23 Feb Randomization is the gold standard for clinical trials so that we avoid bias. Randomization ensures the treatment allocation is provided by chance and not by choice, either on the part of th
nutrition writing question and need a sample draft to help me learn.
Complete the randomization spreadsheet as instructed in the tutorial. You will need to generate two sets of random numbers: one for the simple randomization process and one for the block randomization process. You will turn in the spreadsheet as well as a short text explaining the process and discussing the averages and t-tests. Were these processes (simple randomization and block randomization) successful in creating equal groups?
Tutorial: https://mediaplus.asu.edu/embedded?id=d2d0e4b6-81a…
Requirements: spreadsheet and a short text
How To Randomize
Adapted from:
Randomization
Randomization is the gold standard for clinical trials so that we avoid bias. Randomization ensures the treatment allocation is provided by chance and not by choice, either on the part of the clinician or the patient/recipient. To allow for randomization you need a treatment group and a control group. The control group can be standard care, a sham procedure or in the event of no recognized standard of care, a placebo maybe considered. Consultation with a statistician is highly recommended.
The method of randomization can vary:
from a simple sequence written within envelopes (this can be prone to unblinding or human error and must be controlled within the institution)
a remotely administered computer, web-based or phone randomization system
Simple randomization
Simple randomization is analogous to repeated fair coin tossing
preserves complete unpredictability of each intervention assignment
best achieved using a random numbers table or a computer generated random number sequence
other manual methods such as coin-toss, dice throw, card shuffling are acceptable methods but can be subverted, cannot be audited and can be difficult to implement in practice
Where to find a random numbers table:
Download from the internet:
Or use Excel to generate 2 random groups:
Command in excel is RANDBETWEEN (1,2)
(or use randomization websites such as: ?)
Problems with simple randomization
for small sample sizes can be quite imbalanced between the randomized treatment groups (over time if the sample is large enough this will even out)
if recruitment is over a long period simple randomization could lead to imbalances in baseline characteristics of the treatment groups if the type of patients being enrolled changes over time and a long series of assignments to one treatment occurs
Blocking (random permuted blocks)
Blocking is a method to deal with the imbalances caused by simple randomization.
It is one form of restricted randomization which aims to create unbiased comparison groups of about the same size throughout the trial.
block sizes can vary from 2 to 20 (the smallest block size is determined by adding up the allocation ratio e.g. 1:1 ratio = block size of 2; 2:2:1 ratio = block size of 5)
smaller block sizes can be susceptible to subversion of the randomization sequence because it is possible to guess future allocations on the basis of past allocations
it is recommended that the block sizes used are randomly varied to avoid this problem especially if the trial has limited blinding (randomly permuted blocks)
Blocking Example: Block size of 4 (e.g., there are 4 treatments all participants will undergo)?
Stratification
Stratification involves dividing the sample to be studied according to prognostic factors.
stratification aims to control for imbalances in baseline characteristics between the treatment groups
stratification can only be used with restricted randomization schemes (usually blocking) not with simple randomization
only variables observed and recorded before randomization can be used for stratification
it is generally only practical to include at most two or three stratification variables
variables used for stratification should be easy to observe and reasonably free of measurement error
by reducing imbalances on prognostic factors stratification can increase the statistical power and precision of small trials but with samples of 50 per group the statistical gain will be minimal
stratification is recommended for multicenter trials with the trial center used as the stratification variable this will control for differences in the study population due to environmental, social, demographic and other factors related to the clinic or center
