Statistical considerations for clinical trials
and scientific experiments

Find:

Study Type	Measurement Type
Parallel study:	Success or Failure	Quantitative Measurement	Time to an Event
Crossover study:		Quantitative Measurement
Study to find an association:		Quantitative Measurement

Updates:

• 2010-May-14: move to JavaScript-based Quantitative Measurement tools.
• 2015-Jan-07: move to JavaScript-based Fisher Exact test.

These calculations are based on assumptions which may not be true for the clinical trial you are planning. We do not guarantee the accuracy of these calculations or their suitability for your trial. We suggest that you speak to a biostatistical consultant when planning a clinical trial. Please contact us if you have any questions or problems using this software.

The author of these tools is David A. Schoenfeld, Ph.D. (dschoenfeld@partners.org), with support from the Massachusetts General Hospital Mallinckrodt General Clinical Research Center, Research Resources Division, National Institutes of Health, National Center for Advancing Translational Sciences.

Definitions

Sample size:

The number of patients or experimental units required for the trial.

Power:

The probability that a clinical trial will have a significant(positive) result, that is have a p-value of less than the specified significance level(usually 5%). This probability is computed under the assumption that the treatment difference or strength of association equals the minimal detectable difference.

Minimal detectable difference:

The smallest difference between the treatments or strength of association that you wish to be able to detect. In clinical trials this is the smallest difference that you believe would be clinically important and biologically plausible. In a study of association it is the smallest change in the dependent(outcome variable, response), per unit change in the independent(input variable, covariate) that is plausible.

Parallel design:

A parallel designed clinical trial compares the results of a treatment on two separate groups of patients. The sample size calculated for a parallel design can be used for any study where two groups are being compared.

Crossover study:

A crossover study compares the results of a two treatment on the same group of patients. The sample size calculated for a crossover study can also be used for a study that compares the value of a variable after treatment with it's value before treatment. The standard deviation of the outcome variable is expressed as either the within patient standard deviation or the standard deviation of the difference. The former is the standard deviation of repeated observations in the same individual and the latter is the standard deviation of the difference between two measurements in the same individual.

Study to find an association:

A study to find an association determines if a variable, the dependent variable, is affected by another, the independent variable. For instance, a study to determine whether blood pressure is affected by salt intake.

Success/Failure:

The outcome of the study is a variable with two values, usually treatment success or treatment failure.

Measurement:

The outcome of the study is a continuous measurement.

Time to an Event:

The outcome of the study is a time, such as the time to death, or relapse. Some patients will not have been observed to relapse. These observations are said to be censored.