Concordance Between Two Personality Disorder Instruments With Psychiatric Inpatients Henry J. Jackson, Joe Gazis, Raymond P. Rudd, and Jane Edwards Eighty-two psychiatric inpatients received axis II diagnoses on the Milton Clinical Multiaxial Inventory (MCMI-1)-a self-report instrument-and the Structured Interview for DSM-III Personality (SIDP). Those two instruments were then compared in terms of personality disorder categories and trait-scores (dimensions). Essentially, with the exception of the borderline category, concordance between the two instruments was poor on all scales. Bayesian statistics confirmed the obtained results. The adequacy of the MCMI-I as an index of DSM-Ill personality disorders is questioned. Copyright 0 1991 by W.B. Saunders Company

D

SM-III’ gave impetus for clinicians to diagnose underlying personality disorders in patients with axis I disorders. The resultant poor agreement among clinicians in making DSM-III personality disorder diagnoses’ has been instrumental in the development and use of questionnaires and structured interviews. Such instruments have included the Personality Diagnostic Questionnaire (PDQ),’ the Structured Interview for DSM-III Personality (SIDP),4 and the Personality Disorder Examination (PDE).5 The Millon Multiaxial Clinical Inventory (MCMI)” appeared before the development of the aforementioned instruments, and parallel to the development of the DSM-III personality disorder criteria set. Theodore Millon was a highly influential member of the DSM-III Personality Disorder Committee that developed the DSM-III criteria set. However, his MCMI-1” was not explicitly based on the DSM-III nomenclature. Rather, the MCMI-1 reflected Millon’s own detailed theory of personality and psychopathology.’ Advantages of the MCMI-1 as outlined by O’Callaghan et al., ’ include its attempted coverage of axis I and axis II disorders (including all the DSM-III personality disorders); the provision of dimensional and category scores; the self-report format, which allows for rapid administration and scoring; and, the fact that it has been normed for psychiatric populations. Millon’ has argued that his MCMI-1 is a good index of most DSM-III personality disorder categories, although considerable disagreement exists on this point.“,” This is a crucial issue, as Dana and Cantrell” have argued that typically the MCMI-1 is used by practitioners as a DSM-III measure. In fact, the MCMI-1 has been used in several studies to diagnose personality disorders in patients recovering from obsessive-compulsive and depressive disorders,13 and in agoraphobic and panic disorder patients.‘4-16 have used both structured interviews and selfReich and his co11eagues’7~20 report instruments (SIDP, MCMI-1, and PDQ) to arrive at personality disorder

From the NH and MRC Schizophrenia Research Program and Departments of Psychology, Royal Park Hospital, and Albert Park Clinic, ParkviNe, Australia. Address reprint requests to Henry J. Jackson, Ph.D., NH and MRC Schizophrenia Research Unit, Royal Park Hospital, Private Bag No. 3, Parkville, vie 3052, Australia. Copyright 0 1991 by W.B. Saunders Company 0010-440X/91/3203-0008$03.00/0

