| Autor: |
Sharon E. Davis, Henry Ssemaganda, Jejo D. Koola, Jialin Mao, Dax Westerman, Theodore Speroff, Usha S. Govindarajulu, Craig R. Ramsay, Art Sedrakyan, Lucila Ohno-Machado, Frederic S. Resnic, Michael E. Matheny |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
BMC Medical Research Methodology, Vol 23, Iss 1, Pp 1-15 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
1471-2288 |
| DOI: |
10.1186/s12874-023-01913-9 |
| Popis: |
Abstract Background Validating new algorithms, such as methods to disentangle intrinsic treatment risk from risk associated with experiential learning of novel treatments, often requires knowing the ground truth for data characteristics under investigation. Since the ground truth is inaccessible in real world data, simulation studies using synthetic datasets that mimic complex clinical environments are essential. We describe and evaluate a generalizable framework for injecting hierarchical learning effects within a robust data generation process that incorporates the magnitude of intrinsic risk and accounts for known critical elements in clinical data relationships. Methods We present a multi-step data generating process with customizable options and flexible modules to support a variety of simulation requirements. Synthetic patients with nonlinear and correlated features are assigned to provider and institution case series. The probability of treatment and outcome assignment are associated with patient features based on user definitions. Risk due to experiential learning by providers and/or institutions when novel treatments are introduced is injected at various speeds and magnitudes. To further reflect real-world complexity, users can request missing values and omitted variables. We illustrate an implementation of our method in a case study using MIMIC-III data for reference patient feature distributions. Results Realized data characteristics in the simulated data reflected specified values. Apparent deviations in treatment effects and feature distributions, though not statistically significant, were most common in small datasets (n |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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