Search

We examine differential validity of the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) diagnoses assessed by the fully-structured Composite International Diagnostic Interview Version 3.0 (CIDI) among Latino, non-Latino Black, and non-Latino White adolescents in comparison to gold standard diagnoses derived from the Schedule for Affective Disorders and Schizophrenia for School-age Children (K-SADS). Results are based on the National Comorbidity Survey Replication Adolescent Supplement, a national US survey of adolescent mental health. Clinicians re-interviewed 347 adolescent/parent dyads with the K-SADS. Sensitivity and/or specificity of CIDI diagnoses varied significantly by ethnicity/race for four of ten disorders. Modifications to algorithms sometimes reduced bias in prevalence estimates, but at the cost of reducing individual-level concordance. These findings document the importance of assessing fully-structured diagnostic instruments for differential accuracy in ethnic/racial subgroups. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Objectives. We examined potential pathways by which time in the United States may relate to differences in the predicted probability of past-year psychiatric disorder among Latino immigrants as compared with US-born Latinos. Methods. We estimated predicted probabilities of psychiatric disorder for US-born and immigrant groups with varying time in the United States, adjusting for different combinations of covariates. We examined 6 pathways by which time in the United States could be associated with psychiatric disorders. Results. Increased time in the United States is associated with higher risk of psychiatric disorders among Latino immigrants. After adjustment for covariates, differences in psychiatric disorder rates between US-born and immigrant Latinos disappear. Discrimination and family cultural conflict appear to play a significant role in the association between time in the United States and the likelihood of developing psychiatric disorders. Conclusions. Increased perceived discrimination and family cultural conflict are pathways by which acculturation might relate to deterioration of mental health for immigrants. Future studies assessing how these implicit pathways evolve as contact with US culture increases may help to identify strategies for ensuring maintenance of mental health for Latino immigrants. [ABSTRACT FROM AUTHOR]

We consider a Markov structure for partially unobserved time-varying compliance classes in the Imbens–Rubin (1997, The Annals of Statistics 25, 305–327) compliance model framework. The context is a longitudinal randomized intervention study where subjects are randomized once at baseline, outcomes and patient adherence are measured at multiple follow-ups, and patient adherence to their randomized treatment could vary over time. We propose a nested latent compliance class model where we use time-invariant subject-specific compliance principal strata to summarize longitudinal trends of subject-specific time-varying compliance patterns. The principal strata are formed using Markov models that relate current compliance behavior to compliance history. Treatment effects are estimated as intent-to-treat effects within the compliance principal strata. [ABSTRACT FROM AUTHOR]

In assessing the mechanism of treatment efficacy in randomized clinical trials, investigators often perform mediation analyses by analyzing if the significant intent-to-treat treatment effect on outcome occurs through or around a third intermediate or mediating variable: indirect and direct effects, respectively. Standard mediation analyses assume sequential ignorability, i.e. conditional on covariates the intermediate or mediating factor is randomly assigned, as is the treatment in a randomized clinical trial. This research focuses on the application of the principal stratification (PS) approach for estimating the direct effect of a randomized treatment but without the standard sequential ignorability assumption. This approach is used to estimate the direct effect of treatment as a difference between expectations of potential outcomes within latent subgroups of participants for whom the intermediate variable behavior would be constant, regardless of the randomized treatment assignment. Using a Bayesian estimation procedure, we also assess the sensitivity of results based on the PS approach to heterogeneity of the variances among these principal strata. We assess this approach with simulations and apply it to two psychiatric examples. Both examples and the simulations indicated robustness of our findings to the homogeneous variance assumption. However, simulations showed that the magnitude of treatment effects derived under the PS approach were sensitive to model mis-specification. Copyright © 2009 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Motivated by the need to meaningfully implement the Institute of Medicine's (IOM's) definition of health care disparity, this paper proposes statistical frameworks that lay out explicitly the needed causal assumptions for defining disparity measures. Our key emphasis is that a scientifically defensible disparity measure must take into account the direction of the causal relationship between allowable covariates that are not considered to be contributors to disparity and non-allowable covariates that are considered to be contributors to disparity, to avoid flawed disparity measures based on implausible populations that are not relevant for clinical or policy decisions. However, these causal relationships are usually unknown and undetectable from observed data. Consequently, we must make strong causal assumptions in order to proceed. Two frameworks are proposed in this paper, one is the conditional disparity framework under the assumption that allowable covariates impact non-allowable covariates but not vice versa. The other is the marginal disparity framework under the assumption that non-allowable covariates impact allowable ones but not vice versa. We establish theoretical conditions under which the two disparity measures are the same and present a theoretical example showing that the difference between the two disparity measures can be arbitrarily large. Using data from the Collaborative Psychiatric Epidemiology Survey, we also provide an example where the conditional disparity is misled by Simpson's paradox, whereas the marginal disparity approach handles it correctly. Copyright © 2008 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This article presents analyses of a representative sample of U.S. Latinos (N = 2,540) to investigate whether family cohesion moderates the effects of cultural conflict on psychological distress. The results for the aggregated Latino group suggest a significant association between family cohesion and lower psychological distress, and the combination of strong family cohesion with presence of family cultural conflict is associated with higher psychological distress. However, this association differs by Latino groups. In this study, no association for Puerto Ricans is seen; Cuban results are similar to the aggregate group, family cultural conflict in Mexicans is associated with higher psychological distress whereas family cohesion in other Latinos is associated with higher psychological distress. Implications of these findings are discussed to unravel the differences in family dynamics across Latino subethnic groups. [ABSTRACT FROM AUTHOR]