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1991: pp 252-260

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diagnoses in outpatients presenting with panic disorder or major depression. Although not directly concerned with the lack of agreement among the instruments, and despite generally not using statistical analyses to determine concordance, it is clear there were differences among instruments. For example, it appeared that the MCMI-1 yielded a greater number of personality disorder diagnoses than the SIDP, specifically, schizoid, narcissistic, dependent, passiveaggressive, and clusters 2 and 3.‘9.20Reich’7-20 also adopted a procedure of accepting the consensus personality disorder diagnosis as agreement as to the presence of a specific personality disorder on two of the three personality disorder instruments. This consensus approach might be considered overly conservative, particularly as the MCMI-1 was not designed to precisely reflect DSM-III criteria.“)-l’ In a soon-to-be-published report, we report on the types and frequencies of personality disorders and traits among 87 inpatients.” That sample consisted of patients with principal diagnoses of schizophrenia (n = 35) or affective disorder (n = 30), and, a third group consisting of 22 patients who did not meet the DSM-III/R criteria for schizophrenia or a mood disorder. In the discussion of our results, we raised the issue of whether self-report measures, such as the MCMI-1, were less accurate compared with assessor-rated and interview-based instruments, such as the PDE.” It would seem important to determine whether the MCMI-1 accords with lengthier, more time-consuming, interviewer-assessed instruments such as the SIDP or PDE, which are explicitly based on DSM-III/R criteria.4.5 The aim of the present study was to examine the concordance between the SIDP and MCMI-1, and then apply Bayesian statistics to determine whether the MCMI-1 and the SIDP identified the same personality disorders in the same individuals. Even if the concordance was somewhat short of perfection, one might be prepared to accept this sacrifice because of the previously mentioned attributes of the MCMI-1.’ The SIDP is explicitly based on DSM-III criteria and was adopted as the gold standard as it has been used in a number of studies.“‘.” There may be objections to the use of the SIDP in this way, as its adequacy as an index of DSM-III has never been tested. Nevertheless, we felt reluctant to rely on clinician judgement as the gold standard, as clinicians generally tend to underdiagnose personality disorders2’ and are unreliable when asked to make personality disorder diagnoses using unstructured methods.’ Therefore, in the absence of any gold standard, we decided to elevate the SIDP to DSM-III marker status. METHOD Subjects Eighty-two inpatients (51 males, 31 females) with DSM-III/R’~” diagnoses and who had all given written, informed consent, were interviewed with the SIDP and the MCMI-1. (Eighty-seven patients were included in the study by Jackson et al.” However, for five patients SIDP data were not complete, so they could not be included in the present investigation.) Patients with diagnoses of mental retardation, dementia, or delirium were excluded. Thirty-one patients were diagnosed as having recent-onset schizophrenia, 30 had primary unipolar affective disorders (13 with major depression, six with dysthymia, and 11 with both major depression and dysthymia), and 21 had nondepressive and nonschizophrenic disorders, including six with no axis I disorder, five with substance dependence or abuse. five with psychotic disorder-not othenvise specified, two with adjustment disorder, one with panic disorder, one with delusional disorder, and one with anorexia nervosa. Fuller diagnostic details are provided by Jackson et al.” Each of the three axis I diagnostic groups consisted of a consecutive

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series of patients. The total sample (N = 82) had a mean age (*SD) of 31.20 -C 10.72 years, a mean education of 10.46 ? 2.53 years, and a mean number of 3.32 +- 4.97 admissions.

Materials The MCMI-I constructed by Millon consists of 11 subscales that measure pathological personality disorders (PDs) and nine clinical symptom syndromes. The MCMI-1 provides both dimensional (or base-rate [BR] scores and categorical scores (BR score of 85 or more indicates the presence of a diagnostic category). The SIDE consists of 160 questions in 15 sections covering all the DSM-III axis II criteria. Each section is arranged according to similarities in personality features (e.g., low self-esteem/dependence, veracity/stability) found across DSM-III axis II categories. This format minimizes information regarding specific diagnoses until ratings have been collated after the interview. Sections begin with a request for the patient to focus on usual behavior, rather than on periods of illness. Most of the questions are open-ended to encourage maximum data about the patient’s long-term functioning. The administration of the SIDP takes approximately 60 to 90 minutes to complete. The Stmctured Clinical Interview for DSM-III-R (SCID-P) is a structured diagnostic interview developed by Spitzer et al.” that allows a trained interviewer to make DSM-III-R axis I diagnoses. The Royal Park Multidiagnostic Instrument for Psychosis (RPMIP)” is a semistructured diagnostic interview that allows a trained interviewer to make DSM-III axis I psychotic disorder diagnoses. Fuller details of the MCMI-1, SCID-P, and RPMIP are provided by Jackson et al.2’

