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. PURPOSE: 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. LIMITATIONS: 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.
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. PURPOSE: 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. LIMITATIONS: 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.
Authors: Wolfgang Hueber; Bruce E Sands; Steve Lewitzky; Marc Vandemeulebroecke; Walter Reinisch; Peter D R Higgins; Jan Wehkamp; Brian G Feagan; Michael D Yao; Marek Karczewski; Jacek Karczewski; Nicole Pezous; Stephan Bek; Gerard Bruin; Bjoern Mellgard; Claudia Berger; Marco Londei; Arthur P Bertolino; Gervais Tougas; Simon P L Travis Journal: Gut Date: 2012-05-17 Impact factor: 23.059
Authors: Robin M J M van Geel; Josep Tabernero; Elena Elez; Johanna C Bendell; Anna Spreafico; Martin Schuler; Takayuki Yoshino; Jean-Pierre Delord; Yasuhide Yamada; Martijn P Lolkema; Jason E Faris; Ferry A L M Eskens; Sunil Sharma; Rona Yaeger; Heinz-Josef Lenz; Zev A Wainberg; Emin Avsar; Arkendu Chatterjee; Savina Jaeger; Eugene Tan; Kati Maharry; Tim Demuth; Jan H M Schellens Journal: Cancer Discov Date: 2017-03-31 Impact factor: 39.397
Authors: Jigar R Desai; Edward A Bowen; Mark M Danielson; Rajasekhar R Allam; Michael N Cantor Journal: J Am Med Inform Assoc Date: 2013-02-28 Impact factor: 4.497
Authors: Ivanka Galeva; Michael W Konstan; Mark Higgins; Gerhild Angyalosi; Florian Brockhaus; Simon Piggott; Karen Thomas; Alexander G Chuchalin Journal: Curr Med Res Opin Date: 2013-06-05 Impact factor: 2.580
Authors: Kert Viele; Scott Berry; Beat Neuenschwander; Billy Amzal; Fang Chen; Nathan Enas; Brian Hobbs; Joseph G Ibrahim; Nelson Kinnersley; Stacy Lindborg; Sandrine Micallef; Satrajit Roychoudhury; Laura Thompson Journal: Pharm Stat Date: 2013-08-05 Impact factor: 1.894