Summarizing historical information on controls in clinical trials.
BACKGROUND
Historical information is always relevant when designing clinical trials, but it might also be incorporated in the analysis. It seems appropriate to exploit past information on comparable control groups.


OBJECTIVE
Phase IV and proof-of-concept trials are used to discuss aspects of summarizing historical control data as prior information in a new trial. The importance of a fair assessment of the similarity of control parameters is emphasized.


METHODS
The methodology is meta-analytic-predictive. Heterogeneity of control parameters is expressed via the between-trial variation, which is the key parameter determining the prior effective sample size and its upper bound (prior maximum sample size).


RESULTS
For a Phase IV trial (930 control patients in 11 historical trials) between-trial heterogeneity was fairly small, resulting in a prior effective sample size of approximately 90 patients. For a proof-of-concept trial (363 patients in four historical trials) heterogeneity was moderate to substantial, resulting in a prior effective sample size of approximately 20. For another proof-of-concept trial (14 patients in one historical trial), assuming substantial heterogeneity implied a prior effective sample size of 7. The prior effective sample size can only be large if the amount of historical data is large and between-trial heterogeneity is small. The prior effective sample size is bounded by the prior maximum sample size (ratio of within- to between-trial variance), irrespective of the amount of historical data.


CONCLUSIONS
The meta-analytic-predictive approach assumes exchangeability of control parameters across trials. Due to the difficulty to quantify between-trial variability, sensitivity of conclusions regarding assumptions and type of inference should be assessed.


CONCLUSIONS
The use of historical control information is a valuable option and may lead to more efficient clinical trials. The proposed approach is attractive for nonconfirmatory trials, but under certain circumstances extensions to the confirmatory setting could be envisaged as well.
Novartis Pharma, Basel, Switzerland. beat.neuenschwander@novartis.com
Beat
B
Neuenschwander
Gorana
G
Capkun-Niggli
Michael
M
Branson
David J
DJ
Spiegelhalter
D017327:Clinical Trials, Phase IV as Topic / Q000379:methods*; D035061:Control Groups; D016208:Databases, Factual; D006801:Humans; D015201:Meta-Analysis as Topic; D015233:Models, Statistical; D012107:Research Design; D018401:Sample Size