Rasch scaling procedures for informing development of a valid Fetal Surveillance Education Program multiple-choice assessment

<p>Abstract</p> <p>Background</p> <p>It is widely recognised that deficiencies in fetal surveillance practice continue to contribute significantly to the burden of adverse outcomes. This has prompted the development of evidence-based clinical practice guidelines by the Royal Australian and New Zealand College of Obstetricians and Gynaecologists and an associated Fetal Surveillance Education Program to deliver the associated learning. This article describes initial steps in the validation of a corresponding multiple-choice assessment of the relevant educational outcomes through a combination of item response modelling and expert judgement.</p> <p>Methods</p> <p>The Rasch item response model was employed for item and test analysis and to empirically derive the substantive interpretation of the assessment variable. This interpretation was then compared to the hierarchy of competencies specified a priori by a team of eight subject-matter experts. Classical Test Theory analyses were also conducted.</p> <p>Results</p> <p>A high level of agreement between the hypothesised and derived variable provided evidence of construct validity. Item and test indices from Rasch analysis and Classical Test Theory analysis suggested that the current test form was of moderate quality. However, the analyses made clear the required steps for establishing a valid assessment of sufficient psychometric quality. These steps included: increasing the number of items from 40 to 50 in the first instance, reviewing ineffective items, targeting new items to specific content and difficulty gaps, and formalising the assessment blueprint in light of empirical information relating item structure to item difficulty.</p> <p>Conclusion</p> <p>The application of the Rasch model for criterion-referenced assessment validation with an expert stakeholder group is herein described. Recommendations for subsequent item and test construction are also outlined in this article.</p

Support of personalized medicine through risk-stratified treatment recommendations - an environmental scan of clinical practice guidelines

BACKGROUND: Risk-stratified treatment recommendations facilitate treatment decision-making that balances patient-specific risks and preferences. It is unclear if and how such recommendations are developed in clinical practice guidelines (CPGs). Our aim was to assess if and how CPGs develop risk-stratified treatment recommendations for the prevention or treatment of common chronic diseases. METHODS: We searched the United States National Guideline Clearinghouse for US, Canadian and National Institute for Health and Clinical Excellence (United Kingdom) CPGs for heart disease, stroke, cancer, chronic obstructive pulmonary disease and diabetes that make risk-stratified treatment recommendations. We included only those CPGs that made risk-stratified treatment recommendations based on risk assessment tools. Two reviewers independently identified CPGs and extracted information on recommended risk assessment tools; type of evidence about treatment benefits and harms; methods for linking risk estimates to treatment evidence and for developing treatment thresholds; and consideration of patient preferences. RESULTS: We identified 20 CPGs that made risk-stratified treatment recommendations out of 133 CPGs that made any type of treatment recommendations for the chronic diseases considered in this study. Of the included 20 CPGs, 16 (80%) used evidence about treatment benefits from randomized controlled trials, meta-analyses or other guidelines, and the source of evidence was unclear in the remaining four (20%) CPGs. Nine CPGs (45%) used evidence on harms from randomized controlled trials or observational studies, while 11 CPGs (55%) did not clearly refer to harms. Nine CPGs (45%) explained how risk prediction and evidence about treatments effects were linked (for example, applying estimates of relative risk reductions to absolute risks), but only one CPG (5%) assessed benefit and harm quantitatively and three CPGs (15%) explicitly reported consideration of patient preferences. CONCLUSIONS: Only a small proportion of CPGs for chronic diseases make risk-stratified treatment recommendations with a focus on heart disease and stroke prevention, diabetes and breast cancer. For most CPGs it is unclear how risk-stratified treatment recommendations were developed. As a consequence, it is uncertain if CPGs support patients and physicians in finding an acceptable benefit- harm balance that reflects both profile-specific outcome risks and preferences