Statin Use and Incidence of Parkinson's Disease in Women from the French E3N Cohort Study.

BACKGROUND: Statins represent candidates for drug repurposing in Parkinson's disease (PD). Few studies examined the role of reverse causation, statin subgroups, and dose-response relations based on time-varying exposures. OBJECTIVES: We examined whether statin use is associated with PD incidence while attempting to overcome the limitations described previously, especially reverse causation. METHOD: We used data from the E3N cohort study of French women (follow-up, 2004-2018). Incident PD was ascertained using multiple sources and validated by experts. New statin users were identified through linked drug claims. We set up a nested case-control study to describe trajectories of statin prescriptions and medical consultations before diagnosis. We used time-varying multivariable Cox proportional hazards regression models to examine the statins-PD association. Exposure indexes included ever use, cumulative duration/dose, and mean daily dose and were lagged by 5 years to address reverse causation. RESULTS: The case-control study (693 cases, 13,784 controls) showed differences in case-control trajectories, with changes in the 5 years before diagnosis in cases. Of 73,925 women (aged 54-79 years), 524 developed PD and 11,552 started using statins in lagged analyses. Ever use of any statin was not associated with PD (hazard ratio [HR] = 0.87, 95% confidence interval [CI] = 0.67-1.11). Alternatively, ever use of lipophilic statins was significantly associated with lower PD incidence (HR = 0.70, 95% CI = 0.51-0.98), with a dose-response relation for the mean daily dose (P-linear trend = 0.02). There was no association for hydrophilic statins. CONCLUSION: Use of lipophilic statins at least 5 years earlier was associated with reduced PD incidence in women, with a dose-response relation for the mean daily dose. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Identifying Protective Drugs for Parkinson's Disease in Health-Care Databases Using Machine Learning.

BACKGROUND: Available treatments for Parkinson's disease (PD) are only partially or transiently effective. Identifying existing molecules that may present a therapeutic or preventive benefit for PD (drug repositioning) is thus of utmost interest. OBJECTIVE: We aimed at detecting potentially protective associations between marketed drugs and PD through a large-scale automated screening strategy. METHODS: We implemented a machine learning (ML) algorithm combining subsampling and lasso logistic regression in a case-control study nested in the French national health data system. Our study population comprised 40,760 incident PD patients identified by a validated algorithm during 2016 to 2018 and 176,395 controls of similar age, sex, and region of residence, all followed since 2006. Drug exposure was defined at the chemical subgroup level, then at the substance level of the Anatomical Therapeutic Chemical (ATC) classification considering the frequency of prescriptions over a 2-year period starting 10 years before the index date to limit reverse causation bias. Sensitivity analyses were conducted using a more specific definition of PD status. RESULTS: Six drug subgroups were detected by our algorithm among the 374 screened. Sulfonamide diuretics (ATC-C03CA), in particular furosemide (C03CA01), showed the most robust signal. Other signals included adrenergics in combination with anticholinergics (R03AL) and insulins and analogues (A10AD). CONCLUSIONS: We identified several signals that deserve to be confirmed in large studies with appropriate consideration of the potential for reverse causation. Our results illustrate the value of ML-based signal detection algorithms for identifying drugs inversely associated with PD risk in health-care databases. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

High-Dimensional Propensity Score-Adjusted Case-Crossover for Discovering Adverse Drug Reactions from Computerized Administrative Healthcare Databases.

INTRODUCTION: Increasing availability of medico-administrative databases has prompted the development of automated pharmacovigilance signal detection methodologies. Self-controlled approaches have recently been proposed. They account for time-independent confounding factors that may not be recorded. So far, large numbers of drugs have been screened either univariately or with LASSO penalized regressions. OBJECTIVE: We propose and assess a new method that combines the case-crossover self-controlled design with propensity scores (propensity score-adjusted case-crossover) built from high-dimensional data-driven variable selection, to account for co-medications or possibly other measured confounders. METHODS: Comparison with the univariate and LASSO case-crossover was performed from simulations and a real-data study. Multiple regressions (LASSO, propensity score-adjusted case-crossover) accounted for co-medications and no other covariates. For the univariate and propensity score-adjusted case-crossover methods, the detection threshold was based on a false discovery rate procedure, while for LASSO, it relied on the Akaike Information Criterion. For the real-data study, two drug safety experts evaluated the signals generated from the analysis of 4099 patients with acute myocardial infarction from the French national health database. RESULTS: On simulations, our approach ranked the signals similarly to the LASSO and better than the univariate method while controlling the false discovery rate at the prespecified level, contrary to the univariate method. The LASSO provided the best sensitivity at the cost of larger false discovery rate estimates. On the application, our approach showed similar performances to the LASSO and better performances than the univariate method. It highlighted 43 signals out of 609 drug candidates: 22 (51%) were considered as potentially pharmacologically relevant, including seven (16%) regarded as highly relevant. CONCLUSIONS: Our findings show the interest of a propensity score combined with a case-crossover for pharmacovigilance. They also confirm that indication bias remains a challenge when mining medico-administrative databases.

