A semiparametric model for between-subject attributes: Applications to beta-diversity of microbiome data.

The human microbiome plays an important role in our health and identifying factors associated with microbiome composition provides insights into inherent disease mechanisms. By amplifying and sequencing the marker genes in high-throughput sequencing, with highly similar sequences binned together, we obtain operational taxonomic units (OTUs) profiles for each subject. Due to the high-dimensionality and nonnormality features of the OTUs, the measure of diversity is introduced as a summarization at the microbial community level, including the distance-based beta-diversity between individuals. Analyses of such between-subject attributes are not amenable to the predominant within-subject-based statistical paradigm, such as t-tests and linear regression. In this paper, we propose a new approach to model beta-diversity as a response within a regression setting by utilizing the functional response models (FRMs), a class of semiparametric models for between- as well as within-subject attributes. The new approach not only addresses limitations of current methods for beta-diversity with cross-sectional data, but also provides a premise for extending the approach to longitudinal and other clustered data in the future. The proposed approach is illustrated with both real and simulated data.

On assessing model fit for distribution-free longitudinal models under missing data.

The generalized estimating equation (GEE), a distribution-free, or semi-parametric, approach for modeling longitudinal data, is used in a wide range of behavioral, psychotherapy, pharmaceutical drug safety, and healthcare-related research studies. Most popular methods for assessing model fit are based on the likelihood function for parametric models, rendering them inappropriate for distribution-free GEE. One rare exception is a score statistic initially proposed by Tsiatis for logistic regression (1980) and later extended by Barnhart and Willamson to GEE (1998). Because GEE only provides valid inference under the missing completely at random assumption and missing values arising in most longitudinal studies do not follow such a restricted mechanism, this GEE-based score test has very limited applications in practice. We propose extensions of this goodness-of-fit test to address missing data under the missing at random assumption, a more realistic model that applies to most studies in practice. We examine the performance of the proposed tests using simulated data and demonstrate the utilities of such tests with data from a real study on geriatric depression and associated medical comorbidities.

Causal inference for Mann-Whitney-Wilcoxon rank sum and other nonparametric statistics.

The nonparametric Mann-Whitney-Wilcoxon (MWW) rank sum test is widely used to test treatment effect by comparing the outcome distributions between two groups, especially when there are outliers in the data. However, such statistics generally yield invalid conclusions when applied to nonrandomized studies, particularly those in epidemiologic research. Although one may control for selection bias by using available approaches of covariates adjustment such as matching, regression analysis, propensity score matching, and marginal structural models, such analyses yield results that are not only subjective based on how the outliers are handled but also often difficult to interpret. A popular alternative is a conditional permutation test based on randomization inference [Rosenbaum PR. Covariance adjustment in randomized experiments and observational studies. Statistical Science 2002; 17(3):286-327]. Because it requires strong and implausible assumptions that may not be met in most applications, this approach has limited applications in practice. In this paper, we address this gap in the literature by extending MWW and other nonparametric statistics to provide causal inference for nonrandomized study data by integrating the potential outcome paradigm with the functional response models (FRM). FRM is uniquely positioned to model dynamic relationships between subjects, rather than attributes of a single subject as in most regression models, such as the MWW test within our context. The proposed approach is illustrated with data from both real and simulated studies.

Distribution-free models for longitudinal count responses with overdispersion and structural zeros.

Overdispersion and structural zeros are two major manifestations of departure from the Poisson assumption when modeling count responses using Poisson log-linear regression. As noted in a large body of literature, ignoring such departures could yield bias and lead to wrong conclusions. Different approaches have been developed to tackle these two major problems. In this paper, we review available methods for dealing with overdispersion and structural zeros within a longitudinal data setting and propose a distribution-free modeling approach to address the limitations of these methods by utilizing a new class of functional response models. We illustrate our approach with both simulated and real study data.

Characteristics of young rural Chinese suicides: a psychological autopsy study.

