A comparison of entropy balance and probability weighting methods to generalize observational cohorts to a population: a simulation and empirical example.

PURPOSE: We compared methods to control bias and confounding in observational studies including inverse probability weighting (IPW) and stabilized IPW (sIPW). These methods often require iteration and post-calibration to achieve covariate balance. In comparison, entropy balance (EB) optimizes covariate balance a priori by calibrating weights using the target's moments as constraints. METHODS: We measured covariate balance empirically and by simulation by using absolute standardized mean difference (ASMD), absolute bias (AB), and root mean square error (RMSE), investigating two scenarios: the size of the observed (exposed) cohort exceeds the target (unexposed) cohort and vice versa. The empirical application weighted a commercial health plan cohort to a nationally representative National Health and Nutrition Examination Survey target on the same covariates and compared average total health care cost estimates across methods. RESULTS: Entropy balance alone achieved balance (ASMD ≤ 0.10) on all covariates in simulation and empirically. In simulation scenario I, EB achieved the lowest AB and RMSE (13.64, 31.19) compared with IPW (263.05, 263.99) and sIPW (319.91, 320.71). In scenario II, EB outperformed IPW and sIPW with smaller AB and RMSE. In scenarios I and II, EB achieved the lowest mean estimate difference from the simulated population outcome ($490.05, $487.62) compared with IPW and sIPW, respectively. Empirically, only EB differed from the unweighted mean cost indicating IPW, and sIPW weighting was ineffective. CONCLUSION: Entropy balance demonstrated the bias-variance tradeoff achieving higher estimate accuracy, yet lower estimate precision, compared with IPW methods. EB weighting required no post-processing and effectively mitigated observed bias and confounding. Copyright © 2016 John Wiley & Sons, Ltd.

Real-world Effectiveness of Liraglutide vs. Sitagliptin Among Older Patients with Type 2 Diabetes Enrolled in a Medicare Advantage Prescription Drug Plan: A Retrospective Observational Study.

INTRODUCTION: Liraglutide and sitagliptin were compared on glycemic control and all-cause healthcare costs over a 1-year period among older adults with type 2 diabetes (65-89 years) enrolled in a national Medicare Advantage Prescription Drug health plan. METHODS: This was a retrospective study in which the index date was the first prescription fill for liraglutide or sitagliptin between 25 January 2010 and 31 December 2014. Post-index treatment persistence and glycosylated hemoglobin (HbA1c) at baseline and 1 year (± 90 days) post-index date were required. Patients were excluded if their record included use of insulin during the baseline period. Inverse probability of treatment weighting using stabilized weights was employed with final covariate adjusted regression modeling to estimate the primary outcome (mean change in HbA1c) and secondary outcomes (achieving glycemic goal and costs), each at 1-year post-index date. RESULTS: Overall, 3056 patients met the selection criteria, of whom 218 filled prescriptions for liraglutide and 2838 for sitagliptin. Adjusted mean change in HbA1c at 1 year post-index was - 0.42 with liraglutide versus - 0.12 with sitagliptin (P = 0.0012). Adjusted odds of achieving the treatment goals of HbA1c < 7% and achieving an HbA1c reduction of ≥ 1% were higher for  those on liraglutide than for those on sitagliptin (1.68, 95% confidence interval [CI] 1.25-2.24 and 1.76, 95% CI 1.31-2.36), respectively. Total healthcare costs in those achieving an HbA1c of < 7% were not significantly different between treatment groups but were higher within the liraglutide group for those achieving an HbA1c < 8%. CONCLUSIONS: When compared to sitagliptin, liraglutide was associated with greater achievement of an HbA1c < 7% over a 1-year period in an older population. This finding was not associated with a statistically significant increase in all-cause total healthcare costs, although costs were slightly higher in the liraglutide group than in the sitagliptin group.

Cardiovascular Safety of Concomitant Use of Atypical Antipsychotics and Long-Acting Stimulants in Children and Adolescents With ADHD.

