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Richard B. Francoeur, Ph.D. Associate Professor of Social Work Adelphi University

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Richard B. Francoeur, Ph.D. Associate Professor of Social Work Adelphi University

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  1. Validating psychometric measures as abuse potential signals to inform a shorter drug safety questionnaire: A proposed study of adverse events and clusters that simultaneously predict a psychometric scale and unique variation in scale items Richard B. Francoeur, Ph.D.NIDA/FDA/CPDD Conference on “Science of Abuse Liability Assessment” November 10, 2011

  2. Richard B. Francoeur, Ph.D. Associate Professor of Social Work Adelphi University Social Work Building, 1 South Avenue Garden City, NY 11530 Affiliate, Center for the Psychosocial Study of Health & Illness, Columbia University francoeur@adelphi.edu

  3. Richard B. Francoeur, Ph.D., is the sole or lead author of influential peer-reviewed articles in palliative care, including a highly cited publication about interacting cancer symptom clusters that predict depressive malaise in patients initiating palliative radiation. He is now interested in expanding the scope of his work from illness symptoms and clusters into a similar focus on adverse events and clusters that signal abuse potential of drug formulations used in palliative care. Francoeur has created two methodological innovations (one recently published) that will be useful in detecting and interpreting interactions among adverse events that co-occur as a cluster. He will estimate latent trait models in which adverse events and clusters predict both a psychometric scale and additional, unique variation in scale items. These models can be used to reveal contexts of abuse potential, and to assess the validity of psychometric scales and items as signals of abuse potential, which may inform development of shorter safety questionnaires. Francoeur needs access to data from human abuse potential trials to demonstrate this novel approach. He invites those open to sharing data (at least positive control and placebo conditions) to contact him (francoeur@adelphi.edu). As the P.I. on previous projects, Francoeur has attracted funding from the NIMH, the Hartford Foundation (Hartford Geriatric Social Work Faculty Scholar Program), and the Open Society Institute (Project on Death in America).

  4. Abstract • In this poster, I introduce the multiple indicators-multiple causes (MIMIC) model (i.e., a confirmatory factor analysis predicted by multiple regression) as an attractive approach to assess when adverse events (AEs) and psychometric measures converge as signals of abuse potential • In this application of MIMIC, specific AEs and interacting AE clusters predict a latent trait (construct), which is represented as an error-conditioned psychometric scale. (This optimal scale is derived based on factor loadings of the mutually co-occurring variation across all scale items) • At this same level of the latent trait (construct), these AEs and interacting AE clusters simultaneously predict any remaining unique variation in each psychometric item (also error-conditioned) that is not reflected in the latent trait

  5. Abstract (cont.) • A series of MIMIC models can inform the creation of shorter, streamlined, and validated questionnaires for safety studies of a particular drug, or perhaps drug class, by identifying entire psychometric scales (and subscales) from the same domain—and individual items across any of these similar scales—that are best predicted by AEs and AE clusters • I outline a proposed study I will conduct to demonstrate this approach. The study will also report a means I recently developed to overcome a major limitation in obtaining unique estimates for these fully-specified, “under-identified” MIMIC models

  6. I. Background • In human abuse potential trials for the same drug or drug class, there is interest in developing a shorter, streamlined, and validated psychometric questionnaire that is given less frequently (U.S. Food and Drug Administration, 2010; Schoedel & Sellers, 2008) • An important goal of the FDA and drug companies • Safety trials would be more cost-effective to conduct • Standardization across studies would improve cross-study comparisons

  7. The questionnaire should retain only psychometric scales (or specific scale items) that contribute uniquely to identify signals of abuse potential • All adverse events (AEs) should be captured • AEs that are not related to psychometric scales or items could be due to participant burden from frequent administration of multiple and overlapping scales or to limitations in domains tapped by the measures • Responses to psychometric scales or items that cannot be linked to reported AEs could be tapping unreported adverse events without clear behavioral referents • Sub-acute effects do not pass threshold to be realized as AE

  8. Stronger signals occur when detected AEs can be linked to heightened 1) scores for specific psychometric scales; or 2) responses on specific items (Mansbach et al., 2010) • The psychometric responses may occur in the same, prior, or next period • Select from the set of psychometric scales for each latent trait (construct): • The scale that reveals the strongest signal(s) from AEs and clusters [based on indirect effect(s)] • The remaining scale(s) that reveal the next-strongest signal(s) from other AEs/clusters [also based on indirect effect(s)] • Specific item(s) from any scale that provide unique signal(s) from any AEs/clusters [based on direct effect(s)] • See Figure 1 (next on slide 9) and Figure Legends (slides 10-11) for a visual representation, and definitions, of indirect and direct effects

