Skip to main content
SpringerLink
Log in

Biostatistics in Clinical Trials

  • Chapter
  • First Online:
Quantitative Methods in Pharmaceutical Research and Development

  • 805 Accesses

Abstract

Biostatistics plays an important role in the design, conduct, and reporting of clinical trials. In recent years, the recognition and appreciation of biostatistics have greatly improved. Biostatisticians are not only responsible for the quantitative properties of clinical trial designs and for analyzing data from clinical trials, they are also respected partners of the clinical teams contributing to strategic discussions and helping solve day-to-day issues on studies and clinical programs. Biostatisticians also drive the development and implementation of innovative statistical methodology.

This chapter provides a brief overview of basic biostatistical principles and analysis methods used in clinical trials. In the Introduction, we cover basic biostatistical principles of clinical trials including population and sample, errors and bias, choice of a control arm, randomization, allocation, and blinding. Phases of clinical development and commonly used statistical designs are described in Sect. 1.2. A brief overview of the most commonly used probability models and basic principles of estimation and inference with an emphasis on modeling techniques are given in Sect. 1.3. Where appropriate, references to available software procedures in R and/or SAS are provided. Finally, we highlight some important considerations for clinical trials, such as defining appropriate estimands, handling outcomes with missing data, applying multiplicity adjustments, analyzing subgroups, planning multiregional clinical trials, and evaluating drug safety.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 139.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  • Agresti A (2002). Categorical Data Analysis. Second Edition. Wiley Series in Probability and Statistics.

    Google Scholar 

  • Alosh M, Bretz F, Huque M (2014). Advanced multiplicity adjustment methods in clinical trials. Statistics in Medicine 33(4).

    Google Scholar 

  • Alosh M, Fritsch K, Huque M, Mahjoob K, Pennello G, Rothmann M, Russek-Cohen E, Smith F, Wilson S & Yue L (2015). Statistical considerations on subgroup analysis in clinical trials. Statistics in Biopharmaceutical Research 7: 286-303.

    Google Scholar 

  • Alosh M, Huque MF, Bretz F, D’Agostino R (2017). Tutorial on statistical considerations on subgroup analysis in confirmatory clinical trials. Statistics in Medicine, 36 (8): 1334-1360.

    MathSciNet  Google Scholar 

  • Amit O, Heiberger RM, and Lane PW (2018). Graphical approaches to the analysis of safety data from clinical trials. Pharmaceutical Statistics 7: 20–35.

    Google Scholar 

  • Armitage P, Berry G (1994). Statistical Methods in Medical Research, 3rd ed. Oxford: Blackwell.

    Google Scholar 

  • Austin PC, Lee DS, Fine JP. (2016). Introduction to the Analysis of Survival Data in the Presence of Competing Risks. Circulation. 6133(6):601–609.

    Google Scholar 

  • Baer L, Ivanova A (2013). When should the sequential parallel comparison design be used in clinical trials? Clinical Investigation 3:832–833.

    Google Scholar 

  • Benjamini Y, Hochberg Y (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society, 57: 289–300.

    MathSciNet  MATH  Google Scholar 

  • Benjamini Y, Yekutieli D (2005). False discovery rate-adjusted multiple confidence intervals for selected parameters. Journal of the American Statistical Association 100: 71-81.

    MathSciNet  MATH  Google Scholar 

  • Berry SM & Berry DA (2004). Accounting for multiplicities in assessing drug safety: A three-level hierarchical mixture model. Biometrics 60: 418-426.

    MathSciNet  MATH  Google Scholar 

  • Berry SM, Carlin BP, Lee JJ, and Muller P (2011). Bayesian Adaptive Methods for Clinical Trials. CRC Press, Taylor and Francis Group, Boca Raton, FL.

    Google Scholar 

  • Binkowitz B, Ibia E (2011). Multiregional clinical trials: An introduction from an industry perspective. Ther Innov Regul Sci., 45:569–73.

