Lois A. Gelfand

The Personalized Advantage Index: translating research on prediction into individualized treatment recommendations. A demonstration.

Background Advances in personalized medicine require the identification of variables that predict differential response to treatments as well as the development and refinement of methods to transform predictive information into actionable recommendations. Objective To illustrate and test a new method for integrating predictive information to aid in treatment selection, using data from a randomized treatment comparison. Method Data from a trial of antidepressant medications (N = 104) versus cognitive behavioral therapy (N = 50) for Major Depressive Disorder were used to produce predictions of post-treatment scores on the Hamilton Rating Scale for Depression (HRSD) in each of the two treatments for each of the 154 patients. The patients own data were not used in the models that yielded these predictions. Five pre-randomization variables that predicted differential response (marital status, employment status, life events, comorbid personality disorder, and prior medication trials) were included in regression models, permitting the calculation of each patients Personalized Advantage Index (PAI), in HRSD units. Results For 60% of the sample a clinically meaningful advantage (PAI≥3) was predicted for one of the treatments, relative to the other. When these patients were divided into those randomly assigned to their “Optimal” treatment versus those assigned to their “Non-optimal” treatment, outcomes in the former group were superior (d = 0.58, 95% CI .17—1.01). Conclusions This approach to treatment selection, implemented in the context of two equally effective treatments, yielded effects that, if obtained prospectively, would rival those routinely observed in comparisons of active versus control treatments.

Mediation analysis: a retrospective snapshot of practice and more recent directions.

R. Baron and D. A. Kennys (1986) paper introducing mediation analysis has been cited over 9,000 times, but concerns have been expressed about how this method is used. The authors review past and recent methodological literature and make recommendations for how to address 3 main issues: association, temporal order, and the no omitted variables assumption. The authors briefly visit the topics of reliability and the confirmatory-exploratory distinction. In addition, to provide a sense of the extent to which the earlier literature had been absorbed into practice, the authors examined a sample of 50 articles from 2002 citing R. Baron and D. A. Kenny and containing at least 1 mediation analysis via ordinary least squares regression. A substantial proportion of these articles included problematic reporting; as of 2002, there appeared to be room for improvement in conducting such mediation analyses. Future literature reviews will demonstrate the extent to which the situation has improved.

A Placebo-Controlled Randomized Clinical Trial of Naltrexone in the Context of Different Levels of Psychosocial Intervention

BACKGROUND Naltrexone is approved for the treatment of alcohol dependence when used in conjunction with a psychosocial intervention. This study was undertaken to examine the impact of 3 types of psychosocial treatment combined with either naltrexone or placebo treatment on alcohol dependency over 24 weeks of treatment: (1) Cognitive-Behavioral Therapy (CBT) + medication clinic, (2) BRENDA (an intervention promoting pharmacotherapy) + medication clinic, and (3) a medication clinic model with limited therapeutic content. METHODS Two hundred and forty alcohol-dependent subjects were enrolled in a 24-week double-blind placebo-controlled study of naltrexone (100 mg/d). Subjects were also randomly assigned to 1 of 3 psychosocial interventions. All patients were assessed for alcohol use, medication adherence, and adverse events at regularly scheduled research visits. RESULTS There was a modest main treatment effect for the psychosocial condition favoring those subjects randomized to CBT. Intent-to-treat analyses suggested that there was no overall efficacy of naltrexone and no medication by psychosocial intervention interaction. There was a relatively low level of medication adherence (50% adhered) across conditions, and this was associated with poor outcome. CONCLUSIONS Results from this 24-week treatment study demonstrate the importance of the psychosocial component in the treatment of alcohol dependence. Moreover, results demonstrate a substantial association between medication adherence and treatment outcomes. The findings suggest that further research is needed to determine the appropriate use of pharmacotherapy in maximizing treatment response.

