QT analysis: A guide for statistical programmers. Prabhakar Munkampalli Statistical Analyst II Hyderabad, 7 th September 2012

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1 QT analysis: A guide for statistical programmers Prabhakar Munkampalli Statistical Analyst II Hyderabad, 7 th September 2012

2 Agenda ECG ICH E14 Thorough QT/QTc study Role of Statistical Programmer References

3 Disclaimer All opinions expressed in this presentation are the author s personal views, and do not reflect the views or opinions of Novartis 3 QT analysis: A guide for Statistical Programmers Prabhakar M. 7th Sep 2012 CONSPIC: JAIPUR 2012 Business Use Only

4 ECG (Electrocardiogram) A test that records the electrical activity of the heart. Used to test for irregularities in how the heart functions. P WAVE: DEPOLARIZATION OF ATRIA QRS: DEPLOLARIZATION OF VENTRICLES T: REPOLARIZATION OF VENTRICLES 4 QT analysis: A guide for Statistical Programmers Prabhakar M. 7th Sep 2012 CONSPIC: JAIPUR 2012 Business Use Only

5 Torsades de pointes An undesirable property of some drugs is their ability to delay cardiac repolarization, an effect that can be measured as prolongation of the QT interval on the surface electrocardiogram (ECG). TdP can degenerate into ventricular fibrillation, leading to sudden death. Most drugs that have caused TdP clearly increase both the absolute QT and the QTc (hereafter called QT/QTc). 5 QT analysis: A guide for Statistical Programmers Prabhakar M. 7th Sep 2012 CONSPIC: JAIPUR 2012 Business Use Only

6 ECG example 6 QT analysis: A guide for Statistical Programmers Prabhakar M. 7th Sep 2012 CONSPIC: JAIPUR 2012 Business Use Only

7 Torsades de pointes 7 Presentation Title Presenter Name Date Subject Business Use Only

8 Torsades de pointes Documented cases of TdP (fatal and non-fatal) associated with the use of a drug have resulted in the withdrawal from the market of several drugs and relegation of other drugs to second-line status. 8 QT analysis: A guide for Statistical Programmers Prabhakar M. 7th Sep 2012 CONSPIC: JAIPUR 2012 Business Use Only

9 QT correction One difficultly of QT interpretation is that the QT interval gets shorter as the heart rate increases. [ QT α 1/ RR] This problem can be solved by correcting the QT time for heart rate using two formulae. Bazett correction : QTcB= QT / RR 0.5 Fridericia s correction: QTcF= QT / RR 0.33 Other correction formulae 9 QT analysis: A guide for Statistical Programmers Prabhakar M. 7th Sep 2012 CONSPIC: JAIPUR 2012 Business Use Only

10 ICH E14 QT analysis is necessary... Systemic bioavailability Non antiarrhythmic drugs Approved drugs with new dose or route that results in higher exposure (ie...cmax or AUC) New indication Drugs with chemical or Pharmacological class with associated with QT effect or TdP 10 QT analysis: A guide for Statistical Programmers Prabhakar M. 7th Sep 2012 CONSPIC: JAIPUR 2012 Business Use Only

11 Thorough QT/QTc study Not the first trial Threshold level of regulatory concern, 5 ms evidenced by the 95% confidence interval around the mean effect of 10 ms Carried out in healthy volunteers Provides guidance for later trials 11 QT analysis: A guide for Statistical Programmers Prabhakar M. 7th Sep 2012 CONSPIC: JAIPUR 2012 Business Use Only

12 Role of Statistical/Clinical Programmer Programmer Review of protocol and RAP Review of ecrf Planning for analysis datset Study design TLF Cross over/ Parallel 12 QT analysis: A guide for Statistical Programmers Prabhakar M. 7th Sep 2012 CONSPIC: JAIPUR 2012 Business Use Only

13 Inclusion / Exclusion Criterion Excluded Subjects/Patients with - A marked baseline prolongation of QT/QTc interval (e.g., repeated demonstration of a QTc interval >450 ms) A history of additional risk factors for TdP (e.g., heart failure, hypokalemia, family history of Long QT Syndrome) The use of concomitant medications that prolong the QT/QTc interval. 13 QT analysis: A guide for Statistical Programmers Prabhakar M. 7th Sep 2012 CONSPIC: JAIPUR 2012 Business Use Only

