Developmental Trajectories of Nonsuicidal Self‐Injury in Adolescence and Intrapersonal/Interpersonal Risk Factors

This 3‐wave study investigated the developmental trajectories of nonsuicidal self‐injury (NSSI) and intrapersonal/interpersonal risk factors among 3,381 Chinese adolescents (56.2% females) aged from 13 to 17 years during a 1‐year period. Using an accelerated longitudinal design and latent class growth analysis, we identified four subgroups of NSSI trajectories: negligible (74.6%), experimental (12.8%), moderate decreasing (10.8%), and high fluctuating (1.9%). Adolescents reporting both intrapersonal (i.e., impulsive behaviors and depression) and interpersonal (i.e., unstable relationships and parental criticism) risk factors were significantly more likely to follow the latter three trajectories. The findings of this study suggest there is heterogeneity in NSSI development among adolescents and highlight the contributions of both intrapersonal and interpersonal risk factors in the engagement in NSSI.     

Use of likelihood ratio tests to detect outliers under the variance shift outlier model

In this paper, we revisit the alternative outlier model of Thompson [A note on restricted maximum likelihood estimation with an alternative outlier model, J. Roy. Stat. Soc. Ser. B 47 (1985), pp. 53–55] for detecting outliers in the linear model. Gumedze et al. [A variance shift model for detection of outliers in the linear mixed model, Comput. Statist. Data Anal. 54 (2010), pp. 2128–2144] called this model the variance shift outlier model (VSOM). The basic idea behind the VSOM is to detect observations with inflated variance and isolate them for further investigation. The VSOM is appealing because it downweights an outlier in the analysis, with the weighting determined automatically as part of the estimation procedure. We set up the VSOM as a linear mixed model and then use the likelihood ratio test (LRT) statistic as an objective measure for determining whether the weighting is required, i.e. whether the observation is an outlier. We also derived one-step updates of the variance parameter estimates based on observed, expected and average information matrices to obtain one-step LRT statistics which usually require less computation. Both the fully iterated and one-step LRTs are functions of the squared standard residuals from the null model and therefore can be computed directly without the need to fit the VSOM. We investigated the properties of the likelihood ratio tests and compare them. An extension of the model to detect a group of outliers is also given. We illustrate the proposed methodology using simulated datasets and a real dataset.     

Application of sensitivity analysis to incomplete longitudinal CD4 count data

In this paper, we investigate the effect of tuberculosis pericarditis (TBP) treatment on CD4 count changes over time and draw inferences in the presence of missing data. We accounted for missing data and conducted sensitivity analyses to assess whether inferences under missing at random (MAR) assumption are sensitive to not missing at random (NMAR) assumptions using the selection model (SeM) framework. We conducted sensitivity analysis using the local influence approach and stress-testing analysis. Our analyses showed that the inferences from the MAR are robust to the NMAR assumption and influential subjects do not overturn the study conclusions about treatment effects and the dropout mechanism. Therefore, the missing CD4 count measurements are likely to be MAR. The results also revealed that TBP treatment does not interact with HIV/AIDS treatment and that TBP treatment has no significant effect on CD4 count changes over time. Although the methods considered were applied to data in the IMPI trial setting, the methods can also be applied to clinical trials with similar settings.