THE TERMINATION PHASE: THERAPISTS’ PERSPECTIVE ON THE THERAPEUTIC RELATIONSHIP AND OUTCOME

The termination phase of treatment is recognized as a significant aspect of the therapy process and yet remains vastly understudied in psychotherapy literature. In the present study, therapists’ perspectives were used to examine how three elements of the therapy relationship (working alliance, real relationship and transference) during the termination phase relate to perceived client sensitivity to loss, termination phase evaluation and overall treatment outcome. Self-report data was gathered from 233 therapists, recruited from two Divisions of the American Psychological Association. Therapists completed measures for their work with a client with whom they could identify a termination phase of treatment. Results revealed that the working alliance and real relationship during the termination phase related positively to termination phase evaluation and overall treatment outcome, whereas negative transference during the termination phase related negatively to overall treatment outcome. Therapists’ perceptions of client sensitivity to loss related positively to both negative and positive transference during the termination phase. Post-hoc analyses revealed only the working alliance during the termination phase uniquely predicted overall treatment outcome in a model with the three therapy relationship elements examined together. On the other hand, all three therapy relationship variables during the termination phase uniquely predicted termination phase evaluation, when examined together. Limitations and implications of these findings are discussed, and recommendations for future study are suggested.

CAUSAL INFERENCE WITH A CONTINUOUS TREATMENT AND OUTCOME: ALTERNATIVE ESTIMATORS FOR PARAMETRIC DOSE-RESPONSE FUNCTIONS WITH APPLICATIONS.

Causal inference with a continuous treatment is a relatively under-explored problem. In this dissertation, we adopt the potential outcomes framework. Potential outcomes are responses that would be seen for a unit under all possible treatments. In an observational study where the treatment is continuous, the potential outcomes are an uncountably infinite set indexed by treatment dose. We parameterize this unobservable set as a linear combination of a finite number of basis functions whose coefficients vary across units. This leads to new techniques for estimating the population average dose-response function (ADRF). Some techniques require a model for the treatment assignment given covariates, some require a model for predicting the potential outcomes from covariates, and some require both. We develop these techniques using a framework of estimating functions, compare them to existing methods for continuous treatments, and simulate their performance in a population where the ADRF is linear and the models for the treatment and/or outcomes may be misspecified. We also extend the comparisons to a data set of lottery winners in Massachusetts. Next, we describe the methods and functions in the R package causaldrf using data from the National Medical Expenditure Survey (NMES) and Infant Health and Development Program (IHDP) as examples. Additionally, we analyze the National Growth and Health Study (NGHS) data set and deal with the issue of missing data. Lastly, we discuss future research goals and possible extensions.