A comparison of traditional and Rasch cut points for assessing clinically important change in health-related quality of life among patients with asthma

PROMs Following Root Canal Treatment and Surgical Endodontic Treatment

The FDI is currently working on developing a tool to encompass patient-reported outcome measures (PROMs) within the overall assessment of outcomes of endodontic treatment. The outcome of endodontic treatment has traditionally been determined by various clinical and radiographic criteria. However, these parameters do not address the impact of treatment on a patient's oral health-related quality of life (OHRQoL). OHRQoL, a crucial PROM, can be used to understand treatment outcome from a patient-centred perspective, thus improving clinician-patient communication whilst guiding decision-making. This focussed review aims to recount the OHRQoL of patients following nonsurgical root canal treatment and surgical endodontic treatment, with a specific focus on the minimal important difference (MID; the minimum score changes of an outcome instrument for a patient to register a clinically significant change in their OHRQoL and/or oral condition) and the methods used to determine it. The current evidence indicates that the OHRQoL of patients requiring root canal treatment is poorer than those without such need. Accordingly, the literature suggests that OHRQoL improves following nonsurgical or surgical endodontic treatment. However, study methodologies vary widely, and conclusions cannot be drawn with high confidence, nor can MID recommendations be provided. Well-designed clinical studies with baseline measurements and appropriate follow-up time frames are therefore needed. Despite that the literature is rife with outcome studies, research on PROMs is an area that deserves greater attention, particularly in relation to the MID. Determining the MID will facilitate the understanding of changes in outcome scores from the patients' perspective, thus allowing for more informed decision-making in clinical practice.

Health-related quality of life following treatment for extremity soft tissue sarcoma

BACKGROUND AND OBJECTIVES

The primary objective of this study was to estimate the change in health-related quality of life (HRQL) 1 year following treatment for extremity soft tissue sarcoma (STS), measured by the EQ-5D. Secondary objectives included determining clinical variables associated with HRQL at 1 year, estimating the proportion with a clinically important difference (CID) in HRQL, and evaluating variability within EQ-5D domains.

METHODS

Patients over the age of 16 years, treated for a localized extremity STS, were included. The EQ-5D change score from pre-treatment to 1-year follow-up was determined. The association of clinical variables with EQ-5D scores was estimated using a linear regression model. The proportion of patients with a CID in HRQL score was determined. A vector analysis of the EQ-5D domains was undertaken.

RESULTS

The mean EQ-5D change score was 0.02. Age, sex, disease status, and initial EQ-5D score were associated with EQ-5D score at 1 year. There was a CID improvement in 32% and a deterioration in 24%. The anxiety and depression domain demonstrated the most change between baseline and 1 year after treatment.

CONCLUSION

Most patients maintain a high level of HRQL following treatment for extremity STS. J. Surg. Oncol. 2016;114:821-827. © 2016 2016 Wiley Periodicals, Inc.

Item response theory analysis of Centers for Disease Control and Prevention Health-Related Quality of Life (CDC HRQOL) items in adults with arthritis

Background

Examine the feasibility of performing an item response theory (IRT) analysis on two of the Centers for Disease Control and Prevention health-related quality of life (CDC HRQOL) modules – the 4-item Healthy Days Core Module (HDCM) and the 5-item Healthy days Symptoms Module (HDSM). Previous principal components analyses confirm that the two scales both assess a mix of mental (CDC-MH) and physical health (CDC-PH). The purpose is to conduct item response theory (IRT) analysis on the CDC-MH and CDC-PH scales separately.

Methods

2182 patients with self-reported or physician-diagnosed arthritis completed a cross-sectional survey including HDCM and HDSM items. Besides global health, the other 8 items ask the number of days that some statement was true; we chose to recode the data into 8 categories based on observed clustering. The IRT assumptions were assessed using confirmatory factor analysis and the data could be modeled using an unidimensional IRT model. The graded response model was used for IRT analyses and CDC-MH and CDC-PH scales were analyzed separately in flexMIRT.

Results

The IRT parameter estimates for the five-item CDC-PH all appeared reasonable. The three-item CDC-MH did not have reasonable parameter estimates.

