Feasibility and acceptability of intensive, real-time biobehavioral data collection using ecological momentary assessment, salivary biomarkers, and accelerometers among middle-aged African Americans.

Perceived racial discrimination is linked to unhealthy behaviors and stress-related morbidities. A compelling body of research indicates that perceived racial discrimination may contribute to health disparities among African Americans (AAs). The purposes of this study were to describe the study protocol including data collection procedures and study measures and to evaluate the feasibility and acceptability of intensive biobehavioral data collection using ecological momentary assessment (EMA), salivary biomarkers, and accelerometers over 7 days among middle-aged AAs with a goal of understanding the relationships between perceived racial discrimination and biobehavioral responses to stress. Twelve AA men and women participated in the feasibility/acceptability study. They completed surveys, anthropometrics, and received in-person training in EMA and saliva sample collection at baseline. Participants were asked to respond to the random prompt text message-based EMA five times a day, wear an accelerometer daily for 7 days, and to self-collect saliva samples four times a day for 4 consecutive days. The EMA surveys included perceived racial discrimination, affective states, lifestyle behaviors, and social and physical contexts. The mean EMA response rate was 82.8%. All participants collected saliva samples four times a day for 4 consecutive days. About 83% of participants wore the accelerometer on the hip 6 out of 7 days. Despite the perception that the intensive nature of assessments would result in high participant burden, the acceptability of the study procedures was uniformly favorable.

Estimation approaches in cognitive diagnosis modeling when attributes are hierarchically structured.

BACKGROUND: Although research in cognitive psychology suggests refraining from investigating cognitive skills inisolation, many cognitive diagnosis model (CDM) examples do not take hierarchical attribute structures into account. When hierarchical relationships among the attributes are not considered, CDM estimates may be biased. METHOD: The current study, through simulation and real data analyses, examines the impact of different MMLE-EM approaches on the item and person parameter estimates of the G-DINA, DINA and DINO models when attributes have a hierarchical structure. A number of estimation approaches that can result from modifying either the Q-matrix or prior distribution are proposed. Impact of the proposed approaches on item parameter estimation accuracy and attribute classification are investigated. RESULTS: For the G-DINA model estimation, the Q-matrix type (i.e, explicit vs. implicit) has greater impact than structuring the prior distribution. Specifically, explicit Q-matrices result in better item parameter recovery and higher correct classification rates. In contrast, structuring the prior distribution is more influential on item and person parameter estimates for the reduced models. When prior distribution is structured, the Q-matrix type has almost no influence on item and person parameter estimates of the DINA and DINO models. CONCLUSION: We can conclude that the Q-matrix type has a significant impact on CDM estimation, especially when the estimating model is G-DINA.

Modelling continuous abstinence rates over time from clinical trials of pharmacological interventions for smoking cessation.

BACKGROUND AND AIM: It is useful, for theoretical and practical reasons, to be able to specify functions for continuous abstinence over time in smoking cessation attempts. This study aimed to find the best-fitting models of mean proportion abstinent with different smoking cessation pharmacotherapies up to 52 weeks from the quit date. METHODS: We searched the Cochrane Database of Systematic Reviews to identify randomized controlled trials (RCTs) of pharmacological treatments to aid smoking cessation. For comparability, we selected trials that provided 12 weeks of treatment. Continuous abstinence rates for each treatment at each follow-up point in trials were extracted along with methodological details of the trial. Data points for each pharmacotherapy at each follow-up point were aggregated where the total across contributing studies included at least 1000 participants per data point. Continuous abstinence curves were modelled using a range of different functions from the quit date to 52-week follow-up. Models were compared for fit using R2 and Bayesian information criterion (BIC). RESULTS: Studies meeting our selection criteria covered three pharmacotherapies [varenicline, nicotine replacement therapy (NRT) and bupropion] and placebo. Power functions provided the best fit (R2  > 0.99, BIC < 17.0) to continuous abstinence curves from the target quit date in all cases except for varenicline, where a logarithmic function described the curve best (R2  = 0.99, BIC = 21.2). At 52 weeks, abstinence rates were 22.5% (23.0% modelled) for varenicline, 16.7% (16.0% modelled) for bupropion, 13.0% (12.4% modelled) for NRT and 8.3% (8.9% modelled) for placebo. For varenicline, bupropion, NRT and placebo, respectively, 55.9, 65.0, 62.3 and 56.5% of participants who were abstinent at the end of treatment were still abstinent at 52 weeks. CONCLUSIONS: Mean continuous abstinence rates up to 52 weeks from initiation of smoking cessation attempts in clinical trials can be modelled using simple power functions for placebo, nicotine replacement therapy and bupropion and a logarithmic function for varenicline. This allows accurate prediction of abstinence rates from any time point to any other time point up to 52 weeks.

