The Role of Gender Norms on Intimate Partner Violence Among Newly Married Adolescent Girls and Young Women in India: A Longitudinal Multilevel Analysis.

Gender norms have been posited to impact intimate partner violence (IPV), but there is scant evidence of the longitudinal association between community-level gender norms and IPV. Using longitudinal data on 3,965 married girls surveyed in India, we fitted mixed-effects ordinal and binary logistic regression models for physical IPV intensity and occurrence of sexual IPV. We found a 26% increase in the odds that women experience frequent physical IPV per one unit increase in greater community-level equitable gender norms. We did not find an association between community-level equitable gender norms and sexual IPV. Findings suggest that the relationship between gender norms and physical and sexual IPV differs, indicating the need for tailored interventions for different types of IPV.

Role of marriage, motherhood, son preference on adolescent girls' and young women's empowerment: Evidence from a panel study in India.

BACKGROUND: Marriage is a key determinant of health and well-being of adolescent girls and young women (AGYW) in India. It is a key life event in which girls move to their marital households, often co-residing with their in-laws and begin childbearing. The change in the normative environment in conjunction with cultural norms surrounding son preference influences women's overall life course. However, there is scant research about the association between these life transitions and changes in empowerment among AGYW in India. METHODS: Using two waves of data from prospective cohort panel dataset that followed unmarried (6,065 observations in each wave) and married AGYW (3,941 observations from each wave) over a three-year period from Uttar Pradesh and Bihar, we examined how marriage, childbearing, and having a son is associated with changes in AGYW's empowerment, especially considering whether AGYW marry into patrilocal households (household with in-laws) as an effect modifier. Empowerment indicators included freedom of movement or mobility, decision-making power, access to economic using Kabeer's framework as our theoretical approach. RESULTS: Marriage was associated with lower freedom of movement with a pronounced effect on those who co-resided with their in-laws. Marriage was associated with greater decision-making power for AGYW who did not co-reside with the in-laws. Motherhood was positively correlated with greater freedom of movement, marginally higher intrahousehold decision-making power, and better access to economic resources. No statistically significant evidence that having at least one son compared to having daughters only (or no daughters) conferred additional changes in girls' freedom of movement, intrahousehold decision-making power, and access to economic resources. CONCLUSION: Findings highlight the importance of understanding the vulnerabilities of being newly married in adolescence and emphasize the need for having interventions that target newly married AGYW along with mothers-in-law to empower them.

Diagnosing and Handling Common Violations of Missing at Random.

Ignorable likelihood (IL) approaches are often used to handle missing data when estimating a multivariate model, such as a structural equation model. In this case, the likelihood is based on all available data, and no model is specified for the missing data mechanism. Inference proceeds via maximum likelihood or Bayesian methods, including multiple imputation without auxiliary variables. Such IL approaches are valid under a missing at random (MAR) assumption. Rabe-Hesketh and Skrondal (Ignoring non-ignorable missingness. Presidential Address at the International Meeting of the Psychometric Society, Beijing, China, 2015; Psychometrika, 2023) consider a violation of MAR where a variable A can affect missingness of another variable B also when A is not observed. They show that this case can be handled by discarding more data before proceeding with IL approaches. This data-deletion approach is similar to the sequential estimation of Mohan et al. (in: Advances in neural information processing systems, 2013) based on their ordered factorization theorem but is preferable for parametric models. Which kind of data-deletion or ordered factorization to employ depends on the nature of the MAR violation. In this article, we therefore propose two diagnostic tests, a likelihood-ratio test for a heteroscedastic regression model and a kernel conditional independence test. We also develop a test-based estimator that first uses diagnostic tests to determine which MAR violation appears to be present and then proceeds with the corresponding data-deletion estimator. Simulations show that the test-based estimator outperforms IL when the missing data problem is severe and performs similarly otherwise.

Ignoring Non-ignorable Missingness.