14 Study design of Thorough QT/QTc study Based on study objectives and PK/PD of compound. Typically Randomized, controlled trial, appropriate blinding and concurrent placebo group. Positive control group (pharmacological or nonpharmacological) to establish assay sensitivity. E.g. Moxifloxacin Crossover: Small number of subjects as individual subject acts as a control. Simples heart rate correction. Parallel : Drugs with long half lives, and when Multiple dose treatment groups to be compared. 14 QT analysis: A guide for Statistical Programmers Prabhakar M. 7th Sep 2012 CONSPIC: JAIPUR 2012 Business Use Only

15 Number and Timings of recordings First in human study information like Cmax, Tmax should help to estimate peak concentration of relevant analyte and plan for timing of ECG collection. Optimal timing to cover range of concentration for PK/PD analysis. Number and timings of ECG recordings depends on patient population, endpoint, statistical model, sample size, and cost effectiveness. Even though, peak serum concentration does not always correspond to the peak effect on QT/QTc interval, care should be taken to perform ECG recordings at time points around the Cmax. 15 QT analysis: A guide for Statistical Programmers Prabhakar M. 7th Sep 2012 CONSPIC: JAIPUR 2012 Business Use Only

16 Examples of Tables, Listings and Figures 16 QT analysis: A guide for Statistical Programmers Prabhakar M. 7th Sep 2012 CONSPIC: JAIPUR 2012 Business Use Only

17 17 QT analysis: A guide for Statistical Programmers Prabhakar M. 7th Sep 2012 CONSPIC: JAIPUR 2012 Business Use Only

18 18 QT analysis: A guide for Statistical Programmers Prabhakar M. 7th Sep 2012 CONSPIC: JAIPUR 2012 Business Use Only

19 Table 4 Newly occurring ECG Qualitative abnormality Abnormality Type Finding Treat. A Treat. B Treat. C N=xxx N=xxx N=xxx Total n % Total n % Total n % Any new ECG abnormality xx xx xx.x xx xx xx.x xx xx xx.x Rhythm xx xx xx.x xx xx xx.x xx xx xx.x Atrial flutter xx xx xx.x xx xx xx.x xx xx xx.x Atrial fibrillation xx xx xx.x xx xx xx.x xx xx xx.x Junctional Rhythm xx xx xx.x xx xx xx.x xx xx xx.x Morphology xx xx xx.x xx xx xx.x xx xx xx.x RAA xx xx xx.x xx xx xx.x xx xx xx.x LAA xx xx xx.x xx xx xx.x xx xx xx.x 19 QT analysis: A guide for Statistical Programmers Prabhakar M. 7th Sep 2012 CONSPIC: JAIPUR 2012 Business Use Only

20 20 QT analysis: A guide for Statistical Programmers Prabhakar M. 7th Sep 2012 CONSPIC: JAIPUR 2012 Business Use Only

21 Data collection : CRF page 21 QT analysis: A guide for Statistical Programmers Prabhakar M. 7th Sep 2012 CONSPIC: JAIPUR 2012 Business Use Only

22 Variables Quantitative variables Ventricular rate PR interval QRS duration QT interval Fridericia QT interval Bazett QT interval uncorrected RR interval Qualitative variables Evaluation type rhythm arrhythmia Conduction morphology MI st t waves u waves ECG interpretation 22 QT analysis: A guide for Statistical Programmers Prabhakar M. 7th Sep 2012 CONSPIC: JAIPUR 2012 Business Use Only

23 Checks related to source data (1) Source data: quantitative and qualitative variables in same database? 2 possibilities in the data transfer: Quantitative variables in one dataset + Qualitative variables in one dataset 2 datasets Quantitative variables + Qualitative variables in one dataset 1 dataset E.g. 1 Source ECG dataset OR Variable: ECG identification can be used to identify the duplicates in the dataset. Suggestion: Create 2 different analysis datasets to avoid duplication of information. 23 QT analysis: A guide for Statistical Programmers Prabhakar M. 7th Sep 2012 CONSPIC: JAIPUR 2012 Business Use Only

