Archive for January, 2011

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

Like what you read? Get blogs delivered right to your inbox as I post them so you can start standing out in your job and career. There is not a better way to learn or review college level stats topics than by reading, It’s A Math, Math World 

Email Marketing You Can Trust

It’s A Math, Math World (Clinical Trial Bias)

 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, we want to ensure that our response is not biased. By definition, bias is systematic error introduced into sampling or testing by selecting or encouraging one outcome over another. If the same error occurs in the treated and untreated groups, then the study is still internally valid, but systematic error can cause an entire collection of measurements too lose their meaning.

There are 2 types of bias we will look at: (1) Treatment-related, and (2) Non-treatment related.

Treatment related bias: Systematic error that is treatment related can really harm a trial.  This is a bias related to treatment assignment that affects the observed treatment differences in the trial. It is important to ensure, to the best of one’s ability, that no such bias exists before the trial begins.

Treatment related bias can occur in 3 ways:

  • During treatment assignment:  This occurs through an assignment process that allows groups to be different at baseline.
  • During treatment process:  If you have comparable groups, you may treat them differently other than the assigned treatment administration. For example, one group might receive systematically better care than another.
  • During Measurement or Data Collection process: In terms of measurement, one may listen more carefully to heart sounds on mercury column when taking BP because they think that group will produce a person with higher BP. In terms of data collection, an investigator might document adverse events more carefully because he/she thinks they are more serious because one treatment is more dangerous than another.

Non-treatment related bias:  This is study error not related to treatment assignment. This can cause a “conservative bias” that can make it more difficult to detect a treatment effect. Conservative bias is not good for developer of treatment or patients with condition of interest because it is harder to determine whether treatment is effective against primary outcome.

Requirements to reduce bias:

  • Establish comparable study groups that are free of selection bias.
  • Use a data collection schedule in which the probability of observing an event is the same for all patients.
  • Use data collection procedures that are reproducible and standardized over all treatment groups.

Methods of Bias control:

  • Masking (blinding):

 This is used to conceal the intervention assignment from either the patient, investigator or both.

  • Randomization:

This is used to create comparable treatment and control groups.

  • Standardization:

Written treatment protocol

Tested forms and other documentation, including manuals

Written definitions of what response is

Standard equipment that is tested and calibrated

Training and certification of study personnel

  • Surveillance:

Each trial must be carefully and independently monitored to ensure protocol and regulatory compliance.

Like what you read? Get blogs delivered right to your inbox as I post them so you can start standing out in your job and career. There is not a better way to learn or review college level stats topics than by reading, It’s A Math, Math World 

Email Marketing You Can Trust