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For most of the sample size procedures in PASS for survival, the user may choose to solve for sample size, power, or the population effect size in some manner. This page provides a brief description of the tools that are available in PASS for power and sample size analysis of survival methods. For several of these procedures, you may specify the proportion of individuals in the control and treatment groups, the accrual time, and the total time.
The first of the three procedures computes the sample size and power for non-inferiority tests under the assumption of proportional hazards. The Cox’s Proportional Hazards Model procedure is documented in Chow, Shao, and Wang (2008) which summarizes the work of Schoenfeld (1981, 1983). The exponential model procedure is based on the unconditional method of Chow, Shao, and Wang (2008) which, in turn, is based on the conditional methods of Lachin and Foulkes (1986). In PASS, there is an analytic procedure for group-sequential logrank tests as well as four simulation procedures.
A variety of spending function options are available in the simulation procedures, including Hwang-Shi-DeCani, O’Brien-Fleming, and Pocock types.
In sequential designs, one or more intermediate analyses of the emerging data are conducted to evaluate whether the experiment should be continued. This procedure computes conditional and predicted power for the case when a logrank test is used to compare the hazard rate of a treatment group to that of a control group.
The Cox Regression procedure in PASS calculates power and sample size for testing the hypothesis that ?1=0 versus the alternative that ?1=B .
You are consulting for a clinical research group planning a trial to compare survival rates for proposed and standard cancer treatments.
Use the TWOSAMPLESURVIVAL statement with the TEST=LOGRANK option to compute the required sample size for the log-rank test. The required sample size per group to achieve a power of 0.8 is 228 if the median loss time is 20 years for the proposed treatment.
The SAS Power and Sample Size application (PSS) is a desktop application that provides easy access to power analysis and sample size determination techniques.
This section describes the statistical tasks that are available with the application as well as its principal features. PSS provides multiple input parameter options, stores the results in a project format, displays power curves, and produces narratives for the results.
SAS is used in: medical research institutions, academic institutions, pharmaceutical industry, financial institutions, etc. From the Windows Desktop, select the Start menu, the Programs menu, the SAS menu, and finally SAS 9.3 (English).
NOTE: The appearance of your computer screen will differ depending on which version of Windows you are using.
The Editor (Program Editor) window is a text editor that facilitates writing SAS programs (code). The active window can be changed by clicking on that window with the mouse, or by selecting the desired window from the Window menu. We will now consider a simple program, which creates a SAS data set using a SAS data step (DATA) and calculates simple descriptive statistics (sample size, mean, and standard deviation) using a SAS procedure (PROC). Data sets can be entered in the SAS programming code or can be read in from a variety of external sources, such as text files, and Microsoft Excel.
SAS indicates that data set WORK.ONE was created and includes one variable with 6 observations.
The WORK prefix for data set ONE indicates the SAS library name where SAS stores the data set.
By default the output from your program, in this case your title and means, will appear in HTML format in the results viewer window. This is different than previous versions of SAS, where the program output was sent as a text listing to the output window by default. In some cases the formatted HTML output may be preferred as it is easier to read, but in other circumstances the text based listing format may be easier to work with. This is done by selecting Tools Options Preferences from the menu at the top of the main SAS window and opening the Results tab (mnemonic TOPR). Like the program editor, the contents of the results viewer, output and log windows can also be saved.



