Response shift in coronary artery disease.

PURPOSE: Patients with coronary artery disease (CAD) experience significant angina symptoms and lifestyle changes. Revascularization procedures can result in better patient-reported outcomes (PROs) than optimal medical therapy (OMT) alone. This study evaluates the impact of response shift (RS) on changes in PROs of patients with CAD across treatment strategies. METHODS: Data were from patients with CAD in the Alberta Provincial Project on Outcome Assessment in Coronary Heart Disease (APPROACH) registry who completed the 16-item Canadian version of the Seattle Angina Questionnaire at 2 weeks and 1 year following a coronary angiogram. Multi-group confirmatory factor analysis (MG-CFA) was used to assess measurement invariance across treatment groups at week 2. Longitudinal MG-CFA was used to test for RS according to receipt of coronary artery bypass grafting (CABG), percutaneous coronary intervention (PCI), or optimal medical therapy (OMT) alone. RESULTS: Of the 3116 patients included in the analysis, 443 (14.2%) received CABG, 2049(65.8%) PCI, and the remainder OMT alone. The MG-CFA revealed a partial-strong invariance across the treatment groups at 2 weeks (CFI = 0.98, RMSEA [90% CI] = 0.05 [0.03, 0.06]). Recalibration RS was detected on the Angina Symptoms and Burden subscale and its magnitude in the OMT, PCI, and CABG groups were 0.32, 0.28, and 0.53, respectively. After adjusting for RS effects, the estimated target changes were largest in the CABG group and negligible in the OMT group. CONCLUSION: Adjusting for RS is recommended in studies that use SAQ-CAN to assess changes in patients with CAD who have received revascularization versus OMT alone.

Response shift in parent-reported psychopathology in children with chronic physical illness.

PURPOSE: Because physical-mental comorbidity in children is relatively common, this study tested for response shift (RS) in children with chronic physical illness using a parent-reported measure of child psychopathology. METHODS: Data come from Multimorbidity in Children and Youth across Life-course (MY LIFE), a prospective study of n = 263 children aged 2-16 years with physical illness in Canada. Parents provided information on child psychopathology using the Ontario Child Health Study Emotional Behavioral Scales (OCHS-EBS) at baseline and 24 months. Oort's structural equation modeling was used to test for different forms of RS in parent-reported assessments between baseline and 24 months. Model fit was evaluated using root mean square error of approximation (RMSEA), comparative fit index (CFI), and standardized root mean residual (SRMR). RESULTS: There were n = 215 (81.7%) children with complete data and were included in this analysis. Of these, n = 105 (48.8%) were female and the mean (SD) age was 9.4 (4.2) years. A two-factor measurement model provided good fit to the data [RMSEA (90% CI) = 0.05 (0.01, 0.10); CFI = 0.99; SRMR = 0.03]. Non-uniform recalibration RS was detected on the conduct disorder subscale of the OCHS-EBS. This RS effect had negligible impact on the longitudinal change in externalizing and internalizing disorders construct over time. CONCLUSIONS: Response shift detected on the conduct disorder subscale of the OCHS-EBS, indicated that parents of children with physical illness may recalibrate their responses on child psychopathology over 24 months. Researchers and health professionals should be aware of RS when using the OCHS-EBS to assess child psychopathology over time.

Generating synthetic data from administrative health records for drug safety and effectiveness studies.

Introduction: Administrative health records (AHRs) are used to conduct population-based post-market drug safety and comparative effectiveness studies to inform healthcare decision making. However, the cost of data extraction, and the challenges associated with privacy and securing approvals can make it challenging for researchers to conduct methodological research in a timely manner using real data. Generating synthetic AHRs that reasonably represent the real-world data are beneficial for developing analytic methods and training analysts to rapidly implement study protocols. We generated synthetic AHRs using two methods and compared these synthetic AHRs to real-world AHRs. We described the challenges associated with using synthetic AHRs for real-world study. Methods: The real-world AHRs comprised prescription drug records for individuals with healthcare insurance coverage in the Population Research Data Repository (PRDR) from Manitoba, Canada for the 10-year period from 2008 to 2017. Synthetic data were generated using the Observational Medical Dataset Simulator II (OSIM2) and a modification (ModOSIM). Synthetic and real-world data were described using frequencies and percentages. Agreement of prescription drug use measures in PRDR, OSIM2 and ModOSIM was estimated with the concordance coefficient. Results: The PRDR cohort included 169,586,633 drug records and 1,395 drug types for 1,604,734 individuals. Synthetic data for 1,000,000 individuals were generated using OSIM2 and ModOSIM. Sex and age group distributions were similar in the real-world and synthetic AHRs. However, there were significant differences in the number of drug records and number of unique drugs per person for OSIM2 and ModOSIM when compared with PRDR. For the average number of days of drug use, concordance with the PRDR was 16% (95% confidence interval [CI]: 12%-19%) for OSIM2 and 88% (95% CI: 87%-90%) for ModOSIM. Conclusions: ModOSIM data were more similar to PRDR than OSIM2 data on many measures. Synthetic AHRs consistent with those found in real-world settings can be generated using ModOSIM. Synthetic data will benefit rapid implementation of methodological studies and data analyst training.