BACKGROUND: Patterns of suicide rates in China differ in many ways from those in the West. This study aimed to identify the risk factors characteristic for young rural Chinese suicides. METHOD: This was a case-control psychological autopsy (PA) study. The samples were suicides and living controls (both aged 15-34 years) from 16 rural counties of China. We interviewed two informants for each suicide and each control with pretested and validated instruments to estimate psychosocial, psychiatric and other risk factors for suicides. RESULTS: The prevalence of mental disorders was higher among the young Chinese who died by suicide than among the living controls, but was lower than among suicides in the West. Marriage was not a protecting factor for suicide among young rural Chinese women, and never-married women who were involved in relationships were about three times more likely to commit suicide than single women who were unattached. Religion/religiosity was not a protecting factor in Chinese suicide, as it tended to be stronger for suicides than for controls. Impulsivity was significantly higher for suicides than for controls. Psychological strain, resulting from conflicting social values between communist gender equalitarianism and Confucian gender discrimination, was associated significantly with suicide in young rural Chinese women, even after accounting for the role of psychiatric illness. CONCLUSIONS: Risk factors for suicide in rural China are different from those in the West. Psychological strain plays a role in suicide. Suicide prevention programs in China should incorporate culture-specific considerations.

A U-statistics-based approach for modeling Cronbach coefficient alpha within a longitudinal data setting.

Cronbach coefficient alpha (CCA) is a classic measure of item internal consistency of an instrument and is used in a wide range of behavioral, biomedical, psychosocial, and health-care-related research. Methods are available for making inference about one CCA or multiple CCAs from correlated outcomes. However, none of the existing approaches effectively address missing data. As longitudinal study designs become increasingly popular and complex in modern-day clinical studies, missing data have become a serious issue, and the lack of methods to systematically address this problem has hampered the progress of research in the aforementioned fields. In this paper, we develop a novel approach to tackle the complexities involved in addressing missing data (at the instrument level due to subject dropout) within a longitudinal data setting. The approach is illustrated with both clinical and simulated data.

Comparing Multiple Sensitivities and Specificities with Different Diagnostic Criteria: Applications to Sexual Abuse and Sexual Health Research.

When comparing sensitivities and specificities from multiple diagnostic tests, particularly in biomedical research, the different test kits under study are applied to groups of subjects with the same disease status for a disease or medical condition under consideration. Although this process gives rise to clustered or correlated test outcomes, the associated inference issues are well recognized and have been widely discussed in the literature. In mental health and psychosocial research, sensitivity and specificity have also been widely used to study the reliability of instrument for diagnosing mental health and psychiatric conditions and assessing certain behavioral patterns. However, unlike biomedical applications, outcomes are often obtained under varying reference standards or different diagnostic criteria, precluding the application of existing methods for comparing multiple diagnostic tests to such a research setting. In this paper, we develop a new approach to address these problems (including that of missing data) by extending recent work on inference using inverse probability weighted estimates. The approach is illustrated with data from two studies in sexual abuse and health research as well as a limited simulation study, with the latter used to study the performance of the proposed procedure.

Inducing lifestyle regularity in recovering bipolar disorder patients: results from the maintenance therapies in bipolar disorder protocol.

On the basis of theories we articulated in earlier papers (Ehlers et al 1988: Arch Gen Psychiatry 45:948-952, 1993: Depression 1:285-293), we have developed an adjunctive psychosocial intervention for patients with bipolar 1 disorder. Central to this intervention is the establishment of regularity in daily routines. In this report, we present data from a controlled investigation comparing this new treatment, interpersonal and social rhythm therapy (IPSRT), with a conventional medication clinic approach. Despite comparable changes in symptomatology over a treatment period lasting up to 52 weeks, subjects assigned to IPSRT (n = 18) show significantly greater stability (p = .047) of daily routines with increasing time in treatment, while subjects assigned to the medication clinic condition (n = 20) show essentially no change in their social routines as measured by Social Rhythm Metric (SRM-Monk et al 1990: J Nerv Ment Dis 178(2):120-126) score. We conclude that IPSRT is capable of influencing lifestyle regularity in patients with bipolar 1 disorder, with the possible benefit of protection against future affective episodes.

Pretreatment REM sleep and subjective sleep quality distinguish depressed psychotherapy remitters and nonremitters.

