A study of alternative approaches to non-normal latent trait distributions in item response theory models used for health outcome measurement

Hungarian PROMIS-29+2: psychometric properties and population reference values

OBJECTIVES

This study aims to assess psychometric properties of the Hungarian PROMIS-29+2 profile measure and provide general population reference values for Hungary.

METHODS

An adult general population sample (n = 1700) completed PROMIS-29+2 v2.1 in an online survey. The following psychometric properties were assessed: floor and ceiling effect, convergent validity with SF-36v1 domains, internal consistency (McDonald's omega), unidimensionality, local independence, monotonicity, graded response model (GRM) fit and differential item functioning (DIF). Age- and gender-specific reference values were established using the US item calibrations.

RESULTS

Depending on scale orientation, high floor or ceiling effects were observed for all domains (25.2-60.7%) except for sleep disturbance. McDonald's omega for domains ranged from 0.87-0.97. Unidimensionality, local independence and monotonicity were supported and the GRM adequately fitted for all but one domains. The sleep disturbance domain demonstrated item misfit, response level disordering and low discrimination ability, particularly for item Sleep116 ('refreshing sleep'). Strong correlations were observed between PROMIS-29+2 and corresponding SF-36 domains (r_s=│0.60│ to │0.78│). No DIF was detected for most sociodemographic characteristics. Problems with physical function, pain interference and social roles tended to increase, whereas problems with anxiety, depression, fatigue and cognitive function declined with age (p < 0.01). In all domains except for cognitive function, more health problems occurred in females than in males (p < 0.001).

CONCLUSION

The Hungarian PROMIS-29+2 shows satisfactory psychometric properties; however, the sleep disturbance domain substantially underperforms that requires further attention. Population reference values were generated that facilitate the interpretation of health outcomes in various patient populations.

A Common Measurement Scale for Self-Report Instruments in Mental Health Care

Abstract. The diversity of measures in clinical psychology hampers a straightforward interpretation of test results, complicates communication with the patient, and constitutes a challenge to the implementation of measurement-based care. In educational research and assessment, it is common practice to convert test scores to a common metric, such as T scores. We recommend applying this also in clinical psychology and propose and test a procedure to arrive at T scores approximating a normal distribution that can be applied to individual test scores. We established formulas to estimate normalized T scores from raw scale scores by regressing IRT-based θ scores on raw scores. With data from a large population and clinical samples, we established crosswalk formulas. Their validity was investigated by comparing calculated T scores with IRT-based T scores. IRT and formulas yielded very similar T scores, supporting the validity of the latter approach. Theoretical and practical advantages and disadvantages of both approaches to convert scores to common metrics and alternative approaches are discussed. Provided that scale characteristics allow for their computation, T scores will help to better understand measurement results, which makes it easier for patients and practitioners to use test results in joint decision-making about the course of treatment.

Overfactoring in rating scale data: A comparison between factor analysis and item response theory

Educational and psychological measurement is typically based on dichotomous variables or rating scales comprising a few ordered categories. When the mean of the observed responses approaches the upper or the lower bound of the scale, the distribution of the data becomes skewed and, if a categorical factor model holds in the population, the Pearson correlation between variables is attenuated. The consequence of this correlation attenuation is that the traditional linear factor model renders an excessive number of factors. This article presents the results of a simulation study investigating the problem of overfactoring and some solutions. We compare five widely known approaches: (1) The maximum-likelihood factor analysis (FA) model for normal data, (2) the categorical factor analysis (FAC) model based on polychoric correlations and maximum likelihood (ML) estimation, (3) the FAC model estimated using a weighted least squares algorithm, (4) the mean corrected chi-square statistic by Satorra-Bentler to handle the lack of normality, and (5) the Samejima's graded response model (GRM) from item response theory (IRT). Likelihood-ratio chi-square, parallel analysis (PA), and categorical parallel analysis (CPA) are used as goodness-of-fit criteria to estimate the number of factors in the simulation study. Our results indicate that the maximum-likelihood estimation led to overfactoring in the presence of skewed variables both for the linear and categorical factor model. The Satorra-Bentler and GRM constitute the most reliable alternatives to estimate the number of factors.

Should suicidal ideation be regarded as a dimension, a unipolar trait or a mixture? A model-based analysis at the score level

Screening questionnaires administered in community samples may allow to early identify suicidal ideation (S.I.). Although the results found in these samples suggest that S.I. behaves like a unipolar trait or a quasi-trait, it is routinely assessed using procedures developed for bipolar traits. Therefore, the main aim of this study is to determine whether there is a basis for modelling S.I. as a bipolar trait, a unipolar trait, or a quasi-trait with two classes of individuals (symptomatic and asymptomatic). In a community sample and mainly at the scoring level, we compare the results provided by fitting three models based on different assumptions: GRM (bipolar traits), LL-GRM (unipolar traits) and FMA (quasi-traits). 773 Spanish participants answered a S.I. and a life satisfaction questionnaires. GRM and LL-GRM provided equivalent results at the structural level, but not at the scoring level, especially in the conditional and marginal accuracy of the estimated scores. While the GRM scores are highly accurate only in a narrow range well above the mean, the LL-GRM scores are highly accurate in a much wider range around the mean. They also have different implications for the prediction of life satisfaction. FMA results suggest that an asymptomatic and a symptomatic class could not be clearly differentiated. In conclusion, LL-GRM would make it possible to accurately measure a larger number of subjects in a community sample than GRM, leaving fewer cases of vulnerable people unidentified. These results should be considered by researchers and professionals when deciding which modellings to use for screening purposes.

