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Purpose: This study assessed the prevalence of physical and cognitive doping in recreational triathletes with two different randomized response models, that is, the Cheater Detection Model (CDM) and the Unrelated Question Model (UQM). Since both models have been employed in assessing doping, the major objective of this study was to investigate whether the estimates of these two models converge. Material and Methods: An anonymous questionnaire was distributed to 2,967 athletes at two triathlon events (Frankfurt and Wiesbaden, Germany). Doping behavior was assessed either with the CDM (Frankfurt sample, one Wiesbaden subsample) or the UQM (one Wiesbaden subsample). A generalized likelihood-ratio test was employed to check whether the prevalence estimates differed significantly between models. In addition, we compared the prevalence rates of the present survey with those of a previous study on a comparable sample. Results: After exclusion of incomplete questionnaires and outliers, the data of 2,017 athletes entered the final data analysis. Twelve-month prevalence for physical doping ranged from 4% (Wiesbaden, CDM and UQM) to 12% (Frankfurt CDM), and for cognitive doping from 1% (Wiesbaden, CDM) to 9% (Frankfurt CDM). The generalized likelihood-ratio test indicated no differences in prevalence rates between the two methods. Furthermore, there were no significant differences in prevalences between the present (undertaken in 2014) and the previous survey (undertaken in 2011), although the estimates tended to be smaller in the present survey. Discussion: The results suggest that the two models can provide converging prevalence estimates. The high rate of cheaters estimated by the CDM, however, suggests that the present results must be seen as a lower bound and that the true prevalence of doping might be considerably higher. [ABSTRACT FROM AUTHOR]

Study Objective To estimate the 12-month prevalence of cognitive-enhancing drug use. Design Paper-and-pencil questionnaire that used the randomized response technique. Setting University in Mainz, Germany. Participants A total of 2569 university students who completed the questionnaire. Measurements and Main Results An anonymous, specialized questionnaire that used the randomized response technique was distributed to students at the beginning of classes and was collected afterward. From the responses, we calculated the prevalence of students taking drugs only to improve their cognitive performance and not to treat underlying mental disorders such as attention-deficit-hyperactivity disorder, depression, and sleep disorders. The estimated 12-month prevalence of using cognitive-enhancing drugs was 20%. Prevalence varied by sex (male 23.7%, female 17.0%), field of study (highest in students studying sports-related fields, 25.4%), and semester (first semester 24.3%, beyond first semester 16.7%). To our knowledge, this is the first time that the randomized response technique has been used to survey students about cognitive-enhancing drug use. Conclusion Using the randomized response technique, our questionnaire provided data that showed a high 12-month prevalence of cognitive-enhancing drug use in German university students. Our study suggests that other direct survey techniques have underestimated the use of these drugs. Drug prevention programs need to be established at universities to address this issue. [ABSTRACT FROM AUTHOR]

This article derives the power curves for a Wald test that can be applied to randomized response models when small prevalence rates must be assessed (e.g., detecting doping behavior among elite athletes). These curves enable the assessment of the statistical power that is associated with each model (e.g., Warner's model, crosswise model, unrelated question model, forced-choice models, item count model, cheater detection model). This power analysis can help in choosing the optimal model and sample size and in setting model parameters in survey studies. The general framework can be applied to all existing randomized response model versions. The Appendix of this article contains worked-out numerical examples to demonstrate the power analysis for each specific model. [ABSTRACT FROM AUTHOR]