  9. II. Models to Inform a Shorter Questionnaire Figure 1. The Multiple Indicators-Multiple Causes (MIMIC) Model: A confirmatory factor analysis predicted by multiple regression Factor-Analytic “Multiple Indicators”: Observed Items for a Latent Trait Tapped by a Psychometric Scale Regression-Based “Multiple Causes”: Co-Occurring Adverse Events (AEs) and Their Interaction Res. Error Res. Error Res. Error Item #1 Item #2 ... Last Item Direct Effects AE #1 Indirect Effects (A) Indirect Effects (B) Res. Error AE #2 Latent Trait (or Construct) Tapped by a Psychometric Scale AE #1 AE #2

  10. Figure Legends • “Indirect” effects to the items are revealed by solid-line, unidirectional paths mediated by the latent psychometric trait • Each observed item is predicted by the latent trait [Indirect Effects (A)], which is predicted by both adverse events (AE) and their interaction [Indirect Effects (B)] • Indirect Effects (A): The item loadings from the confirmatory factor analysis portion of the MIMIC. Constitute the shared effects across all observed items based on their mutually “co-occurring variation” that reveal the variation in the latent trait • Indirect Effects (B): The multiple regression effects by the AEs and their interaction in predicting this revealed variation in the latent trait • Indirect Effects (B) are useful for identifying scales of the same latent trait that show stronger abuse potential signals

  11. Figure Legends • “Direct” effects are represented by dashed-line,unidirectional paths. (For clarity, shown to first observed item only, but in the proposed study will be specified to all items) • These are additional, and unmediated, multiple regression effects by the AEs and their interaction that directly predict the observed items, thus revealing differential item functioning (DIF) • DIF is based on each observed item’s “unique variation” that does NOT tap the latent trait predicted by the indirect effects • Useful for detecting observed items from the same scale that 1) show different signals of abuse potential than the overall scale; and/or 2) may be strongly biased as indicators of the latent trait • Short blue arrows labeled Res. Error indicate that error is conditioned from each observed item and the overall scale

  12. AE-Psychometric Relationships in MIMIC • To recap, Figure 1 (slide 9) and its legends reveal that MIMIC analyses of safety trial data can identify single and interacting AEs that predict two sources of psychometric variation simultaneously • But consistent, accurate, and meaningful AE coding must occur first • AE data coded within MedDRA that are consistent with data from psychometric instruments helps corroborate the validity of both (Mansbach et al., 2010; see slides 15-16 for AE coding in MedDRA) • MIMIC analyses with AE data coded in MedDRA can simultaneously • 1) Identify individual, co-occurring, and interacting AEs that may signal potential for drug abuse or misuse; and • 2) Reveal which standardized psychometric instruments—as well as specific items from any of the instruments—are most sensitive and specific to the occurrence of these AEs

  13. MIMIC Flexibility for Adequate Statistical Power • The sample size may be based on the number of case observations across all participants, study arms, and periods • Although human abuse potential studies use few participants [typically 20 to 40; Schoedel & Sellers (2008)], each individual provides multiple case observations for a MIMIC based on increases in drug doses for the new drug formulation, positive control medication, and placebo • Set of dummy codes for all but one participant, may factor out response heterogeneity by nesting observations in participants • Datasets across human abuse potential studies may be combined to test a drug formulation, or even a drug class, in a single MIMIC model • Dummy variable identifies each study or run “multi-group” MIMIC

  14. Invalidity and Indeterminacy in MIMIC • When direct effects are included, only fully specified, “saturated” MIMIC models can be assured to be valid… • Estimates from unsaturated MIMIC models vary based on which direct effect(s) to scale items we decide not to estimate [i.e., fix regression parameter(s) to zero] • In Figure 1 (slide 9), items with no direct effects exclude dashed lines. Results shift as we add/retract direct effects • ...But fully specified, “saturated” models do not converge to unique estimates (e.g., Grayson et al., 2000) • In Figure 1 (slide 9), a dashed line to every item would result in a non-converging, inestimable MIMIC • In my study, I will apply an approach I recently developed to overcome this limitation in obtaining unique estimates for these fully-specified, “under-identified” MIMIC models