The classical missing at random (MAR) assumption, as defined by Rubin (Biometrika 63:581-592, 1976), is often not required for valid inference ignoring the missingness process. Neither are other assumptions sometimes believed to be necessary that result from misunderstandings of MAR. We discuss three strategies that allow us to use standard estimators (i.e., ignore missingness) in cases where missingness is usually considered to be non-ignorable: (1) conditioning on variables, (2) discarding more data, and (3) being protective of parameters.

The Role of Conditional Likelihoods in Latent Variable Modeling.

In psychometrics, the canonical use of conditional likelihoods is for the Rasch model in measurement. Whilst not disputing the utility of conditional likelihoods in measurement, we examine a broader class of problems in psychometrics that can be addressed via conditional likelihoods. Specifically, we consider cluster-level endogeneity where the standard assumption that observed explanatory variables are independent from latent variables is violated. Here, "cluster" refers to the entity characterized by latent variables or random effects, such as individuals in measurement models or schools in multilevel models and "unit" refers to the elementary entity such as an item in measurement. Cluster-level endogeneity problems can arise in a number of settings, including unobserved confounding of causal effects, measurement error, retrospective sampling, informative cluster sizes, missing data, and heteroskedasticity. Severely inconsistent estimation can result if these challenges are ignored.

A randomized controlled trial of the Transdiagnostic Intervention for Sleep and Circadian Dysfunction (TranS-C) to improve serious mental illness outcomes in a community setting.

OBJECTIVE: To determine if the Transdiagnostic Intervention for Sleep and Circadian Dysfunction (TranS-C) improves functional impairment, psychiatric symptoms, and sleep and circadian functioning. METHOD: Adults diagnosed with serious mental illness (SMI) and sleep and circadian dysfunction (N = 121) were randomly allocated to TranS-C plus usual care (TranS-C + UC; n = 61; 8 individual weekly sessions) or 6 months of Usual Care followed by Delayed Treatment with TranS-C (UC-DT; n = 60). Schizophrenia (45%) and anxiety disorders (47%) were common. Blind assessments were conducted pre-treatment, post-treatment, and 6 months later (6FU). The latter two were the post-randomization points of interest. The location was Alameda County Behavioral Health Care Services (ACBHCS), a Community Mental Health Center (CMHC) in California. RESULTS: For the primary outcomes, relative to UC-DT, TranS-C + UC was associated with reduction in functional impairment (b = -3.18, p = 0.025, d = -0.58), general psychiatric symptoms (b = -5.88, p = 0.001, d = -0.64), sleep disturbance (b = -5.55, p < .0001, d = -0.96), and sleep-related impairment (b = -9.14, p < .0001, d = -0.81) from pre-treatment to post-treatment. These effects were maintained to 6-month follow-up (6FU; d = -0.42 to -0.82), except functional impairment (d = -0.37). For the secondary outcomes, relative to UC-DT, TranS-C + UC was associated with improvement in sleep efficiency and on the Sleep Health Composite score from pre-treatment to 6FU. TranS-C + UC was also associated with reduced total wake time and wake time variability from pre-treatment to post-treatment, as well as reduced hallucinations and delusions, bedtime variability, and actigraphy measured waking activity count variability from pre-treatment to 6FU. CONCLUSIONS: A novel transdiagnostic treatment, delivered within a CMHC setting, improves selected measures of functioning, symptoms of comorbid disorders, and sleep and circadian outcomes. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

A Dyadic IRT Model.

