Corroborating characteristics of single pivotal trial evidence supporting FDA approval of novel cancer therapies

Introduction: Novel cancer therapies are often approved with evidence from a single pivotal trial alone. There are concerns about the credibility of this evidence. Higher validity may be indicated by five methodological and statistical characteristics of pivotal trial evidence that were described by the US Food and Drug Administration (FDA) which may corroborate the reliance on a single trial alone for approval decisions. Methods: We did a meta-epidemiologic evaluation of all single pivotal trials supporting FDA approval of novel drugs and therapeutic biologicals for cancers between 2000 and 2016. For each trial, we determined the presence of these five characteristics, which we operationalized as (1) large and multicenter trial ( ≥ 200 patients; more than one center); consistent treatment benefits across (2) multiple patient subgroups (in view of FDA reviewers), (3) multiple endpoints (including overall survival , progression-free survival, response rate, health related quality of life) and (4) multiple treatment comparisons (e.g. multi-arm studies); (5) “statistically very persuasive” results (p-values <0.00125). Results: Thirty-five of 100 approvals were based on evidence from a single pivotal trial without any further supporting evidence on beneficial effects (20 randomized controlled trials and 15 single-arm trials). The number increased substantially from 1 approval before 2006 to 23 after 2011. Sixty-six percent (23/35) of the trials were large multicenter trials (median 301 patients and 63 centers). Consistent effects were demonstrated across subgroups in 66% (23/35), across endpoints in 43% (15/35), and across multiple comparisons in 3% (1/35). Very low p-values for the primary endpoint were seen in 34% (12/35). At least one of the corroborating characteristics was present in 94% (33/35) of all approvals, two or more were present in 54% (19/35) and none had all characteristics. Conclusions and relevance: Single pivotal trials typically have some of the corroborating characteristics, but often only one or two. These characteristics need to be better operationalized, defined and reported and whether single trials with such characteristics provide similar evidence about benefits and harms of novel treatments as multiple trials would do, needs to be shown. primary, OS, RR) had statistically significant favorable effects, we also searched for potential benefits on HRQoL. We also searched for any p-value < 0.05 reported in relationship to effects on the primary outcome, OS, PFS, RR or HRQoL in reviews of approvals based on SATs, and counted such studies as fulfilling the “Cons istency of treatmen t effects across multiple endpoints” and “statistically very persuasive” criterion. indications for two are on efficacy the same single pivotal trial. No on of beneficial effects also for endpoint. FDA statement not conclusive or interpreted by us not to be consistent across subgroups. CLL, lymphocytic applicable 1) These two cancer indications of the same novel drug are based on efficacy data from the same single pivotal trial. 2) No statements indicating benefits on quality of life found 3) characteristic met because beneficial effects also shown for (co-)primary endpoint. 4) FDA statement not conclusive or interpreted by us not to be consistent across subgroups. *) PFS, of life.

[1]  L. Trinquart,et al.  Design analysis indicates Potential overestimation of treatment effects in randomized controlled trials supporting Food and Drug Administration cancer drug approvals. , 2018, Journal of clinical epidemiology.

[2]  J. Ioannidis,et al.  The Comparative Effectiveness of Innovative Treatments for Cancer (CEIT-Cancer) project: Rationale and design of the database and the collection of evidence available at approval of novel drugs , 2018, Trials.

[3]  L. Hemkens,et al.  How to use FDA drug approval documents for evidence syntheses , 2018, British Medical Journal.

[4]  A. V. Morant,et al.  European Marketing Authorizations Granted Based on a Single Pivotal Clinical Trial: The Rule or the Exception? , 2018, Clinical pharmacology and therapeutics.

[5]  Arnoud J Templeton,et al.  Magnitude of Clinical Benefit of Cancer Drugs Approved by the US Food and Drug Administration , 2018, Journal of the National Cancer Institute.

[6]  Fares Alahdab,et al.  Treatment Effect in Earlier Trials of Patients With Chronic Medical Conditions: A Meta‐Epidemiologic Study , 2018, Mayo Clinic proceedings.

[7]  A. Krist "Needs More Research"-Implications of the Proteus Effect for Researchers and Evidence Adopters. , 2018, Mayo Clinic proceedings.

