National Academies Press: OpenBook
« Previous: 4 General Guidelines
Suggested Citation:"References." Institute of Medicine. 2001. Small Clinical Trials: Issues and Challenges. Washington, DC: The National Academies Press. doi: 10.17226/10078.
×

Page 94

References

Alderson, P. 1996. Equipoise as a means of managing uncertainty: personal, communal and proxy. Journal of Medical Ethics 22: 135–139.

Altman, L. K. 1996. Safety of catheter into the heart is questioned, startling doctors. The New York Times, September 18, p. A1.

Altman, L. K. 2000. Ethical and patient information concerns: who goes first? Presentation to the Institute of Medicine Committee on Strategies for Small-Number-Participant Clinical Research Trials, September 28, Washington, D.C.

Armitage, P. 1975. Sequential Medical Trials. New York: Wiley & Sons.

Becker, B. J. 2000. Meta-analysis of small n clinical trials. Presentation to the Institute of Medicine Committee on Strategies for Small-Number-Participant Clinical Research Trials, September 28, Washington, D.C.

Begg, C., M. Cho, S. Eastwood, R. Horton, D. Moher, I. Olkin, R. Pitkin, D. Rennie, K. Schulz, D. Simel, and D. Stroup. 1996. Improving the quality of reporting of randomized controlled trials: The CONSORT Statement. Journal of the American Medical Association 276: 637–639.

Bhaumik, D. K., and R. D. Gibbons. An upper prediction limit for the arithmetic mean of a lognormal random variable. Submitted for publication.

Bock, R. D. 1979. Univariate and multivariate analysis of time-structured data. In : Longitudinal Research in the Study of Behavior and Development . J. R. Nesselroade and P. B. Baltes, eds. New York: Academic Press.

Bock, R. D. 1983. The discrete Bayesian. In : Principles of Modern Psychological Measurement . H. Wainer and S. Messick, eds. Hillsdale, NJ: Earlbaum.

Bock, R. D., ed. 1989. Multilevel Analysis of Educational Data . New York: Academic Press.

Suggested Citation:"References." Institute of Medicine. 2001. Small Clinical Trials: Issues and Challenges. Washington, DC: The National Academies Press. doi: 10.17226/10078.
×

Page 95

Boers, M., P. Brooks, V. Strand, and P. Tugwell. 1998. The OMERACT Filter for Outcome Measures in Rheumatology. Journal of Rheumatology 25: 198–199.

Boers, M., P. Brooks, V. Strand, and P. Tugwell. 1999. OMERACT IV: Introduction. Fourth International Consensus Conference on Outcome Measures in Rheumatology. Journal of Rheumatology 26: 199–200.

Brody, J. 1997. Alzheimer studies thwarted. The New York Times, March 5, p. C10.

Bryk, A. S., and S. W. Raudenbush. 1987. Application of hierarchical linear models to assessing change. Psychological Bulletin 101: 47–158.

Bryk, A. S., and S. W. Raudenbush. 1992. Hierarchical Linear Models: Applications and Data Analysis Methods . Newbury Park, CA: Sage Publications.

Cappelleri, J. C., J. P. Ioannidis, C. H. Schmid, S. D. de Ferranti, M. Aubert, T. C. Chalmers, and J. Lau. 1996. Large trials vs meta-analysis of smaller trials: how do their results compare? Journal of the American Medical Association 276: 1332–1338.

Center, B. A., R. J. Skiba, and A. Casey 1986. A methodology for the quantitative synthesis of intra-subject design research. Journal of Special Education 19: 387–400.

Chi, E. M., and G. C. Reinsel. 1989. Models of longitudinal data with random effects and AR(1) errors. Journal of the American Statistical Association 84: 452–459.

Chou, Y., and D. Owen. 1986. One-sided distribution-free simultaneous prediction limits for p future samples. Journal of Quality Technology 18: 96–98.

Clark, P. I., and P. E. Leaverton. 1994. Scientific and ethical issues in the use of placebo controls in clinical trials. Annual Review of Public Health 15: 19–38.