We propose a dyadic Item Response Theory (dIRT) model for measuring interactions of pairs of individuals when the responses to items represent the actions (or behaviors, perceptions, etc.) of each individual (actor) made within the context of a dyad formed with another individual (partner). Examples of its use include the assessment of collaborative problem solving or the evaluation of intra-team dynamics. The dIRT model generalizes both Item Response Theory models for measurement and the Social Relations Model for dyadic data. The responses of an actor when paired with a partner are modeled as a function of not only the actor's inclination to act and the partner's tendency to elicit that action, but also the unique relationship of the pair, represented by two directional, possibly correlated, interaction latent variables. Generalizations are discussed, such as accommodating triads or larger groups. Estimation is performed using Markov-chain Monte Carlo implemented in Stan, making it straightforward to extend the dIRT model in various ways. Specifically, we show how the basic dIRT model can be extended to accommodate latent regressions, multilevel settings with cluster-level random effects, as well as joint modeling of dyadic data and a distal outcome. A simulation study demonstrates that estimation performs well. We apply our proposed approach to speed-dating data and find new evidence of pairwise interactions between participants, describing a mutual attraction that is inadequately characterized by individual properties alone.

Bayesian Comparison of Latent Variable Models: Conditional Versus Marginal Likelihoods.

Typical Bayesian methods for models with latent variables (or random effects) involve directly sampling the latent variables along with the model parameters. In high-level software code for model definitions (using, e.g., BUGS, JAGS, Stan), the likelihood is therefore specified as conditional on the latent variables. This can lead researchers to perform model comparisons via conditional likelihoods, where the latent variables are considered model parameters. In other settings, however, typical model comparisons involve marginal likelihoods where the latent variables are integrated out. This distinction is often overlooked despite the fact that it can have a large impact on the comparisons of interest. In this paper, we clarify and illustrate these issues, focusing on the comparison of conditional and marginal Deviance Information Criteria (DICs) and Watanabe-Akaike Information Criteria (WAICs) in psychometric modeling. The conditional/marginal distinction corresponds to whether the model should be predictive for the clusters that are in the data or for new clusters (where "clusters" typically correspond to higher-level units like people or schools). Correspondingly, we show that marginal WAIC corresponds to leave-one-cluster out cross-validation, whereas conditional WAIC corresponds to leave-one-unit out. These results lead to recommendations on the general application of the criteria to models with latent variables.

Trifactor Models for Multiple-Ratings Data.

In this study we extend and assess the trifactor model for multiple-ratings data in which two different raters give independent scores for the same responses (e.g., in the GRE essay or to subset of PISA constructed-responses). The trifactor model was extended to incorporate a cross-classified data structure (e.g., items and raters) instead of a strictly hierarchical structure. we present a set of simulations to reflect the incompleteness and imbalance in real-world assessments. The effects of the rate of missingness in the data and of ignoring differences among raters are investigated using two sets of simulations. The use of the trifactor model is also illustrated with empirical data analysis using a well-known international large-scale assessment.

Monte Carlo local likelihood approximation.

We propose the Monte Carlo local likelihood (MCLL) method for approximating maximum likelihood estimation (MLE). MCLL initially treats model parameters as random variables, sampling them from the posterior distribution as in a Bayesian model. The likelihood function is then approximated up to a constant by fitting a density to the posterior samples and dividing the approximate posterior density by the prior. In the MCLL algorithm, the posterior density is estimated using local likelihood density estimation, in which the log-density is locally approximated by a polynomial function. We also develop a new method that allows users to efficiently compute standard errors and the Bayes factor. Two empirical and three simulation studies are provided to demonstrate the performance of the MCLL method.

Modifying the Impact of Eveningness Chronotype ("Night-Owls") in Youth: A Randomized Controlled Trial.

