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Abstract. Modern prediction methods from machine learning (ML) and artificial intelligence (AI) are becoming increasingly popular, also in the field of psychological assessment. These methods provide unprecedented flexibility for modeling large numbers of predictor variables and non-linear associations between predictors and responses. In this paper, we aim to look at what these methods may contribute to the assessment of criterion validity and their possible drawbacks. We apply a range of modern statistical prediction methods to a dataset for predicting the university major completed, based on the subscales and items of a scale for vocational preferences. The results indicate that logistic regression combined with regularization performs strikingly well already in terms of predictive accuracy. More sophisticated techniques for incorporating non-linearities can further contribute to predictive accuracy and validity, but often marginally.

OBJECTIVE: Many individuals with an eating disorder do not receive appropriate care. Low-threshold interventions could help bridge this treatment gap. The study aim was to evaluate the effectiveness of Featback, a fully automated online self-help intervention, online expert-patient support and their combination. METHOD: A randomized controlled trial with a 12-month follow-up period was conducted. Participants aged 16 or older with at least mild eating disorder symptoms were randomized to four conditions: (1) Featback, a fully automated online self-help intervention, (2) chat or email support from a recovered expert patient, (3) Featback with expert-patient support and (4) a waiting list control condition. The intervention period was 8 weeks and there was a total of six online assessments. The main outcome constituted reduction of eating disorder symptoms over time. RESULTS: Three hundred fifty five participants, of whom 43% had never received eating disorder treatment, were randomized. The three active interventions were superior to a waitlist in reducing eating disorder symptoms (d = -0.38), with no significant difference in effectiveness between the three interventions. Participants in conditions with expert-patient support were more satisfied with the intervention. DISCUSSION: Internet-based self-help, expert-patient support and their combination were effective in reducing eating disorder symptoms compared to a waiting list control condition. Guidance improved satisfaction with the internet intervention but not its effectiveness. Low-threshold interventions such as Featback and expert-patient support can reduce eating disorder symptoms and reach the large group of underserved individuals, complementing existing forms of eating disorder treatment. PUBLIC SIGNIFICANCE STATEMENT: Individuals with eating-related problems who received (1) a fully automated internet-based intervention, (2) chat and e-mail support by a recovered individual or (3) their combination, experienced stronger reductions in eating disorder symptoms than those who received (4) usual care. Such brief and easy-access interventions play an important role in reaching individuals who are currently not reached by other forms of treatment. ispartof: INTERNATIONAL JOURNAL OF EATING DISORDERS vol:55 issue:10 pages:1361-1373 ispartof: location:United States status: published

ispartof: EUROPEAN JOURNAL OF PSYCHOLOGICAL ASSESSMENT vol:37 issue:2 pages:81-85 status: published

Background Adding short-term psychodynamic psychotherapy (STPP) to antidepressants increases treatment efficacy, but it is unclear which patients benefit specifically. This study examined efficacy moderators of combined treatment (STPP + antidepressants) v. antidepressants for adults with depression. Methods For this systematic review and meta-analysis (PROSPERO registration number: CRD42017056029), we searched PubMed, PsycINFO, Embase.com, and the Cochrane Library from inception to 1 January 2022. We included randomized clinical trials comparing combined treatment (antidepressants + individual outpatient STPP) v. antidepressants in the acute-phase treatment of depression in adults. Individual participant data were requested and analyzed combinedly using mixed-effects models (adding Cochrane risk of bias items as covariates) and an exploratory machine learning technique. The primary outcome was post-treatment depression symptom level. Results Data were obtained for all seven trials identified (100%, n = 482, combined: n = 238, antidepressants: n = 244). Adding STPP to antidepressants was more efficacious for patients with high rather than low baseline depression levels [B = −0.49, 95% confidence interval (CI) −0.61 to −0.37, p < 0.0001] and for patients with a depressive episode duration of >2 years rather than B = −0.68, 95% CI −1.31 to −0.05, p = 0.03) and than 1–2 years (B = −0.86, 95% CI −1.66 to −0.06, p = 0.04). Heterogeneity was low. Effects were replicated in analyses controlling for risk of bias. Conclusions To our knowledge, this is the first study that examines moderators across trials assessing the addition of STPP to antidepressants. These findings need validation but suggest that depression severity and episode duration are factors to consider when adding STPP to antidepressants and might contribute to personalizing treatment selection for depression.

