Mixed models for data from thorough QT studies: Part 2. One-step assessment of conditional QT prolongation

Pharmaceutical Statistics

Volumn 10, Issue 4, 2011, Pages 293-301

  Schall, Robert ^a,b  

^a UNIVERSITY OF THE FREE STATE   (South Africa)

^b IQVIA   (United States)

Author keywords

conditional QT prolongation; ECG; mixed models; QT interval; repeated measures cross over study; thorough QT study

Indexed keywords

ACCURACY; ANALYSIS OF COVARIANCE; ARTICLE; CONTROLLED STUDY; NONHUMAN; QT PROLONGATION; UNSTRUCTURED INTERVIEW;

ANALYSIS OF VARIANCE; DATA INTERPRETATION, STATISTICAL; HUMANS; LONG QT SYNDROME; MODELS, STATISTICAL; PHARMACEUTICAL PREPARATIONS; TOXICITY TESTS;

EID: 79956128896     PISSN: 15391604     EISSN: 15391612     Source Type: Journal    
DOI: 10.1002/pst.465     Document Type: Article

References (18)

1. International Conference on Harmonization. The clinical evaluation of QT/QTc interval prolongation and proarrhythmic potential for non-antiarrhythmic drugs. ICH Harmonised Tripartite Guideline E14, Step 4, 2005. Available at: (accessed 18 March 2010).
2. Li L, Desai M, Desta Z, Flockhart D., QT analysis: a complex answer to a 'simple' problem. Statistics in Medicine 2004; 23: 2625-2643. (Pubitemid 39206460)
3. Senn S, Julious S., Measurement in clinical trials: a neglected issue for statisticians? Statistics in Medicine 2009; 28: 3189-3209.
4. Schall R, Ring A., Statistical characterization of QT prologation. Journal of Biopharmaceutical Statistics 2010; 20: 543-562.
5. Shah A, Hajian G., A maximum likelihood approach for estimating the QT correction factor using mixed effects model. Statistics in Medicine 2003; 22: 1901-1909. (Pubitemid 36603562)
6. Dmitrienko A, Smith B., Repeated-measures models in the analysis of QT interval. Pharmaceutical Statistics 2003; 2: 175-190.
7. Patterson SD, Jones B, Zariffa N., Modeling and interpreting QTc prolongation in clinical pharmacology studies. Drug Information Journal 2005; 39: 437-445. (Pubitemid 41609523)
8. Ring A., Statistical models for the heart rate correction of the QT Interval. Statistics in Medicine 2010; 29: 786-796.
9. Schall R, Ring A., Mixed models for data from thorough QT trials: Part 1. Assessment of marginal QT prolongation. Pharmaceutical Statistics 2010; DOI: 10.1002/pst.463.
10. Wang Y, Pan G, Balch A., Bias and variance evaluation of QT interval correction methods. Journal of Biopharmaceutical Statistics 2008; 18: 427-450. (Pubitemid 351891684)
11. SAS Institute Inc. SAS/STAT 9.1 User's guide, Vol. 4. SAS Institute Inc: Cary, 2004.
12. Jones B, Kenward MG., Design and analysis of cross-over trials (2nd edn). Chapman and Hall/CRC Press: London, 2003.
13. Huber PJ., The behavior of maximum likelihood estimates under nonstandard conditions. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Vol. 1, Le Cam LM, Neyman J, (eds). University of California Press: Berkeley, 1967; 221-233.
14. Liang KY, Zeger SL., Longitudinal data analysis using generalized linear models. Biometrika 1986; 73: 13-22.
15. Diggle PJ, Liang KY, Zeger SL., Analysis of longitudinal data. Clarendon Press: Oxford, 1994.
16. Brown H, Prescott R., Applied mixed models in medicine (2nd edn). Wiley: Hoboken, 2006.
17. Malik M, Farbom P, Batchvarov V, Hnatkova K, Camm AJ., Relation between QT and RR intervals is highly individual among healthy subjects: implications for heart rate correction of the QT interval. Heart 2002; 87: 220-228. (Pubitemid 34177674)
18. Kenward MG, Roger JH., Small sample inference for fixed effects from restricted maximum likelihood. Biometrics 1997; 53: 983-997. (Pubitemid 27391027)