OBJECTIVE: To determine whether an intervention to reduce eveningness chronotype improves sleep, circadian, and health (emotional, cognitive, behavioral, social, physical) outcomes. METHOD: Youth aged 10 to 18 years with an evening chronotype and who were "at risk" in 1 of 5 health domains were randomized to: (a) Transdiagnostic Sleep and Circadian Intervention for Youth (TranS-C; n = 89) or (b) Psychoeducation (PE; n = 87) at a university-based clinic. Treatments were 6 individual, weekly 50-minute sessions during the school year. TranS-C addresses sleep and circadian problems experienced by youth by integrating evidence-based treatments derived from basic research. PE provides education on the interrelationship between sleep, stress, diet, and health. RESULTS: Relative to PE, TranS-C was not associated with greater pre-post change for total sleep time (TST) or bed time (BT) on weeknights but was associated with greater reduction in evening circadian preference (pre-post increase of 3.89 points, 95% CI = 2.94-4.85, for TranS-C, and 2.01 points, 95% CI = 1.05-2.97 for PE, p = 0.006), earlier endogenous circadian phase, less weeknight-weekend discrepancy in TST and wakeup time, less daytime sleepiness, and better self-reported sleep via youth and parent report. In terms of functioning in the five health domains, relative to PE, TranS-C was not associated with greater pre-post change on the primary outcome. However, there were significant interactions favoring TranS-C on the Parent-Reported Composite Risk Scores for cognitive health. CONCLUSION: For at-risk youth, the evidence supports the use of TranS-C over PE for improving sleep and circadian functioning, and improving health on selected outcomes. CLINICAL TRIAL REGISTRATION INFORMATION: Triple Vulnerability? Circadian Tendency, Sleep Deprivation and Adolescence. https://clinicaltrials.gov; NCT01828320.

CFA Models with a General Factor and Multiple Sets of Secondary Factors.

We propose a class of confirmatory factor analysis models that include multiple sets of secondary or specific factors and a general factor. The general factor accounts for the common variance among manifest variables, whereas multiple sets of secondary factors account for the remaining source-specific dependency among subsets of manifest variables. A special case of the model is further proposed which constrains the specific factor loadings to be proportional to the general factor loadings. This proportional model substantially reduces the number of model parameters while preserving the essential structure of the general model. Furthermore, the proportional model allows for the interpretation of latent variables as the expected values of the observed manifest variables, decomposition of the variances, and the inclusion of interactions, similar to generalizability theory. We provide two applications to illustrate the utility of the proposed class of models.

Pairwise residuals and diagnostic tests for misspecified dependence structures in models for binary longitudinal data.

Maximum likelihood estimation of models for binary longitudinal data is typically inconsistent if the dependence structure is misspecified. Unfortunately, diagnostics specifically designed for detecting such misspecifications are scant. We develop residuals and diagnostic tests based on comparing observed and expected frequencies of response patterns over time in the presence of arbitrary time-varying and time-invariant covariates. To overcome the sparseness problem, we use lower-order marginal tables, such as two-way tables for pairs of time-points, aggregated over covariate patterns. Our proposed pairwise concordance residuals are valuable for exploratory diagnostics and for constructing both generic tests for misspecified dependence structure as well as targeted adjacent pair concordance tests for excess serial dependence. The proposed methods are straightforward to implement and work well for general situations, regardless of the number of time-points and the number and types of covariates.

Can integrating the Memory Support Intervention into cognitive therapy improve depression outcome? Study protocol for a randomized controlled trial.

BACKGROUND: The Memory Support Intervention was developed in response to evidence showing that: (1) patient memory for treatment is poor, (2) poor memory for treatment is associated with poorer adherence and poorer outcome, (3) the impact of memory impairment can be minimized by the use of memory support strategies and (4) improved memory for treatment improves outcome. The aim of this study protocol is to conduct a confirmatory efficacy trial to test whether the Memory Support Intervention improves illness course and functional outcomes. As a "platform" for the next step in investigating this approach, we focus on major depressive disorder (MDD) and cognitive therapy (CT). METHOD/DESIGN: Adults with MDD (n = 178, including 20% for potential attrition) will be randomly allocated to CT + Memory Support or CT-as-usual and will be assessed at baseline, post treatment and at 6 and 12 months' follow-up (6FU and 12FU). We will compare the effects of CT + Memory Support vs. CT-as-usual to determine if the new intervention improves the course of illness and reduces functional impairment (aim 1). We will determine if patient memory for treatment mediates the relationship between treatment condition and outcome (aim 2). We will evaluate if previously reported poor treatment response subgroups moderate target engagement (aim 3). DISCUSSION: The Memory Support Intervention has been developed to be "transdiagnostic" (relevant to a broad range of mental disorders) and "pantreatment" (relevant to a broad range of types of treatment). This study protocol describes a "next step" in the treatment development process by testing the Memory Support Intervention for major depressive disorder (MDD) and cognitive therapy (CT). If the results are promising, future directions will test the applicability to other kinds of interventions and disorders and in other settings. TRIAL REGISTRATION: ClinicalTrials.gov, ID: NCT01790919 . Registered on 6 October 2016.