Transitions between versions of the International Classification of Diseases and chronic disease prevalence estimates from administrative health data: a population-based study.

BACKGROUND: Diagnosis codes in administrative health data are routinely used to monitor trends in disease prevalence and incidence. The International Classification of Diseases (ICD), which is used to record these diagnoses, have been updated multiple times to reflect advances in health and medical research. Our objective was to examine the impact of transitions between ICD versions on the prevalence of chronic health conditions estimated from administrative health data. METHODS: Study data (i.e., physician billing claims, hospital records) were from the province of Manitoba, Canada, which has a universal healthcare system. ICDA-8 (with adaptations), ICD-9-CM (clinical modification), and ICD-10-CA (Canadian adaptation; hospital records only) codes are captured in the data. Annual study cohorts included all individuals 18 + years of age for 45 years from 1974 to 2018. Negative binomial regression was used to estimate annual age- and sex-adjusted prevalence and model parameters (i.e., slopes and intercepts) for 16 chronic health conditions. Statistical control charts were used to assess the impact of changes in ICD version on model parameter estimates. Hotelling's T2 statistic was used to combine the parameter estimates and provide an out-of-control signal when its value was above a pre-specified control limit. RESULTS: The annual cohort sizes ranged from 360,341 to 824,816. Hypertension and skin cancer were among the most and least diagnosed health conditions, respectively; their prevalence per 1,000 population increased from 40.5 to 223.6 and from 0.3 to 2.1, respectively, within the study period. The average annual rate of change in prevalence ranged from -1.6% (95% confidence interval [CI]: -1.8, -1.4) for acute myocardial infarction to 14.6% (95% CI: 13.9, 15.2) for hypertension. The control chart indicated out-of-control observations when transitioning from ICDA-8 to ICD-9-CM for 75% of the investigated chronic health conditions but no out-of-control observations when transitioning from ICD-9-CM to ICD-10-CA. CONCLUSIONS: The prevalence of most of the investigated chronic health conditions changed significantly in the transition from ICDA-8 to ICD-9-CM. These results point to the importance of considering changes in ICD coding as a factor that may influence the interpretation of trend estimates for chronic health conditions derived from administrative health data.

A comparison of methods to address item non-response when testing for differential item functioning in multidimensional patient-reported outcome measures.

PURPOSE: Item non-response (i.e., missing data) may mask the detection of differential item functioning (DIF) in patient-reported outcome measures or result in biased DIF estimates. Non-response can be challenging to address in ordinal data. We investigated an unsupervised machine-learning method for ordinal item-level imputation and compared it with commonly-used item non-response methods when testing for DIF. METHODS: Computer simulation and real-world data were used to assess several item non-response methods using the item response theory likelihood ratio test for DIF. The methods included: (a) list-wise deletion (LD), (b) half-mean imputation (HMI), (c) full information maximum likelihood (FIML), and (d) non-negative matrix factorization (NNMF), which adopts a machine-learning approach to impute missing values. Control of Type I error rates were evaluated using a liberal robustness criterion for α = 0.05 (i.e., 0.025-0.075). Statistical power was assessed with and without adoption of an item non-response method; differences > 10% were considered substantial. RESULTS: Type I error rates for detecting DIF using LD, FIML and NNMF methods were controlled within the bounds of the robustness criterion for > 95% of simulation conditions, although the NNMF occasionally resulted in inflated rates. The HMI method always resulted in inflated error rates with 50% missing data. Differences in power to detect moderate DIF effects for LD, FIML and NNMF methods were substantial with 50% missing data and otherwise insubstantial. CONCLUSION: The NNMF method demonstrated comparable performance to commonly-used non-response methods. This computationally-efficient method represents a promising approach to address item-level non-response when testing for DIF.

Critical examination of current response shift methods and proposal for advancing new methods.

PURPOSE: This work is part of an international, interdisciplinary initiative to synthesize research on response shift in results of patient-reported outcome measures. The objective is to critically examine current response shift methods. We additionally propose advancing new methods that address the limitations of extant methods. METHODS: Based on literature reviews, this critical examination comprises design-based, qualitative, individualized, and preference-based methods, latent variable models, and other statistical methods. We critically appraised their definition, operationalization, the type of response shift they can detect, whether they can adjust for and explain response shift, their assumptions, and alternative explanations. Overall limitations requiring new methods were identified. RESULTS: We examined 11 methods that aim to operationalize response shift, by assessing change in the meaning of one's self-evaluation. Six of these methods distinguish between change in observed measurements (observed change) and change in the construct that was intended to be measured (target change). The methods use either (sub)group-based or individual-level analysis, or a combination. All methods have underlying assumptions to be met and alternative explanations for the inferred response shift effects. We highlighted the need to address the interpretation of the results as response shift and proposed advancing new methods handling individual variation in change over time and multiple time points. CONCLUSION: No single response shift method is optimal; each method has strengths and limitations. Additionally, extra steps need to be taken to correctly interpret the results. Advancing new methods and conducting computer simulation studies that compare methods are recommended to move response shift research forward.

