Quality of life during occlusion therapy for amblyopia from the perspective of the children and from that of their parents, as proxy

BACKGROUND

Parents pity their amblyopic child when they think that they suffer from occlusion therapy. We measured health-related quality of life during occlusion therapy.

METHODS

We developed the Amblyopia Parents and Children Occlusion Questionnaire (APCOQ). It was designed by a focus group of patients, orthoptists and ophthalmologists and consisted of twelve items concerning skin contact of patch, activities, contact with other children, emotions and awareness of necessity to patch. Parents filled out the Proxy Version shortly before the Child Version was obtained from their child. Child Version item scores were compared with Proxy Version item scores and related to the child's age, visual acuity, refraction, angle of strabismus, and cause of amblyopia.

RESULTS

63 children were recruited by orthoptists, and their parents agreed to participate. Three children were excluded: one child with Down-syndrome, one child with cerebral palsy, and one child who had been treated by occlusion therapy. Included were 60 children (mean age 4.57 ± 1.34 SD) and 56 parents. Children had occluded 128 ± 45 SD days at interview. Prior to occlusion, 54 children had worn glasses. Cronbach's α was 0.74 for the Child Version and 0.76 for the Proxy Version. Children judged their quality of life better than their parents did, especially pertaining to skin contact and activities like games and watching TV. Notably, 13 children with initial visual acuity ≥ 0.6 logMAR in the amblyopic eye experienced little trouble with games during occlusion. Quality of life in eight children with strabismus of five years and older correlated negatively (Spearman rank mean rho = -0.43) with angle of strabismus. Children with amblyopia due to both refractive error and strabismus (n = 14) had, relatively, lowest quality of life, also according to their parents, as proxy. Several children did not know why they wore a patch, contrary to what their parents thought.

CONCLUSIONS

Children's quality of life during occlusion therapy is affected less than their parents think, especially regarding skin contact, playing games and watching TV during occlusion. Quality of life correlates negatively with the angle of strabismus in children five years and older. Children do not know why they wear a patch, contrary to what their parents think. Notably, children with low visual acuity in the amblyopic eye, had little difficulty playing games.

Testing for response shift in treatment evaluation of change in self-reported psychopathology amongst secondary psychiatric care outpatients

OBJECTIVES

If patients change their perspective due to treatment, this may alter the way they conceptualize, prioritize, or calibrate questionnaire items. These psychological changes, also called "response shifts," may pose a threat to the measurement of therapeutic change in patients. Therefore, it is important to test the occurrence of response shift in patients across their treatment.

METHODS

This study focused on self-reported psychological distress/psychopathology in a naturalistic sample of 206 psychiatric outpatients. Longitudinal measurement invariance tests were computed across treatment in order to detect response shifts.

RESULTS

Compared with before treatment, post-treatment psychopathology scores showed an increase in model fit and factor loading, suggesting that symptoms became more coherently interrelated within their psychopathology domains. Reconceptualization (depression/mood) and reprioritization (somatic and cognitive problems) response shift types were found in several items. We found no recalibration response shift.

CONCLUSION

This study provides further evidence that response shift can occur in adult psychiatric patients across their mental health treatment. Future research is needed to determine whether response shift implies an unwanted potential bias in treatment evaluation or a desired cognitive change intended by treatment.

Common Item Equating Using the Loglinear Rasch Model

A method is proposed to equate different sets of items administered to different groups of individuals using the Rasch model. A Rasch equating model is formulated that describes one common Rasch scale in different groups with different but overlapping sets of items. The item parameters can then be estimated simultaneously, avoiding different parameter estimates of common items in different groups. The model can be tested globally to test the hypothesis of one common Rasch scale, and the goodness of link can be tested. The method is based on the quasi-loglinear Rasch model.

