title: Age and disease severity predict choice of atypical neuroleptic: a signal detection approach to physicians' prescribing decisions.
abstract: OBJECTIVE
  We used a novel application of a signal detection technique, receiver operator characteristics (ROC), to describe factors entering a physician's decision to switch a patient from a typical high potency neuroleptic to a particular atypical, olanzapine (OLA) or risperidone (RIS).
  
  
  METHODS
  ROC analyses were performed on pharmacy records of 476 VA patients who had been treated on a high potency neuroleptic then changed to either OLA or RIS.
  
  
  RESULTS
  Overall 68% patients switched to OLA and 32% to RIS. The best predictor of neuroleptic choice was age at switch, with 78% of patients aged less than 55 years receiving OLA and 51% of those aged greater than or equal to 55 years receiving OLA (chi(2)=38.2, P<0.001). Further analysis of the former group indicated that adding the predictor of one or more inpatient days to age increased the likelihood of an OLA switch from 78% to 85% (chi(2)=7.3, P<0.01) while further analysis of the latter group indicated that adding the predictor of less than 10 inpatients days to age decreased the likelihood of an OLA switch from 51% to 45% (chi(2)=7.0, P<0.01).
  
  
  CONCLUSIONS
  ROC analyses have the advantage over other analyses, such as regression techniques, insofar as their "cut-points" are readily interpretable, their sequential use forms an intuitive "decision tree" and allows the potential identification of clinically relevant "subgroups". The software used in this analysis is in the public domain (http://mirecc.stanford.edu).
authors.affiliation: Palo Alto Veterans Affairs Health Care System, MC 151Y, Palo Alto, CA, USA. yesavage@stanford.edu
authors.forename: Jerome A
authors.initials: JA
authors.lastname: Yesavage
authors.forename: Jennifer
authors.initials: J
authors.lastname: Hoblyn
authors.forename: Javaid
authors.initials: J
authors.lastname: Sheikh
authors.forename: Jared R
authors.initials: JR
authors.lastname: Tinklenberg
authors.forename: Art
authors.initials: A
authors.lastname: Noda
authors.forename: Ruth
authors.initials: R
authors.lastname: O'Hara
authors.forename: Catherine
authors.initials: C
authors.lastname: Fenn
authors.forename: Martin S
authors.initials: MS
authors.lastname: Mumenthaler
authors.forename: Leah
authors.initials: L
authors.lastname: Friedman
authors.forename: Helena C
authors.initials: HC
authors.lastname: Kraemer
mesh_terms: D000367:Age Factors; D014150:Antipsychotic Agents / Q000009:adverse effects / Q000627:therapeutic use*; D001569:Benzodiazepines / Q000009:adverse effects / Q000627:therapeutic use*; D002755:Choice Behavior; D011307:Drug Prescriptions / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D000077152:Olanzapine; D011446:Prospective Studies; D012372:ROC Curve; D018967:Risperidone / Q000009:adverse effects / Q000627:therapeutic use*; D012559:Schizophrenia / Q000175:diagnosis / Q000188:drug therapy*; D012565:Schizophrenic Psychology; D012720:Severity of Illness Index; D017603:Signal Detection, Psychological
chemical_list: D014150:Antipsychotic Agents; D001569:Benzodiazepines; D018967:Risperidone; D000077152:Olanzapine