    Google Scholar 

  • Bretz F, Koenig F, Brannath W, Glimm E, Posch M (2009). Adaptive designs for confirmatory clinical trials. Statistics in Medicine, 28:1181-1217.

    MathSciNet  Google Scholar 

  • Bretz F, Horthorn T, and Westfall P (2011). Multiple Comparisons Using R. CRC Press, Taylor and Francis Group, Boca Raton, FL.

    Google Scholar 

  • Carroll KJ, Fleming TJ (2013). Statistical Evaluation and Analysis of Regional Interactions: The PLATO Trial Case Study. Statistics in Biopharmaceutical Research, 5(2): 91-101.

    Google Scholar 

  • Chen, J, Quan, H, Binkowitz, B (2010). Assessing consistent treatment effect in a multi-regional clinical trial: A systematic review. Pharm Stat., 9:242–253.

    Google Scholar 

  • Chen J, Quan H (2016). Multiregional Clinical Trials for Simultaneous Global New Drug Development. Boca Raton, FL: Chapman and Hall/CRC.

    Google Scholar 

  • Chow SC, Shao J, Wang H (2003). Sample Size Calculation in Clinical Research. Marcel Dekker, New York.

    MATH  Google Scholar 

  • Chow SC and Chang M (2007). Adaptive Design Methods in Clinical Trials. Chapman & Hall/CRC.

    Google Scholar 

  • Chow SC and Liu JP (2003). Design and Analysis of Clinical Trials, 2nd edition. John Wiley and Sons, New York.

    Google Scholar 

  • Chuang-Stein C, Le V, & Chen W (2001). Recent advancements in the analysis and presentation of safety data. Drug Information Journal 35: 377-397.

    Google Scholar 

  • Chuang-Stein C, Li Y, Kawai N, Komiyama O and Kuribayashi K (2014). Detecting safety signals in subgroups. In: Jiang Q & Xia HA, eds. Quantitative Evaluation of Safety in Drug Development: Design, Analysis and Reporting. Boca Raton, Florida: CRC Press.

    Google Scholar 

  • Crowe BJ, Xia HA, Berlin JA, et al. (2009). Recommendations for safety planning, data collection, evaluation and reporting during drug, biologic and vaccine development: a report of the safety planning, evaluation, and reporting team. Clinical Trials 6: 430-440.

    Google Scholar 

  • Cox DR (1970). Analysis of Binary Data, 1st ed. New York: Chapman and Hall/CRC Press.

    MATH  Google Scholar 

  • Dmitrienko A, Tamhane AC, and Bretz F (2010). Multiple Testing Problems in Pharmaceutical Statistics. CRC Press, Taylor and Francis Group, Boca Raton, FL.

    Google Scholar 

  • Dmitrienko A and D’Agostino R (2013). Traditional multiplicity adjustment methods in clinical trials. Statistics in Medicine 32(29).

    Google Scholar 

  • Donner A (1984). Approaches to sample size estimation in the design of clinical trials—A review. Stat. Med. 3: 199–214.

    Google Scholar 

  • Duke SP, Bancken F, Crowe B, Soukup M, Botsis T and Forshee R (2005). Seeing is believing: good graphic design principles for medical research. Statistics in Medicine 34: 3040-3059.

    MathSciNet  Google Scholar 

  • Ellenberg SS, Fleming TR & DeMets DL (2002). Data monitoring committees in clinical trials: a practical perspective. John Wiley & Sons.

    Google Scholar 

  • EMA (2014). European Medicines Agency. Guideline on the investigation of subgroups in confirmatory clinical trials (Draft). Available at https://www.ema.europa.eu/en/documents/scientific-guideline/draft-guideline-investigation-subgroups-confirmatory-clinical-trials_en.pdf

  • EMA (2016). European Medicines Agency. Guideline on the scientific application and the practical arrangements necessary to implement Commission Regulation (EC) No 507/2006 on the conditional marketing authorisation for medicinal products for human use falling within the scope of Regulation (EC) No 726/2004. Available at https://www.ema.europa.eu/en/documents/scientific-guideline/guideline-scientific-application-practical-arrangements-necessary-implement-commission-regulation-ec/2006-conditional-marketing-authorisation-medicinal-products-human-use-falling_en.pdf

  • EMA (2017). European Medicines Agency. Guideline on multiplicity issues in clinical trials (Draft). Available at https://www.ema.europa.eu/en/documents/scientific-guideline/draft-guideline-multiplicity-issues-clinical-trials_en.pdf

  • Fedorov V, Leonov S. (2013). Optimal Design for Nonlinear Response Models: CRC Press.

