Chenyin Gao
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View article: Doubly robust omnibus sensitivity analysis of externally controlled trials with intercurrent events
Doubly robust omnibus sensitivity analysis of externally controlled trials with intercurrent events Open
Externally controlled trials are crucial in clinical development when randomized controlled trials are unethical or impractical. These trials consist of a full treatment arm with the experimental treatment and a full external control arm. …
View article: Unsupervised Ensemble Learning for Efficient Integration of Pre-trained Polygenic Risk Scores
Unsupervised Ensemble Learning for Efficient Integration of Pre-trained Polygenic Risk Scores Open
View article: Unsupervised Ensemble Learning for Efficient Integration of Pre-trained Polygenic Risk Scores
Unsupervised Ensemble Learning for Efficient Integration of Pre-trained Polygenic Risk Scores Open
The growing availability of pre-trained polygenic risk score (PRS) models has enabled their integration into real-world applications, reducing the need for extensive data labeling, training, and calibration. However, selecting the most sui…
View article: On the Role of Surrogates in Conformal Inference of Individual Causal Effects
On the Role of Surrogates in Conformal Inference of Individual Causal Effects Open
Learning the Individual Treatment Effect (ITE) is essential for personalized decision-making, yet causal inference has traditionally focused on aggregated treatment effects. While integrating conformal prediction with causal inference can …
View article: Doubly protected estimation for survival outcomes utilizing external controls for randomized clinical trials
Doubly protected estimation for survival outcomes utilizing external controls for randomized clinical trials Open
Censored survival data are common in clinical trials, but small control groups can pose challenges, particularly in rare diseases or where balanced randomization is impractical. Recent approaches leverage external controls from historical …
View article: Causal Customer Churn Analysis with Low-rank Tensor Block Hazard Model
Causal Customer Churn Analysis with Low-rank Tensor Block Hazard Model Open
This study introduces an innovative method for analyzing the impact of various interventions on customer churn, using the potential outcomes framework. We present a new causal model, the tensorized latent factor block hazard model, which i…
View article: Real Effect or Bias? Best Practices for Evaluating the Robustness of Real-World Evidence through Quantitative Sensitivity Analysis for Unmeasured Confounding
Real Effect or Bias? Best Practices for Evaluating the Robustness of Real-World Evidence through Quantitative Sensitivity Analysis for Unmeasured Confounding Open
The assumption of no unmeasured confounders is a critical but unverifiable assumption required for causal inference yet quantitative sensitivity analyses to assess robustness of real-world evidence remains underutilized. The lack of use is…
View article: Improving randomized controlled trial analysis via data-adaptive borrowing
Improving randomized controlled trial analysis via data-adaptive borrowing Open
In recent years, real-world external controls have grown in popularity as a tool to empower randomized placebo-controlled trials, particularly in rare diseases or cases where balanced randomization is unethical or impractical. However, as …
View article: Real Effect or Bias? Best Practices for Evaluating the Robustness of Real-World Evidence through Quantitative Sensitivity Analysis for Unmeasured Confounding
Real Effect or Bias? Best Practices for Evaluating the Robustness of Real-World Evidence through Quantitative Sensitivity Analysis for Unmeasured Confounding Open
The assumption of ‘no unmeasured confounders’ is a critical but unverifiable assumption required for causal inference yet quantitative sensitivity analyses to assess robustness of real-world evidence remains underutilized. The lack of use …
View article: Pretest estimation in combining probability and non-probability samples
Pretest estimation in combining probability and non-probability samples Open
Multiple heterogeneous data sources are becoming increasingly available for statistical analyses in the era of big data. As an important example in finite-population inference, we develop a unified framework of the test-and-pool approach t…
View article: Elastic integrative analysis of randomised trial and real-world data for treatment heterogeneity estimation
Elastic integrative analysis of randomised trial and real-world data for treatment heterogeneity estimation Open
We propose a test-based elastic integrative analysis of the randomised trial and real-world data to estimate treatment effect heterogeneity with a vector of known effect modifiers. When the real-world data are not subject to bias, our appr…
View article: Where does the risk lie? Systemic risk and tail risk networks in the Chinese financial market
Where does the risk lie? Systemic risk and tail risk networks in the Chinese financial market Open
This paper studies tail risk connectedness and systemic risk in the Chinese financial market in the post‐crisis period of 2009–2017. We adopt the conditional value at risk ( CoVaR ) and complex theory to construct the tail risk connectedne…
View article: Pretest estimation in combining probability and non-probability samples
Pretest estimation in combining probability and non-probability samples Open
Multiple heterogeneous data sources are becoming increasingly available for statistical analyses in the era of big data. As an important example in finite-population inference, we develop a unified framework of the test-and-pool approach t…
View article: Enhancing convolutional neural network generalizability via low-rank weight approximation
Enhancing convolutional neural network generalizability via low-rank weight approximation Open
Noise is ubiquitous during image acquisition. Sufficient denoising is often an important first step for image processing. In recent decades, deep neural networks (DNNs) have been widely used for image denoising. Most DNN-based image denois…
View article: Soft calibration for selection bias problems under mixed-effects models
Soft calibration for selection bias problems under mixed-effects models Open
Calibration weighting has been widely used to correct selection biases in non-probability sampling, missing data, and causal inference. The main idea is to calibrate the biased sample to the benchmark by adjusting the subject weights. Howe…
View article: Nearest Neighbour Ratio Imputation with Incomplete Multinomial Outcome in Survey Sampling
Nearest Neighbour Ratio Imputation with Incomplete Multinomial Outcome in Survey Sampling Open
Nonresponse is a common problem in survey sampling. Appropriate treatment can be challenging, especially when dealing with detailed breakdowns of totals. Often, the nearest neighbour imputation method is used to handle such incomplete mult…
View article: Nearest neighbor ratio imputation with incomplete multi-nomial outcome in survey sampling
Nearest neighbor ratio imputation with incomplete multi-nomial outcome in survey sampling Open
Nonresponse is a common problem in survey sampling. Appropriate treatment can be challenging, especially when dealing with detailed breakdowns of totals. Often, the nearest neighbor imputation method is used to handle such incomplete multi…