Longitudinal trends in food cravings following Roux-en-Y gastric bypass in an adolescent sample

This is the author's accepted manuscript. Made available by the permission of the publisher.Background Food cravings are more prevalent and potentially problematic for many individuals with obesity. Initial evidence suggests that bariatric surgery has some short-term beneficial effects on cravings in adults, but little is known about the effect on adolescents or the trajectory beyond 6 months. Methods The purpose of the present study was to determine the longitudinal effect of Roux-en-Y gastric bypass (RYGB) on food cravings in a sample of adolescents with severe obesity (body mass index (BMI) ≥40 kg/m2). Sixteen adolescents were recruited and underwent RYGB. Participants completed the Food Craving Inventory before RYGB, and 3, 6, 12, 18, and 24 months postoperatively. The present study took place in a single pediatric tertiary care hospital. Results RYGB produced a negative (cravings decreased as time increased) nonlinear trend for total food cravings as well as for each individual subscale (sweets, high fat foods, carbohydrates, fast food) over the 24-month study period. This means that while cravings decrease postsurgically, there is a decline in the slope with the line reaching asymptote at approximately 18 months. BMI change was not a significant predictor of food cravings, but low statistical power may account for this lack of significance. Conclusion These findings provide preliminary evidence that RYGB decreases food cravings in adolescents

A longitudinal study of several potential mediators of the relationship between child maltreatment and posttraumatic stress disorder symptoms

Child maltreatment is a reliable predictor of post-traumatic stress disorder (PTSD) symptoms. However, not all maltreated children develop PTSD symptoms, suggesting that additional mediating variables explain how certain maltreated children develop PTSD symptoms when others do not. The current study tested three potential mediators of the relationship between child maltreatment and subsequent PTSD symptoms: 1) respiratory sinus arrhythmia reactivity, 2) cortisol reactivity, and 3) experiential avoidance, or the unwillingness to experience painful private events such as thoughts and memories. Maltreated (n = 51) and non-maltreated groups (n = 59) completed a stressor paradigm, a measure of experiential avoidance, and a semi-structured interview of PTSD symptoms. One year later, participants were re-administered the PTSD symptoms interview. Results of a multiple mediator model showed the set of potential mediators mediated the relationship between child maltreatment and subsequent PTSD symptoms. However, experiential avoidance was the only significant specific indirect effect, demonstrating that maltreated children avoiding painful private events after the abuse were more likely to develop a range of PTSD symptoms one year later. These results highlight the importance of experiential avoidance in the development of PTSD symptoms for maltreated children and implications for secondary prevention and clinical intervention models are discussed

The effectiveness of a low-intensity problem-solving intervention for common adolescent mental health problems in New Delhi, India: protocol for a school-based, individually randomized controlled trial with an embedded stepped-wedge cluster randomized controlled recruitment trial

Background Conduct, anxiety and depressive disorders account for over 75% of the adolescent mental health burden globally. The current protocol will test a low-intensity problem-solving intervention for school-going adolescents with common mental health problems in India. The protocol also tests the effects of a classroom-based sensitization intervention on the demand for counselling services in an embedded recruitment trial. Methods We will conduct a two-arm individually randomized controlled trial in six Government-run secondary schools in New Delhi. The targeted sample is 240 adolescents in grades 9-12 with persistent, elevated mental health symptoms and associated impact. Participants will receive either a brief problem-solving intervention delivered over 3 weeks by lay counsellors (intervention), or enhanced usual care comprised of problem-solving booklets (control). Self-reported adolescent mental health symptoms and idiographic problems will be assessed at 6 weeks (co-primary outcomes) and again at 12 weeks post-randomization. In addition, adolescent-reported impact of mental health difficulties, perceived stress, mental wellbeing and clinical remission, as well as parent-reported adolescent mental health symptoms and impact scores, will be assessed at 6 and 12 weeks post-randomization. We will also complete a parallel process evaluation, including estimations of the costs of delivering the interventions. An embedded recruitment trial will apply a stepped-wedge, cluster (class)-randomized controlled design in 70 classes across the six schools. This will evaluate the added impact of a classroom-based sensitization intervention over school-level recruitment sensitization activities on the primary outcome of referral rate into the host trial (i.e. the proportion of adolescents referred as a function of the total sampling frame in each condition of the embedded recruitment trial). Other outcomes will be the proportion of referrals eligible to participate in the host trial, proportion of self-generated referrals, and severity and pattern of symptoms among referred adolescents in each condition. Power calculations were undertaken separately for each trial. A detailed statistical analysis plan will be developed separately for each trial prior to unblinding. Discussion Both trials were initiated on 20 August 2018. A single research protocol for both trials offers a resource-efficient methodology for testing the effectiveness of linked procedures to enhance uptake and outcomes of a school-based psychological intervention for common adolescent mental health problems

Handling Longitudinal MNAR Data

Mplus input files for all model

Specification searches in multilevel structural equation modeling: A Monte Carlo investigation

