Abstract
Experiments such as clinical trials should be carried out with specific objectives. For example, in a trial designed to prevent disease, specific considerations should be made concerning the impact of the trial on the health of the target population, including the participants in the trial. These objectives should be assessed continually in light of data accumulating from the trial. Accumulating evidence should be judged in the context of changing circumstances external to the trial, and the trial's design possibly modified. An important type of modification is stopping the trial. This is a sequential decision problem that can be addressed using a Bayesian approach and the methods of dynamic programming. As an example we consider a vaccine trial for the prevention of haemophilus influenzae type b. The objective we consider is minimizing the number of cases of this disease in a Native American population over a specified horizon. We assess the prior probability distribution of vaccine efficacy. We also assess the probability of regulatory approval for widespread use of the vaccine, depending on the data presented to the regulatory officials. In deciding whether to continue the trial we weigh the impact of the possible future results by their (predictive) probabilities. We address the sensitivity of the optimal stopping policy to the priorprobability distribution, to the assessed probability of regulatory approval, and to the horizon.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 360-378 |
| Number of pages | 19 |
| Journal | Controlled Clinical Trials |
| Volume | 15 |
| Issue number | 5 |
| DOIs | |
| State | Published - 1994 |
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Keywords
- assessing prior information
- decision analysis
- dynamic programming
- Monitoring randomized clinical trials
- optimal stopping
- sequential design
- weighing information gain with effective therapy
ASJC Scopus subject areas
- Pharmacology
Cite this
Decision making during a Phase III randomized controlled trial. / Berry, Donald A.; Wolff, Mark C.; Sack, David Allen.
In: Controlled Clinical Trials, Vol. 15, No. 5, 1994, p. 360-378.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Decision making during a Phase III randomized controlled trial
AU - Berry, Donald A.
AU - Wolff, Mark C.
AU - Sack, David Allen
PY - 1994
Y1 - 1994
N2 - Experiments such as clinical trials should be carried out with specific objectives. For example, in a trial designed to prevent disease, specific considerations should be made concerning the impact of the trial on the health of the target population, including the participants in the trial. These objectives should be assessed continually in light of data accumulating from the trial. Accumulating evidence should be judged in the context of changing circumstances external to the trial, and the trial's design possibly modified. An important type of modification is stopping the trial. This is a sequential decision problem that can be addressed using a Bayesian approach and the methods of dynamic programming. As an example we consider a vaccine trial for the prevention of haemophilus influenzae type b. The objective we consider is minimizing the number of cases of this disease in a Native American population over a specified horizon. We assess the prior probability distribution of vaccine efficacy. We also assess the probability of regulatory approval for widespread use of the vaccine, depending on the data presented to the regulatory officials. In deciding whether to continue the trial we weigh the impact of the possible future results by their (predictive) probabilities. We address the sensitivity of the optimal stopping policy to the priorprobability distribution, to the assessed probability of regulatory approval, and to the horizon.
AB - Experiments such as clinical trials should be carried out with specific objectives. For example, in a trial designed to prevent disease, specific considerations should be made concerning the impact of the trial on the health of the target population, including the participants in the trial. These objectives should be assessed continually in light of data accumulating from the trial. Accumulating evidence should be judged in the context of changing circumstances external to the trial, and the trial's design possibly modified. An important type of modification is stopping the trial. This is a sequential decision problem that can be addressed using a Bayesian approach and the methods of dynamic programming. As an example we consider a vaccine trial for the prevention of haemophilus influenzae type b. The objective we consider is minimizing the number of cases of this disease in a Native American population over a specified horizon. We assess the prior probability distribution of vaccine efficacy. We also assess the probability of regulatory approval for widespread use of the vaccine, depending on the data presented to the regulatory officials. In deciding whether to continue the trial we weigh the impact of the possible future results by their (predictive) probabilities. We address the sensitivity of the optimal stopping policy to the priorprobability distribution, to the assessed probability of regulatory approval, and to the horizon.
KW - assessing prior information
KW - decision analysis
KW - dynamic programming
KW - Monitoring randomized clinical trials
KW - optimal stopping
KW - sequential design
KW - weighing information gain with effective therapy
UR - http://www.scopus.com/inward/record.url?scp=0028046097&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0028046097&partnerID=8YFLogxK
U2 - 10.1016/0197-2456(94)90033-7
DO - 10.1016/0197-2456(94)90033-7
M3 - Article
C2 - 8001357
AN - SCOPUS:0028046097
VL - 15
SP - 360
EP - 378
JO - Controlled Clinical Trials
JF - Controlled Clinical Trials
SN - 0197-2456
IS - 5
ER -