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Statistical approaches to experimental design and data analysis of in vivo studies

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Abstract

The objective of any experiment is to obtain an unbiased and precise estimate of a treatment effect in an efficient manner. Statistical aspects of the design, conduct, and analysis of the experiment play a major role in determining whether this goal is met. We highlight some of the more important statistical issues that pertain to in vivo studies. Particular emphasis is placed on the role of randomization, the number of animals, the utilization of repeated measures data, adjustments for missing data, and dealing with multiple causes of death or treatment failure. The discussion is not intended to be a comprehensive guide to all the statistical issues that can occur in animal experiments. Rather, the objective is to acquaint researchers with components of the experiment that will require careful statistical thought.

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References

  1. Clarke R: Issues in experimental design and endpoint analysis in the study of experimental cytotoxic agents in vivo in breast cancer and other models. Breast Cancer Res Treat 46:255–278, 1997

    Article  PubMed  Google Scholar 

  2. Breslow NE, Day NE: Statististical Methods in Cancer Research: Volume I — The Analysis of Case-Control Studies. IARC Scientific Publications No. 32, Lyon, 1980

  3. Breslow NE, Day NE: Statistical Methods in Cancer Research: Volume II — The Design and Analysis of Cohort Studies. IARC Scientific Publications No. 82, Lyon, 1987

  4. Gart JJ, Krewski D, Lee PN, Tarone RE, Wahrendorf J: Statistical Methods in Cancer Research: Volume III — The Design and Analysis of Long-Term Animal Experiments. IARC Scientific Publications No. 79, Lyon, 1986

  5. Gad S, Weil CS: Statistics and Experimental Design for Toxicologists. Telford, Caldwell NJ, 1988

    Google Scholar 

  6. Daniel WW: Biostatistics: A Foundation for Analysis in the Health Sciences (5th Ed.). Wiley, New York, 1991

    Google Scholar 

  7. Brown BW, Brauner C, Chan A, Gutierrez D, Herson J, Lovato J, Polsley J: STPLAN Version 4.0 — Calculations for Sample Sizes and Related Problems. The University of Texas M.D. Anderson Cancer Center, Department of Biomathematics, 1993

  8. Heitjan DF, Derr JA, Satyaswaroop PG: The multisite tumour transplantation model for human endometrial carcinoma: a statistical evaluation. Cell Prolif 25:193–203, 1992

    PubMed  Google Scholar 

  9. StatXact 3 for Windows — Statistical Software for Exact Nonparametric Inference. CYTEL Software Corporation, Cambridge MA, 1995

  10. Zimmerman DW, Zumbo BD: Parametric alternatives to the Student t-test under violations of normality and homogeneity of variance. Perceptual and Motor Skills 74:835–844, 1992

    Google Scholar 

  11. Zimmerman DW, Zumbo BD: The relative power of parametric and nonparametric statistical methods. In: Keren G, Lewis C (eds) A Handbook of Data Analysis in the Behavioral Sciences: Methodological Issues. Erlbaum, Hillsdale NJ, 1993, pp 481–517

    Google Scholar 

  12. Liang KY, Zeger S: Longitudinal data analysis using generalized linear models. Biometrika 73:13–22, 1986

    Google Scholar 

  13. Liang KY, Zeger S: Regression analysis for correlated data. Annu Rev Pub Health 14:43–68, 1993

    Article  Google Scholar 

  14. Williams DA: Extra-binomial variation in logistic linear models. Appl Statistics 31:144–148, 1982

    Google Scholar 

  15. Liang KY, Hanfelt JJ: On the use of the quasilikelihood method in teratological experiments. Biometrics 50:872–880, 1994

    PubMed  Google Scholar 

  16. Carroll RJ, Ruppert D, Stefanski LA: Measurement Error in Nonlinear Models. Chapman and Hall, London, 1995

    Google Scholar 

  17. Hanfelt JJ, Liang KY: Approximate likelihoods for generalized linear errors-in-variables models. J Royal Statistical Society B 59:627–637, 1997

    Article  Google Scholar 

  18. Morgan BJT: The Analysis of Quantal Response Data. Chapman and Hall, London, 1992

    Google Scholar 

  19. Breslow NE: Extra-Poisson variation in log-linear models. Appl Statistics 33:38–44, 1984

    Google Scholar 

  20. McCullagh P, Nelder JA: Generalized Linear Models (2nd Ed.). Chapman and Hall, London, 1989

    Google Scholar 

  21. Stephens TC: Measurement of tumor cell surviving fraction and absolute number of clonogens per tumor in excision assays. In: Kallman RF (ed) Robust Tumor Models in Experimental Cancer Therapy. Pergamon, New York, 1987, chapter 21

    Google Scholar 

  22. Hanauske AR, Hilsenbeck SG, Von Hoff DD: Human tumor screening. In: Teicher B (ed) Anticancer Drug Development Guide: Preclinical Screening, Clinical Trials, and Approval. Humana, Totowa NJ, 1996, chapter 3

    Google Scholar 

  23. Cox DR, Oakes D: Analysis of Survival Data. Chapman and Hall, London, 1984

    Google Scholar 

  24. Kalbfleish JD, Prentice RL: The Statistical Analysis of Failure Time Data. Wiley, New York, 1980

    Google Scholar 

  25. Hay JW, Wolak FA: A procedure for estimating the unconditional cumulative incidence curve and its variability for the human immunodeficiency virus. Appl Statistics 43:599–624, 1994