    Google Scholar 

  • Fitzmaurice GM, Laird NM, Rotnitzky AG (1993). Regression Models for Discrete Longitudinal Responses (with discussion). Statistical Science, 8: 248-309.

    MATH  Google Scholar 

  • Fitzmaurice GM, Laird NM, Ware JH (2011). Applied Longitudinal Analysis, Second Edition. John Wiley and Sons, Inc.

    Google Scholar 

  • Fitzmaurice G, Davidian M, Verbeke G and Molenberghs G (2009). Longitudinal Data Analysis. Boca Raton, FL: Chapman and Hall/CRC Press.

    MATH  Google Scholar 

  • Friede T, Parsons N, Stallard N. (2012). A conditional error function approach for subgroup selection in adaptive clinical trials. Statistics in Medicine, 31(30):4309-20.

    MathSciNet  Google Scholar 

  • Friedman LM, Furberg CD, DeMets DL, Reboussin DM, Granger CB (2015). Fundamentals of Clinical Trials. 5th Edition. Springer, New York.

    Google Scholar 

  • De Glas, NA, Kiderlen M, Vandenbroucke JP, de Craen AJM, Portielje JEA, van de Velde CJH, Liefers GJ, Bastiaannet E, Le Cessie S (2016). Performing Survival Analyses in the Presence of Competing Risks: A Clinical Example in Older Breast Cancer Patients. Journal of National Cancer Institue, May; 108(5): 10.

    Google Scholar 

  • Gould AL, ed. (2015). “Statistical Methods for Evaluating Safety in Medical Product Development.” Chichester, United Kingdom: John Wiley & Sons Ltd.

    Google Scholar 

  • Hall DB (2001). Zero-Inflated Poisson and Binomial Regression with Random Effects: A Case Study. Biometrics, 56(4):1030-1039.

    MathSciNet  MATH  Google Scholar 

  • He W, Pinheiro J, Kuznetsova O (2014). Practical Considerations for Adaptive Trial Design and Implementation. Springer, New York.

    Google Scholar 

  • Hilbe JM (2007). Negative Binomial Regression. Cambridge, UK: Cambridge University Press.

    Google Scholar 

  • Hochberg Y, Tamhane AC (1987). Multiple Comparison Procedures. Wiley, New York.

    MATH  Google Scholar 

  • Hsu JC (1996). Multiple Comparisons, Theory and Methods. Chapman & Hall/CRC

    Google Scholar 

  • Hu F, Rosenberger WF (2006). The Theory of Response-Adaptive Randomization in Clinical Trials. Wiley Series in Probability and Statistics.

    Google Scholar 

  • Huang X, Tamura RN (2010). Comparison of test statistics for the sequential parallel design. Statistics in Biopharmaceutical Research 2:42–50.

    Google Scholar 

  • Huber PJ (1964). Robust Estimation of a Local Parameter. Annals of Mathematical Statistics, 35: 73-101.

    MathSciNet  MATH  Google Scholar 

  • Huber PJ (1967). The Behavior of Maximum Likelihood Estimates Under Nonstandard Conditions. Proceedings of 5th Berkeley Symposium, 1: 221-233.

    Google Scholar 

  • Hughes S (2014). The prevention of missing data. In: O’Kelly M. and Ratitch B., eds. “Clinical Trials with Missing Data, A Guide for Practitioners”. Wiley, Statistics in Practice. John Wiley & Sons, Ltd, United Kingdom.

