ArticleContemporary clinical trials communications2017
Strategies to exclude subjects who conceal and fabricate information when enrolling in clinical trials.
Article in Contemporary clinical trials communications, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Trial
- Article
- Frequency of concealment, fabrication and falsification of study data by deceptive subjects.Contemporary clinical trials communications · 2021Article
- Article
- A crowdsourced intervention to promote hepatitis B and C testing among men who have sex with men in China: A nationwide online randomized controlled trial.EClinicalMedicine · 2019Article
- Detecting Deception in Our Research Participants: Are Your Participants Who You Think They Are?Alcoholism, clinical and experimental research · 2018Review
- Improving Study Conduct and Data Quality in Clinical Trials of Chronic Pain Treatments: IMMPACT Recommendations.The journal of painArticle
- Safety, Science, or Both? Deceptive Healthy Volunteers: Psychiatric Conditions Uncovered by Objective Methods of Screening.PsychosomaticsArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Clinical trials within the US face an increasing challenge with the recruitment of quality candidates. One readily available group of subjects that have high rates of participation in clinical research are subjects who enroll in multiple trials for the purpose of generating income through study payments. Aside from issues of safety and generalizability, evidence suggests that these subjects employ methods of deception to qualify for the strict entrance criteria of some studies, including concealing information and fabricating information. Including these subjects in research poses a significant risk to the integrity of data quality and study designs. Strategies to limit enrollment of subjects whose motivation is generating income have not been systematically addressed in the literature. The present paper is intended to provide investigators with a range of strategies for developing and implementing a study protocol with protections to minimize the enrollment of subjects whose primary motivation for enrolling is to generate income. This multifaceted approach includes recommendations for advertising strategies, payment strategies, telephone screening strategies, and baseline screening strategies. The approach also includes recommendations for attending to inconsistent study data and subject motivation. Implementing these strategies may be more or less important depending upon the vulnerability of the study design to subject deception. Although these strategies may help researchers exclude subjects with a higher rate of deceptive practices, widespread adoption of subject registries would go a long way to decrease the chances of subjects enrolling in multiple studies or more than once in the same study.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.