    Google Scholar 

  26. Gray RJ: A class of K-sample tests for comparing the cumulative incidence of a competing risk. Ann of Statistics 16:1141–1154, 1988

    Google Scholar 

  27. Aly EEAA, Kochar SC, McKeague IW: Some tests for comparing cumulative incidence functions and cause-specific hazard rates. J Amer Statistical Assoc 89:994–999, 1994

    Google Scholar 

  28. Lin DY: Non-parametric inference for cumulative incidence functions in competing risks studies. Statistics in Med 16:901–910, 1997

    Article  Google Scholar 

  29. Lagakos SW (issue ed): Statistical methods for multiple events data in clinical trials. Statistics in Med 16(8):833–961, 1997

  30. Heiljan DF, Manni A, Santen RJ: Statistical analysis of in vivo tumor growth experiments. J Cancer Res 53:6042–6050, 1993

    Google Scholar 

  31. Diggle PJ, Liang KY, Zeger S: Analysis of Longitudinal Data. Oxford, 1994

  32. Koziol JA, Maxwell DA, Fukushima M, Colmerauer MEM, Pilch, YH: A distribution-free test for tumor-growth curve analyses with application to an animal tumor immunotherapy experiment. Biometrics 37:383–390, 1981

    PubMed  Google Scholar 

  33. Wei L, Lachin JM: Two-sample asymptotically distribution-free tests for incomplete multivariate observations. J Amer Statistical Assoc 79:653–661, 1984

    Google Scholar 

  34. Wei LJ, Johnson WE: Combining dependent tests with incomplete repeated measurements. Biometrika 72:359–364, 1985

    Google Scholar 

  35. Wu MC, Hunsberger S, Zucker D: Testing for differences in changes in the presence of censoring: parametric and non-parametric methods. Statistics in Med 13:635–646, 1994

    Google Scholar 

  36. Rygaard K, Spang-Thomsen M: Quantitation and Gompertzian analysis of tumor growth. Breast Cancer Res Treat 46:303–312, 1997

    Article  PubMed  Google Scholar 

  37. Garg ML, Rao BR, Redmond CK: Maximum-likelihood estimation of the parameters of the Gompertz survival function. Appl Statistics 19:152–159, 1970

    Google Scholar 

  38. Franses PH: Fitting a Gompertz curve (STMA V35 4508). J Operations Res 45:109–113, 1994

    Google Scholar 

  39. Li LY, et al: Antagonistic mutant FGF-2 proteins inhibit angiogenesis and breast cancer xenograft tumorigenesis. Proc Amer Assoc Canc Res Special Conference on Breast Cancer, Keystone CO, 1997

    Google Scholar 

  40. Machado SG, Robinson GA: A direct, general approach based on isobolograms for assessing the joint action of drugs in pre-clinical experiments. Statistics in Med 13:2289–2309, 1994

    Google Scholar 

  41. Leonessa F, Jacobson M, Boyle B, Lippman J, McGarvey M, Clarke R: Effect of tamoxifen on the multidrug-resistant phenotype in human breast cancer cells: isobologram, drug accumulation, and Mr 170,000 glycoprotein (gpl170) binding studies. J Cancer Res 54:441–447, 1994

    Google Scholar 

  42. CARD for Windows: Computer Aided Research and Development by S-Matrix. Westing Software, Corte Madera CA, 1997

    Google Scholar 

  43. Peto R: Guidelines on the analysis of tumour rates and death rates in experimental animals. Br J Cancer 29:101–105, 1974

    PubMed  Google Scholar 

  44. Little RJA: Modeling the drop-out mechanism in repeated-measures studies. J Am Statistical Assoc 90:1112–1121, 1995

    Google Scholar 

  45. Wu MC, Bailey KR: Estimation and comparison of changes in the presence of informative right censoring: conditional linear model. Biometrics 45:939–955, 1989

    PubMed  Google Scholar 

  46. Mori M, Woodworth GG, Woolson RF: Application of empirical Bayes inference to estimation of rate of change in the presence of informative right censoring. Statistics in Med 11:621–631, 1992

    Google Scholar 

  47. Mori M, Woolson RF, Woodworth GG: Slope estimation in the presence of informative right censoring: modeling the number of observations as a geometric random variable. Biometrics 50:39–50, 1994

    PubMed  Google Scholar 

  48. Barnhart HX, Sampson AR: Multiple population models for multivariate random length data — with applications in clinical trials. Biometrics 51:195–204, 1995

    PubMed  Google Scholar 

  49. Robins JM, Rotnitzky A, Zhao LP: Analysis of semiparametric regression models for repeated outcomes in the presence of missing data. J Am Statistical Assoc 90:106–121, 1995

    Google Scholar 

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  1. Lombardi Cancer Center, Georgetown University Medical Center, Washington, DC, U.S.A

    John J. Hanfelt

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Hanfelt, J.J. Statistical approaches to experimental design and data analysis of in vivo studies. Breast Cancer Res Treat 46, 279–302 (1997). https://doi.org/10.1023/A:1005946614343

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  • DOI: https://doi.org/10.1023/A:1005946614343