Continuous time Markov chain approaches for analyzing transtheoretical models of health behavioral change: A case study and comparison of model estimations.

Continuous time Markov chain models are frequently employed in medical research to study the disease progression but are rarely applied to the transtheoretical model, a psychosocial model widely used in the studies of health-related outcomes. The transtheoretical model often includes more than three states and conceptually allows for all possible instantaneous transitions (referred to as general continuous time Markov chain). This complicates the likelihood function because it involves calculating a matrix exponential that may not be simplified for general continuous time Markov chain models. We undertook a Bayesian approach wherein we numerically evaluated the likelihood using ordinary differential equation solvers available from the gnu scientific library. We compared our Bayesian approach with the maximum likelihood method implemented with the R package MSM. Our simulation study showed that the Bayesian approach provided more accurate point and interval estimates than the maximum likelihood method, especially in complex continuous time Markov chain models with five states. When applied to data from a four-state transtheoretical model collected from a nutrition intervention study in the next step trial, we observed results consistent with the results of the simulation study. Specifically, the two approaches provided comparable point estimates and standard errors for most parameters, but the maximum likelihood offered substantially smaller standard errors for some parameters. Comparable estimates of the standard errors are obtainable from package MSM, which works only when the model estimation algorithm converges.

Estimation approaches in cognitive diagnosis modeling when attributes are hierarchically structured / Enfoques de estimación en Modelado Diagnóstico Cognitivo cuando los atributos están estructurados jerárquicamente

BACKGROUND: Although research in cognitive psychology suggests refraining from investigating cognitive skills inisolation, many cognitive diagnosis model (CDM) examples do not take hierarchical attribute structures into account. When hierarchical relationships among the attributes are not considered, CDM estimates may be biased. METHOD: The current study, through simulation and real data analyses, examines the impact of different MMLE-EM approaches on the item and person parameter estimates of the G-DINA, DINA and DINO models when attributes have a hierarchical structure. A number of estimation approaches that can result from modifying either the Q-matrix or prior distribution are proposed. Impact of the proposed approaches on item parameter estimation accuracy and attribute classification are investigated. RESULTS: For the G-DINA model estimation, the Q-matrix type (i.e, explicit vs. implicit) has greater impact than structuring the prior distribution. Specifically, explicit Q-matrices result in better item parameter recovery and higher correct classification rates. In contrast, structuring the prior distribution is more influential on item and person parameter estimates for the reduced models. When prior distribution is structured, the Q-matrix type has almost no influence on item and person parameter estimates of the DINA and DINO models. CONCLUSION: We can conclude that the Q-matrix type has a significant impact on CDM estimation, especially when the estimating model is G-DINA

ANTECEDENTES: a pesar de que investigación en psicología cognitiva sugiere abstenerse de investigar rasgos cognitivos de forma aislada, muchos de los ejemplos en Modelado Diagnóstico Cognitivo (MDC) no tienen en cuenta la estructura jerárquica de los atributos implicados. Sin embargo, las estimaciones que se hagan con los MDC pueden estar sesgadas cuando no se consideran estas relaciones jerárquicas. MÉTODO: a través de la simulación y datos reales, el presente estudio estudia el impacto de diferentes enfoques MMLE-EM en los parámetros estimados para los ítems y las personas según los modelos G-DINA, DINA y DINO cuando los atributos tienen una estructura jerárquica. Se proponen una serie de enfoques de estimación que resultan de modificar la Matriz-Q o la distribución previa. Se investiga el impacto de los enfoques propuestos en la precisión en la estimación de los parámetros de los ítems y la clasificación de atributos. RESULTADO: para la estimación del modelo G-DINA, el tipo de Matriz-Q (es decir, explícita vs. implícita) tiene un impacto mayor al de que la distribución previa esté estructurada. Por el contrario, una distribución previa estructurada influye más sobre la estimación de los parámetros de los ítems y las personas en el caso de los modelos reducidos. CONCLUSIÓN: podemos concluir que el tipo de Matriz-Q tiene un impacto significativo en la estimación de MDC, especialmente en el modelo G-DINA