Presentation (CTI)

OBJECTIVES: In oncology, information uncertainty encompasses uncertainties about diagnosis, prognosis, treatment side effects, effectiveness, and long-term outcomes, which can impact informed decision-making and cause anxiety or confusion. Despite its significance, there is a gap in understanding uncertainty and patients' perspectives on uncertainty in their treatment decision making. This research examines information uncertainty and how patients perceive and value it in their oncology treatment decision-making in discrete choice experiments (DCEs).
METHODS: A review was conducted to characterize information uncertainty in oncology treatment preference, identified from a systematic review of 79 empirical DCEs conducted between 1990 and March 2020. Data on the source and type of information uncertainty, its integration into study design and analysis, and patient perspectives were extracted.
RESULTS: The review identified various types of information uncertainties, including diagnostic, clinical outcome, treatment-related side effects, and prognostic uncertainties. Uncertainty was integrated into study design using different methods. The majority of studies (n = 40, 51%) used probabilistic presentations with alternative representations, such as frequencies, to reflect clinical outcome uncertainty. Qualitative measures (e.g., high or low risk) and visual aids were used to aid comprehension. A few studies (n = 7, 8%) used ambiguity uncertainty (i.e., ranges showing confidence intervals around probability estimates). Advanced modeling approaches, such as mixed logit, latent class models, and hybrid choice models, incorporated respondent heterogeneity and cognitive factors to capture uncertainty in the analysis framework.
CONCLUSIONS: A proportion of published oncology DCEs include uncertainty in their design and analysis. Various types of information uncertainty were included. However, there is limited research on integrating uncertainty, especially prognostic and clinical outcome uncertainty, in the analysis framework and participants' perspectives, which can impact results interpretation. Future research should focus on understanding the impact of informational uncertainty on patient treatment decision-making and integrating uncertainty into the analysis framework.