A Variational Maximization-Maximization Algorithm for Generalized Linear Mixed Models with Crossed Random Effects.

We present a variational maximization-maximization algorithm for approximate maximum likelihood estimation of generalized linear mixed models with crossed random effects (e.g., item response models with random items, random raters, or random occasion-specific effects). The method is based on a factorized variational approximation of the latent variable distribution given observed variables, which creates a lower bound of the log marginal likelihood. The lower bound is maximized with respect to the factorized distributions as well as model parameters. With the proposed algorithm, a high-dimensional intractable integration is translated into a two-dimensional integration problem. We incorporate an adaptive Gauss-Hermite quadrature method in conjunction with the variational method in order to increase computational efficiency. Numerical studies show that under the small sample size conditions that are considered the proposed algorithm outperforms the Laplace approximation.

The transition model test for serial dependence in mixed-effects models for binary data.

Generalized linear mixed models for longitudinal data assume that responses at different occasions are conditionally independent, given the random effects and covariates. Although this assumption is pivotal for consistent estimation, violation due to serial dependence is hard to assess by model elaboration. We therefore propose a targeted diagnostic test for serial dependence, called the transition model test (TMT), that is straightforward and computationally efficient to implement in standard software. The TMT is shown to have larger power than general misspecification tests. We also propose the targeted root mean squared error of approximation (TRSMEA) as a measure of the population misfit due to serial dependence.

A transdiagnostic sleep and circadian treatment to improve severe mental illness outcomes in a community setting: study protocol for a randomized controlled trial.

BACKGROUND: Severe mental illness (SMI) is common, chronic and difficult to treat. Sleep and circadian dysfunctions are prominent correlates of SMI, yet have been minimally studied in ways that reflect the complexity of the sleep problems experienced. Prior treatment studies have been disorder-focused-they have treated a specific sleep problem in a specific diagnostic group. However, real life sleep and circadianproblems are not so neatly categorized, particularly in SMI where features of insomnia overlap with hypersomnia, delayed sleep phase and irregular sleep-wake schedules. Accordingly, the aim of this studyprotocol is to test the hypothesis that a Transdiagnostic Intervention for Sleep and Circadian Dysfunction (TranS-C) will improve functional impairment, disorder-focused symptoms and sleep and circadian functioning. Participants across DSM diagnoses and across common sleep and circadian problems are eligible. The elements of TranS-C are efficacious across SMI in research settings with research-based providers. The next step is to test TranS-C in a community setting. Accordingly, this study is being conducted within Alameda County Behavioral Health Care Services (ACBHCS), the Community Mental Health Centre (CMHC) for Alameda County. METHODS/DESIGN: 120 adults diagnosed with SMI and sleep and circadian dysfunction within ACBHCS will be randomly allocated to TranS-C (n = 60) or 6-months of Usual Care followed by Delayed Treatment with TranS-C (UC-DT; n = 60). TranS-C is modularized and delivered across eight to twelve 50-minute, weekly, individual sessions. All participants will be assessed before and immediately following treatment and again 6 months later. Primary analysis will examine whether TranS-C significantly improves functional impairment, disorder-specific symptoms and sleep and circadian functioning, relative to UC-DT. Exploratory analysis will examine whether improvements in sleep and circadian functioning predict reduction in functional impairment and disorder-specific symptoms, and whether the intervention effects are mediated by improved sleep and circadian functioning and moderated by previously reported risk factors (demographics, symptom severity, medications, psychiatric and medical comorbidity). DISCUSSION: This trial tests an important and understudied mechanism-dysregulated sleep and circadian rhythms-in SMI, a novel transdiagnostic treatment approach, in a community setting so as to contribute to the goal of bridging the gap between research and practice. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT02469233 . Registered on 9 June 2015.

