Meta-Analysis of Single-Case Data: A Monte Carlo Investigation of a Three Level Model

Numerous ways to meta-analyze single-case data have been proposed in the literature, however, consensus on the most appropriate method has not been reached. One method that has been proposed involves multilevel modeling. This study used Monte Carlo methods to examine the appropriateness of Van den Noortgate and Onghena\u27s (2008) raw data multilevel modeling approach to the meta-analysis of single-case data. Specifically, the study examined the fixed effects (i.e., the overall average baseline level and the overall average treatment effect) and the variance components (e.g., the between person within study variance in the average baseline level, the between study variance in the overall average baseline level, the between person within study variance in the average treatment effect) in a three level multilevel model (repeated observations nested within individuals nested within studies). More specifically, bias of point estimates, confidence interval coverage rates, and interval widths were examined as a function of specific design and data factors. Factors investigated included (a) number of primary studies per meta-analysis, (b) modal number of participants per primary study, (c) modal series length per primary study, (d) level of autocorrelation, and (3) variances of the error terms. The results of this study suggest that the degree to which the findings of this study are supportive of using Van den Noortgate and Onghena\u27s (2008) raw data multilevel modeling approach to meta-analyzing single-case data depends on the particular effect of interest. Estimates of the fixed effects tended to be unbiased and produced confidence intervals that tended to overcover but came close to the nominal level as level-3 sample size increased. Conversely, estimates of the variance components tended to be biased and the confidence intervals for those estimates were inaccurate

Meta-Analysis of Single-Case Data: A Monte Carlo Investigation of a Three Level Model

Numerous ways to meta-analyze single-case data have been proposed in the literature, however, consensus on the most appropriate method has not been reached. One method that has been proposed involves multilevel modeling. This study used Monte Carlo methods to examine the appropriateness of Van den Noortgate and Onghena\u27s (2008) raw data multilevel modeling approach to the meta-analysis of single-case data. Specifically, the study examined the fixed effects (i.e., the overall average baseline level and the overall average treatment effect) and the variance components (e.g., the between person within study variance in the average baseline level, the between study variance in the overall average baseline level, the between person within study variance in the average treatment effect) in a three level multilevel model (repeated observations nested within individuals nested within studies). More specifically, bias of point estimates, confidence interval coverage rates, and interval widths were examined as a function of specific design and data factors. Factors investigated included (a) number of primary studies per meta-analysis, (b) modal number of participants per primary study, (c) modal series length per primary study, (d) level of autocorrelation, and (3) variances of the error terms. The results of this study suggest that the degree to which the findings of this study are supportive of using Van den Noortgate and Onghena\u27s (2008) raw data multilevel modeling approach to meta-analyzing single-case data depends on the particular effect of interest. Estimates of the fixed effects tended to be unbiased and produced confidence intervals that tended to overcover but came close to the nominal level as level-3 sample size increased. Conversely, estimates of the variance components tended to be biased and the confidence intervals for those estimates were inaccurate

Iraq War Clinician Guide 50 Traumatized Amputee VI. Treating the Traumatized Amputee

Although injuries resulting from war produce many turbulent and confused emotions, the needs of those who suffer amputations are unique. Amputation or blindness results in a loss of body function and is an insult to the patient's psychological sense of body integrity and competence. In addition to the loss of body parts, service members often must endure other injuries, as well as psychological traumas. Fear of persistent threats, anxiety related to military career curtailment, and reactions to other past overwhelming experiences may all contribute to the complex turmoil with which they struggle. Any of the above by itself is enough to overwhelm one’s psychological equilibrium. Combined with the loss of a limb, eye, or other body part, additional trauma can be exceptionally devastating. Caring for the amputee patient requires a biopsychosocial approach. The initial clinical focus is rightly on medical stabilization. Follow-on rehabilitation focuses on restoring the individual to the greatest physical, psychological, social and economic functioning possible (Haslam et al., 1960; Mendelson, Burech, Phillips, & Kappel, 1986). A successful team approach to rehabilitation includes the patient, physicians, nurses, therapists, and family members working together to create short and long term goals for the patient’s rehabilitation. As the medical injury stabilizes, attention must shift to ensure the psychological well being of the patient and the support of his/her confident reintegration into society. This chapter focuses on the unique psychological needs of the amputee patient. A brief review of the literature on treatment of amputee patients is provided. As members of the Walter Reed Army Medical Center Psychiatry Consultation Liaison Service (WRAMC PCLS) at the military medical center receiving the majority of amputees from Operation Iraqi Freedom, we provide a description of the amputee population treated and the therapeutic practices that have appeared to be most successfully implemented