Objective Although recent statistical and computational developments allow for the empirical testing of psychological theories in ways not previously possible, one particularly vexing challenge remains: how to optimally model the prospective, reciprocal relations between two constructs as they developmentally unfold over time. Several analytic methods currently exist that attempt to model these types of relations, and each approach is successful to varying degrees. However, none provide the unambiguous separation of between-person and within-person components of stability and change over time, components that are often hypothesized to exist in the psychological sciences. The goal of our paper is to propose and demonstrate a novel extension of the multivariate latent curve model to allow for the disaggregation of these effects. Method We begin with a review of the standard latent curve models and describe how these primarily capture between-person differences in change. We then extend this model to allow for regression structures among the time-specific residuals to capture within-person differences in change. Results We demonstrate this model using an artificial data set generated to mimic the developmental relation between alcohol use and depressive symptomatology spanning five repeated measures. Conclusions We obtain a specificity of results from the proposed analytic strategy that are not available from other existing methodologies. We conclude with potential limitations of our approach and directions for future research.

IntroductionMajor depressive disorder, highly prevalent among people with HIV (PWH) globally, including South Africa, is associated with suboptimal adherence to antiretroviral therapy. Globally, there are insufficient numbers of mental health providers and tested depression treatments. This study's aim was to test task‐shared cognitive‐behavioural therapy for adherence and depression (CBT‐AD) in HIV, delivered by clinic nurses in South Africa.MethodsThis was a two‐arm randomized controlled effectiveness trial (recruitment: 14 July 2016 to 4 June 2019, last follow 9 June 2020). One‐hundred‐sixty‐one participants with clinical depression and virally uncontrolled HIV were recruited from primary care clinics providing HIV care, in Khayelitsha, South Africa. Arm 1 was task‐shared, nurse‐delivered CBT‐AD; and arm 2 was enhanced treatment as usual (ETAU). Primary outcomes (baseline to 4 months) were blinded Hamilton Depression Rating Scale (HAM‐D) scores, and weekly adherence via real‐time monitoring (Wisepill). Secondary outcomes were adherence and depression over 4‐, 8‐ and 12‐month follow‐ups, proportion of participants with undetectable viremia and continuous CD4 cell counts at 12 months. Additional analyses involved viral load and CD4 over time.ResultsAt 4 months, the HAMD scores in the CBT‐AD condition improved by an estimated 4.88 points more (CI: –7.86, –1.87, p = 0.0016), and for weekly adherence, 1.61 percentage points more per week (CI: 0.64, 2.58, p = 0.001) than ETAU. Over follow‐ups, CBT‐AD had an estimated 5.63 lower HAMD scores (CI: –7.90, –3.36, p < 0.001) and 23.56 percentage points higher adherence (CI: 10.51, 34.21, p < 0.001) than ETAU. At 12 months, adjusted models indicated that the odds of having an undetectable viremia was 2.51 greater at 12 months (CI: 1.01, 6.66, p = 0.047), and 3.54 greater over all of the follow‐ups (aOR = 3.54, CI: 1.59, 20.50; p = 0.038) for those assigned CBT‐AD. CD4 was not significantly different between groups at 12 months or over time.ConclusionsTask‐shared, nurse‐delivered, CBT‐AD is effective in improving clinical depression, ART adherence and viral load for virally unsuppressed PWH. The strategy of reducing depression to allow patients with self‐care components of medical illness to benefit from adherence interventions is one to extend. Implementation science trials and analyses of cost‐effectiveness are needed to translate findings into clinical practice.Trial RegistrationClinicalTrials.gov Identifier: NCT02696824 https://clinicaltrials.gov/ct2/show/NCT02696824

Amid recent progress in cognitive development research, high-quality data resources are accumulating, and data sharing and secondary data analysis is becoming an increasingly valuable tool. Integrative data analysis (IDA) is an exciting analytical framework that can enhance secondary data analysis in powerful ways. IDA pools item level data across multiple studies to make inferences possible both within and across studies and can be used to test questions not possible in individual contributing studies. Some of the potential benefits of IDA include the ability to study longer developmental periods, examine how the measurement of key constructs changes over time, increase subject heterogeneity, and improve statistical power and capability to study rare behaviors. Our goal in this paper is to provide a brief overview of the benefits and challenges of IDA in developmental research and to identify additional resources that provide more detailed discussions of this topic.