Procedure Some data for the recent-onset schizophrenic group were reported previously.2’,29Other personality disorder studies have deliberately excluded patients diagnosed as having schizophrenia.“.” Presumably, the argument is that schizophrenia trumps or obscures premorbid personality disorders. Hence, we studied recent-onset patients who were very settled as assessed by scores on positive and negative symptom scales and on the basis of clinicians’ judgements. ” The schizophrenic patients were diagnosed by a trained researcher using the RPMIPz8 for 12 patients, or the SCID-P” for 19 patients. All were assessed in the week before discharge from hospital.29 Some data for the affective disorder and mixed disorder groups were also previously reported.” For the latter two groups, diagnostic interviews using the SCID-P were conducted by one of two clinical psychologists. For all 82 patients, the SIDP was administered by one of a second pair of clinical psychologists, and the MCMI-1 was completed by patients once the patient’s mental state was carefully judged to be relatively settled by both ward teams and two of the present authors. All 82 patients were assessed with the SIDP within 24 hours of their completing the MCMI-1. The knowledgeable informant responses will be the subject of another report. For 27 cases, two clinical psychologists were present, providing interrater reliability data for the SIDP diagnoses. These two clinical psychologists who completed the SIDP were not the same two clinical psychologists who determined the axis I diagnoses on the RPMIP or SCID-P. Kappas were used to determine interrater reliability in terms of SIDP category diagnoses, while Pearson’s product-moment correlations were obtained between SIDP trait-scores and MCMI-1 BR scores. Kappas were also used to compare MCMI-1 and SIDP categories, while Pearson’s correlations were obtained between SIDP trait-scores and MCMI-1 BR scores. Although other researchers do not appear to have used SIDP trait-scores, our research group has reported them elsewhere.29,3”Briefly, SIDP trait-scores were derived in the following way: the numbers of criteria met in each personality disorder category were expressed as a proportion of the total number of criteria in that category required to make a diagnosis. Where there were subcriteria, they were, in turn, expressed as a proportion of the criterion. Thus, for example, in the narcissistic category where two subcriteria are necessary to satisfy criterion E, each is worth .50 of the criterion, whereas for criterion B of the antisocial category where three subcriteria are necessary, each one is worth .33. This procedure for obtaining trait-ratings was undertaken without precedent (B. Pfohl, personal communication). In rating the SIDP, the exclusion criterion found in the schizoid, schizotypal, paranoid, and antisocial categories, namely that the patient “does not meet the criteria for schizophrenia,” was disregarded, a procedure consistent with alterations made to these categories in DSM-III-R.26 For the avoidant, borderline, passive-aggressive, schizoid, and antisocial categories, the age criterion was omitted. In Hogg et al., 29the DSM-III criterion D for the schizoid category was included, namely, that

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the patient exhibited “no eccentricities of speech, behavior, or thought characteristic of schizotypal personality disorder” (p 311).’ In the present investigation, that criterion was omitted, thus allowing for the simultaneous diagnosis of both schizoid and schizotypal personality disorders. Finally, the stipulation that passive-aggressive personality disorder should not be given in the presence of other personality disorders was ignored.

RESULTS Interrater Reliabilities

Table 1 shows that interrater reliabilities for the SIDP were moderate to high for four of the five diagnostic categories for which kappas could be calculated, but only fair for dependent personality disorder. For six of the personality disorder diagnoses, kappas could not be calculated, as there were less than five cases of the personality disorder diagnosed by either of the instruments. Pearson’s product-moment correlations were then calculated on SIDP traitscores in an effort to obtain some indication of interrater reliabilities for all personality disorders. As can be deduced from Table 1, the interrater reliability correlations for the SIDP trait-scores ranged from r = .46 to r = 239,with the best correlations being obtained for the DSM-III cluster B disorders (histrionic, borderline, antisocial, and narcissistic). Concordance Between the SIDP and MCMZ-1