Stochastic Curtailment of Questionnaires for Three-Level Classification: Shortening the CES-D for Assessing Low, Moderate, and High Risk of Depression

In clinical assessment, efficient screeners are needed to ensure low respondent burden. In this article, Stochastic Curtailment (SC), a method for efficient computerized testing for classification into two classes for observable outcomes, was extended to three classes. In a post hoc simulation study using the item scores on the Center for Epidemiologic Studies-Depression Scale (CES-D) of a large sample, three versions of SC, SC via Empirical Proportions (SC-EP), SC via Simple Ordinal Regression (SC-SOR), and SC via Multiple Ordinal Regression (SC-MOR) were compared at both respondent burden and classification accuracy. All methods were applied under the regular item order of the CES-D and under an ordering that was optimal in terms of the predictive power of the items. Under the regular item ordering, the three methods were equally accurate, but SC-SOR and SC-MOR needed less items. Under the optimal ordering, additional gains in efficiency were found, but SC-MOR suffered from capitalization on chance substantially. It was concluded that SC-SOR is an efficient and accurate method for clinical screening. Strengths and weaknesses of the methods are discussed.

Connecting clinical and actuarial prediction with rule-based methods

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.

Curtailment: a method to reduce the length of self-report questionnaires while maintaining diagnostic accuracy

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.

Response shifts in mental health interventions: an illustration of longitudinal measurement invariance

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.

Improvement in Detection of Differential Item Functioning Using a Mixture Item Response Theory Model

Usually, methods for detection of differential item functioning (DIF) compare the functioning of items across manifest groups. However, the manifest groups with respect to which the items function differentially may not necessarily coincide with the true source of the bias. It is expected that DIF detection under a model that includes a latent DIF variable is more sensitive to this source of bias. In a simulation study, it is shown that a mixture item response theory model, which includes a latent grouping variable, performs better in identifying DIF items than DIF detection methods using manifest variables only. The difference between manifest and latent DIF detection increases as the correlation between the manifest variable and the true source of the DIF becomes smaller. Different sample sizes, relative group sizes, and significance levels are studied. Finally, an empirical example demonstrates the detection of heterogeneity in a minority sample using a latent grouping variable. Manifest and latent DIF detection methods are applied to a Vocabulary test of the General Aptitude Test Battery (GATB).

Construct validation of the Amblyopia and Strabismus Questionnaire (A&SQ) by factor analysis

Background

The Amblyopia and Strabismus Questionnaire (A&SQ) was previously developed to assess quality of life (QoL) in amblyopia and/or strabismus patients. Here, factor analysis with Varimax rotation was employed to confirm that the questions of the A&SQ correlated to dimensions of quality of life (QoL) in such patients.

Methods

Responses on the A&SQ from three groups were analyzed: healthy adults (controls) (n = 53), amblyopia and/or strabismus patients (n = 72), and a historic cohort of amblyopes born between 1962-1972 and occluded between 1968–1974 (n = 173). The correlations among the responses to the 26 A&SQ items were factor-analysed by Principal Component Analysis (PCA). As the development of the A&SQ was intuitive-deductive, it was expected that the pattern of correlation could be explained by the five a priori hypothesized dimensions: fear of losing the better eye, distance estimation, visual disorientation, diplopia, and social contact and cosmetic problems. Distribution of questions along the factors derived by PCA was examined by orthogonal Varimax rotation.

Results

Data from 296 respondents were analyzed. PCA provided that six factors (cutoff point eigenvalue >1.0) accumulatively explained 70.5% of the variance. All A&SQ dimensions but one matched with four factors found by Varimax rotation (factor loadings >0.50), while two factors pertained to the fifth dimension. The six factors explained 33.7% (social contact and cosmetic problems); 10.3% (near distance estimation); 8.7% (diplopia); 7.2% (visual disorientation); 6.3% (fear of losing the better eye); and 4.3% (far distance estimation), together 70.48% of the item variance.

Conclusion

The highly explained variance in the A&SQ scores by the factors found by the PCA confirmed the a priori hypothesized dimensions of this QoL instrument.

