85% of organisations with clinical trials approved by the FDA in 2013 used nQuery Advisor + nTerim for their sample size software. 

That’s because nQuery Advisor + nTerim is the most trusted, accurate and user friendly sample size software, and why it is favoured by the top Pharma and Biotech companies worldwide.

Use nQuery Advisor + nTerim to:

• Calculate sample size and power for Means, Proportions, Agreement, Regression, Survival Analysis, Nonparametric Test, etc.
• Get assistance with the standard deviation and effect size information you need to make sample size and power analysis computations.
• Generate randomization lists for your studies for simple, advanced and complex designs

Generating randomization lists is an essential part of most clinical trials. nQuery Advisor + nTerim has a built-in menu for creating randomization lists for basic, advanced and complex designs. In just a few minutes you can easily specify a randomization list for your studies. The automated option for mixed block sizes provides assistance in maintaining the double blind. Use nQuery Advisor + nTerim for:

• Random subset of cases - Select a random subset of n cases from N subjects
• Randomization list (Basic)-Enter names for the groups and the total sample size
• Randomization list (Advanced/Complex)

Randomization Options for Complex Designs

Expanded Facilities for Specification of Centers and Stratification Factors

• Separate Specification for Centers and Strata
• Easy Specification of proportions for Combined Strata
• Separate Name for each Center and Stratum plus an Additional Center/Strata ID Code

Improved Output Options for the Randomization Lists

• New Treatment ID Code
• Length of Subject ID Number can be edited
• Starting Subject ID Number for each Center/Strata Group can be Specified
• Sequence Numbers
• Date and Time of Day Stamp for Randomization List Creation

Why It’s Important to Use Mixed Block Sizes?

Using mixed block sizes helps maintain the double blind. For example, a common practice is for a multi-center study to use blocks of size four within each center for comparison of a new drug to a placebo. After randomizing three patients the clinician might notice that two patients have the same adverse event (or a particular lab result) which is believed from prior research to be associated with the new drug. If the clinician presumes a block size of four is being used, he or she might suspect that the next person to enter the study will receive the placebo. This suspicion might unconsciously lead to changes in the choice of patient to enter next, perhaps delaying the entry of a particularly ill patient so they have a better chance of receiving the new drug rather than the placebo. Such potential biases in patient entry procedures or in assessment of treatment success based on guesses about the treatment a particular patient is receiving might jeopardize the integrity of the entire trial.