Abstract
In clinical studies, efficacies of new treatments are usually assessed by comparing averages of new treatment results versus control or placebo. However, averages do not tell the whole story, and the spread of the data may be more relevant. For example, when we assess how a drug reaches various organs, variability of drug concentrations is important, as in some cases too little and in other cases dangerously high levels get through. Also, for the assessment of the pharmacological response to a drug, variabilities may be important. For example, the effects on circadian glucose levels in patients with diabetes mellitus of a slow-release-insulin and acute-release-insulin formula are different. The latter formula is likely to produce more low and high glucose levels than the former formula. Spread or variability of the data is a determinant of diabetic control, and predictor of hypoglycaemic/hyperglycemic events. As an example, in a parallel-group study of n = 200 the former and latter formulas produced mean glucoses of 7.9 and 7.1 mmol/l, while standard deviations were 4.2 and 8.4 mmol/l respectively. This suggests that, although the slow-release formula did not produce a better mean glucose level, it did produce a smaller spread in the data. How do we test these kinds of data. Clinical investigators, although they are generally familiar with testing differences between averages, have difficulties testing differences between variabilities. The current chapter gives examples of situations where variability is more relevant than averages. It also gives simple statistical methods for testing such data. Statistical tests comparing mean values instead of variabilities are relatively simple and are one method everyone seems to learn. It is a service to the readership of this book to put more emphasis on variability.
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References
Almirall J, Bolibar I, Toran P et al (2004) Contribution of C-reactive protein to the diagnosis and assessment of severity of community-acquired pneumonia. Chest 125:1335–1342
Anonymoxus. http://www.itl.nist.gov/div898/handbook/eda/section3/eda35a.htm. Accessed 15 Dec 2011
Chambers HF, Sande MA (1996) Antimicrobial agents: the aminoglycosides. In: Goodman and Gillman’s pharmacological basis of therapeutics, 9th edn. McGraw Hill, New York
Cleophas TJ (2005) Statistical tables to test data closer to expectation than compatible with random sampling. Clin Res Reg Affairs 22:83–92
Cleophas TJ, Van der Meulen J, Zwinderman AH (1998) Nighttime hypotension in hypertensive patients prevented by beta-blockers but not by angiotensin converting enzyme inhibitors or calcium channel blockers. Eur J Intern Med 9:251–257
Hahn GJ, Meeker WQ (1991) Statistical intervals: a guide for practitioners. Wiley, New York
Hauck WW, Anderson S (1984) A new statistical procedure for testing equivalence in two-group comparative bioavailability trials. J Pharmacokinet Biopharm 12:83–91
Kendall MG, Stuart A (1963) Rank correlation methods, 3rd edn. Griffin, London
Neutel JM, Smith DH (1997) The circadian pattern of blood pressure: cardiovascular risk and therapeutic opportunities. Curr Opin Nephrol Hypertens 6:250–256
Petrie A, Sabin C (2000) Medical statistics at a glance. Blackwell Science Ltd, London
Schuirmann DJ (1987) A comparison of the two one-sided test procedures and the proper approach for assessing the equivalence of average bioavailability. J Pharmacokinet Biopharm 15:657–680
Siegel S (1956) Non-parametric methods for behavioural sciences. McGraw Hill, New York
Tothfalusi L, Endrenyl L (2001) Evaluation of some properties of individual bioequivalence from replicate-design studies. Int J Clin Pharmacol Ther 39:162–166
Tukey JW (1977) Exploratory data analysis. Addison-Wesley, Reading
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Cleophas, T.J., Zwinderman, A.H. (2012). Clinical Data Where Variability Is More Important Than Averages. In: Statistics Applied to Clinical Studies. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-2863-9_44
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DOI: https://doi.org/10.1007/978-94-007-2863-9_44
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