Conclusions

The CDC-PH scale is amenable to IRT analysis but the existing The CDC-MH scale is not. We suggest either using the 4-item Healthy Days Core Module (HDCM) and the 5-item Healthy days Symptoms Module (HDSM) as they currently stand or the CDC-PH scale alone if the primary goal is to measure physical health related HRQOL.

Methods for specifying the target difference in a randomised controlled trial: the Difference ELicitation in TriAls (DELTA) systematic review

BACKGROUND

Randomised controlled trials (RCTs) are widely accepted as the preferred study design for evaluating healthcare interventions. When the sample size is determined, a (target) difference is typically specified that the RCT is designed to detect. This provides reassurance that the study will be informative, i.e., should such a difference exist, it is likely to be detected with the required statistical precision. The aim of this review was to identify potential methods for specifying the target difference in an RCT sample size calculation.

METHODS AND FINDINGS

A comprehensive systematic review of medical and non-medical literature was carried out for methods that could be used to specify the target difference for an RCT sample size calculation. The databases searched were MEDLINE, MEDLINE In-Process, EMBASE, the Cochrane Central Register of Controlled Trials, the Cochrane Methodology Register, PsycINFO, Science Citation Index, EconLit, the Education Resources Information Center (ERIC), and Scopus (for in-press publications); the search period was from 1966 or the earliest date covered, to between November 2010 and January 2011. Additionally, textbooks addressing the methodology of clinical trials and International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH) tripartite guidelines for clinical trials were also consulted. A narrative synthesis of methods was produced. Studies that described a method that could be used for specifying an important and/or realistic difference were included. The search identified 11,485 potentially relevant articles from the databases searched. Of these, 1,434 were selected for full-text assessment, and a further nine were identified from other sources. Fifteen clinical trial textbooks and the ICH tripartite guidelines were also reviewed. In total, 777 studies were included, and within them, seven methods were identified-anchor, distribution, health economic, opinion-seeking, pilot study, review of the evidence base, and standardised effect size.

CONCLUSIONS

A variety of methods are available that researchers can use for specifying the target difference in an RCT sample size calculation. Appropriate methods may vary depending on the aim (e.g., specifying an important difference versus a realistic difference), context (e.g., research question and availability of data), and underlying framework adopted (e.g., Bayesian versus conventional statistical approach). Guidance on the use of each method is given. No single method provides a perfect solution for all contexts.

Psychometric evaluation of the Taiwan Chinese version of the EORTC QLQ-PR25 for HRQOL assessment in prostate cancer patients

Objective

To evaluate the psychometric properties of the Taiwan Chinese Version of the EORTC QLQ-PR25 health-related quality of life (HRQOL) questionnaire for patients with prostate cancer.

Methods

135 prostate cancer patients were recruited in the urology outpatient clinic of a university teaching hospital. Each patient completed the EORTC QLQ-PR25 at every clinic visit between 2004 and 2008, totaling 633 assessments. Confirmatory factor analysis and Rasch analysis were used to evaluate the domain- and item-level psychometric properties.

Results

The results supported the unidimensionality of each of the four EORTC QLQ-PR25 domains (urinary, bowel, and hormonal-treatment-related symptoms, and sexual functioning). Item calibrations for each domain were found invariant across the three assessment time periods. The item-person maps showed 71.3% of item coverage for the urinary symptoms domain and 13–42.7% for the other three domains.

Conclusions

The Taiwan Chinese Version of the EORTC QLQ-PR25 questionnaire is reliable and can be used to measure HRQOL over time. Adding new items to each domain may improve its clinical content coverage and measurement precision.

[Modern testing theory and its application in the field of health measurement]

This paper briefly introduces item response theory (IRT) as a typical representation of modern testing theory (MTT), and systematically reviews the processes and contents of the application of IRT in the area of health measurement, including, for example, item bank development, scale revision and computerized adaptive testing. The author presents the potential benefits and the notable problems during health measuring by IRT. Then, the author asserts the need for thorough assessment of feasibility when using the IRT in patient-reported outcome research. Further research based on IRT and computerized adaptive testing in health measurement will be carried out in the field of medical care including traditional Chinese medicine and integrative medicine.

Development of a computerized adaptive test to assess health-related quality of life in adults with asthma

OBJECTIVE

The purpose of this research was to calibrate an item bank for a computerized adaptive test (CAT) of asthma impact on health-related quality of life (HRQOL), test CAT versions of varying lengths, conduct preliminary validity testing, and evaluate item bank readability.

