arXiv subjects

Adel Ahmadi Nadi

Publications and source records attributed to Adel Ahmadi Nadi.

5 recordsLinked to original sources

arXiv2026

Generative AI use in Statistical Research: A Literature Review and Code Generation Case Study

Generative artificial intelligence (GenAI) is a large language model (LLM) that has the ability to generate media based on user-provided prompts. Given the demonstrated capabilities of models such as ChatGPT in information synthesis and programming, there is growing interest in their potential role within the research process. However, little work has evaluated recent GenAI models for research tasks in the domain of statistical research. This case study examines GenAI as a tool for developing a literature review and translating methodology from academic papers into code, for the topic of dynamic treatment regime (DTR) estimation via the dynamic weighted ordinary least squares (dWOLS) approach. Specifically, we utilize ChatGPT-5 and ScholarAI (Sept-Nov 2025 release) in the processes of identifying relevant sources for the literature review, creating summaries of papers, identifying gaps in research, and R code generation to implement methodology. Our findings show that current GenAI models lack the depth and contextual understanding required to accomplish these tasks without careful prompting and supervision of a knowledgeable researcher. Nonetheless, GenAI has potential to increase efficiency of tasks which take advantage of its search and summarization abilities, as well as basic code debugging and algorithm formation. We demonstrate that under a knowledgeable guide, GenAI can function as a research tool, but not as a substitute for methodological expertise.

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arXiv2025

Recent advances in doubly-robust weighted ordinary least squares techniques for dynamic treatment regime estimation

A dynamic treatment regime (DTR) is an approach to delivering precision medicine that uses patient characteristics to guide treatment decisions for optimal health outcomes. Numerous methods have been proposed for DTR estimation, including dynamic weighted ordinary least squares (dWOLS), a regression-based approach that affords double robustness to model misspecification within an easy to implement analytical framework. Initially, the dWOLS approach was developed under the assumptions of continuous outcomes and binary treatment decisions. Motivated by clinical research, subsequent theoretical advancements have extended the dWOLS framework to address binary, continuous and multicategory treatments across various outcome types, including binary, continuous, and survival-type. However, certain scenarios remain unexplored. This paper summarizes the last ten years of extension and application of the dWOLS method, providing a comprehensive and detailed review of the original dWOLS method and its extensions, as well as highlighting its diverse practical applications. We also explore studies that have addressed challenges associated with dWOLS implementation, such as model validation, variable selection, and handling measurement errors. Using simulated data, we present numerical illustrations along with step-by-step implementations in the \texttt{R} environment to facilitate a deeper understanding of dWOLS-based DTR estimation methodologies.

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arXiv2025

Dynamic Risk-Adjusted Monitoring of Time Between Events: Applications of NHPP in Pipeline Accident Surveillance

Monitoring time between events (TBE) is a critical task in industrial settings. Traditional Statistical Process Monitoring (SPM) methods often assume that TBE variables follow an exponential distribution, which implies a constant failure intensity. While this assumption may hold for products with homogeneous quality, it is less appropriate for complex systems, such as repairable systems, where failure mechanisms evolve over time due to degradation or aging. In such cases, the Non-Homogeneous Poisson Process (NHPP), which accommodates time-varying failure intensity, is a more suitable model. Furthermore, failure patterns in complex systems are frequently influenced by risk factors, including environmental conditions and human interventions, and system failures often incur restoration costs. This work introduces a novel approach: a risk-adjusted control chart based on the NHPP model, specifically designed to monitor the ratio of cost to TBE, referred to as the average cost per time unit (AC). The proposed method is evaluated through extensive simulations, demonstrating its superior performance. Additionally, the chart is applied to monitor pipeline accidents over time, accounting for the impact of various risk factors. These results highlight the effectiveness of the developed chart in enhancing monitoring capabilities for complex systems.

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arXiv2025

Risk-Adjusted learning curve assessment using comparative probability metrics

Surgical learning curves are graphical tools used to evaluate a trainee's progress in the early stages of their career and determine whether they have achieved proficiency after completing a specified number of surgeries. Cumulative sum (CUSUM) techniques are commonly used to assess learning curves due to their simplicity, but they face criticism for relying on fixed performance thresholds and lacking interpretability. This paper introduces a risk-adjusted surgical learning curve assessment (SLCA) method that focuses on estimation rather than hypothesis testing, as seen in CUSUM methods. The method is designed to accommodate right-skewed outcomes, such as surgery durations, characterized by the Weibull distribution. To evaluate the learning process, the SLCA approach estimates comparative probability metrics that assess the likelihood of a clinically important difference between the trainee's performance and a standard. Expecting improvement over time, we use weighted estimating equations to give greater weight to recent outcomes. Compared to CUSUM methods, SLCA offers enhanced interpretability, avoids reliance on externally defined performance levels, and emphasizes assessing clinical equivalence or noninferiority. We demonstrate the method's effectiveness through a colorectal surgery dataset case study and a numerical study.

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arXiv2025

Investigating the performance of the Phase II Hotelling T2 chart when monitoring multivariate time series observations

Thanks to high-tech measurement systems like sensors, data are often collected with high frequency in modern industrial processes. This phenomenon could potentially produce autocorrelated and cross-correlated measurements. It has been shown that if this issue is not properly accounted for while designing the control charts, many false alarms may be observed, disrupting the monitoring process efficiency. There are generally two recommended ways to monitor autocorrelated data: fitting a time series model and then monitoring the residuals or directly monitoring the original observations. Although residual charts are popular in the literature because they offer advantages such as ease of implementation, questions have been raised about their efficiency due to the loss of information. This paper develops the methodology for applying Hotelling's T2 chart directly to monitoring multivariate autocorrelated and cross-correlated observations. To model such data, we use the multivariate vector autoregressive time series model of order p >= 1, denoted as VAR(p). We compare the performance of the T2 chart based on the original observations and the residual-based T2 chart using the well-known metric average run length and a newly introduced criterion called first-to-signal. The results indicate that the proposed method consistently outperforms the alternative chart. We also illustrate the proposed method using two examples: one from a steel sheet rolling process (VAR(1) observations) and another from a chemical process (VAR(3) observations).

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