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Original Article |

Individual Growth Curve Analysis Illuminates Stability and Change inPersonality Disorder Features:  The Longitudinal Study of Personality Disorders FREE

Mark F. Lenzenweger, PhD; Matthew D. Johnson, PhD; John B. Willett, PhD
[+] Author Affiliations

Author Affiliations: Department of Psychology,State University of New York at Binghamton, Binghamton (Drs Lenzenweger andJohnson); and Harvard University Graduate School of Education, Cambridge,Mass (Dr Willett).


Arch Gen Psychiatry. 2004;61(10):1015-1024. doi:10.1001/archpsyc.61.10.1015.
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Background  The long-term stability of personality pathology remains an open question. Its resolution will come from prospective, multiwave longitudinal studies using blinded assessments of personality disorders (PD). Informative analysis of multiwave data requires the application of statistical procedures, such as individual growth curve modeling, that can detect and describe individual change appropriately over time. The Longitudinal Study of Personality Disorders, which meets contemporary methodological design criteria, provides the data for this investigation of PD stability and change from an individual growth curve perspective.

Methods  Two hundred fifty subjects were examined for PD features at 3 different time points using the International Personality Disorders Examination during a 4-year study. Stability and change in PD features over time were examined using individual growth modeling.

Results  Fitting of unconditional growth models indicated that statistically significant variation in PD features existed across time in the elevation and rate of change of the individual PD growth trajectories. Fitting of additional conditional growth models, in which the individual elevation and rate-of-change growth parameters were predicted by subjects’ study group membership (no PD vs possible PD), sex, and age at entry into the study, showed that study group membership predicted the elevation and rate of change of the individual growth curves. Comorbid Axis I psychopathology and treatment during the study period were related to elevations of the individual growth trajectories, but not to rates of change.

Conclusions  From the perspective of individual growth curve analysis, PD features show considerable variability across individuals over time. This fine-grained analysis of individual growth trajectories provides compelling evidence of change in PD features over time and does not support the assumption that PD features are traitlike, enduring, and stable over time.

Figures in this Article

Personality disorders (PDs) are assumed to be stable over time. Theprevailing diagnostic nomenclature (DSM-IV,1DSM-III,2 and DSM-R-III3) for these prevalentdisorders4,5 embraces this assumptionin asserting that PDs are “enduring patterns” of behavior thatare “inflexible,” and “stable over time.”1 However, data supporting this core assumption, drawnfrom properly designed longitudinal studies, are essentially nonexistent,and the long-term stability of PDs remains essentially undocumented terrain.

Initial studies of the stability of PDs generally used 2-wave test-retestresearch designs.6 Such studies are typicallyconducted across short time spans and examine the stability of individualdifferences by examining correlation coefficients and comparing group averagesof PD features at the 2 time points. However, the inadequacy of the test-retestdesign for illuminating stability or change has long been noted in the longitudinalresearch methodology literature.710 Twowaves of longitudinal data represent an extremely limited design for investigatingchange because (1) the amount of change between the first and second occasionsof measurement cannot tell us anything about the shape of each person’sindividual growth trajectory between those times, and (2) estimates of truechange are difficult to obtain from the observed 2-wave data.8,10

Alternatively, the methodological superiority of the prospective multiwavelongitudinal design for the study of stability and change has long been welldocumented.712 Inaddition to using a multiwave design, a proper longitudinal study involvesthe collection of data across sensibly spaced intervals, wherein the variableof interest is continuous in nature, can be equated across occasions of measurement,and remains construct valid for the entire study period.10 Moreover,for interview-based assessments, blinded assessment is a critical requirementsuch that the same subject is never seen by the same interviewer more thanonce to ensure against “halo effects,” which diminish validityand elevate stability estimates artifactually.13,14 Finally,the most informative extraction of meaning from longitudinal data for theinvestigation of stability and change takes the individual growth curve (orpath) as the critical unit of analysis.7,10,11

The Longitudinal Study of Personality Disorders (LSPD),4,15 begunin 1990, is a prospective multiwave longitudinal study of personality pathology,normal personality, and temperament sponsored by the National Institute ofMental Health, Washington, DC. A major goal of the LSPD is the life-span studyof the stability of PD symptomatology. The LSPD subjects were drawn initiallyfrom a nonclinical population, thus avoiding the usual confounds attendingthe study of hospital/clinic PD cases (eg, treatment/time confounds, Berkson’sbias, and selection of extreme cases). Herein, we describe an analysis ofindividual growth trajectories of PD features from the LSPD.4,15 Ouranalyses transcend the previous LSPD15 reportby examining stability and change in a more informative manner and by illustratingthe power, richness, and utility of the individual growth curve (IGC) approach(also known as hierarchical linear modeling,16,17 multilevelmodeling,18 covariance components models,19 or random-coefficient regression models20)for longitudinal data analysis in psychopathology.

In the previous LSPD report,15 the stabilityof individual differences in the PDs over time was examined using the between-wavecorrelation approach, and individual differences were found to be relativelystable during the 4-year span. However, this approach provided only thin 2-wavesnapshots of the entire spectrum of interindividual differences in growth.Average levels of PD symptomatology, assessed using repeated-measures multivariateanalysis of variance (MANOVA),15 provided someevidence of PD feature reduction over time (albeit with small effect sizes).However, the previous MANOVA approach (1) could not accommodate the existingunequal spacing of PD assessments across study subjects, (2) did not disentangleimportant aspects of individual change such as initial levels of symptomatologyand rate of change as typically implemented, and (3) implausibly assumed comparablegrowth across all subjects, wherein heterogeneity of growth is more likely.21 As such, although the previously reported correlationaland MANOVA approaches represented a reasonable first pass through the LSPD15 data, they did not tap the true richness therein.An analysis of individual growth trajectories10,11 isrequired at this point to represent this richness and to improve our understandingof the stability or change in personality disorders over time.

