Abstract
The purpose of this chapter is to acquaint the reader with some typically used biostatistical principles and methods in anticancer drug development for summarizing and analyzing data. Understanding and properly interpreting statistics is critically important for drug development. Each stage of development, ranging from preclinical studies to phase III clinical trials, utilizes some form of statistical analysis whether it is as simple as the calculation of a mean or as complex as a longitudinal model with a complicated correlation structure. Proper statistical design and analysis will be critical for making valid inferences and moving to the next phase of research.
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Notes
- 1.
Since log(A) − log(B) = log(A/B), then the equation log(A) − log(B) = 0 is equivalent to log(A/B) = 0. Exponentiation of both sides of the latter leads to the equivalent expression, A/B = 1.
- 2.
For independent events, A and B, Pr(A and B) = Pr(A) × Pr(B). For example, if we assume FTase inhibition and sample type are independent, then from Table 1.3a the probability a sample comprises AML isolates and exhibits FTase inhibition is (57/106) × (88/106) ≈ 0.446. Therefore, out of 106 total samples, we expect 0.446 ´ 106 ≈ 47.3 to be AML isolates exhibiting FTase inhibition – assuming independence. The remaining cells in Table 1.3b are derived in a similar manner.
- 3.
In this example, we assume FTase inhibition levels in AML isolates and buccal samples from the same patient are uncorrelated.
- 4.
For a binary variable with success probability p, the variance is equal to p(1 − p).
- 5.
The terms “multivariable” and “multivariate” are often used interchangeably. Strictly speaking however, a multivariable analysis refers to a statistical model of a single outcome as a function of multiple variables. In contrast, a multivariate analysis refers to the joint modeling of multiple outcomes simultaneously.
- 6.
Let log(p/(1 – p)) = x. Then p = 1/(1 + e–x). For the example in the text, log(p/(1 − p)) = −1.4668 so that p = 1/(1 + e1.4668) ≈ 0.19.
References
Daniel W. Biostatistics: A Foundation for Analysis in the Health Sciences. Wiley: New York; 8th edition (2004).
Katz MH. Multivariable Analysis: A Practical Guide for Clinicians. Cambridge University Press: Cambridge; 2nd edition (2006).
Lancet JE, Gojo I, Gotlib J, Feldman EJ, Greer J, Liesveld JL, Bruzek LM, Morris L, Park Y, Adjei AA, Kaufmann SH, Garrett-Mayer E, Greenberg PL, Wright JJ, and Karp JE. A phase 2 study of the farnesyltransferase inhibitor tipifarnib in poor-risk and elderly patients with previously untreated acute myelogenous leukemia. Blood. 2007;109(4):1387–94.
Leith CP, Kopecky KJ, Godwin J, McConnell T, Slovak ML, Chen IM, Head DR, Appelbaum FR, and Willman CL. Acute myeloid leukemia in the elderly: assessment of multidrug resistance (MDR1) and cytogenetics distinguishes biologic subgroups with remarkably distinct responses to standard chemotherapy. A Southwest Oncology Group Study. Blood. 1997;89(9):3323–9.
Rosner B. Fundamentals of Biostatistics. Duxbury Press: Pacific Grove, CA; 6th edition (2005).
Van Belle G, Heagerty P, Fisher L, Lumley T. Biostatistics: A Methodology for the Health Sciences. Wiley-Interscience: New York; 2nd edition (2004).
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Hill, E.G., Garrett-Mayer, E. (2011). Basic Biostatistics for the Clinical Trialist. In: Garrett-Mayer, E. (eds) Principles of Anticancer Drug Development. Cancer Drug Discovery and Development. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-7358-0_1
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