    Google Scholar 

  • ICH E5 (1998). International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use. E5(R1) Guideline: Ethnic Factors in the Acceptability of foreign Clinical Data. Available at https://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Efficacy/E5_R1/Step4/E5_R1__Guideline.pdf

  • ICH E8 (1997). International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use. E8 Guideline: General Considerations for Clinical Trials. Available at https://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Efficacy/E8/Step4/E8_Guideline.pdf

  • ICH E9 (1998). International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use. E9 Guideline: Statistical Principles in Clinical Trials. Available at https://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Efficacy/E9/Step4/E9_Guideline.pdf

  • ICH E9 R1 (2017). International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use. E9(R1) Draft Guideline: Estimands and Sensitivity Analysis in Clinical Trials. Available at https://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Efficacy/E9/E9-R1EWG_Step2_Guideline_2017_0616.pdf.

  • ICH E10 (2001). International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use. E10 Guideline: Choice of Control Group and Related Issues in Clinical Trials. Available at https://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Efficacy/E10/Step4/E10_Guideline.pdf

  • ICH E17 (2017). International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use. E17 Guideline: General Principles for Planning and Design of Multi-Regional Clinical Trials. Available at https://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Efficacy/E17/E17EWG_Step4_2017_1116.pdf

  • Ivanova A, Qaqish BF, Schoenfeld D (2011). Optimality, sample size and power calculations for the sequential parallel comparison design. Statistics in Medicine, 30: 2793–2803.

    MathSciNet  Google Scholar 

  • Ivanova A, Tamura RN (2015). A two-way enriched clinical trial design: Combining advantages of placebo lead-in and randomized withdrawal. Statistical Methods in Medical Research, 24: 871–890.

    MathSciNet  Google Scholar 

  • Ivanova A, Marchenko O, Jiang Q & Zink RC. (2019). Safety Monitoring and Analysis in Oncology Trials. In: Roychoudhury S & Lahiri S, eds. “Statistical Challenges in Oncology Clinical Development”. Boca Raton, Florida: CRC Press.

    Google Scholar 

  • Izem R, Sanchez-Kam M, Ma H, Zink RC, and Zhao Y (2018). Sources of safety data and statistical strategies for design and analysis: Postmarket Surveillance. Therapeutic Innovation & Regulatory Science52 (2): 159-169.

    Google Scholar 

  • Jenkins M, Stone A, Jennison C (2011). An adaptive seamless phase II/III design for oncology trials with subpopulation selection using correlated survival endpoints. Pharmaceutical Statistics 10: 347–356.

    Google Scholar 

  • Jennison C, Turnbull BW (2000). Group Sequential Methods with Applications to Clinical Trials. Boca Raton, Florida: CRC Press.

    MATH  Google Scholar 

  • Jiang Q, Xia HA, eds. (2014). Quantitative Evaluation of Safety in Drug Development: Design, Analysis and Reporting. Boca Raton, Florida: CRC Press.

    Google Scholar 

  • Kauermann G and Carroll RJ (2001). The Sandwich Variance Estimator: Efficiency Properties and Coverage Probability of Confidence Intervals. Journal of the American Statistical Association, 96: 1387-1396.

    MathSciNet  MATH  Google Scholar 

  • Kleinbaum DG and Klein M (2012a). Survival Analysis: a Self-learning Text, 3rd Edition, New York, NY: Springer, New York.

    MATH  Google Scholar 

  • Kleinbaum DG and Klein M. (2012b). Recurrent Event Survival Analysis. In: Survival Analysis. Statistics for Biology and Health. Springer, New York, NY

    Google Scholar 

  • Krause A, O’Connell M (2012). A Picture Is Worth a Thousand Tables: Graphics in Life Sciences. New York, NY: Springer.

    Google Scholar 

  • Lachin JM (1981). Introduction to sample size determination and power analysis for clinical trials. Control Clin. Trials, 2: 93–113.

    Google Scholar 

  • Lachin JM (1988). Statistical Properties of Randomization in Clinical Trials. Control Clin Trials, 9: 289-311.