SUBJECTS

The 258 subjects in the LSPD15 were drawnfrom a population consisting of 2000 first-year undergraduate students.4 Subjects were assigned to a possible PD (PPD) groupor a no PD (NPD) group according to the International Personality DisorderExamination DSM-III-R–Screen (IPDE-S)4 (response rate, 84.2%). The PPD subjects metthe diagnostic threshold for at least 1 specific DSM-III-R PD, whereas NPD subjects (1) did not meet the DSM-III-R–defined threshold for diagnosis and (2) had fewer than 10 PDfeatures across all disorders. Extensive detail concerning the initial subjectselection procedure and sampling is given elsewhere.4 The258 subjects consisted of 121 men (46.9%) and 137 women (53.1%). The PPD groupincluded 134 (68 men and 66 women); the NPD group, 124 (53 men and 71 women).All subjects gave voluntary written informed consent and received an honorariumof $50 at each wave. Of the initial 258 subjects, 250 completed all 3 assessmentwaves and are included in this analysis. Five PPD and 3 NPD subjects did notcomplete all 3 waves, for a final sample of 64 men (49.6%) and 65 women (50.4%)in the PPD group and 53 men (43.8%) and 68 women (56.2%) in the NPD group.Race and ethnicity in the final sample were as follows: 9 (3.6%) African American;12 (4.8%) Latin or Hispanic; 180 (72.0%) white; 43 (17.2%) Asian-Pacific Islander;2 (0.8%) Native American; and 4 (1.6%) other. Mean age at study entry was18.89 years (SD, 0.51 years). Additional sample characteristics are summarizedin Table 1.

Table Graphic Jump LocationTable 1. Demographic Characteristics of the Longitudinal Study of PersonalityDisorders Sample (N = 250) for Subjects Available at 3 Waves ofthe Study
PD ASSESSMENTS

The LSPD has a prospective multiwave longitudinal design, with subjectsinitially undergoing evaluation at 3 time points (ie, first, second, and fourthyears in college). Although not required for application of individual growthmodeling,10 the LSPD data are balanced, inthat all subjects have 3 waves of data, and time structured, in that everyoneundergoes repeated assessment on the same 3-wave schedule,10 althoughthe time between assessments varies from case to case. Interview assessmentswere conducted by experienced PhD or advanced MSW clinicians. Finally, asthe LSPD is a naturalistic prospective study, subjects were free to seek psychologicaltreatment of their own accord.

The IPDE-S is a 250-item self-administered true-false PD screening inventorydeveloped by Armand W. Loranger, PhD. The diagnostic efficiency and psychometricproperties of the IPDE-S in a 2-stage screen application were described previously.4

The IPDE is the well-known semistructured interview that assesses DSM and ICD-10 PD features2224 and was used in theWorld Health Organization/Alcohol, Drug Abuse, and Mental Health Administation–sponsoredInternational Pilot Study of Personality Disorders.24 The DSM-III-R criteria were assessed in this study. Clinicallyexperienced interviewers received training in IPDE administration and scoringby Dr Loranger and were supervised throughout the project by one of us (M.F.L.)who was blind to the subjects’ identity, putative PD status, and allprevious assessment information. The interrater reliability for IPDE assessmentswas excellent at all 3 waves, ranging from 0.84 to 0.92 for all PD dimensions.The interviewers were blind to the putative PD group status of the subjectsand to all prior LSPD PD assessment data, and subjects never underwent assessmentby the same interviewer more than once.

The Structured Clinical Interview for DSM-III-R–Non-PatientVersion25 is a semistructured DSM-III-R Axis I clinical interview for use with nonpatients. The interviewwas administered first, followed by the IPDE.

STATISTICAL ANALYSIS

We used IGC analysis to investigate change in PD features over time.This method of analyzing within-subject change was popularized by Rogosa andcolleagues7,11 and representsthe most powerful way to assess change in a continuous dimension over timewithin subjects.10,11,16,17 Thedependent variable used in these analyses was the number of PD features ratedas present on the IPDE, which yielded continuous dimensional scores for the11 DSM-III-R Axis II PDs, the 3 clusters (A, B, andC), and total PD features. Dimensional measures of PD ensured the greatestsensitivity to the investigation of stability and change (qualitative diagnoseswould not be appropriate for a study of change in this framework).

The IGC approach hypothesizes that, for each individual, the continuousoutcome variable (eg, the number of PD features) is a specified function oftime, called the individual growth trajectory, plus error. This trajectoryis often specified as a simple linear function of time, in which case it contains2 important unknown individual growth parameters—an intercept and aslope—that determine the shape of individual true growth over time.The individual intercept parameter represents the net elevation of the trajectoryover time, ie, the true mean level of the PD features for the individual atthe onset of the study (or, alternatively, whenever the origin of the timescale has been defined). The individual slope parameter represents the rateof change over time and is the within-person rate of change in PD featuresover time in the present study. Despite the obvious theoretical importanceof individual slope in studies of change, it has not been widely examinedin research on psychopathology. Once an individual growth trajectory has beenspecified (at level 1) to represent the individual change over time, a level2 model can be specified to describe the investigators’ hypotheses aboutthe way that the individual growth parameters contained in the level 1 modelare related to between-subjects factors (eg, subject sex and diagnostic group).