Cochran, W. G. 1968. The effectiveness of adjustment by subclassification in removing bias in observational studies. Biometrics 24: 295–313.

Cox, D. R., and D. V. Hinkley. 1974. Theoretical Statistics . London: Chapman & Hall.

Davis, C. B. 1993. Environmental regulatory statistics. In : Handbook of Statistics.Environmental Statistics, Vol. 12 . C. R. Rao, N. K. Bose, G. S. Maddala, H. D. Vinod, and G. P. Patil, eds. Amsterdam: North-Holland.

Davis, C. B., and R. J. McNichols. 1987. One-sided intervals for at least p of m observations from a normal population on each of r future occasions. Technometrics 29: 359–370.

Day, S. J., and D. G. Altman. 2000. Blinding in clinical trials and other studies. British Medical Journal 321: 504.

Delaney, M. 2000. Small clinical trials: tool or folly? Presentation to the Institute of Medicine Committee on Strategies for Small-Number-Participant Clinical Research Trials , September 28, Washington, D.C.

DeLeeuw, J., and I. Kreft. 1986. Random coefficient models for multilevel analysis. Journal of Educational Statistics 11: 57–85.

Dempster, A. P., D. B. Rubin, and R. K. Tsutakawa. 1981. Estimation in covariance component models. Journal of the American Statistical Society 76: 341–353.

Diggle, P., K. Y. Liang, and S. L. Zeger. 1994. Analysis of Longitudinal Data . New York: Oxford University Press.

Dixon, W. J., and A. M. Mood. 1948. A method for obtaining and analyzing sensitivity data. Journal of the American Statistical Association 43: 109–126.

Dodge, H. F., and H. G. Romig. 1929. A method for sampling inspection. Bell System Technical Journal 8: 613–631.

Drummond, M. F., B. O'Brien, G. L. Stoddart, and G. W. Torrance. 1997. Methods for the Economic Evaluation of Health Care Programmes , 2nd ed. Oxford: Oxford University Press.

Suggested Citation:"References." Institute of Medicine. 2001. Small Clinical Trials: Issues and Challenges. Washington, DC: The National Academies Press. doi: 10.17226/10078.
×

Page 96

Dunnett, C. W. 1955. A multiple comparisons procedure for comparing several treatments with a control. Journal of the American Statistical Association 50: 1096–1121 .

Durham, S., N. Flournoy, and W. F. Rosenberger. 1997. A random walk rule for phase I clinical trials. Biometrics 53: 745–760.

Eddy, D. M., V. Hasselblad, and R. Shachter. 1992. Meta-Analysis by the Confidence Profile Method . The Statistical Synthesis of Evidence . San Diego: Academic Press.

Edgington, E. S. 1996. Randomized single-subject experimental designs. Behaviour Research and Therapy 34: 567–574.

Elston, R. C., and J. E. Grizzle. 1962. Estimation of time-response curves and their confidence bands. Biometrics 18: 148–159.

Emond, J. 2000. Progress in surgery: innovation or research? Presentation to the Institute of Medicine Committee on Strategies for Small-Number-Participant Clinical Research Trials, September 28, Washington, D.C.

Engels, E. A., N. Terrin, M. Barza, and J. Lau. 2000. Meta-analysis of diagnostic tests for acute sinusitis. Journal of Clinical Epidemiology 53: 852–862.

Environmental Protection Agency. 1992. Addendum to the Interim Final Guidance Document . Statistical Analysis of Ground-Water Monitoring Data at RCRA Facilities . Washington, D.C.: Enviromental Protection Agency.

Farewell, V. T., and G. J. D'Angio. 1981. A simulated study of historical controls using real data. Biometrics 37: 169–176.

Faustman, D. L. 2000. Small clinical research trials: needs beyond space. Presentation to the Institute of Medicine Committee on Strategies for Small-Number-ParticipantClinical Research Trials, September 28, Washington, D.C.

