Randomized Phase III Oncology Trials: A Survey and Empirical Bayes Inference

Changyu Shen, Huiping Xu

Research output: Contribution to journalArticle

Abstract

Mandatory registration of eligible clinical trials at clinicaltrials.gov provides an unprecedented opportunity to survey existing trials that is immune to potential publication bias. Phase III oncology clinical trials represent the gold-standard evaluation on new therapeutic interventions to fight one of the most deadly diseases. Yet, the collective performance of these trials is not well understood. Through clinicaltrials.gov, we identified 130 eligible randomized phase III oncology trials in 2008–2012 and extracted results from 122 using clinicaltrials.gov and other sources. We estimated the distribution of the effect size of randomized phase III oncology trials through a new Bayesian deconvolution method, which allows the calculation of several performance measures. We found that about 22% of the interventions for cancer treatment that reach phase III have null or negative efficacy, and another 30% with strong positive efficacy. For the rest interventions, the majority have modest efficacy, which leads to insufficient power. The false positive rate is low at 0.13%, whereas as high as 33.5% of the trials could be false negatives. The distribution of the effect sizes also provide a tangible prior for Bayesian inference of future trials.

Original languageEnglish (US)
Article number49
JournalJournal of Statistical Theory and Practice
Volume13
Issue number3
DOIs
StatePublished - Sep 1 2019

Fingerprint

Oncology
Empirical Bayes
Clinical Trials
Efficacy
Effect Size
Publication Bias
Deconvolution
Bayesian inference
False Positive
Gold
Performance Measures
Registration
Null
Cancer
Evaluation

Keywords

  • Clinical trials
  • Efficacy
  • Empirical Bayes
  • Oncology
  • Phase III

ASJC Scopus subject areas

  • Statistics and Probability

Cite this

Randomized Phase III Oncology Trials : A Survey and Empirical Bayes Inference. / Shen, Changyu; Xu, Huiping.

In: Journal of Statistical Theory and Practice, Vol. 13, No. 3, 49, 01.09.2019.

Research output: Contribution to journalArticle

@article{08cb73d89bf74272a750e5294b5d7dc3,
title = "Randomized Phase III Oncology Trials: A Survey and Empirical Bayes Inference",
abstract = "Mandatory registration of eligible clinical trials at clinicaltrials.gov provides an unprecedented opportunity to survey existing trials that is immune to potential publication bias. Phase III oncology clinical trials represent the gold-standard evaluation on new therapeutic interventions to fight one of the most deadly diseases. Yet, the collective performance of these trials is not well understood. Through clinicaltrials.gov, we identified 130 eligible randomized phase III oncology trials in 2008–2012 and extracted results from 122 using clinicaltrials.gov and other sources. We estimated the distribution of the effect size of randomized phase III oncology trials through a new Bayesian deconvolution method, which allows the calculation of several performance measures. We found that about 22{\%} of the interventions for cancer treatment that reach phase III have null or negative efficacy, and another 30{\%} with strong positive efficacy. For the rest interventions, the majority have modest efficacy, which leads to insufficient power. The false positive rate is low at 0.13{\%}, whereas as high as 33.5{\%} of the trials could be false negatives. The distribution of the effect sizes also provide a tangible prior for Bayesian inference of future trials.",
keywords = "Clinical trials, Efficacy, Empirical Bayes, Oncology, Phase III",
author = "Changyu Shen and Huiping Xu",
year = "2019",
month = "9",
day = "1",
doi = "10.1007/s42519-019-0049-4",
language = "English (US)",
volume = "13",
journal = "Journal of Statistical Theory and Practice",
issn = "1559-8608",
publisher = "Taylor and Francis",
number = "3",

}

TY - JOUR

T1 - Randomized Phase III Oncology Trials

T2 - A Survey and Empirical Bayes Inference

AU - Shen, Changyu

AU - Xu, Huiping

PY - 2019/9/1

Y1 - 2019/9/1

N2 - Mandatory registration of eligible clinical trials at clinicaltrials.gov provides an unprecedented opportunity to survey existing trials that is immune to potential publication bias. Phase III oncology clinical trials represent the gold-standard evaluation on new therapeutic interventions to fight one of the most deadly diseases. Yet, the collective performance of these trials is not well understood. Through clinicaltrials.gov, we identified 130 eligible randomized phase III oncology trials in 2008–2012 and extracted results from 122 using clinicaltrials.gov and other sources. We estimated the distribution of the effect size of randomized phase III oncology trials through a new Bayesian deconvolution method, which allows the calculation of several performance measures. We found that about 22% of the interventions for cancer treatment that reach phase III have null or negative efficacy, and another 30% with strong positive efficacy. For the rest interventions, the majority have modest efficacy, which leads to insufficient power. The false positive rate is low at 0.13%, whereas as high as 33.5% of the trials could be false negatives. The distribution of the effect sizes also provide a tangible prior for Bayesian inference of future trials.

AB - Mandatory registration of eligible clinical trials at clinicaltrials.gov provides an unprecedented opportunity to survey existing trials that is immune to potential publication bias. Phase III oncology clinical trials represent the gold-standard evaluation on new therapeutic interventions to fight one of the most deadly diseases. Yet, the collective performance of these trials is not well understood. Through clinicaltrials.gov, we identified 130 eligible randomized phase III oncology trials in 2008–2012 and extracted results from 122 using clinicaltrials.gov and other sources. We estimated the distribution of the effect size of randomized phase III oncology trials through a new Bayesian deconvolution method, which allows the calculation of several performance measures. We found that about 22% of the interventions for cancer treatment that reach phase III have null or negative efficacy, and another 30% with strong positive efficacy. For the rest interventions, the majority have modest efficacy, which leads to insufficient power. The false positive rate is low at 0.13%, whereas as high as 33.5% of the trials could be false negatives. The distribution of the effect sizes also provide a tangible prior for Bayesian inference of future trials.

KW - Clinical trials

KW - Efficacy

KW - Empirical Bayes

KW - Oncology

KW - Phase III

UR - http://www.scopus.com/inward/record.url?scp=85068657227&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85068657227&partnerID=8YFLogxK

U2 - 10.1007/s42519-019-0049-4

DO - 10.1007/s42519-019-0049-4

M3 - Article

AN - SCOPUS:85068657227

VL - 13

JO - Journal of Statistical Theory and Practice

JF - Journal of Statistical Theory and Practice

SN - 1559-8608

IS - 3

M1 - 49

ER -