    Google Scholar 

  • Lai TL, Robbins H (1978). Adaptive design in regression and control. Proceedings of the National Academy of Sciences of the United States of America, 75: 586–587.

    Google Scholar 

  • Lambert D (1992). Zero-Inflated Poisson Regression, with an Application to Defects in Manufacturing. Technometrics. 34 (1): 1–14.

    MATH  Google Scholar 

  • Lan KG, DeMets DL (1983). Discrete Sequential Boundaries for Clinical Trials, Biometrika, 70(3):659–663.

    MathSciNet  MATH  Google Scholar 

  • LaVange LM (2013). The Role of Statistics in Regulatory Decision Making. Therapeutic Innovation & Regulatory Science, 48: 12–19.

    Google Scholar 

  • LaVange LM, Permutt T (2016). A Regulatory Perspective on Missing Data in the Aftermath of the NRC Report. Statistics in Medicine, 35(17), 2853-64.

    MathSciNet  Google Scholar 

  • LaVange LM (2019). Statistics at FDA: Reflections on the Past Six Years, Statistics in Biopharmaceutical Research, 11:1, 1-12.

    MathSciNet  Google Scholar 

  • Li Z, Durham SD, Flournoy N (1995). An adaptive design for maximization of a contingent binary response: Adaptive Designs, Flournoy N, Rosenberger WF (eds.). Institute of Mathematical Statistics, pp.179–196.

    Google Scholar 

  • Liang KY and Zeger SL (1986). Longitudinal Data Analysis Using Generalized Linear Models, Biometrica, 73: 13-22.

    MathSciNet  MATH  Google Scholar 

  • Liang KY and Zeger SL (1995). Inference based on estimating functions in the presence of nuisance parameters. Statistical Science, 10(2):196–199.

    Google Scholar 

  • Lipkovich I, Dmitrienko A, Denne J & Enas G (2011). Subgroup identification based on differential effect search—A recursive partitioning method for establishing response to treatment in patient subpopulations. Statistics in Medicine 30: 2601–2621.

    MathSciNet  Google Scholar 

  • Lipkovich I, Dmitrienko A, D’Agostino BR (2017). Tutorial in Biostatistics: Data-driven subgroup identification and analysis in clinical trials. Statistics in Medicine, 36:136-196.

    MathSciNet  Google Scholar 

  • Little R and Rubin D (2002). Statistical Analysis with Missing Data. 2nd Edition. John Wiley & Sons, Inc., Hoboken, New Jersey.

    MATH  Google Scholar 

  • Machin D, Campbell MJ, Tan SB, Tan SH (2008). Sample Size Tables for Clinical Studies, 3rd edition. Wiley-Blackwell.

    Google Scholar 

  • Mallinckrodt C, Lipkovich I (2017). Analyzing Longitudinal Clinical Trial Data, A Practical Guide. CRC Press, Taylor and Francis Group, Boca Raton, FL.

    MATH  Google Scholar 

  • Marchenko O, Jiang Q, Chakravarty A, Ke C, Ma H, Maca J, Russek-Cohen E, Sanchez-Kam M, Zink R, Chuang-Stein C (2015) Evaluation and Review of Strategies to Assess Cardiovascular Risk in Clinical Trials in Patients with Type 2 Diabetes Mellitus. Statistics in Biopharmaceutical Research, 7 (4): 253-266

    Google Scholar 

  • Marchenko O, Jiang Q, Ke C, Ma H, Maca J, Levenson M, Liu L, Mehta C, Park S, Russek-Cohen E, Sanchez-Kam M, Zink R, Chuang-Stein C (2017). Statistical Considerations for Cardiovascular Outcome Trials in Patients with Type 2 Diabetes Mellitus. Statistics in Biopharmaceutical Research, 9, 4: 347-360.

    Google Scholar 

  • Marchenko O, Russek-Cohen E, Levenson M, Zink RC, Krukas M & Jiang Q (2018). Sources of safety data and statistical strategies for design and analysis: Real World Insights. Therapeutic Innovation and Regulatory Science, 52 (2): 170-186.