The IGC approach has several advantages. First, interindividual variabilityin assessment intervals can be tolerated in specifying the individual trajectories,unlike repeated-measures MANOVA. Second, although estimation of an individualgrowth trajectory and its precision generally requires more than 2 observationson the individual over time, the IGC modeling approach is highly tolerantof missing data, permitting subjects who have incomplete data to participatein the analyses. Third, using modern software, the IGC modeling approach simultaneouslyuses data on all individuals at every time point to concurrently investigatewithin- and between-individual change, with concomitant improvements in precisionand power. Fourth, when lengthy multiwave data are available, IGC modelingpermits the flexible specification and rich investigation of nonlinear individualchange over time.

In our analyses, the hypothesized levels 1 and 2 statistical modelswere fitted simultaneously to the LSPD data using full-maximum likelihoodestimation and the HLM-5 computer program.26 Weconducted our analyses sequentially. First, we conducted a set of unconditionalgrowth analyses10 in which we posited a linearindividual change trajectory at level 1, but did not attempt to predict interindividualvariation in the growth parameters by between-subject factors. Such unconditionalanalyses are useful for partitioning the outcome variation into variance componentsthat describe the net variation in slope and intercept across individuals.Second, we conducted a set of conditional analyses in which we examined systematicinterindividual differences in intercept and slope as a function of 3 between-subjectpredictors, namely study group (PPD vs NPD), subject sex, and age in yearsat entry into the study. These predictors yield fixed effects in the predictionof the slope and intercept values retained from the level 1 analysis. Thefitting of the level 2 model also yields estimates of residual variance thatdescribe remaining interindividual variability in the individual slopes andintercepts (as well as their covariance) after accounting for the hypothesizedfixed effects, giving rise to the variance components (ie, σ20, σ21, σ01) (Table 2).

Table Graphic Jump LocationTable 2. Definition and Interpretation of Parameters in the MultilevelModel for Growth (Change)

In addition, supplementary analyses were conducted to determine whetherstatistical interactions among the group, sex, and age at entry variableswere required as predictors in the level 2 model or whether inclusion of additionallevel 2 predictors (ie, Axis I disorder and treatment) would substantiallyimprove the model fit for a particular PD dimension. Improvement in modelfit was assessed by comparison of deviance statistics. Fixed-effects and variancecomponents were tested for statistical significance using the provided z statistics (2-tailed). Sample comparisons of simple proportionswere performed using the χ2 test.

CLINICAL CHARACTERISTICS OF THE SAMPLE

As reported previously,15 the lifetime DSM-III-R Axis I diagnoses (Table 3) of the study subjects are for definite and probable disorders.Eighty-one (62.8%) of the PPD subjects received an Axis I diagnosis comparedwith 32 (26.4%) of the NPD subjects (χ21 = 33.30; P<.001). Forty-one PPD subjects (31.8%) vs 21 NPD subjects(17.4%) reported a history of treatment by wave 3 (χ21= 6.97; P<.01). Finally, as of wave3, 16% of the sample received a probable or definite diagnosis for at least1 Axis II PD (or PD not otherwise specified).

Table Graphic Jump LocationTable 3. Definite and Probable Lifetime DSM-III-R Axis I SCID-NP Diagnosesfor Sample of 250 Subjects
ASSESSMENT SCHEDULE CHARACTERISTICS

The PD features of each of the 250 study subjects were assessed 3 timesduring the 4-year study period. The average age of study subjects at the assessmentwaves were 18.89 years (SD, 0.51 years) at wave 1, 19.83 years (SD, 0.54years) at wave 2, and 21.70 years (SD, 0.56 years) at wave 3. The time betweenassessments for each subject was calculated in years, using each subject’sdate of birth and exact assessment date, and then centered on age at entryinto the study for each study subject (with age at entry being included asa predictor at level 2). The mean time from entry into the study (wave 1)to wave 2 was 0.95 years (SD, 0.14 years); from wave 1 to wave 3, 2.82 years(SD, 0.23 years). Centering the assessment intervals on age at entry and includingage at entry as a predictor at level 2 accounts for each subject’s uniquechronological age when he or she began the study and causes the individuallevel 1 intercepts to represent the true value of the wave 1 assessments asthe subjects’ initial status.

IGC MODELING OF PD FEATURES

The IGC modeling analyses were performed separately for the outcomevariables of total PD features, cluster PD features, and each individual DSM PD. The heterogeneity in the individual growth trajectoriesis impressive, and they are plotted, using an exploratory ordinary least squaresapproach, for total PD features in Figure 1.

Place holder to copy figure label and caption
Figure 1.

Ordinary least squares individualgrowth trajectories for total personality disorder (PD) features in 250 subjectsduring the study period. Time is reported in years since the beginning ofthe study and centered for each subject using the subject’s age at entryinto the study.

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Unconditional Analyses: Is Change Present in the Data?

First, an unconditional growth model (ie, containing no level 2 predictors)was fitted for all PD dimensions, providing estimates of the average elevationand rate-of-change parameters and their natural variation across all subjectson entry into the study. The results of these fits are given in Table 4 and Table 5, whichcontain estimates of the fixed-effects and variance components for the unconditionalgrowth trajectories for each of the various PD dimensions. The estimated averageelevation of the individual growth trajectories on entry into the study (intercepts)differed significantly from 0 for all PD dimensions (P<.001;all large effects). In addition, each intercept contained significant variability(σ20) (P<.001) availablefor prediction at level 2 in subsequent conditional models.