For many years, psychiatrists have tried to understand factors involved in response to medications or psychotherapies, in order to personalize their treatment choices. There is now a broad and growing interest in the idea that we can develop models to personalize treatment decisions using new statistical approaches from the field of machine learning and applying them to larger volumes of data. In this pursuit, there has been a paradigm shift away from experimental studies to confirm or refute specific hypotheses towards a focus on the overall explanatory power of a predictive model when tested on new, unseen datasets. In this paper, we review key studies using machine learning to predict treatment outcomes in psychiatry, ranging from medications and psychotherapies to digital interventions and neurobiological treatments. Next, we focus on some new sources of data that are being used for the development of predictive models based on machine learning, such as electronic health records, smartphone and social media data, and on the potential utility of data from genetics, electrophysiology, neuroimaging and cognitive testing. Finally, we discuss how far the field has come towards implementing prediction tools in real‐world clinical practice. Relatively few retrospective studies to‐date include appropriate external validation procedures, and there are even fewer prospective studies testing the clinical feasibility and effectiveness of predictive models. Applications of machine learning in psychiatry face some of the same ethical challenges posed by these techniques in other areas of medicine or computer science, which we discuss here. In short, machine learning is a nascent but important approach to improve the effectiveness of mental health care, and several prospective clinical studies suggest that it may be working already.

Objective: Decision-tree methods are machine-learning methods which provide results that are relatively easy to interpret and apply by human decision makers. The resulting decision trees show how baseline patient characteristics can be combined to predict treatment outcomes for individual patients, for example. This paper introduces GLMM trees, a decision-tree method for multilevel and longitudinal data. Method: To illustrate, we apply GLMM trees to a dataset of 3,256 young people (mean age 11.33, 48% girls) receiving treatment at one of several mental-health service providers in the UK. Two treatment outcomes (mental-health difficulties scores corrected for baseline) were regressed on 18 demographic, case and severity characteristics at baseline. We compared the performance of GLMM trees with that of traditional GLMMs and random forests. Results: GLMM trees yielded modest predictive accuracy, with cross-validated multiple R values of .18 and .25. Predictive accuracy did not differ significantly from that of traditional GLMMs and random forests, while GLMM trees required evaluation of a lower number of variables. Conclusion: GLMM trees provide a useful data-analytic tool for clinical prediction problems. The supplemental material provides a tutorial for replicating the GLMM tree analyses in R.

Background: Suicidality could be associated with specific combinations of biological, social and psychologicalfactors. Therefore, depressive episodes with suicidal ideation could be different from depressive episodeswithout suicidal ideation in terms of latent variable structures.Methods: In this study we compared latent variable structures between suicidal and non-suicidal depressedpatients using confirmatory factor analysis (CFA), following a measurement invariance test procedure. Patients(N = 919) suffering from major depressive disorder were selected from the Netherlands Study of Depression andAnxiety (NESDA) and split into a group that showed no symptoms of suicidal ideation (non-SI; N = 691) and asuicidal ideation (SI) group that had one or more symptoms of suicidal ideation (N = 228). Depression andanxiety symptoms were measured using the short form of the Mood and Anxiety Symptoms Questionnaire(MASQ-D30).Results: CFA implied a difference in latent variable structures between the non-SI sample (CFI 0.957; RMSEA0.041) and the SI sample (CFI 0.900; RMSEA 0.056). Subsequent multiple-group CFA showed violations ofmeasurement invariance. The General distress and Anhedonic depression subscales were best indicated byhopelessness and lack of optimism in the SI sample and by dissatisfaction and not feeling lively in the non-SIsample. Overall, the SI sample had higher scores and lower inter-item correlations on the Anhedonic depressionitems.Limitations: We have included very mild cases of suicidal ideation in our SI sample.Conclusions: On a latent variable level, depression with suicidal ideation differs from depression without suicidalideation. Results encourage further research into the symptom structure of depression among suicidal patients