    Google Scholar 

  • Matange S (2016). Clinical Graphs Using SAS. Cary, North Carolina: SAS Institute Inc.

    Google Scholar 

  • McGlothlin AE, Lewis RJ (2014). Minimal clinically important difference: defining what really matters to patients. JAMA, 312(13):1342-3.

    Google Scholar 

  • O’Kelly M, Ratitch B (2014). Clinical Trials with Missing Data, A Guide for Practitioners. Wiley, Statistics in Practice. John Wiley & Sons, Ltd, United Kingdom.

    Google Scholar 

  • O’Quigley J., Pepe, M., Fisher, L. (1990). Continual reassessment method: a practical design for phase I clinical trials in cancer. Biometrics, 46 (1): 33-48.

    MathSciNet  MATH  Google Scholar 

  • Panel on Handling Missing Data in Clinical Trials, National Research Council (2010). The Prevention and Treatment of Missing Data in Clinical Trials, National Academies Press.

    Google Scholar 

  • Permutt, T., LaVange, L.M., Wilson, S., Lin, D.T.Y., Sridhara, R., Chakravarty, A. (2016a) “A Taxonomy of Estimands for Regulatory Clinical Trials with Discontinuations, Statistics in Medicine, 35(17), 2865-75.

    MathSciNet  Google Scholar 

  • Permutt, T., LaVange, L.M., Hung, H.M.J., Wilson, S., Lin, D.T.Y., Sridhara, R., Chakravarty, A. (2016b). “Sensitivity Analysis for Missing Data in Regulatory Submissions,” Statistics in Medicine, 35(17), 2876-79.

    MathSciNet  Google Scholar 

  • Permutt, T. and Li, F. (2016) “Trimmed Means for Symptom Trials with Drop-Outs,” Pharmaceutical Statistics, 16(1), 20–28.

    Google Scholar 

  • Pierce DA and Peters D (1992). Practical use of higher order asymptotics for multiparameter exponential families. Journal of the Royal Statistical Society. Series B (Methodological), 54(3):701-737.

    Google Scholar 

  • Piantadosi S. (2017). Clinical Trials. A Methodologic Perspective. Third Edition. Wiley Series in Probability and Statistics.

    Google Scholar 

  • Proschan MA, Lan KKG & Wittes JT (2006). Statistical Monitoring of Clinical Trials. New York, New York: Springer.

    MATH  Google Scholar 

  • Quan, H, Li, M, Chen, J (2010). Assessment of consistency of treatment effects in multiregional clinical trials. Drug Inf J., 44:617–632.

    Google Scholar 

  • Rao, C R (1973) Linear Statistical Inference and its Applications: 2nd Edition. New York: Wiley.

    MATH  Google Scholar 

  • Rosenberger, W.F., Lachin, J.M. (2002). Randomization in Clinical Trials, Theory and Practice. John Wiley & Sons, Ltd.

    Google Scholar 

  • Rutherford A (2011). ANOVA and ANCOVA: A GLM Approach, 2nd Edition. John Wiley & Sons, Inc., Hoboken, New Jersey.

    MATH  Google Scholar 

  • Schuck RN, Woodcock J, Zineh I, Stein P, Jarow J, Temple R, Permutt T, LaVange L, Beaver JA, Charlab R, Blumenthal GM, Dorff SE, Leptak C, Lemery S, Rogers H, Chowdhury B, Litwack D, Pacanowski M. (2018). Considerations for developing targeted therapies in low-frequency molecular subsets of a disease. Clinical Pharmacology and Therapeutics, DOI: https://doi.org/10.1002/cpt.1041.

  • Snedecor GW and Cochran WG (1980). Statistical Methods. Seventh Edition. The Iowa State University Press.

    Google Scholar 

  • Spiegelhalter DJ, Abrams KR, and Myles JP (2004). Bayesian Approaches to Clinical Trials and Health-Care Evaluation. Wiley, Statistics in Practice. John Wiley & Sons, Ltd, United Kingdom.

