Power And Sample Size Calculator Mac App Download

Calculate the sample size for the following scenarios (with α=0.05, and power=0.80): 1. You are interested in determining if the average income of college freshman is less than $20,000. So you compute power retrospectively to see if the test was powerful enough or not. This is an empty question. Of course it wasn’t powerful enough – that’s why the result isn’t significant. Power calculations are useful for design, not analysis. (Note: These comments refer to power computed based on the observed effect size and sample size. Use our sample size calculator to rapidly calculate sample size for a multitude of scenarios. Sample size calculator with 100’s of sample size and power calculation procedures; With Plotting Features, easily conduct a variety of “What-If” scenarios to align your final sample size choice with your scientific and budgetary requirements. Sample Size Tables for Clinical Studies David Machin, Michael J. Campbell, Say-Beng Tan, Sze-Huey Tan. Buy from Amazon US - CA - UK - DE - FR - ES - IT. Sample Sizes for Clinical, Laboratory and Epidemiology Studies includes the sample size software (SSS) and formulae and numerical tables needed to design valid clinical studies.

Command:Sample size
Survival analysis (logrank test)

Description

Calculates the required sample size for the comparison of survival rates in two independent groups.

Required input

  • Type I error - alpha: the probability of making a Type I error (α-level, two-sided), i.e. the probability of rejecting the null hypothesis when in fact it is true.
  • Type II error - beta: the probability of making a Type II error (β-level), i.e. the probability of accepting the null hypothesis when in fact it is false.
  • Survival rate Group 1: the hypothesized survival rate in the first group.
  • Survival rate Group 2: the hypothesized survival rate in the second group.
  • Ratio of sample sizes in Group 1 / Group 2: the ratio of the sample sizes in group 1 and 2. Enter 1 for equal sample sizes in both groups. Enter 2 if the number of cases in group 1 must be double of the number of cases in group 2.

Example

You are interested in detecting a difference between survival rates of 0.6 and 0.4. You plan to have twice as many cases in the first group as in the second group.

For α-level you select 0.05 and for β-level you select 0.20 (power is 80%).

Enter the values 0.6 and 0.4 for the Survival rates in Group 1 and Group 2, and enter 2 for the Ratio of sample sizes.

Results

After you click Calculate the program displays the required sample size.

In the example 129 cases are required in Group 1 and 65 cases in Group 2, giving a total of 194 cases.

A table shows the required total sample size for different Type I and Type II Error levels.

Literature

  • Machin D, Campbell MJ, Tan SB, Tan SH (2009) Sample size tables for clinical studies. 3rd ed. Chichester: Wiley-Blackwell.

See also

Recommended book

Sample Size Tables for Clinical Studies
David Machin, Michael J. Campbell, Say-Beng Tan, Sze-Huey Tan

Buy from Amazon US - CA - UK - DE - FR - ES - IT

Sample Sizes for Clinical, Laboratory and Epidemiology Studies includes the sample size software (SSS) and formulae and numerical tables needed to design valid clinical studies. The text covers clinical as well as laboratory and epidemiology studies and contains the information needed to ensure a study will form a valid contribution to medical research.

The authors, noted experts in the field, explain step by step and explore the wide range of considerations necessary to assist investigational teams when deriving an appropriate sample size for their when planned study. The book contains sets of sample size tables with companion explanations and clear worked out examples based on real data. In addition, the text offers bibliography and references sections that are designed to be helpful with guidance on the principles discussed.

Java applets for power and sample size
Select the analysis to be used in your study:This software is intended to be useful in planning statistical studies. It is not intended to be used for analysis of data that have already been collected.
Each selection provides a graphical interface for studying the power of one or more tests. They include sliders (convertible to number-entry fields) for varying parameters, and a simple provision for graphing one variable against another.
Each dialog window also offers a Help menu. Please read the Help menus before contacting me with questions.

The 'Balanced ANOVA' selection provides another dialog with a list of several popular experimental designs, plus a provision for specifying your own model.