  15. III. The Adverse Events (AEs) Focus: Regression-Based “Multiple Causes” in MIMIC Model • AEs coded as MedDRA preferred terms are discrete events (U.S. Food and Drug Administration, 2010) that may be categorized within abuse-related categories • e.g., euphoria-related; dissociative/psychotic; impaired attention, cognition, mood, and psychomotor events; inappropriate affect; and medication tampering (Klein, 2011) • Different AEs within and across these abuse-related categories may co-occur (cluster) within individuals receiving a particular drug dose (Mansbach et al., 2010)

  16. AE clusters involved in statistically significant interactions may be stronger signals for abuse potential than other co-occurring AEs (Mansbach et al., 2010) • If the new drug is a member of a known drug class, test clusters among AEs that are typical of members of the class (Klein, 2011) • Go beyond identifying occurrences of specific types of AE in MedDRA Dose, exposure, frequency, and time of occurrence are other important factors in AE clusters (Klein, 2011) • For instance, there may be specific period(s) after a drug dose in which an AE is more likely to occur, suggesting an AE Type x AE Period interaction • Demographic and biomarker variables (e.g., vital signs, ECGs, urine toxicology) that interact with specific AEs may identify subgroups with greater abuse potential

  17. How Can We Identify Plausible Interacting AE Clusters Before Specifying the MIMIC? First, select as outcome variables the total score for each psychometric scale or subscale and any item with pronounced or unusual endorsement Then use Chi-Square Automatic Interaction Detector (CHAID), a data mining algorithm with Bonferroni-adjusted significance testing Compares all possible pairs of AEs to select the two that form the strongest interaction (e.g., AE #1 x AE #2) when predicting each outcome Then assesses all possible pairs of this interaction with remaining AEs to determine if it can be qualified further (e.g., AE #1 x AE #2 x AE #3) Process repeats until interaction cannot be broken down further The algorithm returns to the remaining AEs to identify the two that form the second strongest interaction based on the remaining variation in the outcome The algorithm repeats until all interactions are identified

  18. AE Interactions Detected by CHAID CHAID findings are comparable to a regression specified with only the selected interaction term(s) and their derivative component terms Advantages: Considers all possible interactions in a single procedure that 1) adjusts for chance effects (i.e., inflated Type I error) due to multiple testing of all interactions; and 2) does not suffer from regression problems such as influential outliers Limitation: Descriptive only. Unlike explanatory regression, unselected AEs and other predictors are not also factored out Statistically significant findings in CHAID may become non-significant in a follow-up explanatory regression Alternative: If there is insufficient statistical power for simultaneous estimation of all effects in a MIMIC model, CHAID may be followed by estimating fully specified moderated regressions that predict each outcome separately

  19. IV. The Psychometric Focus: Factor Analytic “Multiple Indicators” in MIMIC Model Each standardized psychometric scale and its observed items should be tested in a separate MIMIC Common psychometric scales in human abuse potential studies (British Journal of Addiction, 1991): Visual analogue scale items in categories for drug-abuse and other effects: drug-liking, positive effects, negative effects (Bond & Lader, 1974) 14 euphoria & 7 depression items from Addiction Research Center Inventory (ARCI; Cole et al., 1982; Martin et al., 1971) 50 withdrawal symptoms from ARCI Supplemental Questionnaire No. 4 Euphoria, dysphoric mood, and anxiety subscales of Profile on Mood States (POMS; McNair et al., 1971) Items from a drug-class withdrawal checklist (Mansbach et al., 2010)

  20. To detect abuse potential signals from high doses of a prescription drug used to relieve physical symptoms • I need data from Human Abuse Potential Trials and seek to speak with those who may be willing to share data • At a minimum, I need data on established drug formulation(s) that serve as positive control(s) and on placebo for comparison • I also seek data on a new drug formulation: either a new molecular entity at the time of approval or a recent formulation proposed to be a tamper- and abuse-resistant drug (TARD) • Some Options: Opioid formulation for Pain or other CNS active drugs (Stimulants, Depressants, Hallucinogens, Cannabinoids etc.) for various symptoms and indications V. A Proposed MIMIC Study

