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  ["property_scientific_abstract"]=>
  string(1246) "Background: Symptom manifestations can be subtle. Small imprecisions in measurement can lead to over- or under-estimation of change. One strategy to improve measurement is to conduct logical consistency checks between item responses (i.e., cross-sectionally) and across test administrations (i.e., longitudinally) of rating scales, bearing in mind that some degree of inconsistency is to be expected due to subject-based variability. Objective:  Test consistency flags for the YMRS on clinical trial data.  Study Design: International Society for CNS Clinical Trials and Methodology convened an expert Working Group that assembled consistency/inconsistency flags for the Young Mania Rating Scale (YMRS). Participants: Data sets are being requested from sponsors who conducted clinical trials that used the YMRS.  Primary and secondary outcome measures:  Primary outcome measure is the frequency with which each of the potential scoring inconsistencies occurs.  Secondary outcome is the frequency with which ratings have multiple inconsistencies.  Statistical analysis: Descriptive analysis of frequency of occurrence of each of the potential scoring inconsistencies. Trials or investigators will not be identified in the reporting of the results."
  ["project_brief_bg"]=>
  string(1340) "Symptom manifestations can be subtle. Small imprecisions in measurement can lead to over- or under-estimation of change. One strategy to improve measurement is to conduct logical consistency checks between item responses (i.e., cross-sectionally) and across test administrations (i.e., longitudinally) of rating scales, bearing in mind that some degree of inconsistency is to be expected due to subject-based variability. The

International Society for CNS Clinical Trials and Methodology (ISCTM) expert Working Group focusing on improving consistency in measurement has been developing algorithms for flags to identify possible errors in use of rating scales widely used in our field. The model includes developing an algorithm based on the ?expert opinion? of the working group and then testing it in data sets. Recommendations have been published for the Positive and Negative Syndrome Scale (PANSS) (Rabinowitz and Rabinowitz, 2021; Rabinowitz et al., 2017), the Personal and Social Performance (PSP) scale (Rabinowitz et al., 2021), the Montgomery-Asberg Depression Rating Scale (MADRS)(Rabinowitz et al., 2019) and the Hamilton Rating Scale for Depression (HAM-D) (Rabinowitz et al., 2022).  The value of our approach of flagging inconsistencies on improving signal detection has recently been demonstrated (Opler et al., 2021)."
  ["project_specific_aims"]=>
  string(232) "Apply algorithm developed based on expert opinion to detect scoring inconsistencies in the use of the YMRS scale. Algorithm to be applied to data sets from various clinical trials. Recommendations based on this work to be published."
  ["project_study_design"]=>
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    ["label"]=>
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  }
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  string(295) "Data on all subjects with available data on YMRS (Young Mania Rating Scale).  We are requesting YMRS item level data and where available CGI-s data.   All data by visit with sequential subject identifiers. Please note that we do not need patient demographic, psychiatric history, or safety data."
  ["project_main_outcome_measure"]=>
  string(272) "The primary outcome measure is the frequency with which each of the potential scoring inconsistencies("flags") occurs.  Secondary outcome is the frequency with which YMRS administrations have multiple inconsistencies.   Flags are included as an attachment to this request."
  ["project_main_predictor_indep"]=>
  string(193) "Potential scoring inconsistencies are defined based on the inconsistency flags developed by the expert working

group of the International Society for CNS Clinical Trials and Methodology."
  ["project_other_variables_interest"]=>
  string(59) "YMRS item level data to be used to compute and apply flags."
  ["project_stat_analysis_plan"]=>
  string(2833) "The frequency with which each of the expert-derived logical consistency flags occurred in the clinical trial data will be assessed.  Then, using the norms that we derived from the  trials, we will apply all of the following empirically-based checks of outlier rating administrations: (1) Underuse of a value (e.g., rarely choosing a score of 4), (2) Overuse of a value; (3) Disproportionate use of even or odd response choices; (4) ?Long-string,? using the same response on several consecutive items--the assumption is that careless raters may choose the same response option to many questions in a row and that attentive raters will not use the same response option for large numbers of consecutive items;  (5) Inter-item standard deviation which checks for random patterns of response within a YMRS  administration as measured by how much an individual administration strays from its own midpoint across the YMRS items, and (6) Outlier response choices on multiple items as measured by the Mahalanobis distance that shows when an individual is on the outskirts of the multivariate distribution formed by responses to all items.  Outliers were predefined as administrations that were in the top or bottom 2.5 percent of one or more of the measures, or 5% on only one side for skewed items where lower 2.5% does not exist (to approximate a p-value of .05).  To identify the outlier cutting points for overuse or underuse of values, disproportionate use of odd/even values, longest consecutive use of same response choice in each YMRS administration, and intra-item standard deviation and multivariate outliers (Mahalanobis distance), we examined the frequency distributions for each of these measures.