New adaptive lasso approaches for variable selection in automated pharmacovigilance signal detection.

BACKGROUND: Adverse effects of drugs are often identified after market introduction. Post-marketing pharmacovigilance aims to detect them as early as possible and relies on spontaneous reporting systems collecting suspicious cases. Signal detection tools have been developed to mine these large databases and counts of reports are analysed with disproportionality methods. To address disproportionality method biases, recent methods apply to individual observations taking into account all exposures for the same patient. In particular, the logistic lasso provides an efficient variable selection framework, yet the choice of the regularization parameter is a challenging issue and the lasso variable selection may give inconsistent results. METHODS: We propose a new signal detection methodology based on the adaptive lasso. We derived two new adaptive weights from (i) a lasso regression using the Bayesian Information Criterion (BIC), and (ii) the class-imbalanced subsampling lasso (CISL), an extension of stability selection. The BIC is used in the adaptive lasso stage for variable selection. We performed an extensive simulation study and an application to real data, where we compared our methods to the existing adaptive lasso, and recent detection approaches based on lasso regression or propensity scores in high dimension. For both studies, we evaluate the methods in terms of false discoveries and sensitivity. RESULTS: In the simulations and the application, both proposed adaptive weights show equivalent or better performances than the other competitors, with an advantage for the CISL-based adaptive weights. CISL and lasso regression using BIC are solid alternatives. CONCLUSION: Our proposed adaptive lasso is an appealing methodology for signal detection in pharmacovigilance. Although we cannot rely on test theory, our approaches show a low and stable False Discovery Rate in all simulation settings. All methods evaluated in this work are implemented in the adapt4pv R package.

Propensity Score-Based Approaches in High Dimension for Pharmacovigilance Signal Detection: an Empirical Comparison on the French Spontaneous Reporting Database.

Classical methods used for signal detection in pharmacovigilance rely on disproportionality analysis of counts aggregating spontaneous reports of a given adverse drug reaction. In recent years, alternative methods have been proposed to analyze individual spontaneous reports such as penalized multiple logistic regression approaches. These approaches address some well-known biases resulting from disproportionality methods. However, while penalization accounts for computational constraints due to high-dimensional data, it raises the issue of determining the regularization parameter and eventually that of an error-controlling decision rule. We present a new automated signal detection strategy for pharmacovigilance systems, based on propensity scores (PS) in high dimension. PSs are increasingly used to assess a given association with high-dimensional observational healthcare databases in accounting for confusion bias. Our main aim was to develop a method having the same advantages as multiple regression approaches in dealing with bias, while relying on the statistical multiple comparison framework as regards decision thresholds, by considering false discovery rate (FDR)-based decision rules. We investigate four PS estimation methods in high dimension: a gradient tree boosting (GTB) algorithm from machine-learning and three variable selection algorithms. For each (drug, adverse event) pair, the PS is then applied as adjustment covariate or by using two kinds of weighting: inverse proportional treatment weighting and matching weights. The different versions of the new approach were compared to a univariate approach, which is a disproportionality method, and to two penalized multiple logistic regression approaches, directly applied on spontaneous reporting data. Performance was assessed through an empirical comparative study conducted on a reference signal set in the French national pharmacovigilance database (2000-2016) that was recently proposed for drug-induced liver injury. Multiple regression approaches performed better in detecting true positives and false positives. Nonetheless, the performances of the PS-based methods using matching weights was very similar to that of multiple regression and better than with the univariate approach. In addition to being able to control FDR statistical errors, the proposed PS-based strategy is an interesting alternative to multiple regression approaches.