Of children with mental health problems who access specialist help, 50% show reliable improvement on self-report measures at case closure and 10% reliable deterioration. To contextualise these figures it is necessary to consider rates of improvement for those in the general population. This study examined rates of reliable improvement/deterioration for children in a school sample over time. N = 9074 children (mean age 12; 52% female; 79% white) from 118 secondary schools across England provided self-report mental health (SDQ), quality of life and demographic data (age, ethnicity and free school meals (FSM) at baseline and 1 year and self-report data on access to mental health support at 1 year). Multinomial logistic regressions and classification trees were used to analyse the data. Of 2270 (25%) scoring above threshold for mental health problems at outset, 27% reliably improved and 9% reliably deteriorated at 1-year follow up. Of 6804 (75%) scoring below threshold, 4% reliably improved and 12% reliably deteriorated. Greater emotional difficulties at outset were associated with greater rates of reliable improvement for both groups (above threshold group: OR = 1.89, p p p p p p

Childhood adversity is associated with a range of mental disorders, functional impairment and higher health care costs in adulthood. In this study we evaluated if childhood adversity was predictive of adverse clinical and functional outcomes and health care costs in a sample of patients at ultra-high risk (UHR) for developing a psychosis. Structural Equation Modeling was used to examine the effect of childhood adversity on depression, anxiety, transition to psychosis and overall functioning at 4-year follow-up. In addition, we evaluated economic costs of childhood adversity in terms of health care use and productivity loss. Data pertain to 105 UHR participants of the Dutch Early Detection and Intervention Evaluation (EDIE-NL). Physical abuse was associated with higher depression rates (b=0.381, p=0.012) and lower social functional outcome (b=−0.219, p=0.017) at 4-year follow-up. In addition, emotional neglect was negatively associated with social functioning (b=−0.313, p=0.018). We did not find evidence that childhood adversity was associated with transition to psychosis, but the experience of childhood adversity was associated with excess health care costs at follow-up. The data indicate long-term negative effects of childhood adversity on depression, social functioning and health care costs at follow-up in a sample of UHR patients.

Successful smoking cessation appears to be facilitated by identity change, that is, when quitting or nonsmoking becomes part of smokers' and ex-smokers' self-concepts. The current longitudinal study is the first to examine how identity changes over time among smokers and ex-smokers and whether this can be predicted by socioeconomic status (SES) and psychosocial factors (i.e., attitude, perceived health damage, social norms, stigma, acceptance, self-evaluative emotions, health worries, expected social support). We examined identification with smoking (i.e., smoker self-identity) and quitting (i.e., quitter self-identity) among a large sample of smokers (n = 742) and ex-smokers (n = 201) in a cohort study with yearly measurements between 2009 and 2014. Latent growth curve modeling was used as an advanced statistical technique. As hypothesized, smokers perceived themselves more as smokers and less as quitters than do ex-smokers, and identification with smoking increased over time among smokers and decreased among ex-smokers. Furthermore, psychosocial factors predicted baseline identity and identity development. Socioeconomic status (SES) was particularly important. Specifically, lower SES smokers and lower SES ex-smokers identified more strongly with smoking, and smoker and quitter identities were more resistant to change among lower SES groups. Moreover, stronger proquitting social norms were associated with increasing quitter identities over time among smokers and ex-smokers and with decreasing smoker identities among ex-smokers. Predictors of identity differed between smokers and ex-smokers. Results suggest that SES and proquitting social norms should be taken into account when developing ways to facilitate identity change and, thereby, successful smoking cessation. (PsycINFO Database Record

The purpose of this study was to explore the associations between the qualities of different types of relationships in school, social support and loneliness in adolescence. Using a sample (N=1674) of adolescent students randomly selected from middle schools, we found boys' loneliness was influenced by the qualities of opposite-sex, teacher-student and same-sex relationships, whereas girls' loneliness was only influenced by same-sex relationships. Additionally, social support mediated the association between same-sex relationships and teacher-student relationships, and loneliness. Further, the quality of same-sex relationships showed stronger association with boys' loneliness than girls'. Finally, the quality of same-sex relationships showed the strongest association with boys' loneliness comparing with opposite-sex relationships and teacher-student relationships. These findings are discussed to illuminate the possible mechanisms by which interpersonal relationships could influence loneliness. In future research, causal relationships and other influencing factors on loneliness should be examined.