In a final round of analyses, we will evaluate the overlap in flagging by outlier-pattern and expert-derived flags in our clinical trial samples.  In addition, we sought to determine how these flags would perform in a sample of administrations completed in a random fashion to simulate what one might find in the case of careless/inattentive subjects or raters. To accomplish this, we will generate a ?simulated random? dataset using Monte Carlo methods such that responses were uniformly distributed for each item, without regard to the values of other items.

Multivariate outliers were detected using the Mahalanobis distance.  Longest consecutive use of values with the same response was examined using the code for detecting long-string responding.  Intra-item standard deviation which shows how much each YMRS a administration strays from its midpoint across the YMRS items was computed as per Marjanovic et al (2015) as the standard deviation of responses across the items for an individual YMRS administration.  All analyses will be conducted in R version 4.1.3 and using the package ?careless? (Yentes, 2021)."
  ["project_timeline"]=>
  string(272) "The working group of experts has already prepared consistency flags. Once we have obtained data sets we will apply these to the data sets. Data management and analysis are anticipated to take 4 months. We anticipate producing a manuscript for publication within 12 months."
  ["project_dissemination_plan"]=>
  string(597) "As we have previously done for the Positive and Negative Syndrome Scale (PANSS) (Rabinowitz and Rabinowitz, 2021; Rabinowitz et al., 2017), the Personal and Social Performance (PSP) scale (Rabinowitz et al., 2021), the Montgomery-Asberg Depression Rating Scale (MADRS)(Rabinowitz et al., 2019) and the Hamilton Rating Scale for Depression (HAM-D) (Rabinowitz et al., 2022) we anticipate producing a journal manuscript presenting the inconsistency flags for the YMRS, the

frequency of their occurrence in available data and our recommendations. Target journal: Journal of Affective Disorders"
  ["project_bibliography"]=>
  string(2079) "
Opler, M., Negash, S., Tatsumi, K., Cong, L., Starling, B., Komarof, M., Capodilupo, G., Hasebe, M., Echevarria, B., Blattner, R., Citrome, L., 2021. Application of A Novel Analytic Methodology to Improve PANSS Data Quality and Signal Detection in a Global Clinical Trial of Schizophrenia, ISCTM 2021 Autumn Conference, virtual.

Rabinowitz, J., Opler, M., Rabinowitz, A.A., Negash, S., Anderson, A., Fu, D.J., Williamson, D., Kott, A., Davis, L.L., Schooler, N.R., 2021. Consistency checks to improve measurement with the Personal and Social Performance Scale (PSP). Schizophr Res 228, 529-533.

Rabinowitz, J., Rabinowitz, A.A., 2021. Outlier-response pattern checks to improve measurement with the Positive and Negative Syndrome Scale (PANSS). Psychiatry Res 303.

Rabinowitz, J., Rabinowitz, A.A., 2022. Outlier-response pattern checks to improve measurement with the Montgomery-Asberg depression rating scale (MADRS). J Affect Disord 299, 444-448.

Rabinowitz, J., Schooler, N.R., Anderson, A., Ayearst, L., Daniel, D., Davidson, M., Khan, A., Kinon, B., Menard, F., Opler, L., Opler, M., Severe, J.B., Williamson, D., Yavorsky, C., Zhao, J., Isctm_Algorithms_Flags_To_Identify_Clinical_Inconsistency_In_The_Use_Of_Rating_Scales_In_CNS_RCTs_working_group_members, 2017. Consistency checks to improve measurement with the Positive and Negative Syndrome Scale (PANSS). Schizophr Res 190, 74-76.

Rabinowitz, J., Schooler, N.R., Brown, B., Dalsgaard, M., Engelhardt, N., Friedberger, G., Kinon, B.J., Lee, D., Ockun, F., Mahableshwarkar, A., Tsai, J., Williams, J.B.W., Sauder, C., Yavorsky, C., 2019. Consistency checks to improve measurement with the Montgomery-Asberg Depression Rating Scale (MADRS). J Affect Disord 256, 143-147.