OBJECTIVE: This study examined cardiovascular safety of concomitant use of long-acting stimulants (LAS) and atypical antipsychotics (AAP) in children and adolescents with Attention Deficit Hyperactivity Disorder (ADHD). METHOD: The study used 2004-2007 IMS LifeLink™ claims data involving 6- to 16-year-old children with ADHD and at least one LAS prescription from July 2004 to December 2006. Time-dependent Cox regression analysis was performed to evaluate the risk of cardiovascular disease (CVD) events due to concomitant use of LAS and AAP. RESULTS: The analytical cohort consisted of 37,903 children: 538 (1.9%) used LAS and AAP concurrently and the rest used LAS monotherapy. Time-dependent Cox regression analysis revealed no difference in CVD risk among concomitant users of LAS and AAP (hazard ratio = 1.19; 95% confidence interval = [0.60, 2.53]) when compared with users of LAS monotherapy. CONCLUSION: Concomitant use of LAS and AAP was not associated with risk of CVD events in ADHD patients when compared with LAS monotherapy.

Understanding Prediabetes in a Medicare Advantage Population Using Data Adaptive Techniques.

The objective was to identify individuals with undiagnosed prediabetes from administrative data using adaptive techniques. The data source was a national Medicare Advantage Prescription Drug (MAPD) plan administrative data set. A retrospective, cross-sectional study developed and evaluated data adaptive logistic regression, decision tree, neural network, and ensemble predictive models for metabolic syndrome and prediabetes using 3 mutually exclusive cohorts (N = 279,903). The misclassification rate (MCR), average squared error (ASE), c-statistics, sensitivity (SN), and false positive (FP) rates were compared to select the final predictive models. MAPD individuals with continuous enrollment from 2013 to 2014 were included. Metabolic syndrome and prediabetes were defined using clinical guidelines, diagnosis, and laboratory data. A total of 512 variables identified through subject matter expertise in addition to utilizing all data available were evaluated for the modeling. The ensemble model demonstrated better discrimination (c-statistics, MCR, and ASE of 0.83, 0.24, and 0.16, respectively), high SN, and low FP rate in predicting metabolic syndrome than the individual data adaptive modeling techniques. Logistic regression demonstrated better discrimination (c-statistics, MCR, and ASE of 0.67, 0.13, and 0.11 respectively), high SN, and low FP rate in predicting prediabetes than the other adaptive modeling techniques or ensemble methods. The scored data predicted prediabetes in 44% of the MAPD population, which is comparable to 2005-2006 National Health and Nutrition Examination Survey prediabetes rates of 41%. The logistic regression model demonstrated good performance in predicting undiagnosed prediabetes in MAPD individuals.

Positive predictive value between medical-chart body-mass-index category and obesity versus codes in a claims-data warehouse.

OBJECTIVE: To evaluate the positive predictive value of claims-based V85 codes for identifying individuals with varying degrees of BMI relative to their measured BMI obtained from medical record abstraction. METHODS: This was a retrospective validation study utilizing administrative claims and medical chart data from 1 January 2009 to 31 August 2015. Randomly selected samples of patients enrolled in a Medicare Advantage Prescription Drug (MAPD) or commercial health plan and with a V85 claim were identified. The claims-based BMI category (underweight, normal weight, overweight, obese class I-III) was determined via corresponding V85 codes and compared to the BMI category derived from chart abstracted height, weight and/or BMI. The positive predictive values (PPVs) of the claims-based BMI categories were calculated with the corresponding 95% confidence intervals (CIs). RESULTS: The overall PPVs (95% CIs) in the MAPD and commercial samples were 90.3% (86.3%-94.4%) and 91.1% (87.3%-94.9%), respectively. In each BMI category, the PPVs (95% CIs) for the MAPD and commercial samples, respectively, were: underweight, 71.0% (55.0%-87.0%) and 75.9% (60.3%-91.4%); normal, 93.8% (85.4%-100%) and 87.8% (77.8%-97.8%); overweight, 97.4% (92.5%-100%) and 93.5% (84.9%-100%); obese class I, 96.9 (90.9%-100%) and 97.2% (91.9%-100%); obese class II, 97.0% (91.1%-100%) and 93.0% (85.4%-100%); and obese class III, 85.0% (73.3%-96.1%) and 97.1% (91.4%-100%). CONCLUSIONS: BMI categories derived from administrative claims, when available, can be used successfully particularly in the context of obesity research.