BACKGROUND: We compared pretreatment subjective and electroencephalographic sleep measures among depressed patients who remitted with psychotherapy alone and those who did not remit. METHODS: Patients were 111 midlife women with recurrent major depressive disorder. Baseline psychiatric ratings and sleep studies were conducted prior to treatment with weekly interpersonal psychotherapy. Remission was defined as a score of < or = 7 for 3 consecutive weeks on the Hamilton Depression Rating Scale. Clinical and sleep measures were compared between remitters (n = 62) and nonremitters (n = 49) using t tests and random regression. Linear discriminant function analyses were used to categorize remitters and nonremitters on the basis of sleep measures. RESULTS: Treatment nonremitters had significantly worse subjective sleep quality and significantly elevated phasic REM sleep as measured by multivariate and univariate analyses. The linear accumulation of REM activity during sleep occurred at a significantly higher rate in nonremitters than in remitters. Linear discriminant function analyses based on subjective sleep quality and REM activity correctly identified 68.3% of nonremitters and 68.5% of remitters. CONCLUSIONS: These findings highlight the role of subjective and REM sleep measures as correlates of short-term psychotherapy treatment response in major depressive disorder. Disturbed sleep may be a physiological indicator of increased limbic and brain stem arousal.

Mortality of elderly patients with psychiatric disorders.

OBJECTIVE: The goal of this study was to evaluate the impact of common late-life mental disorders on the life expectancy and causes of death of older psychiatric patients. METHOD: The study population consisted of 809 older psychiatric patients who met DSM-III-R criteria for organic mental disorders, mood disorders, or psychotic disorders and who were discharged after a comprehensive multidisciplinary evaluation and acute inpatient treatment for their behavioral disorders. Dates and causes of death during a 5.75-year follow-up period were provided by the Pennsylvania Department of Health. Univariate and multivariate survival procedures were used to compare the survival rates of the three groups to each other and to a reference population of Pennsylvania residents. Causes of death were also tabulated according to ICD-9-CM and compared across the groups. RESULTS: Age, gender, race, and medical comorbidity made significant independent contributions to survival. When these variables were controlled, the survival of patients with organic mental disorders was less than half of that for patients with mood or psychotic disorders. However, all three groups experienced higher rates of mortality than the reference population, with standardized mortality ratios of 1.5 to 2.5. Deaths occurred from the usual spectrum of natural causes, with the exception that patients with mood disorders were more likely to have died from disorders of the digestive system and suicide. CONCLUSIONS: The mental disorders of late life have a significant negative impact on the survival of older psychiatric patients.

Impact of acute psychiatric inpatient treatment on major depression in late life and prediction of response.

OBJECTIVE: The authors conducted a prospective study to examine the sociodemographic and clinical characteristics of elderly inpatients with major depression and their response to acute psychiatric hospitalization. The relation between the descriptive variables and clinical response was also investigated. METHOD: The subjects were 205 consecutively admitted inpatients, whose mean age was 71 years, who met the DSM-III-R criteria for major depression. Each subject received detailed physical, psychiatric, and mental status examinations, along with quantitative assessments of psychiatric symptoms and cognitive performance at admission and at discharge. Management of physical problems was optimized, and patients were treated with a combination of somatic and psychotherapeutic interventions. The average duration of hospitalization was approximately 1 month. RESULTS: Despite considerable medical and psychiatric comorbidity, the patients responded well to treatment, as reflected by a 50% reduction in the average score on the Hamilton Depression Rating Scale. Nearly one-half of the patients had experienced the resolution of their depressive symptoms by the time of discharge. Race, cognitive performance at admission, number of medical problems, use of ECT, and length of hospitalization independently contributed to the prediction of clinical response. Response to treatment was not related to the other sociodemographic variables examined or to any of the indexes of severity of depressive episode. CONCLUSIONS: Short-term psychiatric hospitalization offers an effective and efficient vehicle for the treatment of severe or complicated cases of major depression in the elderly, even when considerable medical and psychiatric comorbidity is present.

Survival differences and trends in patients with AIDS in the United States.

The AIDS surveillance system maintained by the Centers for Disease Control and Prevention (CDC) provides a unique data base for estimating survival after a diagnosis of AIDS for the general AIDS population in the United States. Because patients enrolled in most AIDS clinical trial studies receive unusual medical care that may not be available to the general public and typically have relatively longer survival time, estimates obtained from these studies may not be of direct use in assessing the national health-care needs. Furthermore, such studies are usually of short duration and may not be very informative for long-term health-policy planning. We present survival estimates obtained from the CDC surveillance data for the adult/adolescent AIDS population in the United States and compare their survival and trend in survival on gender, sexual behavior, and injection-drug use status. These estimates provide information for mortality risk after an AIDS diagnosis over a period of 8 years and for trend of survival during the period between 1983 and 1991.

