The ethical and accurate handling of data is paramount in the domain of clinical research. As the demand for data-driven clinical insights continues to grow, researchers face challenges in balancing the need for accuracy with the availability of data and the imperative to protect sensitive information. In situations where quality real patient data is not available, synthetic data can be the most reliable data source from which to derive predictive insights. Synthetic data can be more cost-effective and time-efficient in many cases than acquiring the equivalent real data.
Synthetic data must be differentiated from fake data. In recent years there has been much controversy concerning fake data detected in published journal articles which have previously passed peer review, particularly in an academic context. As one study is generally built upon assumptions formed by the results of another, this preponderance of fake data has really had a catastrophic impact on our ability to trust any published scientific research, regardless of whether the study at hand also contains fake data. It has become clear that the implementation of increased quality control standards for all published research needs to be prioritised.
While synthetic data is not without it’s own pitfalls, the key difference between synthetic and fake data lies in it’s purpose and authenticity. Synthetic data is designed to emulate real-world data for specific use cases, maintaining statistical properties without revealing actual (individual) information. On the other hand, fake data is typically fabricated and may not adhere to any real-world patterns or statistics.
In clinical research, the use of real patient data is fraught with privacy concerns and other ethical considerations. Accurate and consistent patient data can also be hard to come by for other reasons such as heterogeneous recording methods or insufficient disease populations. Synthetic data is emerging as a powerful solution to navigate these limitations. While accurate synthetic data is not a trivial thing to generate, researchers can harness advanced algorithms and models built by expert data scientists to generate synthetic datasets that faithfully mimic the statistical properties and patterns of real-world patient and other data. This allows researchers to simulate and predict relevant clinical outcomes in situations where real data is not readily available, and do so without compromising individual patient privacy.
A large proportion of machine learning models in an AI context are currently being trained on synthetic rather than real data. This is largely because using generative models to create synthetic data tends to be much faster and cheaper than collecting real-world-data. Real-world data can at times lack sufficient diversity to make insights and predictions truly generalisable.
Both the irresponsible use of synthetic data and the generation and application of fake data in academic, industry and clinical research settings can have severe consequences. Whether stemming from dishonesty or incompetence, the misuse of fake data or inaccurate synthetic data poses a threat to the integrity of scientific inquiry.
This following sections define and delineate between synthetic and fake data as well as summarise the key applications of synthetic data in clinical research as compared to the potential pitfalls associated with the unethical use of fake data.
Synthetic Data:
Synthetic data refers to artificially generated data that mimics the statistical properties and patterns of real-world data. It is created using various algorithms, models, or simulations to resemble authentic patient data as closely as possible. It may do so without containing any real-world identifying information about individual patients comprising the original patient sample from which it was derived.
Synthetic data can be used in situations where privacy, security, or confidentiality concerns make it challenging to access or use real patient data. It can also be used in cases where an insufficient volume of quality patient data is available or where existing data is too heterogeneous to draw accurate inferences, such as is typically the case with rare diseases. It can potentially be employed in product testing to create realistic scenarios without subjecting real patients to unnecessary risk.
3 key use cases for synthetic data in clinical research
1. Privacy Preservation:
– Synthetic data allows researchers to conduct analyses and develop statistical models without exposing sensitive patient information. This is particularly crucial in the healthcare and clinical research sectors, where maintaining patient confidentiality is a legal and ethical imperative.
2. Robust Testing Environments:
– Clinical trials and other experiments related to product testing or behavioural interventions often necessitate testing in various scenarios. Synthetic data provides a versatile and secure testing ground, enabling researchers to validate algorithms and methodologies without putting real patients at risk.
3. Data Augmentation for Limited Datasets:
– In situations where obtaining a large and diverse dataset is challenging, synthetic data serves as a valuable tool for augmenting existing datasets. This aids in the development of more robust models and generalisable findings. A data set can be made up of varying proportions of synthetic versus real-world data. For example, a real world data set may be fairly large but lack diversity on the one hand, or small and overly heterogeneous on the other. The methods of generating synthetic data to augment these respective data sets would differ in each case.
Fake Data:
Fake data typically refers to data that is intentionally fabricated or inaccurate due to improper data handling techniques. In situations of misuse it is usually combined with real study data to give misleading results.
Fake data can be used ethically for various purposes, such as placeholder values in a database during development, creating fictional scenarios for training or educational purposes, or generating data for scenarios where realism is not crucial. Unfortunately in the majority of notable academic and clinical cases it has been used with the deliberate intention to mislead by doctoring study results and thus poses a serious threat to the scientific community and the general public.
.There are three key concerns with fake data.
1. Academic Dishonesty:
– Some researchers may be tempted to fabricate data to support preconceived conclusions, meet publication deadlines or attain competitive research grants. After many high profile cases in recent years it has become apparent that this is a pervasive issue across academic and clinical research. This form of academic dishonesty undermines the foundation of scholarly pursuits and erodes the trust placed in research findings.
2. Mishaps and Ineptitude:
– Inexperienced researchers may inadvertently create fake data, whether due to poor data collection practices, computational errors, or other mishaps. This unintentional misuse can lead to inaccurate results, potentially rendering an entire body of research unreliable if it remains undetected.
3. Erosion of Trust and Reproducibility:
– The use of fake data contributes to the reproducibility crisis in scientific research. One study found that 70% of studies cannot be reproduced due to insufficient reporting of data and methods. When results cannot be independently verified, trust in the scientific process diminishes, hindering the advancement of knowledge. The addition of fake data into this scenario makes replication and thus verification of study results all the more challenging.
In an evolving clinical research landscape, the responsible and ethical use of data is paramount. Synthetic data stands out as a valuable tool in protecting privacy, advancing research, and addressing the challenges posed by sensitive information – assuming it is generated as accurately and responsibly as possible. On the other hand, the misuse of fake data undermines the integrity of scientific research, eroding trust and impeding the progress of knowledge and it’s real-world applications. It is important to stay vigilant against bias in data and employ stringent quality control in all data contexts of data handling.