Agreement Between Two Measurements

Although the five proposed agreement methods can be calculated on the basis of similar linear modeling approaches, they differ according to (i) the measured result (the differences or gross observations), (ii) the main focus of the method (compared to CAD or variance components) and (iii) as variance components are used in index expressions. All methods can mask certain areas of disagreement in the data and make implicit assumptions about aspects of variability or modeled relationships. It is therefore important to go beyond the indexes of agreement themselves and to examine the values and assumptions used to calculate them. For example, it is easy to calculate compliance limits without properly considering the variance components used to infer them or without considering the possibility that the devices naturally have different details. We recommend that researchers define in advance the exact shape of the statistical model they will use in a statistical analysis plan, as different models give rise to indications of different chords. For example, we found that the results of certain indices (for example. B CIA) have changed based on the adoption of an activity interaction in the example of COPD. The advantage of this interaction is that it is able to check whether the agreement between the two devices varies from one activity to another. But it has the price of accepting in the model an additional notion that may or may not hold. Cohens – can also be used if the same counsellor evaluates the same patients at two times (for example.

B to 2 weeks apart) or, in the example above, re-evaluated the same response sheets after 2 weeks. Its limitations are: (i) it does not take into account the magnitude of the differences, so it is unsuitable for ordinal data, (ii) it cannot be used if there are more than two advisors, and (iii) it does not distinguish between agreement for positive and negative results – which can be important in clinical situations (for example. B misdiagnosing a disease or falsely excluding them can have different consequences).

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