A critical examination of best dose analysis for determining cognitive-enhancing potential of drugs: studies with rhesus monkeys and computer simulations View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2013-08

AUTHORS

Paul L. Soto, Jesse Dallery, Nancy A. Ator, Brian R. Katz

ABSTRACT

RATIONALE: Best dose analysis involves identifying the dose associated with the greatest improvement in performance for each subject and comparing performances associated with these individually determined best doses to control performances. OBJECTIVES: The current experiments were conducted to examine whether significant best dose effects might result from the selective analysis of data rather than an actual drug effect. METHODS: Experiment 1 examined the effects of nicotine and methylphenidate on delayed matching-to-sample (DMTS) and self-ordered spatial search (SOSS) performances in rhesus monkeys (DMTS: n = 7; SOSS: n = 6) to determine the validity and reliability of best dose effects. Experiment 2 used Monte Carlo computer simulations to estimate the likelihood of obtaining a significant outcome when the best dose method was applied to randomly generated data sets for which no difference existed. RESULTS: Significant effects were obtained when the best dose analysis was applied to performances from nondrug sessions, and best dose performances were not significantly different from the best nondrug performances. The doses identified as best doses from two nicotine dose-response curve determinations were unrelated, and the improvement associated with the best dose observed during the first dose-response curve determination was not reliable when the dose was administered repeatedly. Finally, there was a high likelihood of obtaining a statistically significant difference when no real difference existed. CONCLUSIONS: Best dose analysis for the identification of potential therapeutic agents should be replaced by single-subject designs. More... »

PAGES

611-622

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s00213-013-3070-4

    DOI

    http://dx.doi.org/10.1007/s00213-013-3070-4

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1042108528

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/23529381


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