Biomedical subjects

Gregory McHugo

Publications and source records attributed to Gregory McHugo.

4 recordsLinked to original sources

PubMed2006

Are there distinctive trajectory groups in substance abuse remission over 10 years? An application of the group-based modeling approach.

This paper introduces to mental health services and evaluation researchers a method for analyzing longitudinal data: a group-based modeling approach for applied research. We present the basic formulations of the model and illustrate its application by analyzing the 10-year longitudinal data on substance abuse remission from the New Hampshire Dual Disorders Study. The basic features of the approach are: identifying latent groups with distinctive trajectories, estimating the proportion of the population in each group, linking individual-level covariates to group membership, assigning individuals to different groups, and creating group profiles based on posterior probabilities. We also discuss cautions and the controversies surrounding this approach. The major findings include four groups with distinct trajectories of remission from substance abuse.

Female↗

PubMed2005

Randomized trial of cognitive-behavioral therapy for chronic posttraumatic stress disorder in adult female survivors of childhood sexual abuse.

The authors conducted a randomized clinical trial of individual psychotherapy for women with posttraumatic stress disorder (PTSD) related to childhood sexual abuse (n = 74), comparing cognitive-behavioral therapy (CBT) with a problem-solving therapy (present-centered therapy; PCT) and to a wait-list (WL). The authors hypothesized that CBT would be more effective than PCT and WL in decreasing PTSD and related symptoms. CBT participants were significantly more likely than PCT participants to no longer meet criteria for a PTSD diagnosis at follow-up assessments. CBT and PCT were superior to WL in decreasing PTSD symptoms and secondary measures. CBT had a significantly greater dropout rate than PCT and WL. Both CBT and PCT were associated with sustained symptom reduction in this sample.

Adult↗

PubMed2004

A method for analyzing longitudinal outcomes with many zeros.

Health care utilization and cost data have challenged analysts because they are often correlated over time, highly skewed, and clumped at 0. Traditional approaches do not address all these problems, and evaluators of mental health and substance abuse interventions often grapple with the problem of how to analyze these data in a way that accurately represents program impact. Recently, the traditional 2-part model has been extended to mixed-effects mixed-distribution model with correlated random effects to deal simultaneously with excess zeros, skewness, and correlated observations. We introduce and demonstrate this new method to mental health services researchers and evaluators by analyzing the data from a study of assertive community treatment (ACT). The response variable is the number of days of hospitalization, collected every 6 months over 3 years. The explanatory variable is group: ACT vs. standard case management. Diagnosis (schizophrenia vs. bipolar disorder), time, and the baseline values of hospital days are covariates. Results indicate that clients in the ACT group have a higher probability of hospital admission, but tend to have shorter lengths of stay. The mixed-distribution model provides greater specification of a model to fit these data and leads to more refined interpretation of the results.

Adult↗

PubMed2003

Using discrete-time survival analysis to examine patterns of remission from substance use disorder among persons with severe mental illness.

Investigators in mental health research are often interested in examining critical events such as onset, relapse, and recovery from illness, including substance use disorders. As data on these critical events are often collected at discrete-time intervals (e.g., weekly, monthly, or yearly), discrete-time survival models are more appropriate than well-known continuous-time methods. In this paper, we present discrete-time survival analysis methods at an introductory level. Using data collected every 6 months from a 3-year study of assertive community treatment in New Hampshire, we show that discrete-time survival models can be used to analyze patterns of remission from substance use disorder among clients with severe mental illness. The main questions investigated are (1) when are remissions more likely to occur? and (2) what variables predict remission? The results indicate that remission is more likely to occur in the first 6 months and in the 3rd year of the study. Gender, age, baseline use of substances, and diagnosis are strong predictors of remission.

Case Management↗