Impact of Using A Mixed Data Collection Modality on Statistical Inferences in Decentralized Clinical Trials

Decentralized clinical trials offer the promise of reduced patient burden, faster and more diverse recruitment, and have received regulatory support during the COVID-19 pandemic. However, lack of data accuracy or data validation poses a challenge for fully decentralized trials. A mixed data collection modality where onsite measurements are collected at key time points and decentralized measurements are taken at intermediate time points is attractive operationally. To date, the impact of decentralized measurements (which could presumably be less accurate) taken at intermediate time points on statistical inference on the primary or other key time points has not been evaluated.

In this article we evaluate the estimation and statistical inference for three scenarios: (1) all onsite measurements, (2) a mixture of onsite and decentralized measurements, and (3) all decentralized measurements, in the setting of a chronic weight management trial. We consider scenarios where decentralized measurements have additional within- and between-subject variabilities and/or bias.

In the mixed modality setting, simulation studies showed that the estimation and inference for the key time points with onsite measurements have good properties and are not impacted by the additional variability and bias from intermediate decentralized measurements. However, estimates for intermediate decentralized time points for the mixed modality and estimates for the all decentralized modality measurements have increased variability and bias.

Mixed modality trials can help achieve the benefits of decentralized clinical trials by reducing the number of onsite visits with little impact on statistical inferences for various estimands, compared to traditional (all onsite) clinical trials.