Fearn, T. 1975. A Bayesian approach to growth curves. Biometrika 62: 89–100.

Feiveson, A. H. 2000. Quantitative assessment of countermeasure efficacy for long-term space missions. Presentation to the Institute of Medicine Committee on Strategies for Small-Number- Participant Clinical Research Trials, September 28, Washington, D.C.

Finkelstein, M. O., and B. Levin. 1990. Statisics for Lawyers . New York: Springer-Verlag.

Finkelstein, M. O., B. Levin, and H. Robbins. 1996a. Clinical and prophylactic trials with assured new treatment for those at greater risk. Part I. Introduction. American Journal of Public Health 86: 691–695.

Finkelstein, M. O., B. Levin, and H. Robbins. 1996b. Clinical and prophylactic trials with assured new treatment for those at greater risk. Part II. Examples. American Journal of Public Health 86: 696–705.

Fisher, R. A. 1935. The Design of Experiments . Edinburgh: Oliver and Boyd.

Flournoy, N. (in press). Up-and-down designs. In : Encyclopedia of Envirometrics . London: Wiley.

Flournoy, N., and I. Olkin. 1995. Do small trials square with large ones? Lancet 345: 741–742.

Food and Drug Administration. 1999. International Conference on Harmonisation; E10 Choice of control group in clinical trials: draft guidance. Federal Register 64: 51767–51780.

Freedman, B. 1987. Equipoise and the ethics of clinical research. New England Journal of Medicine 317: 141–145.

Suggested Citation:"References." Institute of Medicine. 2001. Small Clinical Trials: Issues and Challenges. Washington, DC: The National Academies Press. doi: 10.17226/10078.
×

Page 97

Friedman, L. M., C. D. Furberg, and D. L. DeMets. 1996. Fundamentals of Clinical Trials . Boston: Wright.

Gehan, E. A. 1982. Design of controlled clinical trials: use of historical controls. Cancer Treatment Reports 66: 1089–1093.

Gibbons, R. D. 1987a. Statistical models for the analysis of volatile organic compounds in waste disposal sites. Ground Water 25: 572–580.

Gibbons, R. D. 1987b. Statistical prediction intervals for the evaluation of ground-water equality. Ground Water 25: 455–465.

Gibbons, R. D. 1990. A general statistical procedure for ground-water detection monitoring at waste disposal facilities. Ground Water 28: 235–243.

Gibbons, R. D. 1991. Some additional nonparametric prediction limits for ground-water detection monitoring at waste disposal facilities. Ground Water 29: 729–736.

Gibbons, R.D. 1994. Statistical Methods for Groundwater Monitoring . New York: Wiley.

Gibbons, R. D. 1996. Some conceptual and statistical issues in analysis of ground-water monitoring data. Environmetrics 7: 185–199.

Gibbons, R. D. 2000. Mixed-effects models for mental health services research. Health Services and Outcomes Research Methodology 1: 91–129.

Gibbons, J. D., I. Olkin, and M. Sobel. 1979. An introduction to ranking and selection. The American Statistician 33: 185–192.

Gibbons, R. D., and D. Hedeker. 2000. Applications of mixed-effect models in biostatistics. Sankhya 62 Series B: 70–103.

Gibbons, R. D., D. R. Cox, D. R. Grayson, D. K. Bhaumik, J. M. Davis, and R. P. Sharma. (under review). Sequential prediction bounds for identifying differentially expressed genes in replicated microarray experiments. Biometrics.

Goldstein, H. 1986. Multilevel mixed linear model analysis using iterative generalized least squares Biometrika 73: 43–56.

Goldstein, H. 1995. Multilevel Statistical Models , 2nd ed. New York: Halstead Press.

Gow, P. J., and D. Mutimer. 2001. Liver transplantation for an HIV-positive patient in the era of highly active antiretroviral therapy. AIDS 26: 291-292.

Guttman, I. 1970. Statistical Tolerance Regions: Classical and Bayesian . Darien, Conn: Hafner.