Improving outcome for mental disorders by enhancing memory for treatment.

Patients exhibit poor memory for treatment. A novel Memory Support Intervention, derived from basic science in cognitive psychology and education, is tested with the goal of improving patient memory for treatment and treatment outcome. Adults with major depressive disorder (MDD) were randomized to 14 sessions of cognitive therapy (CT)+Memory Support (n = 25) or CT-as-usual (n = 23). Outcomes were assessed at baseline, post-treatment and 6 months later. Memory support was greater in CT+Memory Support compared to the CT-as-usual. Compared to CT-as-usual, small to medium effect sizes were observed for recall of treatment points at post-treatment. There was no difference between the treatment arms on depression severity (primary outcome). However, the odds of meeting criteria for 'response' and 'remission' were higher in CT+Memory Support compared with CT-as-usual. CT+Memory Support also showed an advantage on functional impairment. While some decline was observed, the advantage of CT+Memory Support was evident through 6-month follow-up. Patients with less than 16 years of education experience greater benefits from memory support than those with 16 or more years of education. Memory support can be manipulated, may improve patient memory for treatment and may be associated with an improved outcome.

An autoregressive growth model for longitudinal item analysis.

A first-order autoregressive growth model is proposed for longitudinal binary item analysis where responses to the same items are conditionally dependent across time given the latent traits. Specifically, the item response probability for a given item at a given time depends on the latent trait as well as the response to the same item at the previous time, or the lagged response. An initial conditions problem arises because there is no lagged response at the initial time period. We handle this problem by adapting solutions proposed for dynamic models in panel data econometrics. Asymptotic and finite sample power for the autoregressive parameters are investigated. The consequences of ignoring local dependence and the initial conditions problem are also examined for data simulated from a first-order autoregressive growth model. The proposed methods are applied to longitudinal data on Korean students' self-esteem.

Treating insomnia improves mood state, sleep, and functioning in bipolar disorder: a pilot randomized controlled trial.

OBJECTIVE: To determine if a treatment for interepisode bipolar disorder I patients with insomnia improves mood state, sleep, and functioning. METHOD: Alongside psychiatric care, interepisode bipolar disorder I participants with insomnia were randomly allocated to a bipolar disorder-specific modification of cognitive behavior therapy for insomnia (CBTI-BP; n = 30) or psychoeducation (PE; n = 28) as a comparison condition. Outcomes were assessed at baseline, the end of 8 sessions of treatment, and 6 months later. This pilot was conducted to determine initial feasibility and generate effect size estimates. RESULTS: During the 6-month follow-up, the CBTI-BP group had fewer days in a bipolar episode relative to the PE group (3.3 days vs. 25.5 days). The CBTI-BP group also experienced a significantly lower hypomania/mania relapse rate (4.6% vs. 31.6%) and a marginally lower overall mood episode relapse rate (13.6% vs. 42.1%) compared with the PE group. Relative to PE, CBTI-BP reduced insomnia severity and led to higher rates of insomnia remission at posttreatment and marginally higher rates at 6 months. Both CBTI-BP and PE showed statistically significant improvement on selected sleep and functional impairment measures. The effects of treatment were well sustained through follow-up for most outcomes, although some decline on secondary sleep benefits was observed. CONCLUSIONS: CBTI-BP was associated with reduced risk of mood episode relapse and improved sleep and functioning on certain outcomes in bipolar disorder. Hence, sleep disturbance appears to be an important pathway contributing to bipolar disorder. The need to develop bipolar disorder-specific sleep diary scoring standards is highlighted.