Using a prediction approach to assess agreement between two continuous measurements

Using multiple agreement methods for continuous repeated measures data: a tutorial for practitioners

Background

Studies of agreement examine the distance between readings made by different devices or observers measuring the same quantity. If the values generated by each device are close together most of the time then we conclude that the devices agree. Several different agreement methods have been described in the literature, in the linear mixed modelling framework, for use when there are time-matched repeated measurements within subjects.

Methods

We provide a tutorial to help guide practitioners when choosing among different methods of assessing agreement based on a linear mixed model assumption. We illustrate the use of five methods in a head-to-head comparison using real data from a study involving Chronic Obstructive Pulmonary Disease (COPD) patients and matched repeated respiratory rate observations. The methods used were the concordance correlation coefficient, limits of agreement, total deviation index, coverage probability, and coefficient of individual agreement.

Results

The five methods generated similar conclusions about the agreement between devices in the COPD example; however, some methods emphasized different aspects of the between-device comparison, and the interpretation was clearer for some methods compared to others.

Conclusions

Five different methods used to assess agreement have been compared in the same setting to facilitate understanding and encourage the use of multiple agreement methods in practice. Although there are similarities between the methods, each method has its own strengths and weaknesses which are important for researchers to be aware of. We suggest that researchers consider using the coverage probability method alongside a graphical display of the raw data in method comparison studies. In the case of disagreement between devices, it is important to look beyond the overall summary agreement indices and consider the underlying causes. Summarising the data graphically and examining model parameters can both help with this.

Baroreflex control of muscle sympathetic nerve activity: a nonpharmacological measure of baroreflex sensitivity

The sensitivity of baroreflex control of sympathetic nerve activity (SNA) represents the responsiveness of SNA to changes in blood pressure. In a slightly different analysis, the baroreflex threshold measures the probability of whether a sympathetic burst will occur at a given diastolic blood pressure. We hypothesized that baroreflex threshold analysis could be used to estimate the sensitivity of the sympathetic baroreflex measured by the pharmacological modified Oxford test. We compared four measures of sympathetic baroreflex sensitivity in 25 young healthy participants: the "gold standard" modified Oxford analysis (nitroprusside and phenylephrine), nonbinned spontaneous baroreflex analysis, binned spontaneous baroreflex analysis, and threshold analysis. The latter three were performed during a quiet baseline period before pharmacological intervention. The modified Oxford baroreflex sensitivity was significantly related to the threshold slope (r = 0.71, P < 0.05) but not to the binned (1 mmHg bins) and the nonbinned spontaneous baroreflex sensitivity (r = 0.22 and 0.36, respectively, P > 0.05), which included burst area. The threshold analysis was also performed during the modified Oxford manipulation. Interestingly, we found that the threshold analysis results were not altered by the vasoactive drugs infused for the modified Oxford. We conclude that the noninvasive threshold analysis technique can be used as an indicator of muscle SNA baroreflex sensitivity as assessed by the modified Oxford technique. Furthermore, the modified Oxford method does not appear to alter the properties of the baroreflex.