24 Checks related to source data (2) Source data quantitative variables: Multiple / Single measurement(s) 6 lead pre-dose measurements OR 3 lead post-dose measurements at 1.5, 3, 6 hours Average of these measurements collapsed into 1 record each Get a confirmation on the type of transfer from ECG providers If there are more number records of time-points than expected how to handle? 24 QT analysis: A guide for Statistical Programmers Prabhakar M. 7th Sep 2012 CONSPIC: JAIPUR 2012 Business Use Only

25 Checks related to source data (3) Source data: identification of scheduled and unscheduled visits Very important to have correct time point variable to be used later in the analysis. 25 QT analysis: A guide for Statistical Programmers Prabhakar M. 7th Sep 2012 CONSPIC: JAIPUR 2012 Business Use Only

26 How to report the data, some pointers: (1) Analysis dataset structure: 26 QT analysis: A guide for Statistical Programmers Prabhakar M. 7th Sep 2012 CONSPIC: JAIPUR 2012 Business Use Only

27 How to report the data, some pointers: (2) Various abnormality flags ECG parameter Abnormality flagging 1 QT increase of > 30 msec from baseline 2 QT increase of > 60 msec from baseline 3 QT new > 450 msec and pre-dose value <= 450 msec 4 QT new > 480 msec and pre-dose value <= 480 msec 5 QT new > 500 msec and pre-dose value <= 500 msec 6 PR < 200 msec Normal ranges 7 PR increase > 25% to a value > QRS < 110 msec Normal ranges 9 QRS increase > 25% to a value > HR bpm Normal ranges 11 RR bpm Normal ranges 12 RR + HR RR increase > 25% to HR < RR + HR RR decrease > 25% to HR > QT analysis: A guide for Statistical Programmers Prabhakar M. 7th Sep 2012 CONSPIC: JAIPUR 2012 Business Use Only

28 Single(Multiple) vs. Replicate ECGs Depends on Objectives and end points. If primary objective is to estimate QTc response profile then single ECGs distributed over time is preferable. If primary objective is to estimate change in QTc at a specific point in time, replicate ECGs should reduce variability. For QT/QTc analysis, collection of multiple baseline helps to verify diurnal pattern and the QT to heart relationship for each subject in each period an provide more baseline data for individual correction. 28 QT analysis: A guide for Statistical Programmers Prabhakar M. 7th Sep 2012 CONSPIC: JAIPUR 2012 Business Use Only

29 Choice of baseline Time-matched (complete Day -1 profile) Account for diurnal variation in QTc May be necessary for parallel group design Single predose time point (average of triplicates) May be sufficient for crossover since time-matched comparison with placebo is within a subject 29 QT analysis: A guide for Statistical Programmers Prabhakar M. 7th Sep 2012 CONSPIC: JAIPUR 2012 Business Use Only

30 Analysis of QT/QTc Interval Data Both central tendency (e.g., means, medians) and categorical analyses. Provide relevant information on clinical risk assessment. In clinical trials, a prolongation of QTc > 500 ms during therapy has been a threshold of particular concern. Multiple analyses using different limits are a reasonable approach to this uncertainty, including: Absolute QTc interval prolongation: QTc interval > 450 QTc interval > 480 QTc interval > 500 Change from baseline in QTc interval: QTc interval increases from baseline > 30 QTc interval increases from baseline > QT analysis: A guide for Statistical Programmers Prabhakar M. 7th Sep 2012 CONSPIC: JAIPUR 2012 Business Use Only

31 Interpretation of Thorough QT/QTc Study The results of the thorough QT/QTc study will influence the amount of information collected in later stages of development: A negative thorough QT/QTc study : Collection of on therapy ECG A positive thorough QT/QTc study : Expanded ECG safety evaluation. A negative thorough QT/QTc study but non clinical data is strongly positive: Expanded ECG safety evaluation 31 QT analysis: A guide for Statistical Programmers Prabhakar M. 7th Sep 2012 CONSPIC: JAIPUR 2012 Business Use Only

32 References %2Fdiaj_11191.pdf _Q_As_R1_step4.pdf QT analysis: A guide for Statistical Programmers Prabhakar M. 7th Sep 2012 CONSPIC: JAIPUR 2012 Business Use Only

33 THANK YOU 33 Presentation Title Presenter Name Date Subject Business Use Only

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