Differential item functioning of the SF-12 in a population-based regional joint replacement registry.

BACKGROUND: Joint replacement, an increasingly common procedure amongst older adults, can substantially improve health-related quality of life (HRQoL). However, differential item functioning (DIF) may affect the accurate interpretation of differences in HRQoL amongst patients with different demographic and health status characteristics but the same underlying (i.e., latent) level of the investigated construct. This study tested for DIF in pre-operative SF-12 physical health (PH) and mental health (MH) sub-scale items amongst patients undergoing total hip arthroplasty (THA) and total knee arthroplasty (TKA). METHODS: Data were from a population-based joint replacement registry from the Canadian province of Manitoba. TKA and THA patients who had surgery between 2009 and 2015 and completed a pre-operative assessment were included. DIF was tested using the multiple indicators multiple causes (MIMIC) method with sex, age group, body weight status, and presence of multiple comorbid conditions (i.e., multimorbidity) as covariates. Analyses were stratified by joint type. RESULTS: The study cohort included 8820 patients; 42.1% underwent THA, 57.3% were female, 32.7% were 70+ years, and 52.8% were obese. For each sub-scale, four of the six items exhibited DIF in both THA and TKA groups. Differences in the covariate effect estimates for DIF and No-DIF models on the MH latent variable were largest for age and body weight status for the THA group, and for sex and multimorbidity for the TKA group. All of the differences were small for PH. Multimorbidity had the strongest association with PH and age and sex had the strongest association with MH in the DIF models. CONCLUSIONS: Demographic and health status characteristics influenced SF-12 PH and MH item responses in joint replacement populations, although the size of the effects were not large for PH. We recommend testing and adjusting for DIF effects to ensure comparability of HRQoL measures in joint replacement populations.

Impact of missing data on bias and precision when estimating change in patient-reported outcomes from a clinical registry.

BACKGROUND: Clinical registries, which capture information about the health and healthcare use of patients with a health condition or treatment, often contain patient-reported outcomes (PROs) that provide insights about the patient's perspectives on their health. Missing data can affect the value of PRO data for healthcare decision-making. We compared the precision and bias of several missing data methods when estimating longitudinal change in PRO scores. METHODS: This research conducted analyses of clinical registry data and simulated data. Registry data were from a population-based regional joint replacement registry for Manitoba, Canada; the study cohort consisted of 5631 patients having total knee arthroplasty between 2009 and 2015. PROs were measured using the 12-item Short Form Survey, version 2 (SF-12v2) at pre- and post-operative occasions. The simulation cohort was a subset of 3000 patients from the study cohort with complete PRO information at both pre- and post-operative occasions. Linear mixed-effects models based on complete case analysis (CCA), maximum likelihood (ML) and multiple imputation (MI) without and with an auxiliary variable (MI-Aux) were used to estimate longitudinal change in PRO scores. In the simulated data, bias, root mean squared error (RMSE), and 95% confidence interval (CI) coverage and width were estimated under varying amounts and types of missing data. RESULTS: Three thousand two hundred thirty (57.4%) patients in the study cohort had complete data on the SF-12v2 at both occasions. In this cohort, mixed-effects models based on CCA resulted in substantially wider 95% CIs than models based on ML and MI methods. The latter two methods produced similar estimates and 95% CI widths. In the simulation cohort, when 50% of the data were missing, the MI-Aux method, in which a single hypothetical auxiliary variable was strongly correlated (i.e., 0.8) with the outcome, reduced the 95% CI width by up to 14% and bias and RMSE by up to 50 and 45%, respectively, when compared with the MI method. CONCLUSIONS: Missing data can substantially affect the precision of estimated change in PRO scores from clinical registry data. Inclusion of auxiliary information in MI models can increase precision and reduce bias, but identifying the optimal auxiliary variable(s) may be challenging.

Quantile regression with nominated samples: An application to a bone mineral density study.

This paper studies quantile regression analysis with maxima or minima nomination sampling designs. These designs are often used to obtain more representative samples from the tails of the underlying distribution using the easy to access rank information during the sampling process. We propose new loss functions to incorporate the rank information of nominated samples in the estimation process. Also, we provide an alternative approach that translates estimation problems with nominated samples to corresponding problems under simple random sampling (SRS). Strategies are given to choose proper nomination sampling designs for a given population quantile. Numerical studies show that quantile regression models with maxima (or minima) nominated samples have higher relative efficiencies compared with their counterparts under SRS for analyzing the upper (or lower) tail quantiles of the distribution of the response variable. Results are then implemented on a large cohort study in the Canadian province of Manitoba to analyze quantiles of bone mineral density using available covariates. We show that in some cases, methods based on nomination sampling designs require about one-tenth of the sample used in SRS to estimate the lower or upper tail conditional quantiles with comparable mean squared errors. This is a dramatic reduction in time and cost compared with the usual SRS approach.