A comparative meta-analysis on the variability in test performance among FDA-licensed enzyme immunosorbent assays for HIV antibody testing.

BACKGROUND: Although independently published studies have compared diagnostic test performance among various manufactured enzyme immunosorbent assays (EIAs) used in HIV antibody testing, none have attempted to formally synthesize such results through a comparative meta-analysis. In particular, no estimates of post-FDA approval test performance, in terms of sensitivity and specificity, and their associated variability within each manufacturer, has been reported in the literature, along with an analysis of the relative differences in manufacturer test performance in practice (after FDA approval). METHODS: Retrieval of studies was done using several searching strategies, while retrieval of manufacturer information was done through package inserts and direct contacts. Comparisons of HIV antibody test performance across manufacturers and within a single manufacturer were made based on 16 estimates (from 11 articles) and 33 estimates (from 19 articles), respectively. A generalized linear model, based upon Bayes estimates of sensitivity and specificity, was used to assess the impact of several study-level covariates on the performance of these EIAs, with overall estimates of manufacturer test performance and associated variability obtained based on generalized estimating equations. RESULTS: Estimates of test performance were obtained across studies, with a significant (P < 0.01) difference between manufacturers. The test performance of each manufacturer significantly interacted (P < 0.05) with the following study-level covariates: type of population screened, year of diagnostic testing and study quality. Among a single manufacturer, Abbott, significant improvement in estimates of test sensitivity (P < 0.01) and specificity (P < 0.01) was observed with each newly produced antibody kit. CONCLUSION: Estimates on the relative differences in test performance within each manufacturer may be used for guiding decisions on the choice of EIA test kit for HIV antibodies, given the type of population screened, as well as cost and time considerations. In addition, the results of this meta-analysis may be used in modeling HIV prevalence when used as prior information within a Bayesian framework or for standardizing test results among various manufacturers.

Stressful life events, bipolar disorder, and the "kindling model".

A common misconception is that bipolar disorder is an endogenous process. However, previous research suggests a role for life events in the onset of and recovery from bipolar episodes. Yet, there remains some question as to whether the relationship between life events and onset changes over the course of the disorder as a result of the number of episodes an individual has experienced. Using a rigorous interview measure of stressful life events, the current study tested the kindling model (R. M. Post, 1992), which theorizes that major life events play a diminishing role over the course of illness in bipolar patients. Analyses revealed that the number of episodes experienced does not appear to have a significant effect on bipolar 1 patients' reactivity to external stressors. In addition, the results suggest that a more complex relationship exists among age, stress, and onset of new episodes than can be adequately explained by the kindling model.

A prospective test of criteria for response, remission, relapse, recovery, and recurrence in depressed patients treated with cognitive behavior therapy.

The definitions that are commonly employed to describe the outcome of the depressive disorders are often used in inconsistent ways and remain largely untested. The lack of a standard and valid set of outcome definitions hinders the study of the naturalistic course and treatment of depressive disorders. In the present study, we operationalized definitions for response, remission, relapse, recovery, and recurrence and examined their validity in a sample of depressed patients treated with cognitive behavior therapy. Validity was evaluated by the ability of the definitions to predict subsequent outcome in acute treatment and during a 3 year follow-up period. All five definitions demonstrated moderate to excellent validity. Moreover, we were able to empirically distinguish response from remission, and relapse from recurrence, despite the frequent confusion of these terms in the literature. Several of the findings suggest that continued refinement of the outcome definitions may enhance validity even further.

Latent structure of EEG sleep variables in depressed and control subjects: descriptions and clinical correlates.

In this study, we aimed to determine the latent structure of multiple EEG sleep variables in patients with major depressive disorder (MDD) and in healthy control subjects and to examine associations between sleep factors and clinical variables. Subjects included 109 women with MDD and 54 healthy control women. EEG sleep data were collected prior to any treatment. Principal components analysis (PCA) was conducted on a set of 24 sleep variables. Separate PCAs were run for patients with MDD, control subjects, and a matched group of patients and controls. Other analyses included correlations, t-tests and MANOVA. Each PCA identified four sleep factors that explained 70% of the total variance in individual sleep variables: slow wave sleep, REM sleep, sleep continuity and REM latency/delta sleep ratio (RL/DSR). Patients with MDD and healthy controls differed on the mean value of the sleep continuity factor, and a multivariate analysis of variance based on the PCA identified MDD-control differences in REM sleep and sleep continuity. In the MDD group, slow wave sleep correlated inversely with age and personality disorder symptoms; sleep continuity correlated with subjective sleep quality and anxiety; and RL/DSR correlated inversely with age. The mean value of the REM factor was higher among treatment non-responders than responders. EEG sleep variables have a similar latent structure in women with MDD and in healthy controls. These sleep factors are supported conceptually and empirically, and correlate with clinical measures in women with MDD. Multivariate statistical techniques decrease the risk of Type I and Type II errors when using a large number of collinear sleep measures, and can clarify conceptual issues related to sleep and depression.