Identification of subgroups of patients for which treatment A is more effective than treatment B, and vice versa, is of key importance to the development of personalized medicine. Several tree-based algorithms have been developed for the detection of such treatment-subgroup interactions. In many instances, however, datasets may have a clustered structure, where observations are clustered within, for example, research centers, studies or persons. In the current paper we propose a new algorithm, generalized linear mixed-effects model (GLMM) trees, that allows for detection of treatment-subgroup interactions, as well as estimation of cluster-specific random effects. The algorithm uses model-based recursive partitioning (MOB) to detect treatment-subgroup interactions, and a GLMM for the estimation of random-effects parameters. In a simulation study, we evaluate the performance of GLMM tree and compare it with that of MOB without random-effects estimation. GLMM tree was found to have a much lower Type I error rate than MOB trees without random effects (4% and 33%, respectively). Furthermore, in datasets with treatment-subgroup interactions, GLMM tree recovered the true treatment subgroups much more often than MOB without random effects (in 90% and 61% of the datasets, respectively). Also, GLMM tree predicted treatment outcome differences more accurately than MOB without random effects (average predictive accuracy of .94 and .88, respectively). We illustrate the application of GLMM tree on a patient-level dataset of a meta-analysis on the effects of psycho- and pharmacotherapy for depression. We conclude that GLMM tree is a promising algorithm for the detection of treatment-subgroup interactions in clustered datasets.

This study investigated moderators of intervention response in a fully automated Internet-based monitoring and feedback intervention (‘Featback’) with different levels of therapist support for individuals with eating disorder (ED) symptoms. This study was part of a randomized controlled trial comparing four conditions: 1) Featback, 2) Featback with low-intensity (weekly) therapist support, 3) Featback with high-intensity (three times a week) therapist support, and 4) a waiting list. A total of 273 participants completed baseline and post-intervention assessments. The outcome measure was ED psychopathology. Model-based recursive partitioning was applied. Baseline levels of ED psychopathology were found to moderate intervention response. Specifically, in terms of improvement in symptoms of anorexia nervosa, participants with higher baseline levels of anorectic psychopathology showed better outcomes in the waiting list condition and the Featback conditions with low- and high-intensity therapist support in comparison with Featback without therapist support. In terms of improvement in symptoms of bulimia nervosa, participants with mild to moderate bulimic symptoms had better outcomes in the Featback conditions compared with the waiting list. Thus, the fully automated Internet-based intervention with and without therapist support may be particularly suitable in improving mild to moderate bulimic psychopathology, whereas the intervention without therapist support may be less effective in improving severe anorectic psychopathology. Further investigating differential intervention responses is important, as this could help to optimize the delivery and dissemination of E-health interventions and therapist support, which in turn could help moving toward personalized (E-)care.

Background:\ud \ud Given their length, commonly used scales to assess suicide risk, such as the Beck Scale for Suicide Ideation (SSI) are of limited use as screening tools. In the current study we tested whether deterministic and stochastic curtailment can be applied to shorten the 19-item SSI, without compromising its accuracy.\ud \ud Methods:\ud \ud Data from 366 patients, who were seen by a liaison psychiatry service in a general hospital in Scotland after a suicide attempt, were used. Within 24 h of admission, the SSI was administered; 15 months later, it was determined whether a patient was re-admitted to a hospital as the result of another suicide attempt. We fitted a Receiver Operating Characteristic curve to derive the best cut-off value of the SSI for predicting future suicidal behavior. Using this cut-off, both deterministic and stochastic curtailment were simulated on the item score patterns of the SSI.\ud \ud Results:\ud \ud A cut-off value of SSI≥6 provided the best classification accuracy for future suicidal behavior. Using this cut-off, we found that both deterministic and stochastic curtailment reduce the length of the SSI, without reducing the accuracy of the final classification decision. With stochastic curtailment, on average, less than 8 items are needed to assess whether administration of the full-length test will result in an SSI score below or above the cut-off value of 6.\ud \ud Limitations:\ud \ud New studies using other datasets should re-validate the optimal cut-off for risk of repeated suicidal behavior after being treated in a hospital following an attempt.\ud \ud Conclusions:\ud \ud Curtailment can be used to simplify the assessment of suicidal behavior, and should be considered as an alternative to the full scale.