The numbers of personality disorders identified by the SIDP and the MCMI-1 are shown in Table 1. For five of the 11 categories, the MCMI-1 identified two to six times more personality disorders than the SIDP. For the compulsive, schizotypal, histrionic, and borderline categories, the results were in the opposite Table 1. SIDP Interrater Reliabilities, the Frequency of Personality Disorders Given by the SIDP and MCMI-1, and the Concordance Between the SIDP and MCMI-1 for Each Personality Disorder Classification SIDP Interrater Reliabilities (n = 27) Personality Disorder

Frequencies of Personality Disorders ~ SIDP MCMI-1

Concordance Between MCMI-1 and SIDP ~ Categories (K)

Categories k)

Traits (Pearson’s r)

.61

9 4

24

_ -.03

17

10

.18

.20 .31 .23

Dimensions (Pearson’s r)

Cluster A Paranoid Schizoid Schizotypal

.67

.50 .67 .59

Cluster B Histrionic Borderline Antisocial Narcissistic

.70 .77 -

.70 .79 .89 .76

19 26 7 4

3 15 5 10

.12 .53 .06 .25

.07 .63 .14 .26

.67 54 .46 .73

16 16 8 10

33 46 3 27

.36 .15 .oo .38

.56 .31 .02 .41

Cluster C Avoidant Dependent Compulsive Passive-aggressive

.42 -

11

.19

NOTE. Dashes indicate K values could not be computed, as less than five cases of a disorder were reported by either rater.

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direction, with the SIDP identifying greater numbers of personality disorders than the MCMI-1. Yet, with few exceptions, agreements were extremely poor, with the majority of patients identified using the SIDP not being identified by the MCMI-1. Agreement between the two instruments only reached moderate significance (K < SO) for the borderline category. Although poor, the next best kappas were for the passive-aggressive and avoidant categories. When the SIDP trait-scores and MCMI-1 BR scores were correlated, concordance between the two instruments remained poor for most categories. Again, concordance was best for borderline, followed by passive-aggressive and avoidant, all achieving fair to moderately strong correlations between the SIDP and the MCMI-1. For compulsive, histrionic, and antisocial, the concordance was virtually nonexistent, suggesting that the two instruments were sampling completely different phenomena. Operating Characteristics of the MCM-Personality Disorder Scales

Bayesian statistics were then applied and the operating characteristics of the MCMI-1 personality disorder scales determined. Table 2 depicts the SIDP BRs, sensitivity, specificity, positive predictive power (PPP), and negative predictive power (NPP) indices for each of the personality disorder categories. Widiger et a1.3’ have argued that PPP and NPP are the most useful statistics for the practitioner, since the clinician wants to know, in this case, the probability of a SIDP diagnosis given an MCMI-1 score of 85 or more. Sensitivity and specificity are of less practical utility to the clinician, since they indicate the probability of a patient having a particular test result given the diagnosis. According to the criteria of Widiger et a1.,31only the MCMI-1 borderline diagnosis demonstrated both high PPP and high NPP. Eighty-seven percent of patients with an MCMI-1 BR borderline score of 85 or more also met the criteria for the SIDP borderline

Table 2. SIDP Base Rates, Sensitivity, Specificity, and Positive and Negative Predictive Power of MCMI-1 Personality Disorder Diagnoses SIDP BR

Sensitivity

Specificity

PPP

NPP

Cluster A Paranoid Schizoid Schizotypal

.ll .05 .21

.33 .25 .24

.89 .71 .91

.27 .04 .40

.92 .95 .82

Cluster B Histronic Borderline Antisocial Narcissistic

.23 .32 .08 .05

.I1 .50 .14 .50

.98 .96 .95 .90

.67 .87 .20 .20

.78 .81 .92 .97

.I9

.81 .75 .oo .90

.70 .48 .96 .75

.39 .26 .oo .33

.94

Personality Disorder

Cluster C Avoidant Dependent Compulsive Passive-aggressive

.I9 .lO

.I2

.89 .90 .98

NOTE. SIDP BR refers to the proportion of the total sample that were accorded each specific personality disorder as diagnosed by the SIDP. In other words, it provides a base rate (BR) for each personality disorder according to the SIDP.