Table Graphic Jump LocationTable 4. Parameters of the Unconditional Growth (Baseline) Model ofPD Feature Change From a Prospective Multiwave Longitudinal Perspective: Interceptand Slope (N = 250)* (Because Table 4 is paired with Table 5 and Table 6 is paired with Table 7, we suggest that readers print the PDF and view these pairs on facing pages: Table 4 across from Table 5 and Table 6 across from Table 7.)
Table Graphic Jump LocationTable 5. Parameters of the Unconditional Growth (Baseline) Model ofPD Feature Change From a Prospective Multiwave Longitudinal Perspective: VarianceComponents and Deviance Statistics (N = 250)*

The estimated average rates of change also differed significantly from0 for all of the PD dimensions, except for the paranoid PD dimension, indicatingthat much change over time was evident in the PD data (typically medium orlarge effects). In addition, all of the variance components associated withrate of change (σ21), with the exception of clusterC disorders (compulsive, avoidant, passive-aggressive, and dependent PDs)and paranoid PD, were statistically significant, suggesting that there weresignificant amounts of variation in change that could potentially be predictedin subsequent level 2 models. Finally, the estimated slopes from the unconditionalgrowth analyses support interesting conclusions with respect to the rate atwhich PDs actually change with time. Specifically, we estimate that totalPD features decrease by 1.4 PD features with each passing year. At the levelof clusters, annual rates of change over the study period were as follows:0.35 cluster A features per year, 0.65 cluster B features per year, and 0.40cluster C features per year.

Conditional Analyses

Next, we distinguished the individual growth trajectories by the subjects'group, sex, and age at study entry. We introduced level 2 predictors to explainany between-person variation in the individual elevation and rate-of-changeparameters. The 2 primary between-subjects factors of interest were the groupmembership (PPD vs NPD) and subject sex. In addition, we included each subject’sage at entry into the study as a predictor at level 2 to account for interindividualvariation in change associated with actual age (ie, developmental level).For all 15 models fitted, intermodel comparisons of goodness-of-fit (deviance)statistics disclosed that the inclusion of group, sex, and age at study entryas level 2 predictors significantly improved the fit beyond that achievedin the unconditional growth models (schizoid PD, P < .05;all others, P < .01). The results ofthe conditional analyses are presented in Table6 and Table 7, which includesestimates of the fixed-effects and variance components associated with eachlevel 2 predictor (study group, sex, and age at entry), the approximate P value for testing that these effects are 0 in the population,and an estimate of the effect size correlation coefficient (r).27Table5 also contains estimates of the variance components from the level2 models, which were also tested for statistical significance.

Table Graphic Jump LocationTable 6. Predicting Interindividual Differences in Change of PD byGroup, Sex, and Age at Entry Into Study: Intercept and Slope(N = 250)* (Because Table 4 is paired with Table 5 and Table 6 is paired with Table 7, we suggest that readers print the PDF and view these pairs on facing pages: Table 4 across from Table 5 and Table 6 across from Table 7.)
Table Graphic Jump LocationTable 7. Predicting Interindividual Differences in Change of PD byGroup, Sex, and Age at Entry Into Study: Variance Components and DevianceStatistics (N = 250)*

With respect to elevation of the individual growth trajectories, groupwas a statistically significant predictor of individual elevation parametersin PD features for all PD dimensions across time (all P < .001, except schizotypal PD [P < .002]and schizoid PD [P < .07]), with effectsizes ranging from 0.12 to 0.44 (median, 0.30), indicating medium effects.Sex was less substantially predictive of elevation parameters; although statisticallysignificant (P≤.05) for 8 of 15 curves, most effectsizes were 0.18 or less (median, 0.14), indicating small effects. The PPDstatus corresponded to higher elevation for all PD dimensions, and male subjectsdisplayed higher elevation, except for histrionic and dependent PD. Age atentry into the LSPD predicted little variation in PD growth curve elevations,except for narcissistic PD (P < .09).All of the variance components estimates for elevation (σ20) indicated that there remained significant variation in elevationthat could be modeled beyond the 3 predictors that we had selected.

The critical growth parameter for investigating stability and changein PD features over time is the individual slope parameter, as it directlyindexes the rate and direction of individual change over time. In the level2 prediction of slope, group membership was significantly predictive of therate of change in PD features for total PD features; cluster A, B, and C dimensions;and the paranoid, borderline, narcissistic, histrionic, avoidant, obsessive-compulsive,and dependent PDs (all P≤.05). For these PD dimensions,the median effect size was 0.26, indicating a medium-sized effect, and thedirection of the fixed effects shows that PPD subjects were showing higherrates of change (ie, PD feature declines). Group status was less predictiveof rate of change for schizoid (P < .43),schizotypal (P < .06), passive-aggressive(P < .24), and antisocial (P < .10) PDs. Inspection of the fixed effects for sexindicate that it was less predictive of slope and, therefore, essentiallyunrelated to change in PD features over time, attaining statistical significanceonly for narcissistic PD (P < .03).Overall, the effect sizes for sex in relation to the rate of change were quitesmall (median effect size, r = 0.07). Ageat entry into the LSPD was minimally associated with the rate-of-change parameters(Table 4). Overall, study group statuswas the factor most strongly associated at level 2 with rate of change inthe PDs. For many PD dimensions, namely clusters A and B and schizoid, schizotypal,antisocial, borderline, narcissistic, and histrionic PD, the variance componentestimates for rate of change (σ21) indicated therewas also additional significant variation in elevation that could still bemodeled beyond the 3 predictors that we had selected.