METHODS

Asthma Impact Survey (AIS) bank items that passed focus group, cognitive testing, and clinical and psychometric reviews were administered to adults with varied levels of asthma control. Adults self-reporting asthma (N = 1106) completed an Internet survey including 88 AIS items, the Asthma Control Test, and other HRQOL outcome measures. Data were analyzed using classical and modern psychometric methods, real-data CAT simulations, and known groups validity testing.

RESULTS

A bi-factor model with a general factor (asthma impact) and several group factors (cognitive function, fatigue, mental health, physical function, role function, sexual function, self-consciousness/stigma, sleep, and social function) was tested. Loadings on the general factor were above 0.5 and were substantially larger than group factor loadings, and fit statistics were acceptable. Item functioning for most items and fit to the model was acceptable. CAT simulations demonstrated several options for administration and stopping rules. AIS distinguished between respondents with differing levels of asthma control.

CONCLUSIONS

The new 50-item AIS item bank demonstrated favorable psychometric characteristics, preliminary evidence of validity, and accessibility at moderate reading levels. Developing item banks for CAT can improve the precise, efficient, and comprehensive monitoring of asthma outcomes and may facilitate patient-centered care.

A point of minimal important difference (MID): a critique of terminology and methods

The minimal important difference (MID) is a phrase with instant appeal in a field struggling to interpret health-related quality of life and other patient-reported outcomes. The terminology can be confusing, with several terms differing only slightly in definition (e.g., minimal clinically important difference, clinically important difference, minimally detectable difference, the subjectively significant difference), and others that seem similar despite having quite different meanings (minimally detectable difference versus minimum detectable change). Often, nuances of definition are of little consequence in the way that these quantities are estimated and used. Four methods are commonly employed to estimate MIDs: patient rating of change (global transition items); clinical anchors; standard error of measurement; and effect size. These are described and critiqued in this article. There is no universal MID, despite the appeal of the notion. Indeed, for a particular patient-reported outcome instrument or scale, the MID is not an immutable characteristic, but may vary by population and context. At both the group and individual level, the MID may depend on the clinical context and decision at hand, the baseline from which the patient starts, and whether they are improving or deteriorating. Specific estimates of MIDs should therefore not be overinterpreted. For a given health-related quality-of-life scale, all available MID estimates (and their confidence intervals) should be considered, amalgamated into general guidelines and applied judiciously to any particular clinical or research context.

Estimation of minimally important differences in EQ-5D utility and VAS scores in cancer

Background

Understanding what constitutes an important difference on a HRQL measure is critical to its interpretation. The aim of this study was to provide a range of estimates of minimally important differences (MIDs) in EQ-5D scores in cancer and to determine if estimates are comparable in lung cancer.

Methods

A retrospective analysis was conducted on cross-sectional data collected from 534 cancer patients, 50 of whom were lung cancer patients. A range of minimally important differences (MIDs) in EQ-5D index-based utility (UK and US) scores and VAS scores were estimated using both anchor-based and distribution-based (1/2 standard deviation and standard error of the measure) approaches. Groups were anchored using Eastern Cooperative Oncology Group performance status (PS) ratings and FACT-G total score-based quintiles.

Results

For UK-utility scores, MID estimates based on PS ranged from 0.10 to 0.12 both for all cancers and for lung cancer subgroup. Using FACT-G quintiles, MIDs were 0.09 to 0.10 for all cancers, and 0.07 to 0.08 for lung cancer. For US-utility scores, MIDs ranged from 0.07 to 0.09 grouped by PS for all cancers and for lung cancer; when based on FACT-G quintiles, MIDs were 0.06 to 0.07 in all cancers and 0.05 to 0.06 in lung cancer. MIDs for VAS scores were similar for lung and all cancers, ranging from 8 to 12 (PS) and 7 to 10 (FACT-G quintiles).

Discussion

Important differences in EQ-5D utility and VAS scores were similar for all cancers and lung cancer, with the lower end of the range of estimates closer to the MID, i.e. 0.08 for UK-index scores, 0.06 for US-index scores, and 0.07 for VAS scores.