Systematic review and narrative review lead experts to different cancer trial predictions: a randomized trial

Completeness of clinical evidence citation in trial protocols: A cross-sectional analysis

BACKGROUND

Human protection policies require assessment of how proposed clinical trials relate to prior and ongoing studies testing similar hypotheses. We assessed the extent to which clinical trial protocols cited relevant published and ongoing clinical trials that would have been easily accessible with reference searches.

METHODS

We created a random sample of trial protocols using ClinicalTrials.gov, stratifying by industry and non-industry-sponsored studies. We then conducted reference searches to determine the extent to which protocols cited clinical trials with identical intervention-indication pairings that were accessible in PubMed and ClinicalTrials.gov at the time of trial initiation.

FINDINGS

Of the 101 trial protocols evaluated, 73 had at least one identified citable trial. None contained statements suggesting a systematic search for relevant clinical evidence. Of industry-sponsored trial protocols with at least one identified citable trial, 7 of 23 (30.4%) did not cite any published clinical trials and 10 of 33 (30.3%) did not cite any ongoing relevant trials. Of the non-industry-sponsored trial protocols with at least one identified citable trial, 5 of 28 (17.9%) did not cite any published clinical trials and 14 of 19 (73.7%) did not cite any ongoing trials.

CONCLUSIONS

Clinical trial protocols undercite accessible, relevant trials and do not document systematic searches for relevant clinical trials. Consequently, ethics review committees often receive an incomplete picture of the research landscape if they review protocols similar to those deposited on ClinicalTrials.gov.

FUNDING

This study was funded by the Canadian Institutes of Health Research and the Greenwall Foundation.

Is preclinical research in cancer biology reproducible enough?

The Reproducibility Project: Cancer Biology (RPCB) was established to provide evidence about reproducibility in basic and preclinical cancer research, and to identify the factors that influence reproducibility more generally. In this commentary we address some of the scientific, ethical and policy implications of the project. We liken the basic and preclinical cancer research enterprise to a vast 'diagnostic machine' that is used to determine which clinical hypotheses should be advanced for further development, including clinical trials. The results of the RPCB suggest that this diagnostic machine currently recommends advancing many findings that are not reproducible. While concerning, we believe that more work needs to be done to evaluate the performance of the diagnostic machine. Specifically, we believe three questions remain unanswered: how often does the diagnostic machine correctly recommend against advancing real effects to clinical testing?; what are the relative costs to society of false positive and false negatives?; and how well do scientists and others interpret the outputs of the machine?

Is preclinical research in cancer biology reproducible enough?

The Reproducibility Project: Cancer Biology (RPCB) was established to provide evidence about reproducibility in basic and preclinical cancer research, and to identify the factors that influence reproducibility more generally. In this commentary we address some of the scientific, ethical and policy implications of the project. We liken the basic and preclinical cancer research enterprise to a vast 'diagnostic machine' that is used to determine which clinical hypotheses should be advanced for further development, including clinical trials. The results of the RPCB suggest that this diagnostic machine currently recommends advancing many findings that are not reproducible. While concerning, we believe that more work needs to be done to evaluate the performance of the diagnostic machine. Specifically, we believe three questions remain unanswered: how often does the diagnostic machine correctly recommend against advancing real effects to clinical testing?; what are the relative costs to society of false positive and false negatives?; and how well do scientists and others interpret the outputs of the machine?