IntroductionShort-term psychodynamic psychotherapy (STPP) is an empirically supported treatment that is often used to treat depression. However, it is largely unclear if certain subgroups of depressed patients can benefit specifically from this treatment method. We describe the protocol for a systematic review and meta-analysis of individual participant data (IPD) aimed at identifying predictors and moderators of STPP for depression efficacy.Method and analysisWe will conduct a systematic literature search in multiple bibliographic databases (PubMed, PsycINFO, Embase.com, Web of Science and Cochrane’s Central Register of Controlled Trials), ‘grey literature’ databases (GLIN and UMI ProQuest) and a prospective trial register (http://www.controlled-trials.com). We will include studies reporting (a) outcomes on standardised measures of (b) depressed (c) adult patients (d) receiving STPP. We will next invite the authors of these studies to share the participant-level data of their trials and combine these data to conduct IPD meta-analyses. The primary outcome for this study is post-treatment efficacy as assessed by a continuous depression measure. Potential predictors and moderators include all sociodemographic variables, clinical variables and psychological patient characteristics that are measured before the start of treatment and are assessed consistently across studies. One-stage IPD meta-analyses will be conducted using mixed-effects models.Ethics and disseminationInstitutional review board approval is not required for this study. We intend to submit reports of the outcomes of this study for publication to international peer-reviewed journals in the fields of psychiatry or clinical psychology. We also intend to present the outcomes at international scientific conferences aimed at psychotherapy researchers and clinicians. The findings of this study can have important clinical implications, as they can inform expectations of STPP efficacy for individual patients, and help to make an informed choice concerning the best treatment option for a given patient.PROSPERO registration numberCRD42017056029.

Meta-analyses comparing the accuracy of clinical versus actuarial prediction have shown actuarial methods to outperform clinical methods, on average. However, actuarial methods are still not widely used in clinical practice, and there has been a call for the development of actuarial prediction methods for clinical practice. We argue that rule-based methods may be more useful than the linear main effect models usually employed in prediction studies, from a data and decision analytic as well as a practical perspective. In addition, decision rules derived with rule-based methods can be represented as fast and frugal trees, which, unlike main effects models, can be used in a sequential fashion, reducing the number of cues that have to be evaluated before making a prediction. We illustrate the usability of rule-based methods by applying RuleFit, an algorithm for deriving decision rules for classification and regression problems, to a dataset on prediction of the course of depressive and anxiety disorders from Penninx et al. (2011). The RuleFit algorithm provided a model consisting of 2 simple decision rules, requiring evaluation of only 2 to 4 cues. Predictive accuracy of the 2-rule model was very similar to that of a logistic regression model incorporating 20 predictor variables, originally applied to the dataset. In addition, the 2-rule model required, on average, evaluation of only 3 cues. Therefore, the RuleFit algorithm appears to be a promising method for creating decision tools that are less time consuming and easier to apply in psychological practice, and with accuracy comparable to traditional actuarial methods. (PsycINFO Database Record (c) 2015 APA, all rights reserved)

In mental health care, both clinical and scientific decisions are based on within-subject comparisons of test scores on the same self-report questionnaire at different points in time. To establish the validity of test score comparisons over time, longitudinal measurement invariance should be established. The current study tested whether the 19 item Beck Scale for Suicide Ideation (BSS) is measurement invariant (MI) over time. As the first five items of the scale are often used to screen for the presence of suicidal thoughts, we also tested a model consisting of only the first five items. Psychiatric in- and out-patients (n=475) completed the questionnaire upon admission and after 3 months. By means of confirmatory factor analysis (CFA) we tested whether the parameters of a single factor model were equal over time. All fit indices indicated that both the 19-item questionnaire and the five-item screener were measurement invariant over time. This means that changes in test-scores over time can be attributed to true changes in the construct of interest. These findings legitimate the use of the 19 item scale and the five-item screener in longitudinal assessments.

Objective: To examine how various predictors and subgroups of respondents contribute to the prediction of health care and productivity costs in a cohort of employees. Methods: We selected 1548 employed people from a cohort study with and without depressive and anxiety symptoms or disorders. Prediction rules, using the RuleFit program, were applied to identify predictors and subgroups of respondents, and to predict estimations of subsequent 1-year health care and productivity costs. RESULTS:: Symptom severity and diagnosis of depression and anxiety were the most important predictors of health care costs. Depressive symptom severity was the most important predictor for productivity costs. Several demographic, social, and work predictors did not predict economic costs. Conclusions: Our data suggest that from a business perspective it can be beneficial to offer interventions aimed at prevention of depression and anxiety. Copyright © 2014 by American College of Occupational and Environmental Medicine.

