Behavioral pattern analysis for adherence in clinical trials using sequence mining

Area of Science:

• Clinical research methodology
• Patient behavior analysis
• Data mining in healthcare

Background:

• Clinical trials are essential for drug and therapy approval, requiring consistent patient participation for valid outcomes.
• Patient adherence is a significant challenge in clinical trials, impacting the reliability of safety and efficacy data.
• Traditional data analysis methods may obscure crucial behavioral patterns related to patient engagement.

Purpose of the Study:

• To analyze patient behavioral data from clinical trials, focusing on scheduling dynamics.
• To identify patterns in appointment rescheduling and time-of-day variations that affect patient adherence.
• To explore the utility of sequence mining for understanding and improving patient retention in clinical research.

Main Methods:

• Analysis of a dataset capturing patient behavior across multiple clinical visits.
• Application of the cSPADE algorithm for extracting behavioral subsequences.
• Visualization of extracted patterns using the Sunburst method for enhanced pattern identification.

Main Results:

• Frequent appointment rescheduling and time-of-day variations were identified as significant factors influencing patient adherence.
• A strong association was found between higher rates of appointment changes and increased patient dropout.
• Sequence mining revealed complex behavioral patterns related to adherence that are not evident in tabular data.

Conclusions:

• Flexible scheduling and minimizing appointment rescheduling are crucial for enhancing patient retention in clinical trials.
• Sequence mining provides valuable insights into adherence-related behaviors, informing strategies for improved patient engagement.
• This methodological approach can guide future clinical trial protocol design and operational decision-making.