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Printable Handouts
Navigable Slide Index
- Introduction
- Contents
- Advantages and disadvantages of permuted block design?
- Implementation example of permuted block design
- Advantages of permuted block design
- Disadvantages of permuted block design
- Disadvantages of permuted block design (1)
- Disadvantages of permuted block design (2)
- Disadvantages of permuted block design (3)
- Disadvantages of permuted block design (4)
- Require better alternatives to permuted block design
- Block urn design for high allocation randomness
- Blocking vs. maximum tolerated imbalance
- Urn model for permuted block design
- Urn model for block urn design
- Conditional allocation probability
- Conditional allocation probability - example
- The improved allocation randomness (1:1 allocation)
- The improved randomness (unequal allocations)
- BUD is a better alternative to PBD
- Other better alternatives to PBD (1)
- Other better alternatives to PBD (2)
- Comparison of conditional allocation probabilities for MTI designs
- How to accurately target any unequal allocation?
- Unequal allocation in clinical trials
- Unequal allocations in bayesian adaptive trials
- Complete random – unacceptable treatment imbalance
- Trade off between target accuracy & treatment balance
- The concept model of mass-weighted urn design (MWUD)
- The math model of mass-weighted urn design (MWUD)
- Performance of mass-weighted urn design
- Performance comparison: two-arm unequal allocation
- Performance comparison: three-arm unequal allocation
- Performance comparison: four-arm equal allocation
- Performance comparison: five-arm unequal allocation
- Performance comparison
- Possible scenario of mass deficit
- Probability of mass deficit
- The unconditional allocation probability
- MWUD – a better alternative to PBD for unequal allocations
- How to balance many baseline covariates
- Stratified randomization for baseline covariate balance
- NINDS rt-PA Stroke Study – two more covariate to stratify
- Two versions of the minimization method
- Controversial over the use of minimization
- New concept about baseline covariate balance
- The minimal sufficient balance (MSB) method
- Continuous covariate imbalance control in MSB
- Categorical covariate imbalance control in MSB
- Vote summary and use of biased coin probability in MSB
- Minimal sufficient balance (MBS) for NINDS rt-PA stroke trial – biased coin probability = 0.50
- MBS for NINDS rt-PA stroke trial – biased coin probability = 0.55
- MBS for NINDS rt-PA stroke trial – biased coin probability = 0.65
- MBS for NINDS rt-PA stroke trial – biased coin probability = 1.00
- MBS for NINDS rt-PA stroke trial – Impact of P(bc) on covariate imbalance
- MBS for NINDS rt-PA stroke trial – Controling the treatment allocation imbalance
- MBS for NINDS rt-PA stroke trial – Example of the distribution of 11 baseline covariates (1)
- MBS for NINDS rt-PA stroke trial – Example of the distribution of 11 baseline covariates (2)
- Comparison between minimization and MSB
- Summary
- Thank you!
Topics Covered
- Advantages and disadvantages of permuted block design
- Block urn design for high allocation randomness
- Mass-weighted urn design target any unequal allocation
- Minimal sufficient balance for many baseline covariates
Links
Series:
Categories:
Talk Citation
Zhao, W. (2020, March 29). Innovative and effective subject randomization methods [Video file]. In The Biomedical & Life Sciences Collection, Henry Stewart Talks. Retrieved December 13, 2024, from https://doi.org/10.69645/GAHZ1176.Export Citation (RIS)
Publication History
Financial Disclosures
- Wenle Zhao Has no commercial/financial matters to disclose.
A selection of talks on Methods
Transcript
Please wait while the transcript is being prepared...
0:00
Hi, everyone. My name is Wenle Zhao.
I'm working at the Medical University of South Carolina in the United States.
I'm a professor for Biostats,
mainly working on clinical trials including clinical trial design and implementation.
0:16
This presentation covers the following contents.
First, I will review the advantages and
disadvantages of the commonly used permuted block design.
Then, I will present
three new randomization designs that
overcome the disadvantages of the permuted block design.
At the same time,
we will keep the good properties of that design.
So, the block urn design
offers a much higher allocation randomness
than permuted block design under the same block size.
The mass-weighted urn design can actually target any unequal allocation ratios.
Not only those with small integers like 1-2 or 2-3,
but also those with irrational numbers
like one to the square root of two to the square root of three.
The minimum sufficient balance method prevents
serious imbalance on a larger number of baseline covariates, meanwhile
maintaining a high level of allocation randomness.
1:22
First, let's review the advantages and
disadvantages of the most commonly used permuted block design.
1:31
Permuted block design uses
a pretty generated randomization sequence with a random permutation of blocks.
The first step is to fill each block with treatment assignment based
on the allocation ratio and assign two random numbers to each sequence code.
Here is an example of a drug study with a 1-1 allocation ratio and a block size of six.
Step 2: sort by the first random number and then assign
the drug kit ID accordingly so that
drug kit ID carries no information on treatment assignment.
Step 3: sort by the second random number with
each block and assign randomization sequence number.
Step 4: pack study drug kit by the treatment group in the groups.
Step 5: assign drug kit to a subject based on a randomized sequence number.