Guyatt, G. H. 1986. The treatment of heart failure. A methodological review of the literature. Drugs 32: 538–568.

Guyatt, G. H., D. Sackett, J. Adachi, R. Roberts, J. Chong, D. Rosenbloom, and J. Keller. 1988. A clinician's guide for conducting randomized trials in individual patients. Canadian Medical Association Journal 139: 497–503.

Hahn, G. J., and W. O. Meeker. 1991. Statistical Intervals: A Guide for Practitioners . New York: Wiley.

Harville, D. A. 1977. Maximum likelihood approaches to variance component estimation and to related problems. Journal of the American Statistical Association 72: 320–385.

Hauck, W. W., and S. Anderson. 1999. Some issues in the design and analysis of equivalence trials. Drug Information Journal 33: 109–118.

Hedayat, A. S., A. J. Izenman, and W. G. Zhang. 1996. Random sampling for forensic study of controlled substances, 12–21. In : Proceedings of the Section on Physical and Engineering Sciences American Statistical Association . Alexandria, VA: American Statistical Association.

Suggested Citation:"References." Institute of Medicine. 2001. Small Clinical Trials: Issues and Challenges. Washington, DC: The National Academies Press. doi: 10.17226/10078.
×

Page 98

Hedeker, D. R., R. D. Gibbons, and C. Waternaux. 1999. Sample size estimation for longitudinal designs with attrition. Journal of Educational Statistics 24: 70–93.

Hedges, L. V., and I. Olkin. 1985. Introduction. In : Statistical Methods for Meta-Analysis . I. Olkin and L. V. Hedges, eds. New York: Academic Press.

Heitjan, D. F. 1997. Bayesian interim analysis of phase II cancer clinical trials. Statistics in Medicine 16: 1791–1802.

Heyd, J. M., and B. P. Carlin. 1999. Adaptive design improvements in the continual reassessment method for phase I studies. Statistics in Medicine 18: 1307–1321.

Hillner, B. E., and R. M. Centor. 1987. What a difference a day makes: a decision analysis of adult streptococcal pharyngitis. Journal of General Internal Medicine 2: 244–250.

Hoel, D. G., M. Sobel, and G. Weiss. 1975. A survey of adaptive sampling for clinical trials. In : Perspectives in Biometrics . R. M. Elashoff, ed. New York: Academic Press.

Hsieh, F. Y. 1988. Sample size formulae for intervention studies with the cluster as unit of randomization. Statistics in Medicine 8: 1195–1201.

Institute of Medicine. 1992. Medical Intervention at the Crossroads: Modern Methods of Clinical Investigation . Washington, DC: National Academy Press.

James, W., and C. Stein. 1961. Estimation with quadratic loss. In : Proceedings of the Berkeley Symposium on Mathematical Statistics and Probability . J. Neyman, ed. Berkeley: University of California Press.

Jennison, C. and B. W. Turnbull. 1983. Confidence intervals for a binomial parameter following a multistage test with application to MIL-STD 105D and medical trials. Technometrics 25: 49–58.

Johannessen, T. 1991. Controlled trials in single subjects. 1. Value in clinical medicine. British Medical Journal 303: 173–174.

Jones, R. H. 1993. Longitudinal Data Analysis with Serial Correlation: A State-Space Approach . New York: Chapman & Hall.

Kim, K., and D. L. DeMets. 1992. Sample size determination for group sequential clinical trials with immediate response. Statistics in Medicine 11: 1391–1399.

Kleiber, C., and D. C. Harper. 1999. Effects of distraction on children's pain and distress during medical procedures: a meta-analysis. Nursing Research 48: 44–49.

Kolata, G. 1995. Women resist trials to test marrow transplants. The New York Times , February 15, p. C8.

Kolata, G. 1997. Lack of volunteers thwarts research on prostate cancer. The New York Times , February 12, p. A18.

Kolata, G., and K. Eichenwald. 1999. Business thrives on unproven care, leaving science behind. The New York Times , October 3, p. A1.

