Statistical approaches for the integration of external controls in a cystic fibrosis clinical trial: a simulation and an application.

Development of new therapeutics for a rare disease such as cystic fibrosis (CF) is hindered by challenges in accruing enough patients for clinical trials. Using external controls from well-matched historical trials can reduce prospective trial sizes, and this approach has supported regulatory approval of new interventions for other rare diseases. We consider three statistical methods that incorporate external controls into a hypothetical clinical trial of a new treatment to reduce pulmonary exacerbations in CF patients: 1) inverse probability weighting, 2) Bayesian modeling with propensity score-based power priors, and 3) hierarchical Bayesian modeling with commensurate priors. We compare the methods via simulation study and in a real clinical trial data setting. Simulations showed that bias in the treatment effect was <4% using any of the methods, with type 1 error (or in the Bayesian cases, posterior probability of the null hypothesis) usually <5%. Inverse probability weighting was sensitive to similarity in prevalence of the covariates between historical and prospective trial populations. The commensurate prior method performed best with real clinical trial data. Using external controls to reduce trial size in future clinical trials holds promise and can advance the therapeutic pipeline for rare diseases.

A predictive algorithm to identify ever smoking in medical claims-based epidemiologic studies.

PURPOSE: To develop and validate an algorithm to estimate probability of ever smoking using administrative claims. METHODS: Using population-based samples of Medicare-aged individuals (121,278 Behavioral Risk Factor Surveillance System survey respondents and 207,885 Medicare beneficiaries), we developed a logistic regression model to predict probability of ever smoking from demographic and claims data. We applied the model in 1,657,266 additional Medicare beneficiaries and calculated area under the receiver operating characteristic curve (AUC) using presence or absence of a tobacco-specific diagnosis or procedure code as our "gold standard." We used these "gold standard" and lung/laryngeal cancer codes to over-ride predicted probability as 100%. We calculated Spearman's rho between probability from this full algorithm and smoking assessed in prior Parkinson disease studies, by substituting our observed and prior ("true") smoking-Parkinson disease odds ratios into the attenuation equation. RESULTS: The predictive model contained 23 variables, including basic demographics, high alcohol consumption, asthma, cardiovascular disease and associated risk factors, selected cancers, and indicators of routine medical usage. The AUC was 67.6% (95% confidence interval 67.5%-67.7%) comparing smoking probability to tobacco-specific diagnosis or procedure codes. Spearman's rho for the full algorithm was 0.82. CONCLUSIONS: Ever smoking might be approximated in administrative data for use as a continuous, probabilistic variable in epidemiologic analyses.

A new path for CF clinical trials through the use of historical controls.

BACKGROUND: Given future challenges in conducting large randomized, placebo controlled trials for future CF therapeutics development, we evaluated the potential for using external historical controls to either enrich or replace traditional concurrent placebo groups in CF trials. METHODS: The study included data from sequentially completed, randomized, controlled clinical trials, EPIC and OPTIMIZE respectively, evaluating optimal antibiotic therapy to reduce the risk of pulmonary exacerbation in children with early Pseudomonas aeruginosa infection. The primary treatment effect in OPTIMIZE, the risk of pulmonary exacerbation associated with azithromycin, was re-estimated in alternative designs incorporating varying numbers of participants from the earlier trial (EPIC) as historical controls. Bias and precision of these estimates were characterized. Propensity scores were derived to adjust for baseline differences across study populations, and both Poisson and Cox regression were used to estimate treatment efficacy. RESULTS: Replacing 86 OPTIMIZE placebo participants with 304 controls from EPIC to mimic a fully historically controlled trial resulted an 8% reduction in risk of pulmonary exacerbations (Hazard ratio (HR):0.92 95% CI 0.61, 1.34) when not adjusting for key baseline differences between study populations. After adjustment, a 37% decrease in risk of exacerbation (HR:0.63, 95% CI 0.50, 0.80) was estimated, comparable to the estimate from the original trial comparing the 86 placebo participants to 77 azithromycin participants on azithromycin (45%, HR:0.55, 95% CI: 0.34, 0.86). Other adjusted approaches provided similar estimates for the efficacy of azithromycin in reducing exacerbation risk: pooling all controls from both studies provided a HR of 0.60 (95% x`CI 0.46, 0.77) and augmenting half the OPTIMIZE placebo participants with EPIC controls gave a HR 0.63 (95% CI 0.48, 0.82). CONCLUSIONS: The potential exists for future CF trials to utilize historical control data. Careful consideration of both the comparability of controls and of optimal methods can reduce the potential for biased estimation of treatment effects.

Solvent exposed occupations and risk of Parkinson disease in Finland.

