ReviewAlcoholism, clinical and experimental research2018
Detecting Deception in Our Research Participants: Are Your Participants Who You Think They Are?
Review in Alcoholism, clinical and experimental research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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
3 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.
- Factors associated with phosphatidylethanol (PEth) sensitivity for detecting unhealthy alcohol use: An individual patient data meta-analysis.Alcoholism, clinical and experimental research · 2021Pooled it
- Frequency of concealment, fabrication and falsification of study data by deceptive subjects.Contemporary clinical trials communications · 2021Article
- Methods to reduce the incidence of false negative trial results in substance use treatment research.Current opinion in psychology · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
There is increasing awareness of the potential negative impacts of participant deception on research, including possibly undermining reliability and reproducibility of study findings. These deceptive individuals set their personal interests above the rules of study participation, thereby jeopardizing data quality as well as placing themselves and others at risk. The costs of participant deception are numerous. Overall, it reduces statistical power and may even result in false conclusions about efficacy and safety. To date, most studies have not utilized sufficient methods to detect rule-breaking subjects. The purpose of this article is to bring to the attention of alcohol and other drug researchers issues involving deceptive participants. The review will suggest alcohol-specific as well as more general strategies to identify and thereby minimize enrollment of these deceptive participants. Specifically, we will identify strategies that are employed in different phases of human alcohol research and advance approaches that may be helpful to the field in reducing these contaminants. As a field, we need to be more proactive in identifying the deceptive participant even at the cost of more burdensome study enrollment. In light of the systemic nature and multipronged damage that this emerging pattern of deception inflicts on clinical research, it is imperative that we each assume greater responsibility for our role in mitigating this source of research contamination.
Indexed as
Identifiers
What OpenQuestion holds
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.