Development of the Multiple Myeloma Symptom and Impact Questionnaire: A New Patient-Reported Outcome Instrument to Assess Symptom and Impacts in Patients With Multiple Myeloma.

OBJECTIVES: This study aimed to develop and assess the content validity of a patient-reported outcomes (PROs) instrument to measure symptoms and impacts experienced by patients with active multiple myeloma (MM). METHODS: The PRO instrument was developed using an iterative, mixed-methods approach. The list of concepts was generated based on a review of existing evidence (qualitative studies and literature) and post hoc psychometric evaluations of 2 PRO instruments in 3 clinical trials. A total of 30 adult patients with MM from the United States participated in hybrid concept elicitation/cognitive debriefing interviews to validate the content validity of the newly developed PRO instrument. Translatability assessment was completed in 8 languages. RESULTS: The item generation process resulted in 17 symptom and 9 impact concepts for evaluation. The concept elicitation interviews and analysis were based on the first 25 participants; evidence of saturation was observed. The cognitive debriefing interviews and analysis were based on the last 23 participants across 4 waves of interviews. On the basis patient feedback, 10 items were removed, and 1 item was added to the PRO instrument. The translatability assessment resulted in 1 minor revision. The multiple myeloma symptom and impact questionnaire (MySIm-Q) includes 11 symptom and 6 impact concepts, organized within 8 hypothesized subdomains, with each concept measured using a 5-point verbal rating scale and a 7-day recall period. CONCLUSIONS: The MySIm-Q instrument was developed using rigorous and mixed methodology and with direct input from patients who received a diagnosis of MM. The MySIm-Q has good content validity and is culturally relevant for use in global clinical trials.

Classroom assignments as measures of teaching quality.

We investigate classroom assignments and resulting student work to identify important characteristics of assignments in terms of instructional quality and their validity as measures of teaching quality. We examine assignment quality within a large-scale project exploring multiple measures including classroom observations, teacher knowledge measures, and value-added estimates based on student achievement scores. Analyses included descriptive statistics, multivariate analyses to understand factors contributing to score variance, and correlational analyses exploring the relationship of assignment scores to other measures. Results indicate relatively low demand levels in all teacher assignments, a marked difference in score distributions for mathematics (math) and English language arts (ELA), and a substantial relationship between what was asked of and produced by students. Relationships between assignments scores, classroom characteristics, and other measures of teaching quality are examined for both domains. These findings help us understand the nature of and factors associated with assignment quality in terms of intellectual demand.

Model Similarity, Model Selection, and Attribute Classification.

Selecting the most appropriate cognitive diagnosis model (CDM) for an item is a challenging process. Although general CDMs provide better model-data fit, specific CDMs have more straightforward interpretations, are more stable, and can provide more accurate classifications when used correctly. Recently, the Wald test has been proposed to determine at the item level whether a general CDM can be replaced by specific CDMs without a significant loss in model-data fit. The current study examines the practical consequence of the test by evaluating whether the attribute-vector classification based on CDMs selected by the Wald test is better than that based on general CDMs. Although the Wald test can detect the true underlying model for certain CDMs, it is yet unclear how effective it is at distinguishing among the wider range of CDMs found in the literature. This study investigates the relative similarity of the various CDMs through the use of the newly developed dissimiliarity index, and explores the implications for the Wald test. Simulations show that the Wald test cannot distinguish among additive models due to their inherent similarity, but this does not impede the ability of the test to provide higher correct classification rates than general CDMs, particularly when the sample size is small and items are of low quality. An empirical example is included to demonstrate the viability of the procedure.