## It’s A Math, Math World (Variance Control)

Some of the information in this article (i.e. some definitions and examples) is attributed to a lecture at Rutgers University by Adele Gilpin during the spring 2004 semester.

In any clinical trial, as well as controlling bias, we want to control variance. What is the difference between bias and variance? **Bias causes the sample mean of one treatment group to be larger or smaller than the true mean. On the other hand, variance inflates the variability of the observed treatment group means. Large variance in a clinical trial reduces the power of the statistical tests**.

**Ways to minimize variance in a clinical trial:**

- Design considerations
- Crossover designs:

Ex. (pain score on treatment 1) – (pain score on treatment 2)

Design balances out any order effect of the treatment administration.

- Stratification and matching:

Stratification uses randomization to balance groups on a **few **characteristics.

Matching is an extreme case of randomization in which balancing is done on **several ** characteristics. It is very expensive and labor intensive, and it can cause recruitment flow problems because it can be difficult to find patients if you match on too many variables.

- Increased sample size:

It increases precision of the estimates of treatment effects. It may not be feasible to recruit the necessary number of patients. It also adds expense to an already expensive process.

- Conduct considerations
- Patient selection:

Use of inclusion and exclusion criteria is used to make patient sample as similar as possible. This reduces variance of the response means but also limits the generalizing of the study results to the general population. Thus, we don’t know if treatment works on those excluded and the FDA might only approve the treatment for a specific subpopulation. The market for the product may be limited. Also, using tight inclusion/exclusion criteria can make it more difficult to recruit enough patients.

- Study Site/Investigator selection:

The sited may have different patient profiles (based on geography for example).

Site staff may have different abilities resulting in different treatment outcomes.

- Standardization of Procedures:

Written protocol

Calibration of instruments and measurement conditions

Training of personnel

Site visits

Central readings and assays (use of a central laboratory)

- Analysis Considerations: (note: to be considered in upcoming blog post)

Use of baseline covariates for adjustment (as in linear regression)

Subgroup analysis

Outlier detection and trimming procedures

- Patient Assignment Considerations: (note: to be considered in upcoming blog post)

Randomization

Stratification

Blocking

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