Association of obesity with healthcare resource utilization and costs in a commercial population.

OBJECTIVE: To examine the association of obesity with healthcare resource utilization (HRU) and costs among commercially insured individuals. METHODS: This retrospective observational cohort study used administrative claims from 1 January 2007 to 1 December 2013. The ICD-9-CM status codes (V85 hierarchy) from 2008 to 2012 classified body mass index (BMI) into the World Health Organizations' BMI categories. The date of first observed BMI code was defined as the index date and continuous eligibility for one year pre- and post- index date was ensured. Post-index claims determined individuals' HRU and costs. Sampling weights developed using the entropy balance method and National Health and Nutrition Examination Survey data ensured representation of the US adult commercially insured population. Baseline characteristics were described across BMI classes and associations between BMI categories, and outcomes were examined using multivariable regression. RESULTS: The cohort included 9651 individuals with BMI V85 codes. After weighting, the BMI distribution was: normal (31.1%), overweight (33.4%), obese class I (22.0%), obese class II (8.1%) and obese class III (5.4%). Increasing BMI was associated with greater prevalence of cardiometabolic conditions, including hypertension, type 2 diabetes and metabolic syndrome. The use of antihypertensives, antihyperlipidemics, antidiabetics, analgesics and antidepressants rose with increasing BMI. Greater BMI level was associated with increased inpatient, emergency department and outpatient utilization, and higher total healthcare, medical and pharmacy costs. CONCLUSIONS: Increasing BMI was associated with higher prevalence of cardiometabolic conditions and higher HRU and costs. There is an urgent need to address the epidemic of obesity and its clinical and economic impacts.

Persistence of stimulants in children and adolescents with attention-deficit/hyperactivity disorder.

OBJECTIVE: To examine the persistence of three newly initiated stimulant preparations among Medicaid children and adolescents with attention-deficit/hyperactivity disorder (ADHD) diagnosis. METHODS: A retrospective longitudinal claims analysis was conducted by using Medicaid analytical eXtract data of four states. The study focused on patients between 6 and 19 years of age with ADHD diagnosis and a stimulant prescription from January 2003 to December 2005. Stimulants were grouped into short-acting stimulants (SAS), intermediate-acting stimulants (IAS), and long-acting stimulants (LAS). Persistence was measured by totaling the number of days the patient remained on the index stimulant therapy from the index prescription date provided the refill gap between two consecutive stimulant claims was no more than 30 days. All the stimulant recipients were uniformly followed for 1 year (365 days). Survival time ratios (STR) were calculated by using accelerated failure time models to examine variation in index stimulant persistence for each stimulant class. RESULTS: Among the 46,135 patients with ADHD continuously followed for 1 year, 8,260 were SAS users, 4,314 were IAS users, and 33,561 were LAS users. Children who received IAS medications had 4% shorter persistence (STR, 0.96 [95% confidence interval [CI], 0.93-0.98]) when compared with those who received SAS medications, whereas those who received index LAS medications had 29% longer persistence (STR, 1.29 [95% CI, 1.27-1.32]). Multivariate accelerated failure time models revealed that Blacks and Hispanics had consistently lower persistence than their counterparts. Foster care was positively associated with index stimulant persistence in the three stimulant types. Further, addition of another stimulant and other psychotropic medications significantly improved persistence of index stimulant in all three stimulant classes. CONCLUSIONS: LAS had comparatively longer persistence than other stimulants. An understanding of demographic and clinical characteristics that influence treatment continuation can help improve stimulant persistence rates in ADHD.