Does Testing More Frequently Shorten the Time to Detect Disease Progression?

Abstract

Purpose

With the rise of smartphone devices to monitor health status remotely, it is tempting to conclude that sampling more often will provide a more sensitive means of detecting changes in health status earlier over time, when interventions may improve outcomes.

Methods

The answer to this question is derived in the context of a model where observations are generated from a linear-trend model with independent as well as autocorrelated autoregressive-moving average, or ARMA(1,1), errors.

Results

The results imply a cautionary message that an increase in the sampling frequency may not always lead to a faster detection of trend changes. The benefit of rapid successive observations depends on how observations, taken closely together in time, are correlated.

Conclusions

Shortening the observation period by half can be accomplished by increasing the number of independent observations to maintain the same power for detecting change over time. However, a strategy to detect progression of disease sooner by taking numerous closely spaced measurements over a shortened interval is limited by the degree of autocorrelation among adjacent observations. We provide a statistical model of disease progression that allows for autocorrelation among successive measurements, and obtain the power of detecting a linear change of specified magnitude when equal-spaced observations are taken over a given time interval.

Translational Relevance

New emerging technology for home monitoring of visual function will provide a means to monitor sensory status more frequently. The model proposed here takes into account how successive measurements are correlated, which impacts the number of measurements needed to detect a significant change in status.