Sunday, October 14, 2012

Using Area Under the Curve (AUC) as Clinical Endpoints


Area Under Curve (AUC) has been frequently used as the endpoint measure in clinical trials. We use AUC commonly in clinical pharmacology - Area under the time concentration curve or in diagnostic research – Area Under the ROC curve. The use of AUC is much more broader than what we think. Many clinical endpoints can utilize the AUC as a measure for the aggregate effect over a period of time. Below are some of the examples that I have experienced where AUC is used in clinical trials not for the purpose of pharmacokinetics measure or ROC measure.  

AUC Used in Pain Assessment

In the study of pain medications (usually acute pain medications), the pain intensity or pain relief in scales are measured at pre and serial time points post analgesic drug administration. The Summed Pain Intensity Difference (SPID) and total pain relief (TOTPAR) are usually calculated and used as the efficacy endpoints. TOTPAR is a time-weighted measure of AUC or total area under the pain relief curve and is a summary measure that integrates serial assessments of a subject’s pain over the duration of the study. The area under the pain relief vs. time curve can be used to derive the proportion of patients experiencing typically 50% pain relief over a specified time frame. This can be calculated as the ratio of two AUCs: TOTPAR vs. maxTOTPAR (maximum potential value for TOTPAR) as illustrated in the “Analysis of scale results – summary measures” of pain.

FEV1 AUC

In Asthma and COPD studies, FEV1 can be measured at pre-dose and at several serial time points post the treatment. The AUC will then be calculated from the time-FEV1 curve.

In  DULERA drug label, FEV1 AUC(0-12 hr) was mentioned as the efficacy measure:

FEV1 AUC (0-12hr) was assessed as a co-primary efficacy endpoint to evaluate the contribution of the formoterol component to DULERA. Patients receiving DULERA 100 mcg/5 mcg had significantly higher increases from baseline at Week 12 in mean FEV1 AUC (0-12 hr) compared to mometasone furoate 100 mcg (the primary treatment comparison) and vs. placebo ......"

In a recent news release “Results of Phase II Study of Boehringer Ingelheim's Investigational Bronchodilator for COPD Presented at 2012 ATS International Conference”, FEV1 AUC was used to measure the treatment effect in COPD.


“Results of the study found olodaterol 5 microgram QD provided significant improvement in lung function as measured by FEV1 AUC(0-12) versus twice-daily olodaterol 2 microgram, while twice-daily dosing of olodaterol 5 microgram had a better FEV1 AUC(0-12) profile versus once-daily olodaterol 10 microgram”

AUC in Type 1 Diabetes

In type-1 diabetes research, the main purpose of the treatment is to preserve the beta-cell function. The assessment of beta-cell function is through the measurement of the C-peptide concentration after simulated Mixed Meal Tolerance Test (MMTT) - the gold standard measure of endogenous insulin secretion

In the mixed-meal tolerance test (MMTT), commonly used in the U.S., a liquid meal (Sustacal/Boost) is ingested in the fasting state with timed measurements of C-peptide over the subsequent 2–4 h. The AUC is then calculated for the area under time-C-Peptide curve over 2 hour (AUC0-2hr) or 4 hours (AUC0-4hr) (see Greenbaum at al “Mixed-Meal Tolerance Test Versus Glucagon Stimulation Test for the Assessment of β-Cell Function in Therapeutic Trials in Type 1 Diabetes”).

In type 1 diabetes research, a concept of mean AUC is also used. Mean AUC is calculated by the AUC divided by the time duration (i.e., AUC0-2 hr / 120 minutes or AUC0-4 hr / 240 minutes)

AUCs to Assess the Responsiveness

We recently published a paper “Vigorimeter grip strength in CIDP: a responsive tool that rapidly measures the effect of IVIG – the ICE study” where we used AUCs to compare the responsiveness of two difference measures. Since two different measures used different scales, we had to calculate the SRM (standardized response mean) before we calculated the AUCs for INCAT scale and for Grip Strength . The larger the AUC, the higher the responsiveness to the treatment. The results indicated that the Vigorimeter grip strength could be more sensitive measure comparing to INCAT scale to evaluate the treatment effect of IVIG in CIDP patients.

AUC for Visual Analog Scale (VAS) for Dyspnea in Acute Heart Failure

In FDA's Cardiovascular and Renal Drugs Advisory Committee Meeting in March 27, 2014 for Serelaxin for Acute Heart Failure, one of the statistical issues discussed was the use of VAS AUC to assess the dyspnea in Acute Heart Failure. The FDA presentation included the detail calculation of the VAS-AUC and the results from this endpoint.

4 comments:

  1. Hi

    Can you please let me know whether there are different methods in trapezoidal rule for calculation of AUC?

    Recently I have seen a formula "AUC = (∑mk=2 (tk - tk-1)*0.5*(FEV1k + FEV1k-1)) / (tm - t1)", in which calculation starts from second time point. Generally I have seen only one formula AUC = ∑(Ci + Ci-1)*0.5*(Ti-Ti-1).

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  2. if you use WinNonlin to calculate the AUC, you have a choice of choosing the following methods:
    The linear trapezoidal rule
    The linear/logarithmic trapezoidal rule
    The lin-up/log-down rule

    There is also a method called Simpson's rule to calculate the area under the curve. It is supposed to be more accurate than the trapezoidal rule. See the youtube video at https://www.youtube.com/watch?v=DHahEDt_FU4
    you can also take a look at a SAS paper "Using SAS Software for a Numerical Approximation and the Area under a Curve Computation"

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  3. I am interested in completing an AUC calculation for type 1 diabetes HbA1c values. I am getting stuck on the calculations because all of my participants have varying number of historical HbA1c values available (some participants have been at the clinic for many years and some have not) and the frequency of testing also varies between participants (some people have every 3 months and some may only have one a year, for example). Do you have any suggestions/resources that would help me with this calculation?

    Thank you!

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  4. Great presentation of ROC Curve Data Science form of blog and ROC Curve Data Science tutorial. Very helpful for beginners like us to understand ROC Curve Data Science course. if you're interested to have an insight on ROC Curve Data Science training do watch this amazing tutorial.:-https://www.youtube.com/watch?v=G_pvQYUm8Ik

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