SAS

When you are planning a study or an experiment, you often need to know how many units to sample to obtain a certain power, or you may want to know the power you would obtain with a specific sample size. The power of a hypothesis test is the probability of rejecting the null hypothesis when the alternative hypothesis is true. With an inadequate sample size, you may not reach valid conclusions with your work; with an excessive sample size, you may waste valuable resources. Thus, performing sample size and power computations is often quite important.

The power and sample size calculations depend on the planned data analysis strategy. That is, if the primary hypothesis test is a two-sample t test, then the power calculations must be based on that test. Otherwise, if the sample size calculations and data analyses are not aligned, the results may not be correct.

The SAS/STAT Power and Sample Size application (PSS) is a data analysis tool that provides easy access to power analysis and sample size determination techniques. The application is intended for students and researchers as well as experienced SAS users and statisticians.

Determining sample size requirements ahead of the study is a prospective exercise. You then proceed to select the appropriate number of sampling units and perform data collection and analysis. However, power and sample size calculations are also useful retrospectively. Only prospective power analysis is offered by PSS.

Power and sample size calculations are a function of the specific alternative hypothesis of interest, in addition to other parameters. That is, the power results will vary depending on which value of the alternative hypothesis you specify, so sometimes it is useful to do these analyses for a range of values to determine how sensitive the power analysis is to changes in the alternative hypothesis value. Often, you produce plots of power versus sample size, called power curves, to see how sample size and power affect each other.

The PSS application is a Web browser application. It is accessed either from your own machine or your organization's intranet using the Microsoft Internet Explorer browser. It relies on the SAS/STAT procedures POWER and GLMPOWER for its computations.

Stat Studio is a tool for data exploration and analysis. Stat Studio requires that you have a license for Base SAS, SAS/STAT, and SAS/IML. Stat Studio runs on a PC in the Microsoft Windows operating environment. You can use Stat Studio to do the following:

explore data through graphs linked across multiple windows
transform data
subset data
analyze univariate distributions
discover structure and features in multivariate data
fit and evaluate explanatory models

PROC CDISC contains changes necessary to support the evolving CDISC ODM and SDTM standards, bug fixes, and usage cases submitted to the SAS CDISC team.

ODM
The Operational Data Model (ODM) is a vendor neutral, platform independent format for interchange and archive of data collected in clinical trials. The model represents study metadata, data and administrative data associated with a clinical trial. Only the information that needs to be shared among different software systems during a trial, or archived after a trial is included in the model.

SDTM
The Study Data Tabulation Model (SDTM) defines a standard structure for study data tabulations that are to be submitted as part of a product application to a regulatory authority such as the United States Food and Drug Administration (FDA).

The SDTM was prepared by the CDISC Submission Data Standards (SDS) Team to guide the organization, structure, and format of tabulation datasets for study data submitted to regulatory authorities. Data tabulation datasets are one of four ways to represent the human subject Case Report Tabulation (CRT) and equivalent animal data submitted to the FDA.