    Google Scholar 

  • Stallard N. (2010). A confirmatory seamless phase II/III clinical trial design incorporating short-term endpoint information. Statistics in Medicine. 29:959–971.

    MathSciNet  Google Scholar 

  • Sweeney, Haslett, and Parnell (2018). A General Framework for Modeling Zero Inflation. Preprint.

    Google Scholar 

  • Tamhane A and Gou J (2017). Hochberg Procedure under Negative Dependence. Statistica Sinica 28(1).

    Google Scholar 

  • Thall, P.F., Cook, J.D. (2004). “Dose-finding based on efficacy-toxicity trade-offs”, Biometrics, 60: 684-693.

    MathSciNet  MATH  Google Scholar 

  • US Food and Drug Administration (2006). Guidance for Clinical Trial Sponsors. Establishment and Operation of Clinical Trial Data Monitoring Committees. Available at https://www.fda.gov/media/75398/download

  • US Food and Drug Administration (2011). Brilinta (Ticagrelor) Tablets for Oral Use Label. Available at https://www.accessdata.fda.gov/drugsatfda_docs/label/2015/022433s017lbl.pdf. Revised Sep., 2015.

  • US Food and Drug Administration (2014a). Guidance for Industry. Expedited Programs for Serious Conditions – Drugs and Biologics. Available at https://www.fda.gov/files/drugs/published/Expedited-Programs-for-Serious-Conditions-Drugs-and-Biologics.pdf

  • US Food and Drug Administration (2014b). Pulmonary-Allergy Advisory Committee Meeting Materials. Available at http://www.fda.gov/AdvisoryCommittees/CommitteesMeetingMaterials/Drugs/Pulmonary-AllergyDrugsAdvisoryCommittee/ucm420669.htm

  • US Food and Drug Administration (2015a). Briefing Information for the May 12, 2015Meeting of the Pulmonary-Allergy Drugs Advisory Committee. Available at https://wayback.archive-it.org/7993/20170404155605/https://www.fda.gov/AdvisoryCommittees/CommitteesMeetingMaterials/Drugs/Pulmonary-AllergyDrugsAdvisoryCommittee/ucm446192.htm.

  • US Food and Drug Administration (2015b). Guidance for industry: safety Assessment for IND safety reporting (draft). Available at https://www.fda.gov/downloads/drugs/guidances/ucm477584.pdf.

  • US Food and Drug Administration (2017a). Briefing Information for the June 20, 2017 Meeting of the Endocrine-Metabolic Drugs Advisory Committee. Available at https://www.fda.gov/AdvisoryCommittees/CommitteesMeetingMaterials/Drugs/EndocrinologicandMetabolicDrugsAdvisoryCommittee/ucm563333.htm

  • US Food and Drug Administration (2017b). Guidance for Industry on Multiple Endpoints. Available at https://www.fda.gov/downloads/drugs/guidancecomplianceregulatoryinformation/guidances/ucm536750.pdf

  • US Food and Drug Administration (2017c). FDA Expands Approved Use of Kalydeco to treat Additional Mutations of Cystic Fibrosis. Press Release, available at https://www.fda.gov/NewsEvents/Newsroom/PressAnnouncements/ucm559212.htm

  • US Food and Drug Administration (2017d). Guidance for Industry Developing Targeted Therapies for Low-Frequency Molecular Subsets of a Disease. Available at https://www.fda.gov/ucm/groups/fdagov-public/@fdagov-drugsgen/documents/document/ucm588884.pdf

  • US Food and Drug Administration (2019a). Guidance for Industry. Enrichment Strategies for Clinical Trials to Support Determination of Effectiveness of Human Drugs and Biological Products. Available at https://www.fda.gov/media/121320/download

  • US Food and Drug Administration (2019b). Guidance for Industry on Adaptive Designs for Clinical Trials of Drugs and Biologics. Available at https://www.fda.gov/media/78495/download

  • Wainwright, C.E., Elborn, J.S., Ramsey, B.W., Marigowda, G., Huang, X., Cipolli, M., Colombo, C., Davies, JC, De Boeck K, Flume PA, Konstan MW, McColley SA, McCoy K, McKone EF, Munck A, Ratjen F, Rowe S.M., Waltz, D., and Boyle, M.P. (2015). “Lumacaftor-Ivacaftor in Patients with Cystic Fibrosis Homozygous for Phe508del CFTR,” New England Journal of Medicine, 373, 220-231.

