BACKGROUND: Clinically relevant endpoints cannot be routinely targeted with reasonable power in a small study. Hence, proof-of-concept studies are often powered to a primary surrogate endpoint. However, in acute heart failure (AHF) effects on surrogates have not translated into clinical benefit in confirmatory studies. Although observing an effect on one of many endpoints due to chance is likely, observing concurrent positive trends across several outcomes by chance is usually unlikely. METHODS: Pre-RELAX-AHF, which compared 4 relaxin doses with placebo in AHF, has shown favourable trends versus placebo (one-sided P < 0.10) on six of nine clinical endpoints in the 30 μg/kg/day group. To illustrate evaluation of multiple, correlated clinical endpoints for evidence of efficacy and for dose selection, a permutation method was applied retrospectively. By randomly re-assigning the treatment group to the actual data for each of the 229 subjects, 20,000 permutation samples were constructed. RESULTS: The permutation P value for at least six favourable trends among nine endpoints in any dose groups was 0.0073 (99.9% CI 0.0053-0.0093). This is higher than would be expected if the endpoints were uncorrelated (0.00026), but much lower than the probability of observing one of nine comparisons significant at the traditional two-sided P < 0.05 (0.74). Thus, the result was unlikely due to correlated endpoints or to chance. CONCLUSIONS: Examining consistency of effect across multiple clinical endpoints in a proof-of-concept study may identify efficacious therapies and enable dose selection for confirmatory trials. The merit of the approach described requires confirmation through prospective application in designing future studies.
BACKGROUND: Clinically relevant endpoints cannot be routinely targeted with reasonable power in a small study. Hence, proof-of-concept studies are often powered to a primary surrogate endpoint. However, in acute heart failure (AHF) effects on surrogates have not translated into clinical benefit in confirmatory studies. Although observing an effect on one of many endpoints due to chance is likely, observing concurrent positive trends across several outcomes by chance is usually unlikely. METHODS: Pre-RELAX-AHF, which compared 4 relaxin doses with placebo in AHF, has shown favourable trends versus placebo (one-sided P < 0.10) on six of nine clinical endpoints in the 30 μg/kg/day group. To illustrate evaluation of multiple, correlated clinical endpoints for evidence of efficacy and for dose selection, a permutation method was applied retrospectively. By randomly re-assigning the treatment group to the actual data for each of the 229 subjects, 20,000 permutation samples were constructed. RESULTS: The permutation P value for at least six favourable trends among nine endpoints in any dose groups was 0.0073 (99.9% CI 0.0053-0.0093). This is higher than would be expected if the endpoints were uncorrelated (0.00026), but much lower than the probability of observing one of nine comparisons significant at the traditional two-sided P < 0.05 (0.74). Thus, the result was unlikely due to correlated endpoints or to chance. CONCLUSIONS: Examining consistency of effect across multiple clinical endpoints in a proof-of-concept study may identify efficacious therapies and enable dose selection for confirmatory trials. The merit of the approach described requires confirmation through prospective application in designing future studies.
Authors: Gad Cotter; Adriaan A Voors; Beth Davison Weatherley; Peter S Pang; John R Teerlink; Gerasimos Filippatos; Piotr Ponikowski; Olga Milo-Cotter; Howard Dittrich; Sam L Teichman; Kirkwood F Adams; Mihai Gheorghiade; Marco Metra Journal: Cardiology Date: 2010-09-28 Impact factor: 1.869
Authors: Joseph A Franciosa; Anne L Taylor; Jay N Cohn; Clyde W Yancy; Susan Ziesche; Adeoye Olukotun; Elizabeth Ofili; Keith Ferdinand; Joseph Loscalzo; Manuel Worcel Journal: J Card Fail Date: 2002-06 Impact factor: 5.712
Authors: Gad Cotter; Howard C Dittrich; Beth Davison Weatherley; Daniel M Bloomfield; Christopher M O'Connor; Marco Metra; Barry M Massie Journal: J Card Fail Date: 2008-09-14 Impact factor: 5.712
Authors: John R Teerlink; Marco Metra; G Michael Felker; Piotr Ponikowski; Adriaan A Voors; Beth Davison Weatherley; Alon Marmor; Amos Katz; Jacek Grzybowski; Elaine Unemori; Sam L Teichman; Gad Cotter Journal: Lancet Date: 2009-03-28 Impact factor: 79.321
Authors: Roger J Hajjar; Krisztina Zsebo; Lawrence Deckelbaum; Craig Thompson; Jeff Rudy; Alex Yaroshinsky; Hung Ly; Yoshiaki Kawase; Kim Wagner; Kenneth Borow; Brian Jaski; Barry London; Barry Greenberg; Daniel F Pauly; Richard Patten; Randall Starling; Donna Mancini; Mariell Jessup Journal: J Card Fail Date: 2008-05-27 Impact factor: 5.712
Authors: D T Felson; J J Anderson; M Boers; C Bombardier; M Chernoff; B Fried; D Furst; C Goldsmith; S Kieszak; R Lightfoot Journal: Arthritis Rheum Date: 1993-06
Authors: Javed Butler; Carine E Hamo; James E Udelson; Christopher O'Connor; Hani N Sabbah; Marco Metra; Sanjiv J Shah; Dalane W Kitzman; John R Teerlink; Harold S Bernstein; Gabriel Brooks; Christophe Depre; Mary M DeSouza; Wilfried Dinh; Mark Donovan; Regina Frische-Danielson; Robert J Frost; Dahlia Garza; Udo-Michael Gohring; Jennifer Hellawell; Judith Hsia; Shiro Ishihara; Patricia Kay-Mugford; Joerg Koglin; Marc Kozinn; Christopher J Larson; Martha Mayo; Li-Ming Gan; Pierrre Mugnier; Sekayi Mushonga; Lothar Roessig; Cesare Russo; Afshin Salsali; Carol Satler; Victor Shi; Barry Ticho; Michael van der Laan; Clyde Yancy; Norman Stockbridge; Mihai Gheorghiade Journal: Circ Heart Fail Date: 2017-04 Impact factor: 8.790
Authors: Meaghan Lunney; Marinella Ruospo; Patrizia Natale; Robert R Quinn; Paul E Ronksley; Ioannis Konstantinidis; Suetonia C Palmer; Marcello Tonelli; Giovanni Fm Strippoli; Pietro Ravani Journal: Cochrane Database Syst Rev Date: 2020-02-27