Latent-Trait Latent-Class Analysis of Self-Disclosure in the Work Environment

Based on the literature about self-disclosure, it was hypothesized that different groups of subjects differ in their pattern of self-disclosure with respect to different areas of social interaction. An extended latent-trait latent-class model was proposed to describe these general patterns of self-disclosure. The model was used to analyze the data of 1,113 subjects, tested on extraversion and with respect to their degree of self-disclosure toward different categories of people in the work environment. A model with one latent trait and a latent class variable with three categories was identified. Subjects belonging to the different latent classes differ in their general tendency to self-disclose, in their choice to whom they will show self-disclosure and in the degree to which they are selective in their self-disclosure. The collateral variable extraversion was associated with both latent variables. The association of extraversion with selectivity in self-disclosure was not significant.

Weak measurement invariance with respect to unmeasured variables: an implication of strict factorial invariance

Measurement bias refers to systematic differences across subpopulations in the relation between observed test scores and the latent variant underlying the test scores. Comparisons of subpopulations with the same score on the latent variable can be expected to have the same observed test score. Measurement invariance is therefore one of the key issues in psychological testing. It has been established that strict factorial invariance (SFI) with respect to a selection variable V almost certainly implies weak measurement invariance with respect to V: given SFI, means and variances of observed scores do not depend on V. It is shown that this result can be extended. SFI in groups derived by selection on V has implications not only for V but also for potentially biasing variables W, if W and the selection variable V and/or if W and the factor underlying the observed test scores are statistically dependent. Given SFI with respect to V and prior knowledge concerning these dependencies, it is not necessary to measure and model variables W in order to exclude them as potentially biasing variables if the investigation focuses on groups selected on V.

Investigating Group Differences on Cognitive Tests Using Spearman's Hypothesis: An Evaluation of Jensen's Method

Jensen has posited a research method to investigate group differences in cognitive tests. This method consists of first extracting a general intelligence factor by means of exploratory factor analysis. Secondly, similarity of factor loadings across groups is evaluated in an attempt to ensure that the same constructs are measured. Finally, the correlation is computed between the loadings of the tests on the general intelligence factor and the mean differences between groups on the tests. This part is referred to as a test of "Spearman's Hypothesis", which essentially states that differences in g account for the main part of differences in observed scores. Based on the correlation, inferences are made with respect to group differences in general intelligence. The validity of these inferences is investigated and compared to the validity of inferences based on multi-group confirmatory factor analysis. For this comparison, population covariance matrices are constructed which incorporate violations of the central assumption underlying Jensen's method concerning the existence of g and/or violations of Spearman's Hypothesis. It is demonstrated that Jensen's method is quite insensitive to the violations. This lack of specificity is observed consistently for all types of violations introduced in the present study. Multi-group confirmatory factor analysis emerges as clearly superior to Jensen's method.

Pseudolikelihood estimation of the Rasch model

An estimation method is proposed for the Rasch model on the basis of the pseudolikelihood theory of Arnold and Strauss (1988). A simulation study was conducted to compare the proposed maximum pseudolikelihood estimates with the well known conditional maximum likelihood and unconditional maximum likelihood estimates for the item parameters of the Rasch model. The results show great similarity between the methods.

Guilford's Facet Theory Of Intelligence: An Empirical Comparison Of Models

A general linear model was described for tests constructed in a facet design. Guilford's structure of intellect model and an alternative model were specified as special cases. From the Guilford research seven data sets were selected such that both models could be compared. The comparison was done with oblique and orthogonal factors. Using covariance structure analysis the fit of the models was assessed, and the parameters estimated. Models with orthogonal factors did not fit the data. The fit of the oblique Guilford model was better than the alternative oblique model. For three of the data sets the oblique Guilford model yielded an acceptable fit to the data with parameter estimates that could be interpreted by the structure of intellect model.