Kpamegan E. E., and N. Flournoy. 2001. An optimizing up-and-down design. In : Optimum Design . Boston: Kluwer.

Kraemer, H. C., and S. Thiemann. 1987. How Many Subjects?: Statistical Power Analysis in Research . New York: Sage Publications.

Krishnamurti, L., B. R. Blazar, and J. E. Wagner. 2001. Bone marrow transplantation without myeloablation for sickle cell disease. New England Journal of Medicine 344: 68.

Lai, T. L., B. Levin, H. Robbins, and D. Siegmund. 1980. Sequential medical trials. Proceedings of the National Academy of Sciences USA 77: 3135–3138.

Lai, T. L., and D. Siegmund, eds. 1985. Herbert Robbins Selected Papers . New York: Springer-Verlag.

Suggested Citation:"References." Institute of Medicine. 2001. Small Clinical Trials: Issues and Challenges. Washington, DC: The National Academies Press. doi: 10.17226/10078.
×

Page 99

Laird, N. M. and J. H. Ware. 1982. Random effects models for longitudinal data. Biometrics 38: 963–974.

Lau, J., J. P. A. Ioannidis, and C. H. Schmid, 1997. Quantitative synthesis in systematic reviews. Annals of Internal Medicine 127: 820–826.

Lau, J., J. P. A. Ioannidis, and C. H. Schmid. 1998. Summing up evidence: one answer is not always enough. The Lancet 351: 123–127.

LeLorier, J., G. Gregoire, A. Benhaddad, J. Lapierre, and F. Derderian. 1997. Discrepancies between meta-analyses and subsequent large randomized, controlled trials. New England Journal of Medicine 337: 536–542.

Levin, B., and H. Robbins. 1981. Selecting the highest probability in binomial or multinomial trials. Proceedings of the National Academy of Sciences USA 78: 4663–4666.

Lilford, R. J., and J. Jackson. 1995. Equipoise and the ethics of randomization. Journal of the Royal Society of Medicine 88: 552–559.

Lindley, D. V., and A. F. M. Smith. 1972. Bayes estimation for linear models. Journal of the Royal Statistical Society Series B 34: 1–41.

Lindsey, J. K. 1993. Models for Repeated Measurements . New York: Oxford University Press.

Lipid Research Clinical Program. 1984. The Lipid Research Clinic's Coronary Primary Prevention Trial results. Parts I and II. Journal of the American Medical Association 251: 354–374.

Longford, N. T. 1987. A fast scoring algorithm for maximum likelihood estimation in unbalanced mixed models with nested effects Biometrika 74: 817–827.

Longford, N. T. 1993. Random Coefficient Models . New York: Oxford University Press.

Malakoff, D. 1999. Bayes offers a ‘new' way to make sense of numbers. Science 286: 1460–1464.

Mansour, H., E. V. Nordheim, and J. J. Rutledge. 1985. Maximum likelihood estimation of variance components in repeated measures designs assuming autoregressive errors. Biometrics 41: 287–294.

Matthews, J. N. S. 1995. Small clinical trials: are they all bad? Statistics in Medicine 14: 115–126.

Meinert, C. 1996. Clinical Trials Dictionary: Terminology and Usage Recommendations . Baltimore: Harbor Duvall Press.

Meinert, C. 2000. Future directions for small n clinical research trials. Presentation to the Institute of Medicine Committee on Strategies for Small-Number-Participant Clinical Research Trials, September 28, Washington, D.C.

Mishoe, H. O. 2000. Research needs in developing small n clinical trials. Presentation to the Institute of Medicine Committee on Small-Number-Participant Clinical Research Trials, September 28, Washington, D.C.

Moher, D., D. J. Cook, S. Eastwood, I. Olkin, D. Rennie, and D. F. Stroup. 1999. Improving the quality of reports of meta-analyses of randomised controlled trials: the QUORUM statement. The Lancet 354: 1896–1900.

Olkin, I. 1996. Meta-analysis: current issues in research synthesis. Statistics in Medicine 5: 1253–1257.

