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Results for “multi-cohort sampling”

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PubMed2026

A doubly robust framework for addressing outcome-dependent selection bias in multi-cohort EHR studies.

Selection bias can hinder accurate estimation of association parameters in binary disease risk models using non-probability samples like electronic health records (EHRs). The issue is compounded when participants are recruited from multiple clinics/centers with varying selection mechanisms that may depend on the disease/outcome of interest. Traditional inverse-probability-weighted (IPW) methods, based on constructed parametric selection models, often struggle with misspecifications when selection mechanisms vary across cohorts. This paper introduces a new Joint Augmented Inverse Probability Weighted (JAIPW) method, which integrates individual-level data from multiple cohorts collected under potentially outcome-dependent selection mechanisms, with data from an external probability sample. JAIPW offers double robustness by incorporating a flexible auxiliary score model to address potential misspecifications in the selection models. We outline the asymptotic properties of the JAIPW estimator, and our simulations reveal that JAIPW achieves up to 6 times lower relative bias and 5 times lower root mean square error (RMSE) compared to the best performing joint IPW methods under scenarios with misspecified selection models. Applying JAIPW to the Michigan Genomics Initiative (MGI), a multi-clinic EHR-linked biobank, combined with external national probability samples, resulted in cancer-sex association estimates closely aligned with national benchmark estimates. We also analyzed the association between cancer and polygenic risk scores (PRS) in MGI to illustrate a situation where the exposure variable is not measured in the external probability sample.

Selection Bias↗

PubMed2026

Causal effects of cholelithiasis on hepatopancreatobiliary diseases: a multi-cohort Mendelian randomization study.

BACKGROUND: Cholelithiasis is commonly associated with multiple hepatopancreatobiliary diseases, yet whether these relationships reflect causal mechanisms or shared risk factors remains unclear. METHODS: We performed a phenome-oriented two-sample Mendelian randomization (MR) analysis to evaluate the causal impact of genetic liability to cholelithiasis across hepatopancreatobiliary outcomes. Independent genome-wide significant variants were selected as instrumental variables. Primary analyses used inverse variance weighting, complemented by sensitivity analyses, reverse MR, and multivariable MR adjusting for body mass index (BMI). RESULTS: Genetic predisposition to cholelithiasis was associated with increased risk of acute pancreatitis and extrahepatic cholangiocarcinoma (eCCA), with consistent directionality across datasets.The association with acute pancreatitis was interpreted as a positive control, whereas the null association with alcohol-induced acute pancreatitis served as a negative control. No causal association was observed for portal vein thrombosis. Sensitivity analyses, including MR-PRESSO and MR Steiger filtering, supported the robustness and directionality of the causal estimates. Reverse MR analyses showed no consistent evidence supporting reverse causality. Multivariable MR indicated that observed effects were not fully explained by BMI-related pathways. CONCLUSION: These findings suggest that cholelithiasis susceptibility may contribute to the broader hepatopancreatobiliary disease network, extending its clinical relevance beyond a localized biliary disorder.

Mendelian Randomization Analysis↗

PubMed2026

A Multi-omics Regulated Cell Death Framework Defines Immune Phenotypes and Guides Precision Therapy in Colorectal Cancer.

Colorectal cancer (CRC) is molecularly and immunologically heterogeneous, contributing to variable treatment response. Because regulated cell death (RCD) intersects with tumor metabolism, immune regulation, and therapeutic susceptibility, we built an RCD-centered framework for CRC stratification. Multi-cohort transcriptomic data were used to infer RCD subtypes with non-negative matrix factorization (NMF) and non-negative least squares (NNLS). Genomic, bulk RNA-seq, single-cell RNA-seq, and spatial transcriptomic datasets were integrated to characterize subtype-associated biology. Machine-learning models were developed for immunotherapy response and survival-risk estimation. Candidate compounds were screened by GDSC2-based drug-sensitivity modeling and molecular docking, and FSTL3 was functionally assessed in vitro. The framework separated CRC samples into two RCD-related phenotypes resembling immune-hot and immune-cold states. RCD1 showed immune activation and higher mutational burden, whereas RCD2 showed immune-suppressed features, intratumoral heterogeneity, and aggressive biology. RCD-associated signatures showed potential for predicting immunotherapy response and survival risk. Dasatinib was prioritized for immune-cold, high-risk tumors, with preliminary evidence supporting its activity in CRC cells, while functional assays suggested a role for FSTL3 in growth, invasion, epithelial-mesenchymal transition, and apoptosis regulation. These findings suggest that RCD-based multi-omics analysis may refine CRC stratification and help generate therapeutic hypotheses.

Colorectal cancer↗