    Google Scholar 

  • Wald A (1943). Tests of statistical hypothesis concerning several parameters when the number of observations is large. Transaction of the American Mathematical Society, 54(3): 426–482.

    Google Scholar 

  • Wassmer, G and Brannath, W (2016). Group Sequential and Confirmatory Adaptive Designs in Clinical Trials. Springer series in pharmaceutical statistics, New York: Springer.

    Google Scholar 

  • Willks, S S (1938). The Large-Sample Distribution of the Likelihood Ratio for Testing Composite Hypotheses. The Annals of Mathematical Statistics, 9, 1, 60-62.

    Google Scholar 

  • Wellentin, L., Becker, R. C., Budaj, A., Cannon, C. P., Emanuelsson, H., Held, C., Horrow, J., Husted, S., James, S., Katus, H., Mahaffey, K. W., Sirica, B. M., for the PLATO Investigators (2009), “Ticagrelor versus Clopidogrel in Patients with Acute Coronary Syndromes,” New England Journal of Medicine, 361, 1045-1057.

    Google Scholar 

  • Woodcock, J. & LaVange, L. M. (2017) “Master Protocols to Study Multiple Therapies, Multiple Diseases, or Both,” New England Journal of Medicine, 377,62-70. https://doi.org/10.1056/NEJMra1510062.

    Article  Google Scholar 

  • Yang S, Puggioni G, Harlow L, Redding C (2016). A Comparison of Different Methods of Zero-Inflated Data Analysis and an Application in Health Surveys. Journal of Modern Applied Statistical Methods, 16 (1), Article 29.

    Google Scholar 

  • Yuan Y, Nguyen HQ, and Thall PF (2016). Bayesian Designs for Phase I-II Clinical Trials. CRC Press, Taylor and Francis Group, Boca Raton, FL.

    MATH  Google Scholar 

  • Zink RC, Wolfinger RD and Mann G (2013). Summarizing the incidence of adverse events using volcano plots and time windows. Clinical Trials 10: 398-406.

    Google Scholar 

  • Zhou Y, Ke Chunlei, Jiang Q, Shahin S & Snapinn S (2015). Choosing appropriate metrics to evaluate adverse events in safety evaluation. Therapeutic Innovation and Regulatory Science 49: 398-404.

    Google Scholar 

  • Zink RC, Marchenko O, Sanchez-Kam M, Izem R, Ma H & Jiang Q (2018). Sources of safety data and statistical strategies for design and analysis: Clinical Trials. Therapeutic Innovation & Regulatory Science, 52 (2): 141-158.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Statistics and Data Insights Department, Bayer, Whippany, NJ, USA

    Olga V. Marchenko

  2. Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Lisa M. LaVange

  3. Department of Computer Science and Statistics, University of Rhode Island, Kingston, RI, USA

    Natallia V. Katenka

Authors
  1. Olga V. Marchenko
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lisa M. LaVange
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Natallia V. Katenka
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Olga V. Marchenko .

Editor information

Editors and Affiliations

  1. Statistics and Data Insights Department, Bayer, Whippany, NJ, USA

    Olga V. Marchenko

  2. Department of Computer Science and Statistics, University of Rhode Island, Kingston, RI, USA

    Natallia V. Katenka

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Marchenko, O.V., LaVange, L.M., Katenka, N.V. (2020). Biostatistics in Clinical Trials. In: Marchenko, O.V., Katenka, N.V. (eds) Quantitative Methods in Pharmaceutical Research and Development. Springer, Cham. https://doi.org/10.1007/978-3-030-48555-9_1

Download citation

Publish with us

Policies and ethics

Search

Navigation