APPENDIX G: ADDITIONAL SOURCES OF INFORMATION
附录G：其他的信息来源
There may be a variety of statistical tests that can be used to evaluate any given set of data. This chapter presents several tests for interpreting and managing analytical data, but many other similar tests could also be employed. The chapter simply illustrates the analysis of data using statistically acceptable methods. As mentioned in the Introduction, specific tests are presented for illustrative purposes, and USP does not endorse any of these tests as the sole approach for handling analytical data.
可能有许多统计检验可以用于评估任何给定的数据组。本章展示了一些检验被用来解释和管理分析数据，但是许多其他的相似检验也可以使用。本章使用统计上可授受的方法简单阐述了数据分析。如在“介绍”所述，为了说明的目的，使用了特定的方法，同时USP并不将这些方法中的任何一种检验作为处理分析数据的唯一方法。 Additional information and alternative tests can be found in the references listed below or in many statistical textbooks. 其他的信息和替代的检验可以从下列参考文献或者许多统计教材中获得。
Control Charts:
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1. Manual on Presentation of Data and Control Chart Analysis, 6 ed., American Society for Testing and Materials (ASTM), Philadelphia, 1996.
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2. Grant, E.L., Leavenworth, R.S., Statistical Quality Control, 7 ed., McGraw-Hill, New York, 1996.
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3. Montgomery, D.C., Introduction to Statistical Quality Control, 3 ed., John Wiley and Sons, New York, 1997.
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4. Ott, E., Schilling, E., Neubauer, D., Process Quality Control: Troubleshooting and Interpretation of Data, 3 ed., McGraw-Hill, New York, 2000.
Detectable Differences and Sample Size Determination:
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1. CRC Handbook of Tables for Probability and Statistics, 2 ed., Beyer W.H., ed., CRC Press, Inc., Boca Raton, FL, 1985.
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2. Cohen, J., Statistical Power Analysis for the Behavioral Sciences, 2 ed., Lawrence Erlbaum Associates, Hillsdale, NJ, 1988.
3. Diletti, E., Hauschke, D., Steinijans, V.W., ―Sample size determination for bioequivalence assessment by means of confidence intervals,‖ International Journal of Clinical Pharmacology, Therapy and Toxicology, 1991; 29,1–8. 4. Fleiss, J.L., The Design and Analysis of Clinical Experiments, John Wiley and Sons, New York, 1986, pp. 369–375.
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5. Juran, J.A., Godfrey, B., Juran's Quality Handbook, 5 ed., McGraw-Hill, 1999, Section 44, Basic Statistical Methods.
6. Lipsey, M.W., Design Sensitivity Statistical Power for Experimental Research, Sage Publications, Newbury Park, CA, 1990.
7. Montgomery, D.C., Design and Analysis of Experiments, John Wiley and Sons, New York, 1984.
8. Natrella, M.G., Experimental Statistics Handbook 91, National Institute of Standards and Technology, Gaithersburg, MD, 1991 (reprinting of original August 1963 text).
9. Kraemer, H.C., Thiemann, S., How Many Subjects<: Statistical Power Analysis in Research, Sage Publications, Newbury Park, CA, 1987.
10. van Belle G., Martin, D.C., ―Sample size as a function of coefficient of variation and ratio of means,‖ American Statistician 1993; 47(3):165–167.
11. Westlake, W.J., response to Kirkwood, T.B.L.: ―Bioequivalence testing—a need to rethink,‖ Biometrics 1981; 37:589–594.
General Statistics Applied to Pharmaceutical Data:
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1. Bolton, S., Pharmaceutical Statistics: Practical and Clinical Applications, 3 ed., Marcel Dekker, New York, 1997.
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2. Bolton, S., ―Statistics,‖ Remington: The Science and Practice of Pharmacy, 20 ed., Gennaro, A.R., ed., Lippincott Williams and Wilkins, Baltimore, 2000, pp. 124–158.
3. Buncher, C.R., Tsay, J., Statistics in the Pharmaceutical Industry, Marcel Dekker, New York, 1981.
4. Natrella, M.G., Experimental Statistics Handbook 91, National Institute of Standards and Technology (NIST), Gaithersburg, MD, 1991 (reprinting of original August 1963 text).
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5. Zar, J., Biostatistical Analysis, 2 ed., Prentice Hall, Englewood Cliffs, NJ, 1984.
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6. De Muth, J.E., Basic Statistics and Pharmaceutical Statistical Applications, 3 ed., CRC Press, Boca Raton, FL, 2014.
General Statistics Applied to Analytical Laboratory Data:
1. Gardiner, W.P., Statistical Analysis Methods for Chemists, The Royal Society of Chemistry, London, England, 1997.
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2. Kateman, G., Buydens, L., Quality Control in Analytical Chemistry, 2 ed., John Wiley and Sons, New York, 1993.
3. Kenkel, J., A Primer on Quality in the Analytical Laboratory, Lewis Publishers, Boca Raton, FL, 2000. 4. Mandel, J., Evaluation and Control of Measurements, Marcell Dekker, New York, 1991.