A class of distribution-free models for longitudinal mediation analysis.

Mediation analysis constitutes an important part of treatment study to identify the mechanisms by which an intervention achieves its effect. Structural equation model (SEM) is a popular framework for modeling such causal relationship. However, current methods impose various restrictions on the study designs and data distributions, limiting the utility of the information they provide in real study applications. In particular, in longitudinal studies missing data is commonly addressed under the assumption of missing at random (MAR), where current methods are unable to handle such missing data if parametric assumptions are violated.In this paper, we propose a new, robust approach to address the limitations of current SEM within the context of longitudinal mediation analysis by utilizing a class of functional response models (FRM). Being distribution-free, the FRM-based approach does not impose any parametric assumption on data distributions. In addition, by extending the inverse probability weighted (IPW) estimates to the current context, the FRM-based SEM provides valid inference for longitudinal mediation analysis under the two most popular missing data mechanisms; missing completely at random (MCAR) and missing at random (MAR). We illustrate the approach with both real and simulated data.

Religious involvement and depressive symptoms in primary care elders.

BACKGROUND: Multiple lines of evidence indicate relationships between religious involvement and depression, although the specific nature of the relationships is yet to be clarified. Moreover, there appear to be no well controlled longitudinal studies to date examining this issue in primary care elders. METHOD: The authors assessed the linear and non-linear relationships between three commonly identified types of religious involvement and observer-rated depressive symptoms in 709 primary care elders assessed at baseline and 1-year follow-up. RESULTS: Cross-sectional analyses revealed a curvilinear, U-shaped association between depressive symptoms and organizational religious activity, an inverse linear relationship of depressive symptoms with private religious involvement, and a positive relationship of depressive symptoms with intrinsic religiosity. Longitudinal analyses revealed a U-shaped association between depressive symptoms and private religious involvement, such that those reporting moderate levels of private religiosity at baseline evidenced lower levels of depressive symptoms at 1-year follow-up than those reporting either high or low levels of private religious activity. CONCLUSIONS: The relationships between religious involvement and depression in primary care elders are complex and dependent on the type of religiosity measured. The authors found the strongest evidence for an association of non-organizational, private religious involvement and the severity of depressive symptoms, although further study is warranted using carefully controlled longitudinal designs that test for both linear and curvilinear relationships.

Correlation analysis for longitudinal data: applications to HIV and psychosocial research.

Correlation analysis is widely used in biomedical and psychosocial research for assessing rater reliability, precision of diagnosis and accuracy of proxy outcomes. The popularity of longitudinal study designs has propelled the proliferation in recent years of new methods for longitudinal and other multi-level clustered data designs, such as the mixed-effect models and generalized estimating equations. Despite these advances, research and methodological development on addressing missing data for correlation analysis is woefully lacking. In this paper, we consider non-parametric inference for the product-moment correlation within a longitudinal data setting and address missing data under both the missing completely at random and missing at random assumptions. We illustrate the approach with real study data in mental health and HIV prevention research.

Power analyses for longitudinal study designs with missing data.

Existing methods for power analysis for longitudinal study designs are limited in that they do not adequately address random missing data patterns. Although the pattern of missing data can be assessed during data analysis, it is unknown during the design phase of a study. The random nature of the missing data pattern adds another layer of complexity in addressing missing data for power analysis. In this paper, we model the occurrence of missing data with a two-state, first-order Markov process and integrate the modelling information into the power function to account for random missing data patterns. The Markov model is easily specified to accommodate different anticipated missing data processes. We develop this approach for the two most popular longitudinal models: the generalized estimating equations (GEE) and the linear mixed-effects model under the missing completely at random (MCAR) assumption. For GEE, we also limit our consideration to the working independence correlation model. The proposed methodology is illustrated with numerous examples that are motivated by real study designs.