Registration for a live webinar on 'Precision medicine treatment for anticancer drug resistance' is now open.
See webinar detailsWe noted you are experiencing viewing problems
-
Check with your IT department that JWPlatform, JWPlayer and Amazon AWS & CloudFront are not being blocked by your network. The relevant domains are *.jwplatform.com, *.jwpsrv.com, *.jwpcdn.com, jwpltx.com, jwpsrv.a.ssl.fastly.net, *.amazonaws.com and *.cloudfront.net. The relevant ports are 80 and 443.
-
Check the following talk links to see which ones work correctly:
Auto Mode
HTTP Progressive Download Send us your results from the above test links at access@hstalks.com and we will contact you with further advice on troubleshooting your viewing problems. -
No luck yet? More tips for troubleshooting viewing issues
-
Contact HST Support access@hstalks.com
-
Please review our troubleshooting guide for tips and advice on resolving your viewing problems.
-
For additional help, please don't hesitate to contact HST support access@hstalks.com
We hope you have enjoyed this limited-length demo
This is a limited length demo talk; you may
login or
review methods of
obtaining more access.
Printable Handouts
Navigable Slide Index
- Introduction
- Contents
- Origins of selection bias in clinical trials
- Blackwell-Hodges model (1957)
- Selection bias in studies with unequal allocation
- Selection bias
- Selection bias depends on the guessing strategy
- Directional guessing strategy
- Example: directional strategy with 7:9 permuted block randomization (PBR)
- When the allocation procedure is known, directional strategy maximizes selection bias
- Directional strategy with biasing threshold
- For PBR, increments in Expected Bias Factor (EBF) are small close to the diagonal
- Directional strategy with biasing threshold and large permuted blocks
- When the investigator expects PBR, but brick tunnel randomization (BTR) is used instead
- Conclusions
- References
Topics Covered
- Origins of selection bias in clinical trials
- Blackwell-Hodges model for selection bias
- Derivation of selection bias with unequal allocation
- Directional guessing strategy
- Directional guessing strategy with permuted block randomization
- Directional strategy maximizes selection bias
- Directional strategy with biasing threshold
- Directional strategy with biasing threshold and large permuted blocks
- Brick Tunnel Randomization
- Undisclosed use of the Brick Tunnel Randomization reduces selection bias
Links
Series:
Categories:
Talk Citation
Kuznetsova, O.M. (2020, August 31). Selection bias in studies with unequal allocation [Video file]. In The Biomedical & Life Sciences Collection, Henry Stewart Talks. Retrieved December 13, 2024, from https://doi.org/10.69645/VFRO9711.Export Citation (RIS)
Publication History
Financial Disclosures
- Dr. Kuznetsova has no commercial/financial relationships to disclose
A selection of talks on Pharmaceutical Sciences
Transcript
Please wait while the transcript is being prepared...
0:00
Hi, I'm Olga Kuznetsova.
I'm a Senior Principal Scientist at Merck & Co.
I will talk about selection bias in studies with unequal allocation.
0:13
I will talk about the origins of selection bias in
clinical trials and derivation of selection bias in trials with unequal allocation.
We will discuss directional guessing strategy and
directional guessing strategy with biasing threshold.
Finally, I will show how
Brick Tunnel Randomization introduced by Kuznetsova and Tymofyeyev,
reduces selection bias when the investigator thinks
that Permuted Block Randomization was used in the trial and applies a biasing threshold.
0:46
What are the origins of selection bias in clinical trials?
In an open-label single-center trial,
the investigator knows the sequence of treatment assignments for all allocated patients.
Thus, the investigator might try to guess
the next treatment assignment and allocate
a patient best suited for the guessed treatment.
This introduces selection bias in
the study results by making the treatment groups incomparable.
Selection bias is well studied for equal allocation,
but is less studied for unequal allocation.
We will discuss selection bias in trials with unequal allocation.
1:25
Blackwell and Hodges offer the following model for selection bias.
Consider a study with 1:1 randomization to treatments A and B.
The study compares a normal response variable, y,
between the two groups.
Let us assume that there is no treatment effect.
There are two types of patients,
healthier patients with expected response Mu plus Delta and
sicker patients with expected response Mu minus Delta.
Suppose the investigator wants to bias the results in favor of treatment A.
To do that, the investigator allocates a healthier patient,
when their guess for the next treatment is A,
and a sicker patient when their guess is B.
As a result, group A has healthier patients and a better outcome.