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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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A biocontrol fungus isolated from rotting wood was identified as a Trichoderma strain (named as Trichoderma sp. ZH1) by internal transcribed spacer (ITS) sequences of rRNA genes. The laccase yield of Trametes sp. AH28-2 in cocultivation with Trichoderma sp. ZH1 reached 6,210 U l(-1), approximately identical to those induced by toxic aromatic inducers. Cocultures maintained 60-70 % of their highest laccase activity obtained at 5 days after inoculation of the biocontrol fungus, at least for 20 days. Furthermore, a novel laccase isozyme (LacC) was obtained through the fungal interactions. The molecular weight of LacC is about 64 kDa, and its isoelectric point is 6.6. The temperature and pH optimum for LacC to oxidize guaiacol are 55 degrees C and 5.0, respectively. LacC is stable both at 60 degrees C and pH 4.0-8.0. Furthermore, the K (m) values of LacC for various substrates were also determined. Our work demonstrates a safe strategy for the production of industrial laccases, instead of the traditional method of chemical induction.

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Power analysis constitutes an important component of modern clinical trials and research studies. Although a variety of methods and software packages are available, almost all of them are focused on regression models, with little attention paid to correlation analysis. However, the latter is arguably a simpler and more appropriate approach for modelling concurrent events, especially in psychosocial research. In this paper, we discuss power and sample size estimation for correlation analysis arising from clustered study designs. Our approach is based on the asymptotic distribution of correlated Pearson-type estimates. Although this asymptotic distribution is easy to use in data analysis, the presence of a large number of parameters creates a major problem for power analysis due to the lack of real data to estimate them. By introducing a surrogacy-type assumption, we show that all nuisance parameters can be eliminated, making it possible to perform power analysis based only on the parameters of interest. Simulation results suggest that power and sample size estimates obtained under the proposed approach are robust to this assumption.

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Existing methods for power and sample size estimation for longitudinal and other clustered study designs have limited applications. In this paper, we review and extend existing approaches to improve these limitations. In particular, we focus on power analysis for the two most popular approaches for clustered data analysis, the generalized estimating equations and the linear mixed-effects models. By basing the derivation of the power function on the asymptotic distribution of the model estimates, the proposed approach provides estimates of power that are consistent with the methods of inference for data analysis. The proposed methodology is illustrated with numerous examples that are motivated by real study designs.

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A recently isolated basidiomycete, Trametes sp. strain AH28-2, can be induced to produce a high level of laccases when grown on a cellobiose-asparagine liquid medium. After induction by kraft lignin, two major isozymes were detected in the fermentation supernatant of the fungus. The principal component laccase A, which accounts for about 85% of the total activity, can be purified to electrophoretic homogeneity by three chromatographic steps: DEAE-Sepharose FF, Superdex-200 and Mono-Q. The solution containing purified laccase is blue in color, and the ratio of absorbance at 280 nm to that at 600 nm is 22. The molecular mass of laccase A is estimated to be 62 kDa by SDS-PAGE, 57 kDa by FPLC, and measured as 58522 Da by MALDI mass spectrum. Laccase A is a monomeric glycoprotein with a carbohydrate content of 11-12% and an isoelectric point of 4.2. The optimum pH and temperature for oxidizing guaiacol are 4.5 and 50 degrees C, respectively. The half-life of the enzyme at 75 degrees C is 27 min. The enzyme shows a good stability from pH 4.2 to pH 8.0. The K(m) values of the enzyme toward substrates 2,2'-azino-bis (3-ethylbenzothazoline-6-sulfonate) (ABTS), guaiacol and 2,6-dimethoxyphenol are 25, 420 and 25.5 microM, respectively, and the corresponding V(max) values are 670, 66.8, and 79 microM min(-1) x mg(-1), respectively. Laccase A activity is strongly inhibited by 0.1 mM NaN(3) or 0.1 mM cyanide. Two units of laccase A alone is able to completely oxidize 100 micromol 2,6-chlorophenol in 6 h. In the presence of 1 mM ABTS and 1-hydroxybenzotriazole, 15.0 U laccase A is able to oxidize 45% and 70% of 50 micromol fluorene in 12 and 18 h, respectively. The laccase A gene was cloned by a PCR method, and preliminary analysis of its sequence indicates 87.0% similarity to the corresponding segment in the phenoloxidase gene from Coriolus hirsutus.

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The lack of control over covariates in practice motivates the need for their adjustment when measuring the degree of association between two sets of variables, for which canonical correlation is traditionally used. In most studies however, there is also a lack of control over the attributes of responses for the sets of variables of interest. In particular, a portion of the response variable may be continuous and the other discrete. For such settings, the traditional partial canonical correlation approach is restrictive, since a covariate-adjustment for a set of continuous variables is assumed. By ignoring the assumption of continuous variates and proceeding with a partial canonical correlation analysis in the presence of continuous and discrete variates, results in canonical correlation estimates that are not consistent. In this paper we generalize the traditional partial canonical correlation approach to covariate-adjustment by allowing the response variables to contain continuous, as well as discrete, variates. The methodology is illustrated with a psychiatric application for examining which sleep variables relate to which depressive symptoms, as measured by commonly used constructs that presents with both continuous and discrete outcomes.

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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.

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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.

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Ignoring the limited precision of medical diagnostic tests can incur serious bias in prevalence estimation. Conversely, treating the values of sensitivity and specificity as constants, as in most studies, inevitably underestimates the variability of prevalence estimates. Bayesian inference provides a natural framework with which to integrate the variability in the estimates of sensitivity and specificity with estimation of prevalence. However, the resulting model becomes quite complicated and presents a computational challenge. Recently, Mendoza-Blanco et al. proposed a missing-data approach with simulation-based techniques to deal with the computational difficulties. Although their approach is quite effective in reducing the computational complexity into manageable tasks, their developed methodology is not general enough for modelling the effects of covariates in prevalence estimation. In this paper, we extend their work in this direction by combining their missing-data approach with a latent variable technique for modelling discrete data. The present work also generalizes the methods of Albert and Chib for Bayesian analysis of binary response data with errors in the response. We illustrate the methodology with several real data examples extracted from the literature.

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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.

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