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ABSTRACT: Background: Depressed patients with comorbid personality pathology may fare worse in treatment for depression than those without this additional pathology, and comorbid personality pathology may be associated with superior response in one form of treatment relative to another, though recent findings have been mixed. We aimed to evaluate the effect of personality pathology on time to remission of patients randomly assigned to 1 of 2 treatment strategies for depression and to determine whether personality pathology moderated the effect of treatment assignment on outcome. Method: Individuals undergoing an episode of unipolar major depression (n = 275) received interpersonal psychotherapy (Klerman, Weissman, Rounsaville, & Chevron, 1984) or selective serotonin reuptake inhibitor (SSRI) pharmacotherapy for depression. Depressive symptoms were measured with the HRSD-17. Remission was a mean HRSD-17 score of 7 or below over a period of 3 weeks. Personality disorders were measured according to SCID-II diagnoses, and personality pathology was measured dimensionally by summing the positive probes on the SCID-II. Results: The presence of at least 1 personality disorder was not a significant predictor of time to remission, but a higher level of dimensionally measured personality pathology and the presence of borderline personality disorder were associated with a longer time to remission. Personality pathology did not moderate the effect of treatment assignment on time to remission. Conclusions: The findings suggest that depressed individuals with comorbid personality pathology generally fare worse in treatment for depression, although in this report, the effect of personality pathology did not differ by the type of treatment received. (PsycINFO Database Record (c) 2012 APA, all rights reserved).
Journal of Consulting and Clinical Psychology 07/2012; 80(5):719-29. · 4.85 Impact Factor
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ABSTRACT: Tree-structured survival methods empirically identify a series of covariate-based binary split points, resulting in an algorithm that can be used to classify new patients into risk groups and subsequently guide clinical treatment decisions. Traditionally, only fixed-time (e.g. baseline) values are used in tree-structured models. However, this manuscript considers the scenario where temporal features of a repeated measures polynomial model, such as the slope and/or curvature, are useful for distinguishing risk groups to predict future outcomes. Both fixed- and random-effects methods for estimating individual temporal features are discussed, and methods for including these features in a tree model and classifying new cases are proposed. A simulation study is performed to empirically compare the predictive accuracies of the proposed methods in a wide variety of model settings. For illustration, a tree-structured survival model incorporating the linear rate of change of depressive symptomatology during the first four weeks of treatment for late-life depression is used to identify subgroups of older adults who may benefit from an early change in treatment strategy.
Biometrical Journal 03/2012; 54(2):181-96. · 1.25 Impact Factor
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ABSTRACT: Identifying the predictors of late-life depression that are amenable to change may lead to interventions that result in better and faster remission. Thus, the authors investigated the impact of two different strategies for coping with physical illness on depression in older, primary care patients. Health-oriented goal engagement strategies involve the investment of cognitive and behavioral resources to achieve health goals. Conversely, disengagement strategies involve the withdrawal of these resources from obsolete or unattainable health goals, combined with goal restructuring.
The participants were 271 adults aged >59 years who took part in a two-year randomized clinical trial for treating depression in older adults (Prevention of Suicide in Primary Care Elderly: Collaborative Trial). The use of engagement and disengagement strategies, along with other risk factors for depression, were included in a tree-structured survival analysis to identify subgroups of individuals at risk for not achieving depression remission.
The use of disengagement strategies predicted earlier remission of depression, particularly among more severely depressed older patients. The use of engagement strategies did not predict earlier remission.
Interventions that encourage disengagement from unattainable health goals may promote remission from depression in older, primary care patients.
International Journal of Geriatric Psychiatry 03/2011; 27(2):178-86. · 2.42 Impact Factor
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ABSTRACT: The decision to commit suicide may be impulsive, but lethal suicidal acts often involve planning and forethought. People who attempt suicide make disadvantageous decisions in other contexts, but nothing is known about the way they decide about the future. Can the willingness to postpone future gratification differentiate between individuals prone to serious, premeditated and less serious, unplanned suicidal acts?
Four groups of depressed participants aged 60 and older made choices between smaller immediate and larger delayed monetary rewards: 15 who had made high-lethality suicide attempts, 14 who had made low-lethality suicide attempts, 12 who seriously contemplated suicide, and 42 people with depression, but no history of suicidal thoughts. The reference group was 31 psychiatrically healthy elders.
Individuals who had made low-lethality attempts displayed an exaggerated preference for immediate rewards compared with nonsuicidal depressed and healthy control subjects. Those who had carried out high-lethality suicide attempts were more willing to delay future rewards, compared with low-lethality attempters. Better planned suicide attempts were also associated with willingness to wait for larger rewards. These effects were unchanged after accounting for education, global cognitive function, substance use disorders, psychotropic medications, and possible brain injury from attempts. Discount rates were correlated with having debt, but were not significantly associated with income, hopelessness, depressive severity, premorbid IQ, age at first attempt, or choice of violent means.
Although clinicians often focus on impulsivity in patients at risk for suicide, these data suggest that identifying biological characteristics and treatments for nonimpulsive suicidal older people may be even more important.
Biological psychiatry 02/2011; 70(2):138-44. · 8.93 Impact Factor
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ABSTRACT: Missing covariate data present a challenge to tree-structured methodology due to the fact that a single tree model, as opposed to an estimated parameter value, may be desired for use in a clinical setting. To address this problem, we suggest a multiple imputation algorithm that adds draws of stochastic error to a tree-based single imputation method presented by Conversano and Siciliano (Technical Report, University of Naples, 2003). Unlike previously proposed techniques for accommodating missing covariate data in tree-structured analyses, our methodology allows the modeling of complex and nonlinear covariate structures while still resulting in a single tree model. We perform a simulation study to evaluate our stochastic multiple imputation algorithm when covariate data are missing at random and compare it to other currently used methods. Our algorithm is advantageous for identifying the true underlying covariate structure when complex data and larger percentages of missing covariate observations are present. It is competitive with other current methods with respect to prediction accuracy. To illustrate our algorithm, we create a tree-structured survival model for predicting time to treatment response in older, depressed adults.
Statistics in Medicine 10/2010; 29(29):3004-16. · 1.88 Impact Factor