Successful completion of a formal course in biostatistics or statistics is a prerequisite for this class, but review of the following online learning modules may be very beneficial. Please take the time to review basic biostatistics concepts and see the instructor if you have not fulfilled this prerequisite. Inferential statistics make inferences about populations based on samples from the populations and generally take the form of hypothesis tests or confidence intervals. Note, in addition to categorical and continuous variables, there are identifier variables such as ID or Name.
In a typical survival test procedure where the goal is to estimate the sample size, the user enters power, alpha, and the desired population survival parameters. If you would like to examine the formulas and technical details relating to a specific PASS procedure, we recommend you download and install the free trial of the software, open the desired survival procedure, and click on the help button in the top right corner to view the complete documentation of the procedure. From this window the desired procedure is selected from the menus, the category tree on the left, or with a procedure search. In the first four, you can also specify an accrual pattern, and proportions of subjects lost to follow-up or switching groups. The conditional procedure does not extend to non-inferiority, non-zero null, or equivalence tests as easily as the unconditional method does.
The first procedure uses the Cox’s proportional hazards model and the second assumes an exponential model.
The simulation procedures differ only in the survival input (hazard rates, median survival times, proportion surviving, or mortality).
This may be done to conserve resources or to allow a data monitoring board to evaluate safety and efficacy when subjects are entered in a staggered fashion over a long period of time. Note that ?1 is the change in log hazard for a one-unit change in X1 when the rest of the covariates are held constant. The planned data analysis is a log-rank test to nonparametrically compare the overall survival curves for the two treatments.
The "Standard" curve has only one point, specifying an exponential form with a survival probability of 0.5 at year 5. Only six more patients are required in each group if the median loss time is as short as five years.
The application is intended for students and researchers as well as experienced SAS users and statisticians.
Narratives are descriptions of the input parameters and include a statement about the computed power or sample size. The emphasis is on manipulating data sets and basic statistical procedures such as t-tests, chi-square tests, correlation, and regression. Although several statistical concepts and methods will be reviewed, this course assumes that you have completed an introductory course in biostatistics or in statistics. The Log window displays system messages, errors, and resource usage and is thus used to review program statements. A SAS data step is primarily used to create and modify data sets, and a SAS procedure is primarily used to analyze data. Title statements are optional, but they help provide information about the program and are a useful addition to most analyses.
Note that the new log information and output are appended to the bottom of the Log window and Output or Results Viewer windows respectively. The WORK library is temporary and thus data sets stored here only exist as long as the current SAS session is open and will be deleted when SAS is closed. The following display shows the SAS Results tab with the default settings set to create only HTML output.
To save, select the Editor window to make it active; then select the Save option from the File menu.
To save the results viewer, output or log click on the tab to make the file active and then select the Save option from the File menu.
Like any saved files, these files can be copied to a USB drive or e-mailed as an attachment for safekeeping.
If you need help on how to use PASS sample size estimation software, use the links below to jump to a survival topic. The procedure is run and the output shows a summary of the entries as well as the sample size estimate.


A numeric summary of these is results is produced as well as easy-to-read sample size or power curve graphs. There you will find summaries, formulas, references, discussions, technical details, examples, and validation against published articles for the procedure. Conditional power (a frequentist concept) is the probability that the final result will be significant, given the data obtained up to the time of the interim look. The procedure assumes that this hypothesis will be tested using the Wald (or score) statistic.
The Output window displays output from statistical procedures run within the SAS program; however this is no longer the default.
The active window will have a shaded title bar at the top of the window, and a highlighted windows bar at the bottom of the screen. SAS expects to see data until the next semi-colon, which is on line 12 in this programming code. If the Editor window is mistakenly cleared, immediately press Ctrl Z or select the Undo option from the Edit menu to undo the clear. A summary statement is given, as well as references to the articles from which the formulas for the result were obtained. Predictive power (a Bayesian concept) is the result of averaging the conditional power over the posterior distribution of effect size.
The survival curve for patients on the standard treatment is well known to be approximately exponential with a median survival time of five years. The GROUPSURVIVAL= option assigns the survival curves to the two groups, and the ACCRUALTIME= and FOLLOWUPTIME= options specify the accrual and follow-up times. In SAS 9.3 output is sent to the Results Viewer which opens automatically when you run a procedure that generates output.
Checking and understanding the statements in the log file is essential to proficient programming. The Results Viewer cannot be cleared in this manner; we will tell you how to clear the Results Viewer in Class 2.
For more details about a particular procedure, we recommend you download and install the free trial to really understand how to use PASS software. The distributions of each of the groups under the null and alternative hypotheses can be specified directly using over ten distributions including normal, exponential, Gamma, Uniform, Beta, and Cauchy.
The Results window displays a map of the Output window, and is useful for navigating the results of complicated analyses. Note that the menu options for the SAS toolbar along the top of the screen depend on which window is currently active. A warning does not necessarily mean that there is an error in the program, however the warnings should be read carefully. Patients will be accrued uniformly over two years and then followed for an additional three years past the accrual period. Where appropriate, several alternate ways of entering values for certain parameters are offered. Some loss to follow-up is expected, with roughly exponential rates that would result in about 50% loss with the standard treatment within 10 years. For example, in the two-sample t test analysis, means can be entered for individual groups or as a difference. The loss to follow-up with the proposed treatment is more difficult to predict, but 50% loss would be expected to occur sometime between years 5 and 20. The null mean difference can be specified as a default of zero or can be explicitly entered. You can access each project to review the results or to edit your input parameters and produce another analysis.



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