Assessing Dimensionality in Dichotomous Items When Many Subjects Have All-Zero Responses: An Example From Psychiatry and a Solution Using Mixture Models

Common methods for determining the number of latent dimensions underlying an item set include eigenvalue analysis and examination of fit statistics for factor analysis models with varying number of factors. Given a set of dichotomous items, the authors demonstrate that these empirical assessments of dimensionality often incorrectly estimate the number of dimensions when there is a preponderance of individuals in the sample with all-zeros as their responses, for example, not endorsing any symptoms on a health battery. Simulated data experiments are conducted to demonstrate when each of several common diagnostics of dimensionality can be expected to under- or over-estimate the true dimensionality of the underlying latent variable. An example is shown from psychiatry assessing the dimensionality of a social anxiety disorder battery where 1, 2, 3, or more factors are identified, depending on the method of dimensionality assessment. An all-zero inflated exploratory factor analysis model (AZ-EFA) is introduced for assessing the dimensionality of the underlying subgroup corresponding to those possessing the measurable trait. The AZ-EFA approach is demonstrated using simulation experiments and an example measuring social anxiety disorder from a large nationally representative survey. Implications of the findings are discussed, in particular, regarding the potential for different findings in community versus patient populations.

Design and methodology for an integrative data analysis of coping power: Direct and indirect effects on adolescent suicidality

As suicide rates have risen in the last decade, there has been greater emphasis on targeting early risk conditions for suicidality among youth and adolescents as a form of suicide "inoculation". Two particular needs that have been raised in this nascent literature are a) the dearth of examination of early intervention effects on distal suicide risk that target externalizing behaviors and b) the need to harmonize multiple existing intervention datasets for greater precision in modeling intervention effects on low base rate outcomes such as suicidal behaviors. This project, entitled "Integrative Data Analysis of Coping Power (CP): Effects on Adolescent Suicidality", funded by the National Institute of Mental Health (NIMH), will harmonize and analyze data from 11 randomized controlled trials of CP (total individual-level N = 3183, total school-level N = 189). CP is an empirically-supported, child- and family-focused preventive intervention that focuses on reducing externalizing more broadly among youth who exhibit early aggression, which makes it ideally suited to targeting externalizing pathways to suicidality. The project utilizes three measurement and data analysis frameworks that have emerged across multiple independent disciplines: integrative data analysis (IDA), random treatment effects multilevel modeling (RTE-MLM), and propensity score weighting (PSW). If successful, the project will a) provide initial evidence that CP would have gender-specific indirect effects on suicidality through reductions in externalizing for boys and reductions in internalizing for girls and b) identify optimal conditions under which CP is delivered (e.g., groups, individuals, online) across participants on reductions in suicidality and other key intermediate endpoints.

Commentary: Matching IRT Models to PRO Constructs- Modeling Alternatives, and Some Thoughts on What Makes a Model Different

This commentary is an attempt to present some additional alternatives to the suggestions made by Reise et al. (2021). IRT models as they are used for patient-reported outcome (PRO) scales may not be fully satisfactory when used with commonly made assumptions. The suggested change to an alternative parameterization is critically reflected with the intent to initiate discussion around more comprehensive alternatives that allow for more complex latent structures having the potential to be more appropriate for PRO scales as they are applied to diverse populations.

Between-Item Multidimensional IRT: How Far Can the Estimation Methods Go?

Multidimensional item response models are known to be difficult to estimate, with a variety of estimation and modeling strategies being proposed to handle the difficulties. While some previous studies have considered the performance of these estimation methods, they typically include only one or two methods, or a small number of factors. In this paper, we report on a large simulation study of between-item multidimensional IRT estimation methods, considering five different methods, a variety of sample sizes, and up to eight factors. This study provides a comprehensive picture of the methods’ relative performance, as well as each individual method’s strengths and weaknesses. The study results lead us to make recommendations for applied research, related to which estimation methods should be used under various scenarios.

Item Parameter Estimation in Multistage Designs: A Comparison of Different Estimation Approaches for the Rasch Model

There is some debate in the psychometric literature about item parameter estimation in multistage designs. It is occasionally argued that the conditional maximum likelihood (CML) method is superior to the marginal maximum likelihood method (MML) because no assumptions have to be made about the trait distribution. However, CML estimation in its original formulation leads to biased item parameter estimates. Zwitser and Maris (2015, Psychometrika) proposed a modified conditional maximum likelihood estimation method for multistage designs that provides practically unbiased item parameter estimates. In this article, the differences between different estimation approaches for multistage designs were investigated in a simulation study. Four different estimation conditions (CML, CML estimation with the consideration of the respective MST design, MML with the assumption of a normal distribution, and MML with log-linear smoothing) were examined using a simulation study, considering different multistage designs, number of items, sample size, and trait distributions. The results showed that in the case of the substantial violation of the normal distribution, the CML method seemed to be preferable to MML estimation employing a misspecified normal trait distribution, especially if the number of items and sample size increased. However, MML estimation using log-linear smoothing lea to results that were very similar to the CML method with the consideration of the respective MST design.