Purpose: Factor mixture analysis (FMA) and item response mixture models in the general population have shown that the psychosis phenotype has four classes. This study attempted to replicate this finding in help-seeking people accessing mental health services for symptoms of non-psychotic mental disorders. Methods: All patients (18-35 years old) referred for non-psychotic mental health problems to the secondary mental healthcare service in The Hague between February 2008 to February 2010 (N = 3,694), were included. Patients completed the Prodromal Questionnaire (PQ). Hybrid latent class analysis was applied to explore the number, size and symptom profiles of the classes. Results: The FMA resulted in four classes. Class 1 (N = 1,039, 28.1 %) scored high on conceptual disorganization, inattention and mood disorder. Patients in Class 2 (N = 619, 16.8 %) endorsed almost all PQ-items, were more often screened as being psychotic or at high risk of developing psychosis, without care takers noticing. In Class 3 (N = 1,747, 47.3 %) perplexity, paranoia and negative symptoms were more prevalent. Patients were more often at high risk of developing psychosis. Class 4 (N = 286, 7.7 %) represented the 'normative' group with low probabilities for all items. Discussion: The results support the hypothesis that a representation in four classes of psychotic-like experiences can also be applied in a help-seeking population. © 2013 Springer-Verlag Berlin Heidelberg.

For classification problems in psychology (e.g., clinical diagnosis), batteries of tests are often administered. However, not every test or item may be necessary for accurate classification. In the current article, a combination of classification and regression trees (CART) and stochastic curtailment (SC) is introduced to reduce assessment length of questionnaire batteries. First, the CART algorithm provides relevant subscales and cutoffs needed for accurate classification, in the form of a decision tree. Second, for every subscale and cutoff appearing in the decision tree, SC reduces the number of items needed for accurate classification. This procedure is illustrated by post hoc simulation on a data set of 3,579 patients, to whom the Mood and Anxiety Symptoms Questionnaire (MASQ) was administered. Subscales of the MASQ are used for predicting diagnoses of depression. Results show that CART-SC provided an assessment length reduction of 56%, without loss of accuracy, compared with the more traditional prediction method of performing linear discriminant analysis on subscale scores. CART-SC appears to be an efficient and accurate algorithm for shortening test batteries. © The Author(s) 2013.

Minimizing the respondent burden and maximizing the classification accuracy of tests is essential for efficacious screening for common mental health disorders. In previous studies, curtailment of tests has been shown to reduce average test length considerably, without loss of accuracy. In the current study, we simulate Deterministic (DC) and Stochastic (SC) Curtailment for three self-report questionnaires for common mental health disorders, to study the potential gains in efficiency that can be obtained in screening for these disorders. The curtailment algorithms were applied in an existing dataset of item scores of 502 help-seeking participants. Results indicate that DC reduces test length by up to 37%, and SC reduces test length by up to 46%, with only very slight decreases in diagnostic accuracy. Compared to an item response theory based adaptive test with similar test length, SC provided better diagnostic accuracy. Consequently, curtailment may be useful in improving the efficiency of mental health self-report questionnaires. © 2013 Elsevier Ireland Ltd.

The efficacy of treatments for depression is often measured by comparing observed total scores on self-report inventories, in both clinical practice and research. However, the occurrence of response shifts (changes in subjects’ values, or their standards for measurement) may limit the validity of such comparisons. As most psychological treatments for depression are aimed at changing patients’ values and frame of reference, response shifts are likely to occur over the course of such treatments. In this article, we tested whether response shifts occurred over the course of treatment in an influential randomized clinical trial. Using confirmatory factor analysis, measurement models underlying item scores on the Beck Depression Inventory (Beck & Beamesderfer, 1974) of the National Institute of Mental Health Treatment of Depression Collaborative Research Program (Elkin, Parloff, Hadley, & Autry, 1985) were analyzed. Compared with before treatment, after-treatment item scores appeared to overestimate depressive symptomatology, measurement errors were smaller, and correlations between constructs were stronger. These findings indicate a response shift, in the sense that participants seem to get better at assessing their level of depressive symptomatology. Comparing measurement models of patients receiving psychotherapy and medication suggested that the aforementioned effects were more apparent in the psychotherapy groups. Consequently, comparisons of observed total scores on self-report inventories may yield confounded measures of treatment efficacy. (PsycINFO Database Record (c) 2019 APA, all rights reserved)