Finally, in sensitivity analyses suggested by the arithmetic and distributionalproperties of the dependent variable (as a count of features), we refittedall of our unconditional and conditional models by replacing the existingoutcome by its square root. This yielded a pattern of results completely consistentwith those reported herein for the untransformed PD variables.

The change observed in PD features over the study period is clearlyshown in fitted growth trajectories recovered from the IGC analysis for thetotal PD features index presented in Figure 2. In this figure, we plot fitted growth trajectories for prototypicalmembers of each group and sex. Prototypical PPD subjects display marked changeover time.

Place holder to copy figure label and caption
Figure 2.

Empirical Bayes trajectories recoveredfrom the hierarchical linear modeling analyses for total personality disorder(PD) features in 250 subjects, by study group and subject sex. Time is reportedin years since the beginning of the study and centered for each subject usingthe subject’s age at entry into the study. NPD indicates no PD; PPD,possible PD.

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PRESENCE OF POTENTIAL STATISTICAL INTERACTION EFFECTS IN THE LEVEL2 MODEL

We also investigated whether statistical interactions among the level2 predictors had any impact on the prediction of the PD outcome variables.In 1 set of supplementary IGC analyses, we included a group × sexinteraction term as an additional predictor at level 2, along with the group,sex, and entry age main effects, and we refitted all of the hypothesized statisticalmodels. For 14 of the 15 models fitted (ie, total PD, 3 clusters, and 11 PDs),inclusion of the group × sex interaction did not result inimproved fit over and above corresponding models containing only the maineffects for group, sex, and age at entry. For the antisocial PD outcome, however,inclusion of the group × sex interaction resulted in an improvementin model fit (χ22 = 9.31; P < .01), and the interaction was predictive of elevation(P < .003) but not slope (P = .29). In addition, we refitted all hypothesized modelsand included age at entry × sex and age at entry × groupinteractions, none of which improved model fit over corresponding main effectsmodels.

SUPPLEMENTARY ANALYSES INCLUDING OTHER LEVEL 2 PREDICTORS

We also considered whether other important level 2 variables might predictelevation and/or rate of change in the PD dimensions. One such hypothesizedvariable was the presence of any Axis I disorder for study subjects beforeor during the study period, as it is reasonable to suspect that stabilityof PD features could be influenced by an Axis I disorder in evidence beforeor during the study period. Indeed, 45.2% of the total sample had some formof lifetime (or current) Axis I disorder as of wave 3. In 12 of the 15 fittedPD models, including the presence of an Axis I disorder as a predictor atlevel 2 provided an improvement in model fit beyond the fit achieved withonly the main effects of group, sex, and entry age (P < .05).However, the presence of an Axis I disorder was related significantly onlyto the elevation parameter values (for 9 PD dimensions) and not to any ofthe slope parameters (ie, change).

Another potential level 2 predictor of interest was the presence ofsome form of psychological/psychiatric treatment in subjects before or duringthe study period. In this sample, 24.8% of the subjects reported receivingtreatment at some point during their lives, before or during the study. Whenincluded as a level 2 predictor, treatment exposure provided an improvementin model fit in 10 of 15 cases (all P < .05).The impact of treatment, however, was limited entirely to elevation (intercept)values in 7 of those 10 models (P ≤ .05)(total PD, cluster A and B totals, and paranoid, schizotypal, borderline,and narcissistic PDs) and had no statistically significant relation to rateof change (ie, slope) for any of the 15 PD dimensions.

The current diagnostic nomenclature (DSM-IV)clearly asserts that PDs are “enduring patterns” of behavior thatare “inflexible,” “stable” over time, and “oflong duration.”1 However, empirical datasupporting such assertions are virtually nonexistent. The LSPD4,17 wasbegun 14 years ago, in part to investigate this core DSM assumption. In a previous LSPD report,15 itwas concluded that “PD features display relatively high levels of individualdifference stability and appreciable mean level stability, with some changeoccurring over time,” although “the changes were relatively small.”However, that analysis had limited resolving power for issues of stabilityand change, as it was inadequately sensitive to the heterogeneity of individualgrowth trajectories and the unique spacing of assessments for each subjectin the LSPD. The present study, which used the more powerful IGC analysisapproach, was able to characterize the nature and amount of change observedin PD dimensions in the LSPD subjects more precisely. Specifically, althougha previous MANOVA-based analysis15 hinted atsome change in PD feature levels over time (with small effect sizes), it didnot speak to the rate of change as reflected in the true growth (change) ofeach subject apart from their initial level of PD features. Our IGC analysisclarified that indeed there was considerable change noted in PD features overtime. Group membership (PPD vs NPD) emerged as an important factor that predictednot only the elevation of the individual growth trajectories (at study entry)as a medium-sized effect, but also showed a comparable medium-sized effectin predicting the rates of individual change in PD features over time. Clearevidence of statistically significant individual change was observed for nearlyall PD dimensions studied, and this change was typically and uniformly inthe direction of decreasing PD features over time. In sum, PD features donot appear to be as inflexible and enduring as that suggested by the DSM criteria.13

