Visual graphical analysis: a technique to investigate symptom trajectories over time

Research to address clinical symptoms and the way they change over time in an individual is of paramount importance to healthcare researchers who are interested in improving the quality of life for ill patients. However, typical statistical methods that rely on means can obscure individual trajectories of change. Visual graphical analysis (VGA) is a technique that can provide researchers with an alternative method of quantitative statistical analysis that is more sensitive to individual change and variation.

To (a) describe the use of VGA as a method to evaluate longitudinal data, (b) discuss challenges to using this method, and (c) offer recommendations for future research in which the method could be implemented.

This methodological article uses data collected from a primary study to present the method of VGA. Daily self-reported sore mouth severity scores from patients receiving outpatient chemotherapy were used in this VGA. The steps of VGA include (a) determining inclusion criteria, (b) managing missing data, (c) creating visual graphs, (d) identifying specific patterns, and (e) performing validation and verification.

Because symptoms occur differently for each patient, this method allows researchers to see symptom trajectories on an individual basis. Creation and analysis of longitudinal graphs could be used also to inspect other symptoms or clinical problems such as headaches, fatigue, constipation, nausea and vomiting, and psychological difficulties. The value of VGA is that it allows a researcher to study the patterns of an individual's experience, as opposed to averaging all individuals' responses over time. Although graphical analysis is exploratory in nature, it may lead to enhanced longitudinal recognition of symptoms that might not be identified otherwise by quantitative data analysis using summary statistics.