Sample data obtained via cluster sampling rather than simple random sampling requires the use of specialized multilevel statistical analysis techniques, such as multilevel structural equation modeling, to model within-cluster and between-cluster variation appropriately. Properly modeling both within-cluster and between-cluster variation could be of substantive interest in numerous applied research settings. However, applied researchers typically test only a within-cluster (i.e., individual difference) theory; specifying a between-cluster model in the absence of theory involves a specification search. Consistent with previous specification search studies, this dissertation manipulated the following independent variables: starting model, search method, and method of Type-I error control as independent variables. Further, consistent with previous multilevel research studies, this dissertation also manipulated the number of clusters, cluster sample size, and intraclass correlation magnitude as independent variables. The main dependent variable of interest was which combination of start model, search type, and method of Type-I error control best recovered the population between-cluster model. Additional dependent variables were also examined to assess the precision of specification search efforts. Results showed that a saturated start model, univariate specification search, and no Type-I error control best recovered the population between-cluster model. However, this specification search method recovered the population model in less than one in five attempts at the largest sample size. A majority of the specification searches recovered the population model in less than five percent of all attempts, and the remaining specification search efforts failed to recover the population model under any conditions. Overall, specification search efforts were more likely to produce a notably misspecified model with biased parameter estimates, an under-identified model, or an inadmissible solution. Model complexity, non-normally distributed data, and within-cluster model misspecification were not manipulated as independent variables in this dissertation. Further, the current results were based on a multilevel path model that may or may not generalize to other multilevel designs, such as confirmatory factor analyses and full structural equation models. Model complexity, non-normally distributed data, within-cluster model misspecification, and advanced analysis designs could be incorporated in future multilevel specification search studies by adapting the models used in previous non-multilevel specification search investigations

Paper: Equivalence Testing to Judge Model Fit: A Monte Carlo Simulation

Supplemental Materials for the above pape

Equivalence Testing to Judge Model Fit: A Monte Carlo Simulation

Decades of published methodological research have shown the chi-square test of model fit performs inconsistently and unreliably as a determinant of SEM fit. Likewise, SEM indices of model fit, such as CFI and RMSEA also perform inconsistently and unreliably. Despite rather unreliable ways to statistically assess model fit, researchers commonly rely on these methods for lack of a suitable inferential alternative. Marcoulides and Yuan (2017) have proposed the first inferential test of SEM fit in many years: an equivalence test adaptation of the RMSEA and CFI indices (i.e., RMSEAt and CFIt). However, the ability of this equivalence testing approach to accurately judge acceptable and unacceptable model fit has not been empirically tested. This fully-crossed Monte Carlo simulation evaluated the accuracy of equivalence testing combining many of the same IV conditions used in previous fit index simulation studies, including: sample size (N = 100-1000), model specification (correctly-specified or misspecified), model type (CFA, path analysis, or SEM), number of variables analyzed (low or high), data distribution (normal or skewed), and missing data (none, 10%, or 25%). Results show equivalence testing performs rather inconsistently and unreliably across IV conditions, with acceptable or unacceptable RMSEAt and CFIt model fit index values often being contingent on complex interactions among conditions. Proportional z-tests and logistic regression analyses indicated that equivalence tests of model fit are problematic under multiple conditions, especially those where models are mildly misspecified. Recommendations for researchers are offered, but with the provision that they be used with caution until more research is available

Equivalence Testing to Judge Model Fit: A Monte Carlo Simulation

Decades of published methodological research have shown the chi-square test of model fit performs inconsistently and unreliably as a determinant of structural equation model (SEM) fit. Likewise, SEM indices of model fit, such as comparative fit index (CFI) and root-mean-square error of approximation (RMSEA) also perform inconsistently and unreliably. Despite rather unreliable ways to statistically assess model fit, researchers commonly rely on these methods for lack of a suitable inferential alternative. Marcoulides and Yuan (2017) have proposed the first inferential test of SEM fit in many years: an equivalence test adaptation of the RMSEA and CFI indicies (i.e., RMSEA t and CFI t). However, the ability of this equivalence testing approach to accurately judge acceptable and unacceptable model fit has not been empirically tested. This fully crossed Monte Carlo simulation evaluated the accuracy of equivalence testing combining many of the same independent variable (IV) conditions used in previous fit index simulation studies, including sample size (N = 100-1,000), model specification (correctly specified or misspecified), model type (confirmatory factor analysis [CFA], path analysis, or SEM), number of variables analyzed (low or high), data distribution (normal or skewed), and missing data (none, 10%, or 25%). Results show equivalence testing performs rather inconsistently and unreliably across IV conditions, with acceptable or unacceptable RMSEA t and CFIt model fit index values often being contingent on complex interactions among conditions. Proportional z-tests and logistic regression analyses indicated that equivalence tests of model fit are problematic under multiple conditions, especially those where models are mildly misspecified. Recommendations for researchers are offered, but with the provision that they be used with caution until more research and development is available