What could account for this pattern of change in PD features over time?Variance components estimated in our level 2 analyses showed that additionalvariability in the PD rate-of-change parameters remains that could be predictedby factors beyond our hypothesized predictors of group, sex, and entry age.The presence of an Axis I disorder in a study subject or receipt of treatmentbefore or during the study period had little relationship with rates of changefor the PDs. Fortunately, the IGC modeling approach will allow for the inclusionof additional predictors (eg, personality and temperament) in future effortsto dissect individual change in PD features over time,10,11,28 andsuch future work will be model guided.29

Several caveats should be considered with these data. The present sampleis more homogenous in age, educational achievement, and social class thanthe US population at large, and it consists of young adults. All of thesefeatures may differentially affect the study results. We note adjustment touniversity life across the college years (particularly the freshman-year transition)may have played a role in the changes we observed.15 Also,LSPD subjects were selected from a population of first-year university studentsthat may have been censored for some of the most severely PD-affected individuals.However, 16% of the LSPD sample received a diagnosis of an Axis II conditionby the end of the study period using the highly conservative IPDE and a ratethat accords well with community studies,5,30 and45.2% had a lifetime (or current) Axis I disorder by the end of college. Unfortunately,no other multiwave longitudinal study of PDs includes proper methodologicalsafeguards (eg, blinded assessments, no treatment/time confounds) with whichto compare these results (compare Shea et al31(p2037)) Additional longitudinal studies of PDs using clinical samplesare welcome.

William James32 claimed: “by theage 30, the character has set like plaster, and will never soften again,”which appears true for some aspects of normal personality, but not all.3336 However,the DSM criteria notwithstanding, clinical experiencesuggests that some PD features may diminish over time or be generally lessstable, and our results support this impression. Continued life-span studyof the LSPD subjects will allow us to specify the long-term change or stabilityfor personality pathology. With planned waves 4 and 5 data collections, morecomplex functions (eg, quadratic and cubic) for representing nonlinear individualchange will become estimable.10 We expect thatadditional waves of data will shed further light on this fascinating issueand should provide a more refined appreciation of the natural history of personalitydisorders.

Correspondence: Mark F. Lenzenweger, PhD,Department of Psychology, State University of New York at Binghamton, ScienceIV, Binghamton, NY 13902-6000 (mlenzen@binghamton.edu).

Submitted for Publication: September 17, 2003;final revision received February 25, 2004; accepted March 16, 2004.

Funding/Support: This study was supported inpart by grant MH-45448 from the National Institute of Mental Health, Washington,DC (Dr Lenzenweger).

Acknowledgment: We thank Armand W. Loranger,PhD, for providing training and consultation on the use of the InternationalPersonality Disorder Examination and Lauren Korfine, PhD, for project coordinationin the early phase of the study.

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Rogosa  DRWillett  JB Understanding correlates of change by modeling individual differencesin growth. Psychometrika 1985;50203- 228
Link to Article
Kessler  RCGreenberg  DF Linear Panel Analysis: Models of Quantitative Change.  Orlando, Fla Academic Press Inc1981;
Loranger  AWLenzenweger  MFGartner  ASusman  VHerzig  JZammit  GKGartner  JDAbrams  RCYoung  RC Trait-state artifacts and the diagnosis of personality disorders. Arch Gen Psychiatry 1991;48720- 728
PubMed Link to Article
Nunnally  JBernstein  IH Psychometric Theory. 3rd New York, NY McGraw-Hill Co1994;
Lenzenweger  MF Stability and change in personality disorder features: the LongitudinalStudy of Personality Disorders. Arch Gen Psychiatry 1999;561009- 1015
PubMed Link to Article
Bryk  ASRaudenbush  SW Application of hierarchical linear models to assessing change. Psychol Bull 1987;101147- 158
Link to Article
Raudenbush  SWBryk  AS Hierarchical Linear Models: Applications and DataAnalysis Methods. 2nd Thousand Oaks, Calif Sage Publications2002;
Goldstein  H MultiLevel Statistical Models. 2nd New York, NY John Wiley & Sons Inc1995;
Dempster  APRubin  DBTsutakawa  RK Estimation in covariance components models. J Am Stat Assoc 1981;76341- 353
Link to Article
Longford  N Random coefficient models.  Oxford, England Clarendon1993;
Estes  WK The problem of inference from curves based on grouped data. Psychol Rev 1956;53134- 140
Loranger  AW International Personality Disorder Examination:DSM-IV and ICD-10 Interviews.  Odessa, Fla Psychological Assessment Resources Inc1999;
Loranger  AWSartorius  NAndreoli  ABerger  PBuchheim  PChannabasavanna  SMCoid  BDahl  ADiekstra  RFWFerguson  BJacobsberg  LBMombour  WPull  COno  YRegier  D The International Personality Disorder Examination (IPDE): the WorldHealth Organization/Alcohol, Drug Abuse, and Mental Health AdministrationInternational Pilot Study of Personality Disorders. Arch Gen Psychiatry 1994;51215- 224
PubMed Link to Article
Loranger  AWedSartorius  NedJanca  Aed Assessment and Diagnosis of Personality Disorders:The International Personality Disorder Examination (IPDE).  New York, NY Cambridge University Press1996;
Spitzer  RLWilliams  JBWGibbon  MFirst  M Users Guide for the Structured Clinical Interviewfor DSM-III-R.  Washington, DC American Psychiatric Press1990;
Raudenbush  SWBryk  ASCheong  YFCongdon  R HLM-5 Hierarchical Linear and Nonlinear Modeling.  Lincolnwood, Ill Scientific Software International2000;
Rosenthal  RRosnow  RL Essentials of Behavioral Research: Methods and DataAnalysis. 2nd New York, NY McGraw-Hill Co1991;
Raudenbush  SW Comparing personal trajectories and drawing causal inferences fromlongitudinal data. Annu Rev Psychol 2001;52501- 525
PubMed Link to Article
Depue  RALenzenweger  MF A neurobehavioral dimensional model of personality disorders. Livesley  WJed.The Handbook of PersonalityDisorders New York, NY Guilford Publications2001;
Samuels  JEEaton  WWBienvenu  OJBrown  CCosta  PTNestadt  G Prevalence and correlates of personality disorders in a community sample. Br J Psychiatry 2002;180536- 542
PubMed Link to Article
Shea  MStout  RGunderson  JMorey  LGrilo  CMcGlashan  TSkodol  ADolan-Sewell  RDyck  IZanarini  MKeller  M Short-term diagnostic stability of schizotypal, borderline, avoidant,and obsessive-compulsive personality disorders. Am J Psychiatry 2002;1592036- 2041
PubMed Link to Article
James  W Principles of Psychology. 1 New York, NY Dover Publications Inc1950;
McCrae  RRCosta  PT Personality in Adulthood: A Five-Factor Theory Perspective. 2nd New York, NY Guilford Publications2003;
Roberts  BWDelVecchio  WF The rank order consistency of personality traits from childhood toold age: a quantitative review of longitudinal studies. Psychol Bull 2000;1263- 25
PubMed Link to Article
Caspi  ARoberts  BW Personality continuity and change across the life course. Pervin  LJohn  OPeds.Handbook of Personality:Theory and Research 2nd New York, NY Guilford Publications1999;
Srivastava  SJohn  OPGosling  SDPotter  J Development of personality in early and middle adulthood: set likeplaster or persistent change? J Pers Soc Psychol 2003;841041- 1053
PubMed Link to Article