Note: The dialogs open in separate windows. If you're running this on an Apple Macintosh, the applets' menus are added to the screen menubar -- so, for example, you'll have two 'Help' menus there!

You may also downloadthis software to run it on your own PC.

Note: These require a web browser capable of running Java applets (version 1.1 or higher). If you do not see a selection list above, chances are that you either have disabled Java, your browser is not new enough., or you need to download a JRE plug-in from java.sun.com. Due to a compatibility bug, many plug-ins size the applet window before allowing for an additional strip with a security warning.; drag the bottom of the window downward a bit to compensate.

Power And Sample Size Calculator Mac App Downloads

Citing this software

If you use this software in preparing a research paper, grant proposal, or other prublication, I would appreciate your acknowledging it by citing it in the references. Here is a suggested bibliography entry in APA or 'author (date)' style:
Lenth, R. V. (2006). Java Applets for Power and Sample Size [Computer software]. Retrieved month day, year, from http://www.stat.uiowa.edu/~rlenth/Power.

This form of the citation is appropriate whether you run it online (give the date you ran it) or the stand-alone version (give the date you downloaded it).

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Download to run locally

The file piface.jar may be downloaded so that you can run these applications locally. [Note: Some mail software (that thinks it is smarter than you) renames this file piface.zip. If this happens, simply rename it piface.jar; do not unzip the file.] You may also want the icon file piface.ico if you put it on your desktop or a toolbar. You will need to have the Java Runtime Environment (JRE) or the Java Development Kit (JDK) installed on your system. You probably already have it; but if not, these are available for free download for several platforms from Sun. If you have JDK or JRE version 1.2 or later, then you can probably run the application just by double-clicking on piface.jar. Otherwise, you may run it from the command line in a terminal or DOS window, using a command like
java -jar piface.jar
This will bring up a selector list similar to the one in this web page. A particular dialog can also be run directly from the command line, if you know its name (can be discovered by browsing piface.jar with a zip file utility such as WinZip). For example, the two-sample t-test dialog may be run using
java -cp piface.jar rvl.piface.apps.TwoTGUI

Questions?

This software is made available as-is, with no guarantees; use it at your own risk. I welcome comments on bugs, additional capabilities you'd like to see, etc. I am also willing to provide minimal support if you truly don't understand what inputs are required. However, each applet has a help menu, and I do request that you carefully read that before you e-mail me with questions.
If you need statistical advice on your research problem, you should contact a statistical consultant; and if you want expert advice, you should expect to pay for it. Most universities with statistics departments or statistics programs also offer a consulting service. If you think your research is important, then it is also important to get good advice on the statistical design (i.e., before you start collecting data) and analysis.
If you have carefully read the above two paragraphs, and still find it appropriate to contact me, my e-mail address is russell-lenth@uiowa.edu.