[8]  Joyce Cheng Inference Based on Small Randomized Oncology Clinical Trials: Is the Observed Treatment Effect True? , 2017 .

[9]  J. Ioannidis,et al.  Timing and Characteristics of Cumulative Evidence Available on Novel Therapeutic Agents Receiving Food and Drug Administration Accelerated Approval , 2017, The Milbank quarterly.

[10]  J. Ioannidis,et al.  Sex based subgroup differences in randomized controlled trials: empirical evidence from Cochrane meta-analyses , 2016, British Medical Journal.

[11]  P. Peyton,et al.  Poor agreement in significant findings between meta-analyses and subsequent large randomized trials in perioperative medicine. , 2016, British journal of anaesthesia.

[12]  Harlan M. Krumholz,et al.  Clinical trial evidence supporting FDA approval of novel therapeutic agents, 2005-2012. , 2014, JAMA.

[13]  Christopher W. Jones,et al.  Non-publication of large randomized clinical trials: cross sectional analysis , 2013, BMJ.

[14]  L. Trinquart,et al.  Influence of trial sample size on treatment effect estimates: meta-epidemiological study , 2013, BMJ : British Medical Journal.

[15]  Rebecca M. Turner,et al.  The Impact of Study Size on Meta-analyses: Examination of Underpowered Studies in Cochrane Reviews , 2013, PloS one.

[16]  K. Getz,et al.  Oncology drug development and approval of systemic anticancer therapy by the U.S. Food and Drug Administration. , 2013, The oncologist.

[17]  John P A Ioannidis,et al.  Empirical evaluation of very large treatment effects of medical interventions. , 2012, JAMA.

[18]  Gordon H Guyatt,et al.  Credibility of claims of subgroup effects in randomised controlled trials: systematic review , 2012, BMJ : British Medical Journal.

[19]  J. Ioannidis,et al.  Recommendations for examining and interpreting funnel plot asymmetry in meta-analyses of randomised controlled trials , 2011, BMJ : British Medical Journal.

[20]  Isabelle Boutron,et al.  Single-Center Trials Show Larger Treatment Effects Than Multicenter Trials: Evidence From a Meta-epidemiologic Study , 2011, Annals of Internal Medicine.

[21]  C. Mullins,et al.  Uncertainty in assessing value of oncology treatments. , 2010, The oncologist.

[22]  P. Keegan,et al.  Review of oncology and hematology drug product approvals at the US Food and Drug Administration between July 2005 and December 2007. , 2010, Journal of the National Cancer Institute.

[23]  J. Ioannidis Why Most Discovered True Associations Are Inflated , 2008, Epidemiology.

[24]  Ross J. Harris,et al.  Correction: reported methodologic quality and discrepancies between large and small randomized trials in meta-analyses. , 2008, Annals of internal medicine.

[25]  R. Temple How FDA currently makes decisions on clinical studies , 2005, Clinical Trials.

[26]  Zhenming Shun,et al.  Statistical consideration of the strategy for demonstrating clinical evidence of effectiveness—one larger vs two smaller pivotal studies , 2005 .

[27]  Sara T Brookes,et al.  Subgroup analyses in randomized trials: risks of subgroup-specific analyses; power and sample size for the interaction test. , 2004, Journal of clinical epidemiology.

[28]  Christian Gluud,et al.  Reported Methodologic Quality and Discrepancies between Large and Small Randomized Trials in Meta-Analyses , 2001, Annals of Internal Medicine.

[29]  P. Myles,et al.  Why we need large randomized studies in anaesthesia. , 1999, British journal of anaesthesia.

[30]  L. Fisher One Large, Well-Designed, Multicenter Study as an Alternative to the Usual Fda Paradigm , 1999 .

[31]  J. Ioannidis,et al.  Issues in comparisons between meta-analyses and large trials. , 1998, JAMA.

[32]  Guidance for Industry Providing Clinical Evidence of Effectiveness for Human Drug and Biological Products , 1998 .

[33]  G. Grégoire,et al.  Discrepancies between meta-analyses and subsequent large randomized, controlled trials. , 1997, The New England journal of medicine.

[34]  Stephen Senn,et al.  Statistical Issues in Drug Development , 1997 .