Figures

Place holder to copy figure label and caption
Figure 1.

Ordinary least squares individualgrowth trajectories for total personality disorder (PD) features in 250 subjectsduring the study period. Time is reported in years since the beginning ofthe study and centered for each subject using the subject’s age at entryinto the study.

Graphic Jump Location
Place holder to copy figure label and caption
Figure 2.

Empirical Bayes trajectories recoveredfrom the hierarchical linear modeling analyses for total personality disorder(PD) features in 250 subjects, by study group and subject sex. Time is reportedin years since the beginning of the study and centered for each subject usingthe subject’s age at entry into the study. NPD indicates no PD; PPD,possible PD.

Graphic Jump Location

Tables

Table Graphic Jump LocationTable 1. Demographic Characteristics of the Longitudinal Study of PersonalityDisorders Sample (N = 250) for Subjects Available at 3 Waves ofthe Study
Table Graphic Jump LocationTable 2. Definition and Interpretation of Parameters in the MultilevelModel for Growth (Change)
Table Graphic Jump LocationTable 3. Definite and Probable Lifetime DSM-III-R Axis I SCID-NP Diagnosesfor Sample of 250 Subjects
Table Graphic Jump LocationTable 4. Parameters of the Unconditional Growth (Baseline) Model ofPD Feature Change From a Prospective Multiwave Longitudinal Perspective: Interceptand Slope (N = 250)* (Because Table 4 is paired with Table 5 and Table 6 is paired with Table 7, we suggest that readers print the PDF and view these pairs on facing pages: Table 4 across from Table 5 and Table 6 across from Table 7.)
Table Graphic Jump LocationTable 5. Parameters of the Unconditional Growth (Baseline) Model ofPD Feature Change From a Prospective Multiwave Longitudinal Perspective: VarianceComponents and Deviance Statistics (N = 250)*
Table Graphic Jump LocationTable 6. Predicting Interindividual Differences in Change of PD byGroup, Sex, and Age at Entry Into Study: Intercept and Slope(N = 250)* (Because Table 4 is paired with Table 5 and Table 6 is paired with Table 7, we suggest that readers print the PDF and view these pairs on facing pages: Table 4 across from Table 5 and Table 6 across from Table 7.)
Table Graphic Jump LocationTable 7. Predicting Interindividual Differences in Change of PD byGroup, Sex, and Age at Entry Into Study: Variance Components and DevianceStatistics (N = 250)*