Advice

Here are two very wrong things that people try to do with my software:
  • Retrospective power (a.k.a. observed power, post hoc power). You've got the data, did the analysis, and did not achieve 'significance.' So you compute power retrospectively to see if the test was powerful enough or not. This is an empty question. Of course it wasn't powerful enough -- that's why the result isn't significant. Power calculations are useful for design, not analysis.
    (Note: These comments refer to power computed based on the observed effect size and sample size. Considering a different sample size is obviously prospective in nature. Considering a different effect size might make sense, but probably what you really need to do instead is an equivalence test; see Hoenig and Heisey, 2001.)
  • Specify T-shirt effect sizes ('small', 'medium', and 'large'). This is an elaborate way to arrive at the same sample size that has been used in past social science studies of large, medium, and small size (respectively). The method uses a standardized effect size as the goal. Think about it: for a 'medium' effect size, you'll choose the same n regardless of the accuracy or reliability of your instrument, or the narrowness or diversity of your subjects. Clearly, important considerations are being ignored here. 'Medium' is definitely not the message!
Power Here are three very right things you can do:
  • Use power prospectively for planning future studies. Software such as is provided on this website is useful for determining an appropriate sample size, or for evaluating a planned study to see if it is likely to yield useful information.
  • Put science before statistics. It is easy to get caught up in statistical significance and such; but studies should be designed to meet scientific goals, and you need to keep those in sight at all times (in planning and analysis). The appropriate inputs to power/sample-size calculations are effect sizes that are deemed clinically important, based on careful considerations of the underlying scientific (not statistical) goals of the study. Statistical considerations are used to identify a plan that is effective in meeting scientific goals -- not the other way around.
  • Do pilot studies. Investigators tend to try to answer all the world's questions with one study. However, you usually cannot do a definitive study in one step. It is far better to work incrementally. A pilot study helps you establish procedures, understand and protect against things that can go wrong, and obtain variance estimates needed in determining sample size. A pilot study with 20-30 degrees of freedom for error is generally quite adequate for obtaining reasonably reliable sample-size estimates.
Many funding agencies require a power/sample-size section in grant proposals. Following the above guidelines is good for improving your chances of being funded. You will have established that you have thought through the scientific issues, that your procedures are sound, and that you have a defensible sample size based on realistic variance estimates and scientifically tenable effect-size goals.
To read more, please see the following references:
  • Lenth, R. V. (2001), ``Some Practical Guidelines for Effective Sample Size Determination,' The American Statistician, 55, 187-193.
  • Hoenig, John M. and Heisey, Dennis M. (2001), ``The Abuse of Power: The Pervasive Fallacy of Power Calculations for Data Analysis,' The American Statistician, 55, 19-24.
  • An earlier draft of the Lenth reference above is _here_, and a shorter summary of some comments I made in a panel discussion at the 2000 Joint Statistical Meetings in Indianapolis is _here_.
  • Additional brief comments, prepared as a handout for my poster presentation at the 2001 Joint Statistical Meetings in Atlanta, are _here_.

Accuracy

  • Formula accuracy
    • Most computations are ``exact' in the sense that they are based on exact formulas for sample size, power, etc. The exception is Satterthwaite approximations; see below.
  • Machine accuracy
    • Even with exact formulas, computed values are inexact, as are all double-precision floating-point computations. Many computations (especially noncentral distributions) require summing one or more series, and there is a serious tradeoff between speed and accuracy. The error bound set for cdfs is 1E-8 or smaller, and for quantiles the bound is 1E-6. Actual errors can be much larger due to accumulated errors or other reasons. Quantiles, for example, are computed by numerically solving an equation involving the cdf; thus, in extreme cases, a small error in the cdf can create a large error in the quantile.
    A warning (typically, ``too many iterations') is generated when an error bound is not detected to have been achieved. However, in the case of quantile computations, no warning message is generated for extreme quantiles. If you want a power of .9999 at alpha=.0001, you can expect the computed sample size to not be accurate to the nearest integer! If you specify reasonable criteria, the answers will be pretty reliable.
  • Satterthwaite approximations
    • Some of the dialogs (two-sample t, mixed ANOVA) implement Satterthwaite approximations when certain combinations of inputs require an error term to be constructed. These are of course not exact, even in their formulation. Moreover, the Satterthwaite degrees of freedom is used as-is in computing power from a noncentral t or noncentral F distribution, and this introduces further errors that could be large in some cases. In the two-sample t setting, I'd expect the worst errors to exist when there is a huge imbalance in sample sizes and/or variances. In the dialogs for mixed ANOVA models (either F tests or multiple comparisons/contrasts), I expect these errors to get worse as more variance components are involved, especially when one or more of them is given negative weight.

Links to other sites

  • A review of power software for PCs by Len Thomas and Charles Krebs
  • Interactive page - Michael Friendly (ANOVA designs)
  • Interactive page - David Schoenfeld (clinical trials designs; menu based on study type and measurement type)
  • UnifyPow - A SAS module for sample-size analysis by Ralph O'Brien.
  • SSize - ECHIP sample size calculator for Palm devices (freeware) by Bob Wheeler.
It is not unlikely that one or more of these links is broken. If that happens, please let me know (especially if you can correct it!)

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