Pauker, S. G. 2000. Decision analysis and small clinical trials. Presentation to the Institute of Medicine Committee on Strategies for Small-Number-Participant Clinical Research Trials, September 28, Washington, D.C.

Suggested Citation:"References." Institute of Medicine. 2001. Small Clinical Trials: Issues and Challenges. Washington, DC: The National Academies Press. doi: 10.17226/10078.
×

Page 100

Piantadosi, S. 1997. Clinical Trials: A Methodologic Perspective . New York: Wiley.

Pocock, S. J. 1984. Clinical Trials: A Practical Approach . New York: Wiley.

Pocock, S. J. 1996. The role of external evidence in data monitoring of a clinical trial. Statistics in Medicine 15: 1285–1293.

Robbins, H. 1956. An empirical Bayes approach to statistics. In : Proceedings of the ThirdBerkeley Symposium on Mathematics and Statistical Probability 1954–1955 . J. Neyman, ed. Berkeley: University of California Press.

Robbins, H. 1977. Prediction and estimation for the compound Poisson distribution. Proceedings of the National Academy of Sciences USA 74: 2670–2671.

Robbins, H. 1993. Comparing two treatments under biased allocation. La Gazette des Sciences Mathematique du Quebec 15: 35–41.

Robbins, H., and C. H. Zhang. 1988. Estimating a treatment effect under biased sampling. Proceedings of the National Academy of Sciences USA 85: 3670–3672.

Robbins, H., and C. H. Zhang. 1989. Estimating the superiority of a drug to a placebo when all and only those patients at risk are treated with the drug. Proceedings of the National Academy of Sciences USA 86: 3003–3005.

Robbins, H., and C. H. Zhang. 1991. Estimating a multiplicative treatment effect under biased allocation. Biometrika 78: 349–354.

Rosenbaum, P., and D. Rubin. 1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70: 41-55.

Rosenberg, B. 1973. Linear regression with randomly dispersed parameters. Biometrika 60: 65–72.

Rosenberger, W. F. 1996. New directions in adaptive designs. Statistical Science 11: 137–149.

Royall, R. 1997. Statistical Evidence: A Likelihood Paradigm . London: Chapman & Hall.

Royall, R. 2000. On the probability of observing misleading statistical evidence. Journal of the American Statistical Association 95: 7600–768 (discussion, pp. 768–780).

Sacks, H., T. C. Chalmers, and H. Smith. 1982. Randomized versus historical controls for clinical trials. The American Journal of Medicine 72: 233–240.

Sandborn, W. J., R. McLeod, and D. P. Jewell. 1999. Medical therapy for induction and maintenance and remission in pouchitis: systematic review. Inflammatory Bowel Disease 5: 33–39.

Sarhan, A., and B. Greenberg. 1962. Contributions to Order Statistic . New York: Wiley.

Scruggs, T. E., M. A. Mastropieri, and G. Castro. 1987. The quantitative synthesis of single subject research: methodology and validation. Remedial and Special Education 8: 24–33.

Senn, S. 1993. Cross-Over Trials in Clinical Research . New York: Wiley.

Siegel, J. P. 2000. Small n clinical trials in the regulatory setting. Presentation to the Institute of Medicine Committee on Strategies for Small-Number-Participant Clinical Research Trials, September 28, Washington, D.C.

Stroup, D. F., J. A. Berlin, S. C. Morton, I. Olkin, G. D. Williamson, D. Rennie, D. Moher, B. Becker, T. A. Sipe, and S. B. Thacker. 2000. Meta-analysis of observational studies in epidemilogy. A proposal for reporting. Journal of the American Medical Association 283: 2008–2012.

Sutherland, H. J., E. M. Meslin, and J. E. Till. 1994. What's missing from current clinical trials guidelines? A framework for integrating ethics, science and community context. Journal of Clinical Ethics 5: 297–303.

