dorsal/arxiv
View SchemaProactive Anomaly Screen for Multiple Endpoints Using Bayesian Latent Class Modeling: A k-Step Ahead Approach
| Authors | Yuxi Zhao, Margaret Gamalo |
|---|---|
| Categories | |
| ArXiv ID | 2601.08167vv2 |
| URL | https://arxiv.org/abs/2601.08167 |
| License | http://creativecommons.org/licenses/by-nc-nd/4.0/ |
Abstract
In clinical trials, ensuring the quality and validity of data for downstream analysis and results is paramount, thus necessitating thorough data monitoring. This typically involves employing edit checks and manual queries during data collection. Edit checks consist of straightforward schemes programmed into relational databases, though they lack the capacity to assess data intelligently. In contrast, manual queries are initiated by data managers who manually scrutinize the collected data, identifying discrepancies needing clarification or correction. Manual queries pose significant challenges, particularly when dealing with large-scale data in late-phase clinical trials. Moreover, they are reactive rather than predictive, meaning they address issues after they arise rather than preemptively preventing errors. In this paper, we propose a joint model for multiple endpoints, focusing on primary and key secondary measures, using a Bayesian latent class approach. This model incorporates adjustments for risk monitoring factors, enabling proactive, $k$-step ahead, detection of conflicting or anomalous patterns within the data. Furthermore, we develop individualized dynamic predictions at consecutive time-points to identify potential anomalous values based on observed data. This analysis can be integrated into electronic data capture systems to provide objective alerts to stakeholders. We present simulation results and demonstrate effectiveness of this approach with real-world data.
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"abstract": "In clinical trials, ensuring the quality and validity of data for downstream analysis and results is paramount, thus necessitating thorough data monitoring. This typically involves employing edit checks and manual queries during data collection. Edit checks consist of straightforward schemes programmed into relational databases, though they lack the capacity to assess data intelligently. In contrast, manual queries are initiated by data managers who manually scrutinize the collected data, identifying discrepancies needing clarification or correction. Manual queries pose significant challenges, particularly when dealing with large-scale data in late-phase clinical trials. Moreover, they are reactive rather than predictive, meaning they address issues after they arise rather than preemptively preventing errors. In this paper, we propose a joint model for multiple endpoints, focusing on primary and key secondary measures, using a Bayesian latent class approach. This model incorporates adjustments for risk monitoring factors, enabling proactive, $k$-step ahead, detection of conflicting or anomalous patterns within the data. Furthermore, we develop individualized dynamic predictions at consecutive time-points to identify potential anomalous values based on observed data. This analysis can be integrated into electronic data capture systems to provide objective alerts to stakeholders. We present simulation results and demonstrate effectiveness of this approach with real-world data.",
"arxiv_id": "2601.08167",
"authors": [
"Yuxi Zhao",
"Margaret Gamalo"
],
"categories": [
"stat.OT"
],
"license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
"title": "Proactive Anomaly Screen for Multiple Endpoints Using Bayesian Latent Class Modeling: A k-Step Ahead Approach",
"url": "https://arxiv.org/abs/2601.08167",
"version": "v2"
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