5. Melveger, A.J., ―Statististics in the pharmaceutical analysis laboratory,‖ Analytical Chemistry in a GMP Environment, Miller J.M., Crowther J.B., eds., John Wiley and Sons, New York, 2000.
6. Taylor, J.K., Statistical Techniques for Data Analysis, Lewis Publishers, Boca Raton, FL, 1990. 7. Thode, H.C., Jr., Testing for Normality, Marcel Dekker, New York, NY, 2002.
8. Taylor, J.K., Quality Assurance of Chemical Measurements, Lewis Publishers, Boca Raton, FL, 1987.
9. Wernimont, G.T., Use of Statistics to Develop and Evaluate Analytical Methods, Association of Official Analytical Chemists (AOAC), Arlington, VA, 1985.
10. Youden, W.J., Steiner, E.H., Statistical Manual of the AOAC, AOAC, Arlington, VA, 1975.
Nonparametric Statistics:
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1. Conover, W.J., Practical Nonparametric Statistics, 3 ed., John Wiley and Sons, New York, 1999.
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2. Gibbons, J.D., Chakraborti, S., Nonparametric Statistical Inference, 3 ed., Marcel Dekker, New York, 1992.
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3. Hollander, M., Wolfe, D., Nonparametric Statistical Methods, 2 ed., John Wiley and Sons, NY, 1999.
Outlier Tests:
1. Barnett, V., Lewis, T., Outliers in Statistical Data, 3rd ed., John Wiley and Sons, New York, 1994. 2. B?hrer, A., ―One-sided and two-sided critical values for Dixon's Outlier Test for sample sizes up to n = 30,‖ Economic Quality Control, Vol. 23 (2008), No. 1, pp. 5–13.
3. Davies, L., Gather, U., ―The identification of multiple outliers,‖ Journal of the American Statistical Association (with comments), 1993; 88:782–801.
4. Dixon, W.J., ―Processing data for outliers,‖ Biometrics, 1953; 9(1):74–89.
5. Grubbs, F.E., ―Procedures for detecting outlying observations in samples,‖ Technometrics, 1969; 11:1–21. 6. Hampel, F.R., ―The breakdown points of the mean combined with some rejection rules,‖ Technometrics, 1985; 27:95–107.
7. Hoaglin, D.C., Mosteller, F., Tukey, J., eds., Understanding Robust and Exploratory Data Analysis, John Wiley and Sons, New York, 1983.
8. Iglewicz B., Hoaglin, D.C., How to Detect and Handle Outliers, American Society for Quality Control Quality Press, Milwaukee, WI, 1993.
9. Rosner, B., ―Percentage points for a generalized ESD many-outlier procedure,‖ Technometrics, 1983; 25:165–172. 10. Standard E-178-94: Standard Practice for Dealing with Outlying Observations, American Society for Testing and Materials (ASTM), West Conshohoken, PA, September 1994.
11. Rorabacher, D.B., ―Statistical treatment for rejections of deviant values: critical values of Dixon's ―Q‖ parameter and related subrange ratios at the 95% confidence level,‖ Analytical Chemistry, 1991; 63(2):139–146.
Precision and Components of Variability:
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1. Hicks, C.R., Turner, K.V., Fundamental Concepts in the Design of Experiments, 5 ed., Oxford University Press, 1999 (section on Repeatability and Reproducibility of a Measurement System).
2. Kirk, R.E., Experimental Design: Procedures for the Behavioral Sciences, Brooks/Cole, Belmont, CA, 1968, pp. 61–63.
3. Kirkwood, T.B.L., ―Geometric means and measures of dispersion,‖ Letter to the Editor, Biometrics, 1979; 35(4). 4. Milliken, G.A., Johnson, D.E., Analysis of Messy Data, Volume 1: Designed Experiments, Van Nostrand Reinhold Company, New York, NY, 1984, pp. 19–23.
5. Searle, S.R., Casella, G., McCulloch, C.E., Variance Components, John Wiley and Sons, New York, 1992. 6. Snedecor, G.W., Cochran, W.G., Statistical Methods, 8th ed., Iowa State University Press, Ames, IA, 1989.
7. Standard E-691-87: Practice for Conducting an Interlaboratory Study to Determine the Precision of a Test Method, ASTM, West Conshohoken, PA, 1994.
8. Hauck, W.W., Koch, W., Abernethy, D., Williams, R. ―Making sense of trueness, precision, accuracy, and uncertainty,‖ Pharmacopeial Forum, 2008; 34(3).
Tolerance Interval Determination:
1. Hahn, G.J., Meeker, W.Q., Statistical Intervals: A Guide for Practitioners, John Wiley and Sons, New York, 1991. 2. Odeh, R.E., ―Tables of two-sided tolerance factors for a normal distribution,‖ Communications in Statistics: Simulation and Computation, 1978; 7:183–201.