CLASSICAL AND BAYESIAN INSTRUMENT DEVELOPMENT

Both patient-reported outcome measures (PROMs) and clinician-reported outcome (ClinRO) measures are recognized as essential tools for advocating patient-centered care, an important driving force behind the current U.S. health care system. Close collaborations among the research community and regulatory bodies have been initiated to form standardized guidelines for the development and evaluation of PROMs and many ClinRO measures that often are designed as psychometric instruments with ordinal response scales. Classical (i.e., frequentist) instrument development often is time-consuming and challenged by small samples (e.g., cases of rare diseases). An innovative Ordinal Bayesian Instrument Development (OBID) approach within a Bayesian Item Response Theory (IRT) framework is introduced to overcome both small sample size and ordinal data modeling challenges, through efficient integration of content validity and construct validity analyses. The performance of OBID is evaluated under a simulation setting with three different types of expert bias (i.e., unbiased, moderately biased, and highly biased), and further evaluated with an exact Bayesian leave-one-out cross-validation (LOO-CV) approach using real data applications. Results successfully demonstrated the OBID approach as a promising tool in future PROMs and ClinRO measures development for small populations or rare diseases. Alternatively classical psychometric methodologies are efficient and reliable with relatively large sample sizes. This study also presents the classical psychometric evaluation of the National Database of Nursing Quality Indicators® (NDNQI®) falls with injury measure, an essential ClinRO measure that supports health care quality improvement efforts and continuous injurious falls research

Subgroup identification of early preterm birth (ePTB): informing a future prospective enrichment clinical trial design

Abstract Background Despite the widely recognized association between the severity of early preterm birth (ePTB) and its related severe diseases, little is known about the potential risk factors of ePTB and the sub-population with high risk of ePTB. Moreover, motivated by a future confirmatory clinical trial to identify whether supplementing pregnant women with docosahexaenoic acid (DHA) has a different effect on the risk subgroup population or not in terms of ePTB prevalence, this study aims to identify potential risk subgroups and risk factors for ePTB, defined as babies born less than 34\ua0weeks of gestation. Methods The analysis data ( N\u2009= \u20093,994,872) were obtained from CDC and NCHS\u2019 2014 Natality public data file. The sample was split into independent training and validation cohorts for model generation and model assessment, respectively. Logistic regression and CART models were used to examine potential ePTB risk predictors and their interactions, including mothers\u2019 age, nativity, race, Hispanic origin, marital status, education, pre-pregnancy smoking status, pre-pregnancy BMI, pre-pregnancy diabetes status, pre-pregnancy hypertension status, previous preterm birth status, infertility treatment usage status, fertility enhancing drug usage status, and delivery payment source. Results Both logistic regression models with either 14 or 10 ePTB risk factors produced the same C-index (0.646) based on the training cohort. The C-index of the logistic regression model based on 10 predictors was 0.645 for the validation cohort. Both C-indexes indicated a good discrimination and acceptable model fit. The CART model identified preterm birth history and race as the most important risk factors, and revealed that the subgroup with a preterm birth history and a race designation as Black had the highest risk for ePTB. The c-index and misclassification rate were 0.579 and 0.034 for the training cohort, and 0.578 and 0.034 for the validation cohort, respectively. Conclusions This study revealed 14 maternal characteristic variables that reliably identified risk for ePTB through either logistic regression model and/or a CART model. Moreover, both models efficiently identify risk subgroups for further enrichment clinical trial design

Additional file 1: of A novel method for expediting the development of patient-reported outcome measures and an evaluation of its performance via simulation

Additional Simulation and Application Results. Additional simulation and application results referenced in Sections 3, 4 and 5. (PDF 901 kb