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The STRAND Chart: A survival time control chart

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The STRAND Chart (Survival Time, Risk‐Adjusted, N‐Division Chart) is a new tool for online risk‐adjusted (RA) monitoring of survival outcomes. The chart is drawn in continuous time, making it responsive to change in the process of interest, for example, performance over time of a surgical unit and the procedures that they employ. Though it is difficult to achieve with charts designed for the purpose described, we show that our suggested chart keeps patient ordering intact. We discuss the difficulties maintaining patient ordering poses, making reference to other charts in the literature. Our conclusion is that the best approach to preserving patient ordering on any chart of this nature involves compromising on the fullness of presentation of the recorded data. The chart is divided intoNstrands, each strand relating to a benchmark patient's survival information attndays following treatment,n = 1,2,…,N. We present a simple version of the chart where the strands consist of Bernoulli RA exponentially weighted moving averages, recording RA failure rates attndays. It can detect recent process changeandhistorical change. We illustrate the STRAND Chart using a well‐known UK post cardiac surgery survival dataset, where the nature of a certain cluster in the data can be seen.
Title: The STRAND Chart: A survival time control chart
Description:
The STRAND Chart (Survival Time, Risk‐Adjusted, N‐Division Chart) is a new tool for online risk‐adjusted (RA) monitoring of survival outcomes.
The chart is drawn in continuous time, making it responsive to change in the process of interest, for example, performance over time of a surgical unit and the procedures that they employ.
Though it is difficult to achieve with charts designed for the purpose described, we show that our suggested chart keeps patient ordering intact.
We discuss the difficulties maintaining patient ordering poses, making reference to other charts in the literature.
Our conclusion is that the best approach to preserving patient ordering on any chart of this nature involves compromising on the fullness of presentation of the recorded data.
The chart is divided intoNstrands, each strand relating to a benchmark patient's survival information attndays following treatment,n = 1,2,…,N.
We present a simple version of the chart where the strands consist of Bernoulli RA exponentially weighted moving averages, recording RA failure rates attndays.
It can detect recent process changeandhistorical change.
We illustrate the STRAND Chart using a well‐known UK post cardiac surgery survival dataset, where the nature of a certain cluster in the data can be seen.

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