This article discusses a nested latent class model for analyzing longitudinal randomized trials when subjects do not always adhere to the treatment to which they are randomized. In the Prevention of Suicide in Primary Care Elderly: Collaborative Trial, subjects were randomized to either the control treatment, where they received standard care, or to the intervention, where they received standard care in addition to meeting with depression health specialists. The health specialists educate patients, their families, and physicians about depression and monitor their treatment. Those randomized to the control treatment have no access to the health specialists; however, those randomized to the intervention could choose not to meet with the health specialists, hence receiving only the standard care. Subjects participated in the study for two years where depression severity and adherence to meeting with health specialists were measured at each follow-up. The outcome of interest is the effect of meeting with the health specialists on depression severity. Traditional intention-to-treat and as-treated analyses may produce biased causal effect estimates in the presence of subject noncompliance. Utilizing a nested latent class model that uses subject-specific and time-invariant "superclasses" allows us to summarize longitudinal trends of compliance patterns and to estimate the effect of the intervention using intention-to-treat contrasts within principal strata that correspond to longitudinal compliance behavior patterns. Analyses show that subjects with more severe depression are more likely to adhere to treatment randomization, and those that are compliant and meet with health specialists benefit from the meetings and show improvement in depression. Simulation results show that our estimation procedure produces reasonable parameter estimates under correct model assumptions. [ABSTRACT FROM AUTHOR]

Copyright of Statistics in Medicine is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Few studies have tested the effects of a depression intervention on the risk for death associated with depression.Objective: To test whether an intervention to improve depression care can modify the risk for death.Design: Practice-based, randomized, controlled trial.Setting: 20 primary care practices in New York, New York, and Philadelphia and Pittsburgh, Pennsylvania.Patients: 1226 randomly sampled patients identified through a 2-stage, age-stratified (60 to 74 years and > or =75 years) depression screening.Intervention: Depression care manager working with primary care physicians to provide algorithm-based care.Measurements: Depression status based on clinical interview and vital status at 5 years by using the National Death Index.Results: At baseline, 396 patients met criteria for major depression and 203 patients met criteria for clinically significant minor depression. After a median follow-up of 52.8 months, 223 patients died. Patients with depression in intervention practices were less likely to have died than those in usual care practices (adjusted hazard ratio, 0.67 [95% CI, 0.44 to 1.00]). Risk for death was reduced in patients with major depression (adjusted hazard ratio, 0.55 [CI, 0.36 to 0.84]) but not in patients with clinically significant minor depression (adjusted hazard ratio, 0.97 [CI, 0.49 to 1.92]). The benefit seemed to be almost entirely attributable to a reduction in deaths due to cancer.Limitations: The mechanism for an effect on deaths due to cancer is unclear. Depression status, cause of death, and vital status might have been misclassified.Conclusions: Older primary care patients with major depression in practices that implemented depression care management were less likely to die over a 5-year period than were patients with major depression in usual care practices. The effect seemed to be limited to deaths due to cancer. The mechanism for such an effect is unclear and warrants further investigation. ClinicalTrials.gov registration number: NCT00000367. [ABSTRACT FROM AUTHOR]