Several multivariate models are motivated to answer similar developmental questions regarding within-person (intraindividual) effects between two or more constructs over time, yet the within-person effects tested by each model are distinct. In this paper, we clarify the types of within-person inferences that can be made from each model. Whereas previous research has focused on detecting whether within-person effects exist over development, the present work can be used to understand the nature of these relationships. We compare each modeling approach using an example investigating the concurrent development of mother-child closeness and mother-child conflict. Our findings demonstrate that fundamentally different conclusions about developmental processes may be reached depending on which model is used, and we demonstrate a framework for making sense of seemingly contradictory findings.

In measurement theory causal indicators are controversial and little-understood. Methodological disagreement concerning causal indicators has centered on the question of whether causal indicators are inherently sensitive to interpretational confounding, which occurs when the empirical meaning of a latent construct departs from the meaning intended by a researcher. This article questions the validity of evidence used to claim that causal indicators are inherently susceptible to interpretational confounding. Further, a simulation study demonstrates that causal indicator coefficients are stable across correctly-specified models. Determining the suitability of causal indicators has implications for the way we conceptualize measurement and build and evaluate measurement models.

Sleep problems are prevalent in people living with HIV/AIDS; however, few studies examine how poor sleep affects mental health and quality of life longitudinally. A sample of people living with HIV/AIDS from a randomized trial ( N = 240; mean age = 47.18; standard deviation = 8.3; 71.4% male; 61.2% White) completed measures of depression (Montgomery-Åsberg Depression Rating Scale), health-related quality of life (AIDS Clinical Trial Group Quality of Life Measure), and life satisfaction (Quality of Life Inventory) at baseline and 4, 8, and 12 months. Controlling for time, condition, and relevant interactions, sleep problems significantly predicted worse outcomes over time ( ps < 0.001). Findings have implications for the importance of identifying and treating sleep problems in people living with HIV/AIDS to improve mental health and quality-of-life outcomes.

This study examined longitudinally the additive effect of syndemics, or co-occurring psychosocial problems, on antiretroviral treatment (ART) non-adherence among 390 HIV-positive sexual minority men. Participants completed measures of ART adherence (reduced to a non-adherence score using exploratory factor analysis) and six syndemic conditions. We employed multilevel modeling with the number of syndemics as a longitudinal predictor of non-adherence, and logistic regression with baseline syndemics predicting follow up viral load. Number of syndemics was a significant longitudinal predictor of non-adherence, with each additional syndemic associated with a 0.13 increase in non-adherence (p = 0.004). Each additional syndemic was also associated with 1.27 greater odds of detectable viral load (p = 0.002). Among HIV-positive sexual minority men in this sample, more syndemics were associated with lower ART adherence and greater odds of detectable viral load, suggesting the need for behavioral intervention to facilitate care for this population.

Objective-This study examined the longitudinal effects of co-occurring psychosocial concerns, or syndemics, on HIV-positive sexual minority men's likelihood of engaging in serodiscordant condomless anal sex (CAS), a health behavior with implications for personal and public health.Methods-Participants included 390 HIV-positive sexual minority men from two prior secondary prevention trials. Over the course of the one-year data collection period (up to 5 observations per participant), participants completed self-report measures of CAS, as well as six syndemic factors: post-traumatic stress disorder, childhood sexual abuse, depression, anxiety, alcohol abuse, and poly sub stance/stimulant use. We employed multilevel modeling to examine the longitudinal additive effect of syndemics on serodiscordant CAS (binary) over the one-year period.Results-The number of syndemic conditions was a significant predictor of CAS, with each additional syndemic associated with 1.41 greater odds of CAS (p = 0.0004; 95% CI [1.16, 1.70]). Both the between-(p = 0.0121, 95% CI [1.07, 1.69]) and within-person (p = 0.01, 95% CI [1.11, 2.10]) effects of syndemics were significant predictors, showing that an increase in the number of syndemic conditions across person and time both increased odds of CAS.Conclusions-Interventions addressing HIV-positive sexual minority men's sexual health behaviors should address the potential impact of co-occurring psychosocial concerns that affect Correspondence concerning this article should be addressed to Audrey Harkness, Clinical Research Building (C-204),