  21. Comparisons will be made among increasing doses of a new drug formulation (if available), an established formulation(s) that serves as the positive control(s), as well as the placebo • Saturated MIMIC models will be estimated • The study will provide evidence about which of the psychometric scales and subscales in their entirety—as well as specific items from any of the instruments—provide signals of the drug’s abuse potential, especially those that are consistently predicted by AEs or interacting AE clusters

  22. Finally, sensitive psychometric items/scales from the study of data from recreational drug users will inform a search for similar items/scales used in RCTs with healthy, drug-naïve subjects (Phase 1). • Some Initial Possibilities • The Profile of Mood States is already used used in human abuse potential studies and RCTs; subscales assess euphoria, depressed mood, anxiety (Mansbach et al., 2010) • 2 hallucinations scales in healthy, drug-naïve subjects: • Volunteers emerging from anesthesia (Bowdle et al., 1998) • Experimental subjects given LSD without knowing drug or its effects (Linton & Langs, 1962; Barr et al., 1972; see Strassman et al., 1994); used with other hallucinogens (Faillace et al., 1967)

  23. These scales for healthy, drug-naïve subjects will be important to test in future MIMIC studies • Can findings of abuse potential in recreational drug users be replicated in Phase I RCTs of healthy, drug-naïve individuals? • Consistent findings in both groups will increase our confidence that detected signals of abuse potential are real, and not data artifacts

  24. Barr, HL, Langs, RJ, Holt RR, Goldberger L, & Klein GS. (1972). LSD: Personality and Experience. New York: Wiley-Interscience. • Bond, A, & Lader, M. (1974). The use of analogue scales in rating subjective feelings. Br J Med Psychol, 47, 211-218. • Bowdle, TA, Radant, AD, Cowley, DS, Kharasch, ED, Strassman, RJ, & Roy-Byrne, PP. (1998). Psychedelic effects of ketamine in healthy volunteers: Relationship to steadystate plasma concentrations. Anesthesiology, 88, 82-88. • British Journal of Addiction. (1991). Special Issue: Clinical Testing of Drug Abuse Liability. Br J Addict 86(12), 1525-1652. • Cole, JO, Orzack, MH, Beake, B, Bird, M, & Bar-Tal, Y (1982). Assessment of the abuse liability of buspirone in recreational sedative users. J Clin Psych, 43, 69-75. • Faillace, LA, Vourlekis, A, & Szara, S. (1967). Clinical evaluation of some hallucinogenic tryptamine derivatives. J Nerv Ment Dis, 145, 306-313. • Grayson, DA, Mackinnon, A, Jorm, AF, Creasey, H, & Broe, GA. (2000). Item bias in the Center for Epidemiologic Studies Depression Scale: Effects of physical disorders and disability in an elderly community sample. J Gerontol, 5, P273-P282. • Klein, M. (2011, June 22). FDA Guidance on Abuse Potential-Related Adverse Events Assessment [Oral presentation at College on Drug Abuse Dependence Workshop, Miami, FL]. VI. References

  25. 9. Linton, HB, & Langs, RJ. (1962). Subjective reactions to lysergic acid diethylamide (LSD-25) measured by a questionnaire. Arch Gen Psychiatry 6, 352-368. 10. Mansbach, RS, Schoedel, KA, Kittrelle, JP, & Sellers, EM (2010). The role of adverse events and related safety data in the pre-market evaluation of drug abuse potential. Drug Alcohol Depend, 112, 173-177. 11. Martin, WR, Sloan, JW, Sapira, JD, & Jasinski, DR (1971). Physiologic, subjective, and behavioral effects of amphetamine, methamphetamine, ephedrine, phenmetrazine, and methylphenidate in man. Clin Pharmacol Ther, 12, 245-258. 12. McNair, D, Lorr, M, & Droppleman, L (1971). edITS Manual for the Profile of Mood States Educational and Industrial Testing Service. San Diego, CA. 13. Schoedel, KA, & Sellers, EM. (2008). Assessing abuse liability during drug development: Changing standards and expectations. Clin Pharmacol Ther, 83(4), 622-626. 14. Strassman, RJ, Qualls, CR, Uhlenhuth, EH, & Kellner, R. (1994). Dose-response study of N,N-Dimethyltryptamine in Humans: II. Subjective effects and preliminary results of a new rating scale. Arch Gen Psych, 51(2), 98-108. 15. U.S. Food and Drug Administration. (2010, January 27). Guidance for Industry: Assessment of Abuse Potential of Drugs. http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM198650.pdf

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