observation, and the divisive approach, where all observations are initially contained in one cluster. Hierarchical clustering results are commonly presented in Dendrogram form.

The third-highest level of the Medical Dictionary for Regulatory Activities (MedDRA) Hierarchy, below High Level Group Term (HLGT) and above Preferred Term (PT). An example of an HLT is “Viral upper respiratory tract infections”.

A decision-making rule based on data from an experiment or observational study. A hypothesis test is used to conclude significance of a result based on the sufficiently low likelihood (set by the predefined significance level) that it occurred because of random chance alone.

The computation of replacement values for missing input values.

One or more columns specifying how the Observations are to be classified.

A scripting language used in JMP applications.

A curve based on the survival function estimator from life-time or clinical outcome data. For example, it can be used to measure the proportion of patients living for a given amount of time after treatment, or to measure the time until a tumor disappears.

A column containing descriptive labels that can be printed in the output by certain procedures instead of, or in addition to, the Variable name (which is also known as the SAS Variable Name). Synonymous with SAS Variable Label.

The lowest level of the Medical Dictionary for Regulatory Activities (MedDRA), below Preferred Term (PT). This level is reserved for non-current, vague, ambiguous, truncated, or misspelled terms, or for terms taken from other terminologies that do not conform to MedDRA rules.

A single statement, instruction, or catalog entry that automatically expands into a set of statements, instructions, or text.

An effect measures the extent to which the response depends on the factors involved in the effect. A main effect is the change in the response due to a single factor. For two-level factors, the main effect is the difference between the mean response at the high level of a factor and the Mean response at its low level.

A Clinical Research comparison of average score during baseline and a summary score during the trial for each finding. See Matched Pairs Analysis.

A clinically validated international medical terminology dictionary used by regulatory authorities and the biopharmaceutical industry.

The primary list of items at the top of a , which represent the actions or classes of actions that can be executed. Selecting an item executes an action, opens a Pull-down Menu, or opens a Glossary box that requests additional information.

A value in the SAS System indicating that no data is stored for the Variable in the current Observation. It is indicated by a single dot (.) for a numeric variable or a blank for a character variable.

The value that occurs most often in a probability Distribution or data set.

An argument of proof by contradiction; often known as denying the consequent. It has the general argument form of:

1. If P, then Q.

2. Not Q.

3. Therefore, not P.

A Variable that contains discrete values that do not have a logical order. Includes names and other verbal descriptions.

A Variable that contains only numeric values and related symbols, such as decimal points, plus signs, and minus signs.

Analysis of variance with one between-groups factor. This is useful when you have a nominal Independent Variable and a normally distributed interval Dependent Variable, and you want to compare differences in the means of the dependent variable according to levels of the independent variable.

Analysis of variance used on one nominal Independent Variable and a normally distributed interval Dependent Variable that is repeated at least twice for each subject. This method is equivalent to the paired samples t-test, but allows for two or more nominal variable levels.

The established systematic tendency for humans to be overly optimistic about the outcome of planned actions. The likelihood of positive and negative events are over- and under-estimated, respectively. This tendency varies based on person and type of action.

A plot showing several lines or markers on the Y axis overlaid to a common variable on the X axis. See Overlay Plot.

See Percentile.

The format of a text (.txt) file that is readable with little-to-no-processing. Files in this format cannot be embellished with multiple font styles, underlining, italicization, emboldening, and so on.

The probability of a statistical significance test enabling you to reject the Null Hypothesis when the Alternative Hypothesis is true. Power equals one minus Glossary (the rate of Type II Error).

The probability of an event computed before collection of new data (often based on an experienced expert opinion or rules-of-thumb). An experimenter begins with a prior probability of an event and then revises it in light of new data. Contrast with posterior probability.

The statistical probability that a Statistic is as or more extreme than the observed value, assuming that the Null Hypothesis is true. A smaller p-value enables you to more rigorously reject the null hypothesis.

The effects that cause extraneous variation in your results and have little to do with the questions being addressed. Random effects include factors such as physical differences between the arrays, or batch effects resulting from performing different parts of the experiments at different times, on different days, using different lots of reagents, and so on. Variation resulting from random effects can confound your results and should be eliminated from your analysis.

Techniques for modeling and analyzing several Variables, with the focus on the relationship between dependent and independent variables. Regression analysis is useful in uncovering how values of a Dependent Variable change when a single Independent Variable is varied.

The number of Observations that constitute a statistical sample. For example, the sample size in a study might consist of the number of subjects. Greater sample sizes lead to greater precision and Power for a study design to detect an effect at a given size.

A file with a compressed format used in SAS. Transport files can be used to move SAS libraries, SAS catalogs, and SAS data sets across different operating systems. Files of this format have the extension .xpt.

Every Variable (column) in a SAS data set must have a unique SAS Variable Name. This name must conform to a number of conventions, with notable restrictions on the first character, blank spaces, special characters, and length.

A subject in a Clinical Research study that skips treatments or otherwise does not meet treatment criteria. In clinical data sets, a value of “Screen Failure” is given in the treatment column for this subject.

The proportion of true positives that are correctly identified. Specifically, sensitivity equals the number of true positives divided by the sum of the number of true positives and the number of false negatives.

The proportion of true negatives that are correctly identified. Specifically, specificity equals the number of true negatives divided by the sum of the number of true negatives and the number of false positives.

The standard deviation of the sample mean. It is calculated by dividing the Standard Deviation by the square root of the Sample Size.

Multiple meanings are possible:

- Transformation of a data set to have zero Mean and unit Variance.

- Making all regression coefficients have the same scale.

- Normalization.

A Variable that partitions the data into blocks with similar characteristics.

A plot summarizing the survival of patients in each experimental ARM over the course of a Clinical Research trial. See Survival Plot.

A nonparametric measure of association based on the number of concordances and discordances in paired Observations. Concordance occurs when paired observations vary together, and discordance occurs when paired observations vary differently. Also, used for the truncated product p-Value adjustment method to indicate that there is at least one false Null Hypothesis among those with p-values less than tau when the null hypothesis is rejected.

The process of applying a function to a Variable in order to adjust the variable's range, variability, or both.

A test that assesses the statistical difference between the Means of two different experimental groups. The Test Statistic follows a Student’s t distribution if the Null Hypothesis is supported.

If only one Variable is chosen (one-sample t-test), the null hypothesis is that “the population mean is equal to the given mean”.

An incorrect decision made when a test fails to reject a false Null Hypothesis (H0). This is comparable to a false negative error. Type II error rate is denoted by Glossary, and is related to the Power of a test (power = 1-beta).

A measure of deviation of a group of samples from the mean. It is calculated by squaring the Standard Deviation.

A Scatterplot of the negative log10-transformed p-Values derived from a specific t-test against the log2-fold change in expression. Genes whose expression is decreased lie to the left of the Mean; genes whose expression is increased lie to the right of the mean. See Volcano Plot.