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Introduction Medications for opioid use disorder (MOUD) are the most effective interventions for this condition, yet many patients discontinue treatment. Though adjunct psychosocial treatments are recommended to increase retention and reduce relapse, the scarcity of trained providers hinders access to and utilization of evidence-based interventions. We conducted a Phase 1 study to assess the feasibility of a virtual reality-delivered Mindfulness-Oriented Recovery Enhancement (MORE-VR) intervention for patients receiving MOUD.Patients and Methods Patients receiving buprenorphine or methadone for OUD (N = 34) were scheduled for 8 weekly sessions of MORE-VR. Enrollment and retention rates were analyzed. Participants reported on the usability and acceptability of MORE-VR, opioid use, and craving and affect before and after each VR session. Heart rate was monitored during one session of MORE-VR.Results Twenty-three participants completed four or more MORE-VR sessions (minimum recommended intervention dose). Participants reported high usability and acceptability of MORE-VR, which had an excellent safety profile. Illicit opioid use decreased significantly from pre- to post-treatment (F = 4.44, p=.04). We observed a significant within-session decrease in opioid craving (F = 39.3, p

Abstract Background SWOG S0421 was a large randomized trial comparing docetaxel/prednisone plus placebo (DPP) to docetaxel/prednisone plus atrasentan over 12 cycles for patients with metastatic castration-resistant prostate cancer (mCRPC). The current report presents the PRO results for this trial, an important secondary endpoint. Methods The trial specified two primary PRO endpoints. Palliation of worst pain was based on the Brief Pain Inventory (BPI), where a 2 point difference is defined as clinically meaningful. Improvement of functional status was based on the Functional Assessment of Cancer Therapy – Prostate Cancer Trial Outcome Index (FACT-P TOI); a 5-point difference has been defined as clinically meaningful. We compared rates by arm using chi-square tests. Longitudinal analyses using linear mixed models addressed changes by arm over time. Results Four-hundred eighty-nine patients on each arm were evaluable for PRO endpoint data. There were no differences by arm in clinically meaningful pain palliation (41.7% for DPP vs. 44.0% for DPA, p = .70) or functional status (24.2% for DPP vs. 28.7% for DPA, p = .13). Longitudinal comparisons indicated no differences over time by arm for BPI Worst Pain scores (0.13 points, p = .23). Patients on the DPA arm had improved functional status of 1.78 points on average, a statistically significant (p = .02) but not clinically meaningful difference. Conclusions The SWOG S0421 PRO data showed little evidence of clinically meaningful differences by arm in either pain palliation or functional status.

Structural equation models articulate the assumed measurement and causal relations among variables, imposing discipline on otherwise unstructured and redundant associations that arise in correlational studies. Biomedical research has eschewed such methods, relying on the generally superior causal inference afforded by randomized controlled trials. Increasingly, however, clinical trials incorporate numerous covariates that are measured but unmanipulated. Most clinical trials now also include multiple correlated endpoints, which can generate ambiguous outcome patterns refractory to simple statistical analysis and interpretation. Modern clinical trials are really multivariate longitudinal studies with at best a component of randomized control; as such, structural equation approaches can add rigour and clarity. The analysis of latent variance (LANOVA) conception combines structural equation and experimental analysis of variance legacies. It allows, for any design that can be decomposed into between-group and within-person models, tests on latent means (that is, the means of the unobserved factors) that are directly analogous to their analysis of variance counterparts. LANOVA variables are either outcomes (which may have a time structure), varying covariates (which may have a time structure), or background covariates (which are static). Allowable causal relations are set-recursive: background covariates can affect all other variables; varying covariates can affect current outcomes and later outcomes and covariates; outcomes can affect only later outcomes. All standard ANOVA and ANCOVA hypotheses can then be tested by proper restrictions among the means and intercepts of these latent variables. Structural equation modelling programs can be used to estimate the models and test the hypotheses. I demonstrate the approach by expressing and testing hypotheses appropriate for two clinical studies that evaluate patient-reported outcomes in populations with solid and haematological malignancies. Copyright © 2003 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]