References

American Psychiatric Association,Committee on Nomenclature and Statistics, Diagnostic and Statistical Manual of Mental Disorders,Fourth Edition.  Washington, DC American Psychiatric Association1994;
American Psychiatric Association,Committee on Nomenclature and Statistics, Diagnostic and Statistical Manual of Mental Disorders,Third Edition.  Washington, DC American Psychiatric Association1980;
American Psychiatric Association,Committee on Nomenclature and Statistics, Diagnostic and Statistical Manual of Mental Disorders,Third Edition, Revised.  Washington, DC American Psychiatric Association1987;
Lenzenweger  MFLoranger  AWKorfine  LNeff  C Detecting personality disorders in a nonclinical population: applicationof a 2-stage procedure for case identification. Arch Gen Psychiatry 1997;54345- 351
PubMed Link to Article
Torgersen  SKringlen  ECramer  V The prevalence of personality disorders in a community sample. Arch Gen Psychiatry 2001;58590- 596
PubMed Link to Article
McDavid  JDPilkonis  PA The stability of personality disorder diagnoses. J Personal Disord 1996;101- 15
Link to Article
Rogosa  DBrandt  DZimowski  M A growth curve approach to the measurement of change. Psychol Bull 1982;92726- 748
Link to Article
Willett  JB Questions and answers in the measurement of change. Rothkopf  Eed.Review of Research in Education(1988-1989) Washington, DC American Educational Research Association1988;
Nesselroade  JRedBaltes  PBed Longitudinal Research in the Study of Behavior andDevelopment.  Orlando, Fla Academic Press Inc1979;
Singer  JDWillett  JB Applied Longitudinal Data Analysis: Modeling Changeand Event Occurrence.  New York, NY Oxford University Press Inc2003;
Rogosa  DRWillett  JB Understanding correlates of change by modeling individual differencesin growth. Psychometrika 1985;50203- 228
Link to Article
Kessler  RCGreenberg  DF Linear Panel Analysis: Models of Quantitative Change.  Orlando, Fla Academic Press Inc1981;
Loranger  AWLenzenweger  MFGartner  ASusman  VHerzig  JZammit  GKGartner  JDAbrams  RCYoung  RC Trait-state artifacts and the diagnosis of personality disorders. Arch Gen Psychiatry 1991;48720- 728
PubMed Link to Article
Nunnally  JBernstein  IH Psychometric Theory. 3rd New York, NY McGraw-Hill Co1994;
Lenzenweger  MF Stability and change in personality disorder features: the LongitudinalStudy of Personality Disorders. Arch Gen Psychiatry 1999;561009- 1015
PubMed Link to Article
Bryk  ASRaudenbush  SW Application of hierarchical linear models to assessing change. Psychol Bull 1987;101147- 158
Link to Article
Raudenbush  SWBryk  AS Hierarchical Linear Models: Applications and DataAnalysis Methods. 2nd Thousand Oaks, Calif Sage Publications2002;
Goldstein  H MultiLevel Statistical Models. 2nd New York, NY John Wiley & Sons Inc1995;
Dempster  APRubin  DBTsutakawa  RK Estimation in covariance components models. J Am Stat Assoc 1981;76341- 353
Link to Article
Longford  N Random coefficient models.  Oxford, England Clarendon1993;
Estes  WK The problem of inference from curves based on grouped data. Psychol Rev 1956;53134- 140
Loranger  AW International Personality Disorder Examination:DSM-IV and ICD-10 Interviews.  Odessa, Fla Psychological Assessment Resources Inc1999;
Loranger  AWSartorius  NAndreoli  ABerger  PBuchheim  PChannabasavanna  SMCoid  BDahl  ADiekstra  RFWFerguson  BJacobsberg  LBMombour  WPull  COno  YRegier  D The International Personality Disorder Examination (IPDE): the WorldHealth Organization/Alcohol, Drug Abuse, and Mental Health AdministrationInternational Pilot Study of Personality Disorders. Arch Gen Psychiatry 1994;51215- 224
PubMed Link to Article
Loranger  AWedSartorius  NedJanca  Aed Assessment and Diagnosis of Personality Disorders:The International Personality Disorder Examination (IPDE).  New York, NY Cambridge University Press1996;
Spitzer  RLWilliams  JBWGibbon  MFirst  M Users Guide for the Structured Clinical Interviewfor DSM-III-R.  Washington, DC American Psychiatric Press1990;
Raudenbush  SWBryk  ASCheong  YFCongdon  R HLM-5 Hierarchical Linear and Nonlinear Modeling.  Lincolnwood, Ill Scientific Software International2000;
Rosenthal  RRosnow  RL Essentials of Behavioral Research: Methods and DataAnalysis. 2nd New York, NY McGraw-Hill Co1991;
Raudenbush  SW Comparing personal trajectories and drawing causal inferences fromlongitudinal data. Annu Rev Psychol 2001;52501- 525
PubMed Link to Article
Depue  RALenzenweger  MF A neurobehavioral dimensional model of personality disorders. Livesley  WJed.The Handbook of PersonalityDisorders New York, NY Guilford Publications2001;
Samuels  JEEaton  WWBienvenu  OJBrown  CCosta  PTNestadt  G Prevalence and correlates of personality disorders in a community sample. Br J Psychiatry 2002;180536- 542
PubMed Link to Article
Shea  MStout  RGunderson  JMorey  LGrilo  CMcGlashan  TSkodol  ADolan-Sewell  RDyck  IZanarini  MKeller  M Short-term diagnostic stability of schizotypal, borderline, avoidant,and obsessive-compulsive personality disorders. Am J Psychiatry 2002;1592036- 2041
PubMed Link to Article
James  W Principles of Psychology. 1 New York, NY Dover Publications Inc1950;
McCrae  RRCosta  PT Personality in Adulthood: A Five-Factor Theory Perspective. 2nd New York, NY Guilford Publications2003;
Roberts  BWDelVecchio  WF The rank order consistency of personality traits from childhood toold age: a quantitative review of longitudinal studies. Psychol Bull 2000;1263- 25
PubMed Link to Article
Caspi  ARoberts  BW Personality continuity and change across the life course. Pervin  LJohn  OPeds.Handbook of Personality:Theory and Research 2nd New York, NY Guilford Publications1999;
Srivastava  SJohn  OPGosling  SDPotter  J Development of personality in early and middle adulthood: set likeplaster or persistent change? J Pers Soc Psychol 2003;841041- 1053
PubMed Link to Article

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