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diagnosis. That was considerably higher than the sensitivity of the MCMI-1 borderline diagnosis, where only 50% of patients meeting the criteria for a SIDP borderline diagnosis also obtained an MCMI-1 BR score of 85 or more for this category. In other words, based on this sample’s SIDP BR of .32 for the borderline diagnosis, one would have an 87% chance of predicting the correct diagnosis of borderline (SIDP), if a patient’s MCMI-1 borderline score was 85 or more. Yet that latter score would only identify 50% of the total number of SIDP borderline diagnoses in this sample. However, these levels of PPP and sensitivity may alter in psychiatric settings in which the SIDP BR for the borderline diagnosis is significantly different. The NPP and specificity rates for borderline were both adequate, indicating that on the basis of this sample, there is an 81% chance of correctly predicting the absence of a SIDP borderline diagnosis with an MCMI-1 BR borderline score below 85, and correctly identifying 96% of the total number of patients in this sample who do not have a diagnosis of borderline as determined by the SIDP. It is noteworthy that specificity and NPP were typically high for virtually all personality disorder categories. (This may, in many cases, be partly an artifact of the low SIDP BRs.) Nevertheless, the MCMI-1 would only seem useful when a MCMI-1 personality disorder scale can also predict (PPP) or identify (sensitivity) the presence of a SIDP personality disorder with a high degree of accuracy. Four other MCMI-1 personality disorder categories had comparable (one case) or superior (three cases) sensitivity rates to borderline personality disorder. Unfortunately, all four categories were clearly inferior to the borderline diagnosis in terms of PPP rates. As can be seen from Table 2, with the exception of histrionic where PPP was .67, the rest of the MCMI-1 personality disorder scales recorded uniformly low PPP rates (.OOto .40). Overall, the aforementioned results indicate that the MCMI-1 has more diagnostic power with respect to correctly indicating the absence of a particular personality disorder than it does with respect to correctly predicting its presence, the one exception being the borderline category. A caveat seems appropriate. High PPP rates may be influenced by high SIDP BRs of the disorders and low PPP rates may be influenced by low SIDP BRs of the disorders. Therefore, in the present study, one should be cautious in interpreting the data for the schizoid, narcissistic, and antisocial categories, given the low SIDP BRs of these disorders. DISCUSSION

The present study indicated moderate concordance between the MCMI-1 and SIDP for the borderline category and this result was bolstered by the strong interrater reliability data for the SIDP borderline category. The majority of the MCMI-1 personality scales demonstrated poor concordance with their SIDP counterparts. In fact, for some diagnostic categories, the two instruments identified/diagnosed completely different individuals (e.g., compulsive). These results may be somewhat compromised in the case of some personality categories by low SIDP BRs, e.g., schizoid and narcissistic, and/or only fair SIDP interrater reliability coefficients, e.g., compulsive, dependent, and paranoid. The best SIDP interrater reliabilities were obtained for the cluster B personality disorders (antisocial, histrionic, borderline, and narcissistic), and particularly