Rabinowitz, J., Williams, J.B.W., Anderson, A., Fu, D.J., Hefting, N., Kadriu, B., Kott, A., Mahableshwarkar, A., Sedway, J., Williamson, D., Yavorsky, C., Schooler, N.R., 2022. Consistency checks to improve measurement with the Hamilton Rating Scale for Depression (HAM-D). J Affect Disord 302, 273-279.
"
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    string(46) "yoda_project_protocol_-_2022-5090_-_2022-11-21"
    ["filename"]=>
    string(50) "yoda_project_protocol_-_2022-5090_-_2022-11-21.pdf"
    ["filesize"]=>
    int(27921)
    ["url"]=>
    string(99) "https://yoda.yale.edu/wp-content/uploads/2023/08/yoda_project_protocol_-_2022-5090_-_2022-11-21.pdf"
    ["link"]=>
    string(92) "https://yoda.yale.edu/data-request/2022-5090/yoda_project_protocol_-_2022-5090_-_2022-11-21/"
    ["alt"]=>
    string(0) ""
    ["author"]=>
    string(4) "1363"
    ["description"]=>
    string(0) ""
    ["caption"]=>
    string(0) ""
    ["name"]=>
    string(46) "yoda_project_protocol_-_2022-5090_-_2022-11-21"
    ["status"]=>
    string(7) "inherit"
    ["uploaded_to"]=>
    int(6188)
    ["date"]=>
    string(19) "2023-08-09 16:57:43"
    ["modified"]=>
    string(19) "2023-08-09 19:10:43"
    ["menu_order"]=>
    int(0)
    ["mime_type"]=>
    string(15) "application/pdf"
    ["type"]=>
    string(11) "application"
    ["subtype"]=>
    string(3) "pdf"
    ["icon"]=>
    string(62) "https://yoda.yale.edu/wp/wp-includes/images/media/document.png"
  }
  ["project_review_link"]=>
  array(21) {
    ["ID"]=>
    int(10465)
    ["id"]=>
    int(10465)
    ["title"]=>
    string(36) "yoda_project_review_-_2022-5090_site"
    ["filename"]=>
    string(40) "yoda_project_review_-_2022-5090_site.pdf"
    ["filesize"]=>
    int(1315723)
    ["url"]=>
    string(89) "https://yoda.yale.edu/wp-content/uploads/2023/08/yoda_project_review_-_2022-5090_site.pdf"
    ["link"]=>
    string(82) "https://yoda.yale.edu/data-request/2022-5090/yoda_project_review_-_2022-5090_site/"
    ["alt"]=>
    string(0) ""
    ["author"]=>
    string(4) "1363"
    ["description"]=>
    string(0) ""
    ["caption"]=>
    string(0) ""
    ["name"]=>
    string(36) "yoda_project_review_-_2022-5090_site"
    ["status"]=>
    string(7) "inherit"
    ["uploaded_to"]=>
    int(6188)
    ["date"]=>
    string(19) "2023-08-09 16:57:36"
    ["modified"]=>
    string(19) "2023-08-09 19:10:43"
    ["menu_order"]=>
    int(0)
    ["mime_type"]=>
    string(15) "application/pdf"
    ["type"]=>
    string(11) "application"
    ["subtype"]=>
    string(3) "pdf"
    ["icon"]=>
    string(62) "https://yoda.yale.edu/wp/wp-includes/images/media/document.png"
  }
  ["project_highlight_button"]=>
  array(3) {
    ["title"]=>
    string(22) "Publication Available!"
    ["url"]=>
    string(78) "https://www.sciencedirect.com/science/article/pii/S0165032723012879?via%3Dihub"
    ["target"]=>
    string(6) "_blank"
  }
  ["request_data_partner"]=>
  string(15) "johnson-johnson"
  ["search_order"]=>
  string(5) "-8510"
  ["request_overridden_res"]=>
  string(1) "3"
  ["principal_investigator"]=>
  array(7) {
    ["first_name"]=>
    string(0) ""
    ["last_name"]=>
    string(0) ""
    ["degree"]=>
    string(0) ""
    ["primary_affiliation"]=>
    string(0) ""
    ["email"]=>
    string(0) ""
    ["state_or_province"]=>
    string(0) ""
    ["country"]=>
    string(0) ""
  }
}
data partner
array(1) {
  [0]=>
  string(15) "johnson-johnson"
}


pi country
array(1) {
  [0]=>
  string(6) "Israel"
}


pi affil
array(1) {
  [0]=>
  string(8) "Academia"
}


products
array(4) {
  [0]=>
  string(16) "risperdal-consta"
  [1]=>
  string(7) "topamax"
  [2]=>
  string(9) "risperdal"
  [3]=>
  string(6) "invega"
}


num of trials
array(1) {
  [0]=>
  string(2) "17"
}


res
array(1) {
  [0]=>
  string(1) "3"
}