{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T10:44:12Z","timestamp":1763981052438,"version":"3.45.0"},"reference-count":33,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T00:00:00Z","timestamp":1762905600000},"content-version":"vor","delay-in-days":11,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["ascpt.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Clinical Translational Sci"],"published-print":{"date-parts":[[2025,11]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>With the growing availability of large healthcare databases for clinical science, mitigating unmeasured confounding has emerged as a major issue in pharmacoepidemiologic studies. Extensions of causal inference methods to high\u2010dimensional settings could help address this problem, but studies comparing their performance in real\u2010world databases are still lacking. This study aims to compare the ability to reduce the measured and indirectly measured confounding of three causal inference methods adapted to a real\u2010world high\u2010dimensional database using a machine learning LASSO algorithm: G\u2010computation (GC), Targeted Maximum Likelihood estimation (TMLE) and Propensity Score with overlap or stabilized inverse probability treatment weighting. This large\u2010scale empirical study was based on the French National Healthcare Claims Database (SNDS), consisting of 2,172,702 pregnancies \u200922\u2009weeks of gestation over the period 2011\u20132014. We used a set of 42 negative and 13 positive reference drugs related to prematurity risk. For each reference drug, the logarithm of the odds ratio for prematurity and its 95% confidence interval were estimated using each method. The proportions of false positive and true positive associations were calculated and compared between the methods. All methods yielded fewer false positives than a crude model based on a minimal set of adjusted covariates. TMLE produced the lowest proportion of false positives (45.2%), followed by GC (47.6%). GC yielded the highest proportion of true positives (92.3%). Our results confirm the interest of causal inference methods exploiting the wealth of data in healthcare databases, especially GC in terms of performance and ease of implementation.<\/jats:p>","DOI":"10.1111\/cts.70394","type":"journal-article","created":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T10:47:17Z","timestamp":1762944437000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Evaluation of Methods Adjusting for Unmeasured Confounding Using Large Healthcare Databases: An Empirical Study Concerning Drugs Inducing Prematurity"],"prefix":"10.1111","volume":"18","author":[{"given":"Chi\u2010Hong","family":"Duong","sequence":"first","affiliation":[{"name":"High\u2010Dimensional Biostatistics for Drug Safety and Genomics, CESP, Universit\u00e9 Paris\u2010Saclay, UVSQ, Universit\u00e9 Paris\u2010Sud, Inserm  Villejuif 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Paris\u2010Sud, Inserm  Villejuif France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jonathan","family":"Cottenet","sequence":"additional","affiliation":[{"name":"Biostatistics and Bioinformatics (DIM) Dijon University Hospital  Dijon France"},{"name":"Faculty of Medicine University of Bourgogne\u2010Franche\u2010Comt\u00e9  Dijon France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Catherine","family":"Quantin","sequence":"additional","affiliation":[{"name":"High\u2010Dimensional Biostatistics for Drug Safety and Genomics, CESP, Universit\u00e9 Paris\u2010Saclay, UVSQ, Universit\u00e9 Paris\u2010Sud, Inserm  Villejuif France"},{"name":"Biostatistics and Bioinformatics (DIM) Dijon University Hospital  Dijon France"},{"name":"Faculty of Medicine University of Bourgogne\u2010Franche\u2010Comt\u00e9  Dijon France"},{"name":"Clinical Epidemiology\/Clinical Trials Unit Clinical Investigation Center 1432  Dijon 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