Background: Few studies have tested the effects of a depression intervention on the risk for death associated with depression. Objective: To test whether an intervention to improve depression care can modify the risk for death. Design: Practice-based, randomized, controlled trial. Setting: 20 primary care practices in New York, New York, and Philadelphia and Pittsburgh, Pennsylvania. Patients: 1226 randomly sampled patients identified through a 2-stage, age-stratified (60 to 74 years and ≥75 years) depression screening. Intervention: Depression care manager working with primary care physicians to provide algorithm-based care. Measurements: Depression status based on clinical interview and vital status at 5 years by using the National Death Index. Results: At baseline, 396 patients met criteria for major depression and 203 patients met criteria for clinically significant minor depression. After a median follow-up of 52.8 months, 223 patients died. Patients with depression in intervention practices were less likely to have died than those in usual care practices (adjusted hazard ratio, 0.67 [95% CI, 0.44 to 1.00]). Risk for death was reduced in patients with major depression (adjusted hazard ratio, 0.55 [CI, 0.36 to 0.84]) but not in patients with clinically significant minor depression (adjusted hazard ratio, 0.97 [CI, 0.49 to 1.92]). The benefit seemed to be almost entirely attributable to a reduction in deaths due to cancer. Limitations: The mechanism for an effect on deaths due to cancer is unclear. Depression status, cause of death, and vital status might have been misclassified. Conclusions: Older primary care patients with major depression in practices that implemented depression care management were less likely to die over a 5-year period than were patients with major depression in usual care practices. The effect seemed to be limited to deaths due to cancer. The mechanism for such an effect is unclear and warrants further investigation. [ABSTRACT FROM AUTHOR]

Objective: Our purpose was to determine the personal characteristics associated with different patterns of adherence to the antidepressant citalopram in a primary care trial of depression management. Method: The study sample consisted of 228 adults aged 60 years and older recruited from primary care settings and who participated in a depression intervention. The intervention consisted of services of trained care managers, who offered recommendations to physicians following a clinical algorithm and helped patients with treatment adherence. Adherence to the antidepressant citalopram was measured using pill counts. We employed the latent class model to classify patients according to the pattern of adherence to citalopram over time. We examined the association of sociodemographic characteristics, depression status, cognitive status, and medical comorbidity with the resulting classes of adherence. Results: The latent class model generated three classes of adherence: known to be adherent, unknown adherence, and known to be nonadherent. Participants who were white were more likely to be in the known to be adherent class than in the known to be nonadherent class (odds ratio (OR) = 10.38, 95% confidence interval (CI) [3.47, 31.12]). Married participants were less likely to be in the unknown adherence class than the known to be nonadherent class (OR = 0.28, 95% CI [0.09, 0.85]). No associations between age, gender, education level, depression status, cognitive status, or medical comorbidity and classes of adherence were found. Conclusions: We found stronger relationships between ethnicity and marital status with patterns of adherence to citalopram than we did other personal characteristics. Identification of a subgroup of patients at particularly high risk of nonadherence is important for the development of adherence interventions. [ABSTRACT FROM AUTHOR]

A data monitoring committee (DMC) is a group of clinicians and biostatisticians appointed by study sponsors who provide independent assessment of the safety, scientific validity and integrity of clinical trials. In the United States, the Food and Drug Administration requires the formation of DMC in all trials that assess new interventions. DMC are also strongly recommended in other clinical studies that have substantial safety issues, that have double-blind treatment assignment or that are expected to have a major impact on clinical practice. They are important in clinical research in psychiatry because they provide an added layer of protection for the vulnerable populations that are often enrolled in such studies. This report describes the role, formation and operation of DMC. [ABSTRACT FROM AUTHOR]