INTRODUCTION: Epidemiologic and toxicology studies suggest that exposure to various solvents, especially chlorinated hydrocarbon solvents, might increase Parkinson disease (PD) risk. METHODS: In a population-based case-control study in Finland, we examined whether occupations with potential for solvent exposures were associated with PD. We identified newly diagnosed cases age 45-84 from a nationwide medication reimbursement register in 1995-2014. From the population register, we randomly selected non-PD controls matched on sex, along with birth and diagnosis years (age). We included 11,757 cases and 23,236 controls with an occupation in the 1990 census, corresponding to age 40-60. We focused on 28 occupations with ≥ 5% probability of solvent exposure according to the Finnish Job Exposure Matrix. We estimated odds ratios (ORs) and 95% confidence intervals (CIs) by logistic regression modeling, adjusting for age, sex, socioeconomic status, and smoking probability. RESULTS: Similar proportions of cases (5.5%) and controls (5.6%) had an occupation with potential exposure to any solvents. However, all occupations with a point estimate above one, and all significantly or marginally significantly associated with PD (electronic/telecommunications worker [OR = 1.63, 95% CI 1.05-2.50], laboratory assistant [OR = 1.40, 95% CI 0.98-1.99], and machine/engine mechanic [OR = 1.23, 95% CI 0.99-1.52]) entailed potential for exposure to chlorinated hydrocarbon solvents, specifically. Secondary analyses indicated exposure to polycyclic aromatic hydrocarbons and some metals might contribute to the association for mechanics. CONCLUSION: PD risk might be slightly increased in occupations with potential exposure to chlorinated hydrocarbon solvents. Confirmation is required in additional studies that adjust for other occupational exposures and smoking.

Fractures in the prodromal period of Parkinson disease.

OBJECTIVE: To examine the association between fractures and Parkinson disease (PD) during the 5-year prodromal phase as compared to controls. METHODS: We performed a population-based case-control study of Medicare beneficiaries in the United States from 2004 to 2009. We identified 89,632 incident PD cases and 117,760 comparable controls 66-90 years of age in 2009. PD case status was the outcome, and noncranial fracture the independent variable. We used logistic regression models to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for association between fracture and PD in yearly time intervals prior to PD diagnosis/control reference date, after adjusting for covariates. RESULTS: There were 39,606 total fractures (25.4% cases, 14.3% controls) over the 5 years prior to the PD diagnosis/control reference date. PD was positively associated with fractures even after adjusting for age, sex, race/ethnicity, Charlson comorbidity index, alcohol use, tobacco use, and osteoporosis. The association between PD and fracture was evident at yearly time windows prior to PD diagnosis/control reference date. The association between PD and each type of fracture strengthened as the PD diagnosis/control reference date approached (all time interaction p values ≤0.02). Among beneficiaries with a mechanism of injury, the majority were attributed to falls (74.6% cases, 72.8% controls). CONCLUSION: Fractures occur more commonly during the prodromal period of PD compared to controls, especially as diagnosis date approached, suggesting that patients with PD may experience unrecognized motor and nonmotor symptoms.

Traumatic brain injury in the prodromal period of Parkinson's disease: A large epidemiological study using medicare data.

OBJECTIVE: Studies suggest a greater risk of Parkinson's disease (PD) after traumatic brain injury (TBI), but it is possible that the risk of TBI is greater in the prodromal period of PD. We aimed to examine the time-to-TBI in PD patients in their prodromal period compared to population-based controls. METHODS: We identified 89,790 incident PD cases and 118,095 comparable controls aged > 65 years in 2009 using Medicare claims data. Using data from the preceding 5 years, we compared time-to-TBI in PD patients in their prodromal period to controls. We estimated hazard ratios (HRs) and 95% confidence intervals (CIs) for TBI in a Cox regression, while adjusting for age, sex, race/ethnicity, modified Charlson comorbidity index, smoking, and alcohol use. RESULTS: Risk of TBI was greater in PD patients in their prodromal period across all age and sex groups, with HRs consistently increasing with proximity to PD diagnosis. HRs ranged from 1.64 (95% CI, 1.52, 1.77) 5 years preceding diagnosis to 3.93 (95% CI, 3.74, 4.13) in the year before. The interaction between PD, TBI, and time was primarily observed for TBI attributed to falls. Motor dysfunction and cognitive impairment, suggested by corresponding International Classification of Diseases, Ninth Revision codes, partially mediated the PD-TBI association. INTERPRETATION: There is a strong association between PD and a recent TBI in the prodromal period of PD. This association strengthens as PD diagnosis approaches and may be a result of undetected nonmotor and motor symptoms, but confirmation will be required. Ann Neurol 2017;82:744-754.

A predictive model to identify Parkinson disease from administrative claims data.

OBJECTIVE: To use administrative medical claims data to identify patients with incident Parkinson disease (PD) prior to diagnosis. METHODS: Using a population-based case-control study of incident PD in 2009 among Medicare beneficiaries aged 66-90 years (89,790 cases, 118,095 controls) and the elastic net algorithm, we developed a cross-validated model for predicting PD using only demographic data and 2004-2009 Medicare claims data. We then compared this model to more basic models containing only demographic data and diagnosis codes for constipation, taste/smell disturbance, and REM sleep behavior disorder, using each model's receiver operator characteristic area under the curve (AUC). RESULTS: We observed all established associations between PD and age, sex, race/ethnicity, tobacco smoking, and the above medical conditions. A model with those predictors had an AUC of only 0.670 (95% confidence interval [CI] 0.668-0.673). In contrast, the AUC for a predictive model with 536 diagnosis and procedure codes was 0.857 (95% CI 0.855-0.859). At the optimal cut point, sensitivity was 73.5% and specificity was 83.2%. CONCLUSIONS: Using only demographic data and selected diagnosis and procedure codes readily available in administrative claims data, it is possible to identify individuals with a high probability of eventually being diagnosed with PD.