Understanding processes of change: How some patients reveal more than others – and some groups of therapists less – about what matters in psychotherapy

Abstract Objective: We identify difficulties researchers encounter in psychotherapy process-outcome investigations, and we describe several limitations of the popular “variance accounted for” approach to understanding the effects of psychotherapy. Methods & Results: Using data simulations, we show how the expected correlation between an excellent measure of therapy quality and outcome would be surprisingly small (approximately .25) under conditions likely to be common in psychotherapy research. Even when we modeled conditions designed to increase the likelihood that strong process-outcome relationships would be observed, we found that the expected correlations were still only in the modest range (.38–.51). Conclusions: We discuss the implications of our analysis for the interpretation of process-outcome findings as well as for design considerations in future investigations.

Sequence of improvement in depressive symptoms across cognitive therapy and pharmacotherapy.

BACKGROUND The authors examined the patterns of improvement in cognitive and vegetative symptoms of major depression in individuals treated with cognitive therapy (CT) or pharmacotherapy (PT). METHOD Outpatients diagnosed with major depressive disorder (n=180) were randomized to receive either CT or PT. Cognitive and vegetative symptoms of major depression were measured by the Beck Depression Inventory-II at baseline and regularly throughout 16 weeks of treatment. RESULTS Multivariate hierarchical linear modeling demonstrated the same patterns of change over time for cognitive and vegetative symptoms within CT and within PT. LIMITATIONS Self-report measures may not be sufficiently specific to capture subtle differences in improvements between vegetative and cognitive symptoms. CONCLUSIONS These results are consistent with Becks [Beck, A.T., 1984, November. Cognition and theory [Letter to the editor]. Arch. Gen. Psychiatry 41, 1112-1114.] hypothesis that CT and PT have a similar site of action, which when targeted, results in changes in both cognitive and vegetative features.

Dynamical systems theory in psychology: assistance for the lay reader is required

Dynamical Systems Theory (DST) has generated interest and excitement in psychological research, as demonstrated by the recent statement, “…the dynamical perspective has emerged as a primary paradigm for the investigation of psychological processes at different levels of personal and social reality” (Vallacher et al., 2010, p. 263). What is less clear to the authors is the degree to which this excitement is justified. Like many psychology researchers, we were initially unfamiliar with the concepts, terminology, and techniques used in DST modeling (an approach that was developed in physics), which made it difficult to judge applications of DST in the articles we encountered. After reading introductory DST material, we developed some opinions about how authors of DST-related articles could help psychologists who are not familiar with DST [hereafter referred to as “lay reader(s)”] to begin to make judgments about their work. In order for DST to be a useful methodology for psychology research, we believe, DST-based work must be reasonably accessible to other psychologists. Although we are restricting our discussion to the application of DST methodology to clinical psychology research, we believe that the following three recommendations may be applied to the field of psychology more broadly.

Mediation Analysis with Survival Outcomes: Accelerated Failure Time vs. Proportional Hazards Models.

Objective: Survival time is an important type of outcome variable in treatment research. Currently, limited guidance is available regarding performing mediation analyses with survival outcomes, which generally do not have normally distributed errors, and contain unobserved (censored) events. We present considerations for choosing an approach, using a comparison of semi-parametric proportional hazards (PH) and fully parametric accelerated failure time (AFT) approaches for illustration. Method: We compare PH and AFT models and procedures in their integration into mediation models and review their ability to produce coefficients that estimate causal effects. Using simulation studies modeling Weibull-distributed survival times, we compare statistical properties of mediation analyses incorporating PH and AFT approaches (employing SAS procedures PHREG and LIFEREG, respectively) under varied data conditions, some including censoring. A simulated data set illustrates the findings. Results: AFT models integrate more easily than PH models into mediation models. Furthermore, mediation analyses incorporating LIFEREG produce coefficients that can estimate causal effects, and demonstrate superior statistical properties. Censoring introduces bias in the coefficient estimate representing the treatment effect on outcome—underestimation in LIFEREG, and overestimation in PHREG. With LIFEREG, this bias can be addressed using an alternative estimate obtained from combining other coefficients, whereas this is not possible with PHREG. Conclusions: When Weibull assumptions are not violated, there are compelling advantages to using LIFEREG over PHREG for mediation analyses involving survival-time outcomes. Irrespective of the procedures used, the interpretation of coefficients, effects of censoring on coefficient estimates, and statistical properties should be taken into account when reporting results.