Blog Archives

Interpretation of the AUC

September 13, 2018
By
Interpretation of the AUC

The AUC* or concordance statistic c is the most commonly used measure for diagnostic accuracy of quantitative tests. It is a discrimination measure which tells us how well we can classify patients in two groups: those with and those without the outcome of interest. Since the measure is based on ranks, it is not sensitive Related Post Simple Experiments with...

Read more »

Interpretation of the AUC

September 12, 2018
By
Interpretation of the AUC

The AUC* or concordance statistic c is the most commonly used measure for diagnostic accuracy of quantitative tests. It is a discrimination measure which tells us how well we can classify patients in two groups: those with and those without the outcome of interest. Since the measure is based on ranks, it is not sensitive The post Interpretation of...

Read more »

Prediction Interval, the wider sister of Confidence Interval

June 15, 2018
By
Prediction Interval, the wider sister of Confidence Interval

In this post, I will illustrate the use of prediction intervals for the comparison of measurement methods. In the example, a new spectral method for measuring whole blood hemoglobin is compared with a reference method. But first, let's start with discussing the large difference between a confidence interval and a prediction interval. Prediction interval versus Related PostSix Sigma DMAIC...

Read more »

Comparing methods using a prediction interval

June 13, 2018
By
Comparing methods using a prediction interval

In this post, I will illustrate the use of prediction intervals for the comparison of measurement methods. In the example a new spectral method for measuring whole blood hemoglobin is compared with a reference method. First, let’s start with discussing the large difference between a confidence interval and a prediction interval. Prediction interval versus Confidence The post Comparing methods...

Read more »

Taking the baseline measurement into account

May 13, 2018
By
Taking the baseline measurement into account

In Randomized Controlled Trials (RCTs), a “Pre” measurement is often taken at baseline (before randomization), and treatment effects are measured at one or more time point(s) after randomization (“Post” measurements). There are many ways to take the baseline measurement into account when comparing 2 groups in a classic pre-post design with one post measurement. In The post Taking the...

Read more »

Search R-bloggers

Sponsors

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)