Psychometric properties of the PROMIS-57 questionnaire, Norwegian version

Purpose

The aims of this cross-sectional study were to explore reliability and validity of the Norwegian version of the Patient-Reported Outcome Measurement System^®—Profile 57 (PROMIS-57) questionnaire in a general population sample, n = 408, and to examine Item Response properties and factor structure.

Methods

Reliability measures were obtained from factor analysis and item response theory (IRT) methods. Correlations between PROMIS-57 and RAND-36-item health survey (RAND36) were examined for concurrent and discriminant validity. Factor structure and IRT assumptions were examined with factor analysis methods. IRT Item and model fit and graphic plots were inspected, and differential item functioning (DIF) for language, age, gender, and education level were examined.

Results

PROMIS-57 demonstrated excellent reliability and satisfactory concurrent and discriminant validity. Factor structure of seven domains was supported. IRT assumptions were met for unidimensionality, local independence, monotonicity, and invariance with no DIF of consequence for language or age groups. Estimated common variance (ECV) per domain and confirmatory factor analysis (CFA) model fit supported unidimensionality for all seven domains. The GRM IRT Model demonstrates acceptable model fit.

Conclusions

The psychometric properties and factor structure of Norwegian PROMIS-57 were satisfactory. Hence, the 57-item questionnaire along with PROMIS-29, and the corresponding 8 and 4 item short forms for physical function, anxiety, depression, fatigue, sleep disturbance, social participation ability and pain interference, are considered suitable for use in research and clinical care in Norwegian populations. Further studies on longitudinal reliability and sensitivity in patient populations and for Norwegian item calibration and/or reference scores are needed.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11136-021-02906-1.

An Item Response Theory-Based Scoring of the South Oaks Gambling Screen-Revised Adolescents

The South Oaks Gambling Screen-Revised Adolescent (SOGS-RA) is one of the most widely used screening tools for problem gambling among adolescents. In this study, item response theory was used for computing measures of problem gambling severity that took into account how much information the endorsed items provided about the presence of problem gambling. A zero-inflated mixture two-parameter logistic model was estimated on the responses of 4,404 adolescents to the South Oaks Gambling Screen-Revised Adolescent to compute the difficulty and discrimination of each item, and the problem gambling severity level (θ score) of each respondent. Receiver operating characteristic curve analysis was used to identify the cutoff on the θ scores that best distinguished daily and nondaily gamblers. This cutoff outperformed the common cutoff defined on the sum scores in identifying daily gamblers but fell behind it in identifying nondaily gamblers. When screening adolescents to be subjected to further investigations, the cutoff on the θ scores must be preferred to that on the sum scores.

Psychometric properties of the Patient-Reported Outcomes Measurement Information System (PROMIS®) pediatric item bank peer relationships in the Dutch general population

PURPOSE

This study aimed to validate the PROMIS Pediatric item bank v2.0 Peer Relationships and compare reliability of the full item bank to its short form, computerized adaptive test (CAT) and the social functioning (SF) subscale of the Pediatric Quality of Life Inventory (PedsQL™).

METHODS

Children aged 8-18 (n = 1327), representative of the Dutch population completed the Peer Relationships item bank. A graded response model (GRM) was fit to the data. Structural validity was assessed by checking item-fit statistics (S-X², p < 0.001 = misfit). For construct validity, a moderately strong correlation (> 0.50) was expected between Peer Relationships and the PedsQL SF subscale. Cross-cultural DIF between U.S. and NL was assessed using logistic regression, where an item with McFadden's pseudo R² > 0.02 was considered to have DIF. Percentage of participants reliably measured was assessed using the standard error of measurement (SEM) < 0.32 as a criterion (reliability of 0.90). Relative efficiency ((1-SEM²)/n_items) was calculated to compare how well the instruments performed relative to the amount of items administered.

RESULTS

In total, 527 (response rate: 39.7%) children completed the PROMIS v2.0 Peer Relationships item bank (n_items = 15) and the PedsQL™ (n_items = 23). Structural validity of the Peer Relationships item bank was sufficient, but one item displayed misfit in the GRM model (S-X² < 0.001); 5152R1r ("I played alone and kept to myself"). The item 733R1r ("I was a good friend") was the only item that displayed cross-cultural DIF (R² = 0.0253). The item bank correlated moderately high (r = 0.61) with the PedsQL SF subscale Reliable measurements were obtained at the population mean and > 2SD in the clinically relevant direction. CAT outperformed all other measures in efficiency. Mean T-score of the Dutch general population was 46.9(SD 9.5).

CONCLUSION

The pediatric PROMIS Peer Relationships item bank was successfully validated for use within the Dutch population and reference data are now available.