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for the antisocial category, which is consistent with previous research findings with both structured% and unstructured diagnostic methods.* As has been noted elsewhere,29 the fair interrater reliability data for paranoid, dependent, and compulsive could be due to a number of factors, including the inferential nature of some of the questions, the differing rater thresholds for abnormal behavior, or residual present state factors. The question as to whether the MCMI-1 constitutes an adequate measure of DSM-III has been debated elsewhere.7,9-11Millon’ argued that his borderline, schizotypal, and narcissistic personality syndromes in MCMI-1 were consonant with DSM-III. There is some support for this perspective in the current study, at least as regards the borderline category. However, even for that disorder, concordance between the MCMI-1 and SIDP would be more aptly described as moderate for both the trait-scores and the full disorder. Conversely, while the SIDP interrater reliability data were strong for narcissistic and moderate for schizotypal (both SIDP trait and category scores), the concordance between the two instruments was extremely poor. Millon7,9 has been critical of the DSM-III antisocial and passive-aggressive categories. For both, the SIDP interrater reliabilities were high, but concordance between the SIDP and MCMI-1 was poor to fair, irrespective of whether traits or categories were considered. Similarly, our study failed to sustain the view that the paranoid and dependent MCMI-1 categories were “very similar to axis II” (p 377) as proposed by Widiger et al.,” although our results may be compromised by the SIDP interrater reliabilities, which were only fair to moderate. The MCMI-1 has been criticized for being oversensitive to pathology and not adequately mirroring the DSM-III criteria.” A major difficulty with the MCMI-1, and perhaps any similar self-report measure, is that it is unable to limit itself to the premorbid state: therefore, the respondents may confuse their axis I syrnptomatology with their axis II traits, particularly if they have long durations of axis I illness. Conversely, the SIDP interviewers went to great lengths to ensure that the patients gave responses about their premorbid personality, that is, before the onset of their axis I illness. We also selected a schizophrenic sample consisting of patients with few admissions (mean, 1.80, SD 0.9). To the best of our knowledge, our study is the first endeavor to directly gauge the statistical concordance of the MCMI-1 with a DSM-III-based instrument. Final caveats are in order: the results obtained by comparing the SIDP and MCMI-1 may vary across different settings. As a corollary of this, the application of Bayesian statistics may yield different results with different populations. Second, as touched on earlier, discordance between the SIDP and MCMI could indicate that the instruments were measuring different constructs. But SIDP interrater reliability was only fair for some categories. Therefore, our results may reflect, in part, a confounded mixture of unreliability and differences in construct validity. Third, if a different gold standard had been adopted, by using another instrument such as the PDE, again different results may have been obtained. Notwithstanding the caveats, on the basis of our obtained results, we would concur with Dana and Cantrell’* in that the MCMI-1 personality disorder scales insufficiently match their DSM-III criteria counterparts. One cannot be confident that diagnoses obtained via the MCMI-1 are in fact accurate DSM-III diagnoses.

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Of course, Millon’s’ conceptualization of personality disorders ultimately may possess greater validity than the DSM-III, but for now at least, DSM-III/R has more currency. Millon’ proposed that while the MCMI-1 provided a good index of DSM-III, he was of the opinion that MCMI-113’ (using DSM-III-R personality disorder category labels) would be even better. That perspective has yet to be subjected to empirical investigation. ACKNOWLEDGMENT We thank Rita Lombardi

and Pam Lambert

for typing the manuscript.

REFERENCES 1. American Psychiatric Association: Diagnostic and Statistical Manual (ed 3, Revised) (DSM-IIIR). Washington, DC, APA, 1987 2. Mellsop G, Varghese F, Joshua S, et al: The reliability of axis II of DSM-III. Am J Psychiatry 139:1360-1361, 1982 3. Hyler S, Rieder R, Spitzer R, et al: Personality Diagnostic Questionnaire (PDQ). New York. NY, State Psychiatric Institute, Biometrics Research, 1983 4. Pfohl B, Stangl D, Zimmerman M: Structured Interview for DSM-III Personality (ed 2) (SIDP). Ames, IA, University of Iowa, 1983 5. Loranger A: Personality Disorder Examination (PDE). Yonkers, NY, DV Communications, 1988 6. Millon T: Millon Clinical Multiaxial Inventory Manual (ed 3). Minneapolis, MN, National Computer Systems, 1983 7. Millon T: Disorders of Personality. DSM-III. Axis II. New York, NY, Wiley, 1981 8. O’Callaghan T, Bates GW, Jackson HJ, et al: The clinical utility of the Millon Clinical Multiaxial Inventory Depression subscales. Aust Psycho1 25:45-61.1990 9. Millon T: The MCMI provides a good assessment of DSM-III disorders: The MCMI-II will prove even better. J Pers Assess 49:379-391,1985 10. Widiger TA, Williams JBW, Spitzer RL, et al: The MCMI as a measure of DSM-III. J Pers Assess 491366-378, 1985 11. Widiger TA, Williams JBW, Spitzer RL, et al: The MCMI and DSM-III: A brief rejoinder to Millon. J Pers Assess 50:198-204, 1986 12. Dana RH, Cantrell JD: An update on the Millon Multiaxial Inventory (MCMI). J Clin Psychiatry 44:760-763, 1988 13. Joffe RT, Swinson RP, Regan JJ: Personality features of obsessive-compulsive disorder. Am J Psychiatry 145:1127-1129, 1988 14. Mavissakalian M, Hamann MS: DSM-III personality disorder in agoraphobia. Compr Psychiatry 27~471-479. 1986 15. Mavissakalian M, Hamann MS: DSM-III personality disorder in agoraphobia II: Changes with treatment. Compr Psychiatry 28:356-361, 1987 16. Mavissakalian M, Hamann MS: Correlates of DSM-III personality disorder in panic disorder and agoraphobia. Compr Psychiatry 29:53.5-544, 1988 17. Reich J: Sex distribution of DSM-III personality disorder in psychiatric outpatients. Am J Psychiatty 144:485-488,1987 18. Reich JH: DSM-III personality disorders and the outcome of treated panic disorder. Am J Psychiatry 145:1149-1152.1988 19. Reich J, Troughton E: Comparison of DSM-III personality disorders in recovered, depressed and panic disorder patients. J Nerv Ment Dis 176:300-304, 1988 20. Reich J, Noyes R, Troughton E: Dependent personality disorder associated with phobic avoidance in patients with panic disorder. Am J Psychiatry 144:323-327, 1987 21. Jackson HJ, Rudd R, Gazis J, et al: Using the MCMI-I to diagnose personality disorders in inpatients: Axis I/axis II associations and sex differences. Aust Psycho1 (in press, 1991) 22. Alnaes R, Torgersen S: The relationship between DSM-III symptom disorders (axis I) and personality disorders (axis II) in an outpatient population. Acta Psychiatr Stand 78:385-492, 1988 23. Pfohl B, Stangl D, Zimmerman M: The implications of DSM-III personality disorders for patients with major depression. J Atfect Disord 7:309-318, 1984

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24. Stangl D, Pfohl B, Zimmerman M, et al: A Structured Interview for the DSM-III Personality Disorders. Arch Gen Psychiatry 42:591-596,1985 25. Hyler S, Rieder RO, Williams JBW, et al: A comparison of clinical and self-report diagnoses of DSM-III personality disorders in 552 patients. Compr Psychiatry 30:170-178, 1989 26. American Psychiatric Association: Diagnostic and Statistical Manual (ed 3, revised) (DSM-IIIR). Washington DC, APA, 1987 27. Spitzer RL, Williams JBW, Gibbon M: Structured Clinical Interview for DSM-III (SCID-P). New York, NY, State Psychiatric Institute, Biometrics Research, 1986 28. McGorry PD, Kaplan I, Dosseter C, et al: The Royal Park Multi-diagnostic Instrument for Psychosis. A Comprehensive Assessment Procedure for the Acute Psychotic Episode (RPMIP). Melbourne, Australia, Monash University Department of Psychological Medicine, 1988 29. Hogg BM, Jackson HJ, Rudd RP, et al: Diagnosing personality disorders in recent-onset schizophrenia. J Nerv Ment Dis 178:194-199, 1990 30. Nazikian H, Rudd R, Edwards J, et al: Personality disorder assessment for psychiatric inpatients. Aust NZ J Psychiatry 24:37-46, 1990 31. Widiger TA, Hurt SW, Frances A, et al: Diagnostic efficiency and DSM-III. Arch Gen Psychiatry 41:1005-1012,1984 32. Millon T: Millon Clinical Multiaxial Inventory (ed 2) (MCMI-II). Minneapolis, MN, National Computer Systems, 1985