Suggested Citation:"References." Institute of Medicine. 2001. Small Clinical Trials: Issues and Challenges. Washington, DC: The National Academies Press. doi: 10.17226/10078.
×

Page 101

Temple, R. 1996. Problems in interpreting active control equivalence trials. Accountability in Research 4: 267–275.

Thall, P. F. 2000. Bayesian strategies for small n clinical trials. Presentation to the Institute of Medicine Committee on Strategies for Small-Number-Participant Clinical Research Trials, September 28, Washington, D.C.

Thall, P. F., and K. T. Russell. 1998. A strategy for dose-finding and safety monitoring based on efficacy and adverse outcomes in phase I/II clinical trials. Biometrics 54: 251–264.

Thall, P. F., and H. G. Sung. 1998. Some extensions and applications of a Bayesian strategy for monitoring multiple outcomes in clinical trials. Statistics in Medicine 17: 1563–1580.

Thall, P. F., R. M. Simon, and E. H. Estey. 1995. Bayesian sequential monitoring designs for single-arm clinical trials with multiple outcomes. Statistics in Medicine 14: 357–379.

Thall, P. F., R. M. Simon, and Y. Shen. 2000. Approximate Bayesian evaluation of multiple treatment effects. Biometrics 56: 213–219.

Truog, R. 1992. Randomized controlled trials: lessons from ECMO. Clinical Research 40: 519–527.

Tugwell, P. and C. Bombardier. 1982. A methodologic framework for developing and selecting endpoints in clinical trials. Journal of Rheumatology 9: 758–762.

Vico, L., P. Collet, A. Guignandon, M. Lafage-Proust, T. Thomas, and M. Rehailia. 2000. Effects of long-term microgravity exposure on cancellous and cortical weight-bearing bones of cosmonauts. The Lancet 355: 1607–1611.

Villar, J., G. Carroli, and J. M. Belizan. 1995. Predictability of meta-analyses of randomized controlled trials. The Lancet 345: 772–776.

Ware, J. H. 1989. Investigating therapies of potentially great benefit: ECMO. Statistical Science 4: 298–340.

Whitehead, J. 1992. Overrunning and underrunning in sequential clinical trials. Controlled Clinical Trials 13: 106–121.

Whitehead, J. 1997. The Design and Analysis of Sequential Clinical Trials . New York: Wiley.

Whitehead, J. 1999. A unified theory for sequential clinical trials. Statistics in Medicine 18: 2271–2286.

Whitehead, J. and H. Brunier, 1995. Bayesian decision procedures for dose determining experiments. Statistics in Medicine 14: 885–893 (discussion, pp. 895–899).

World Medical Association. 1964. Declaration of Helsinki . Adopted by the 18th WorldMedical Assembly Helsinki, Finland, and amended by the 29th World Medical Assembly Tokyo, Japan, 1975, the 35th World Medical Assembly Venice, Italy, 1983, and the 41st World Medical Assembly Hong Kong, 1 . Edinburgh: World Medical Association.

World Medical Association. 2000. Declaration of Helsinki: Ethical Principles for Medical Research Involving Human Subjects (5th rev). Edinburgh: World Medical Association.

Zelen, M. 1969. Play the winner rule and the controlled clinical trial. Journal of the American Statistical Association 64: 131–146.

Zivin, J. A. 2000. Understanding clinical trials. Scientific American 282: 69–75.

Zucker, D. 2000. N-of-1 trials and combining N-of-1 trials to assess treatment

Suggested Citation:"References." Institute of Medicine. 2001. Small Clinical Trials: Issues and Challenges. Washington, DC: The National Academies Press. doi: 10.17226/10078.
×

Page 102

effectiveness. Presentation to the Institute of Medicine Committee on Strategies for Small-Number-Participant Clinical Research Trials, September 28, Washington, D.C.

Zucker, D. R., C. H. Schmid, M. W. McIntosh, R. B. D'Agostino, H. P. Selker, and J. Lau. 1997. Combining single patient trials to estimate population treatment effects and to evaluate individual patient responses to treatment. Journal of Clinical Epidemiology 50: 401–410.

Suggested Citation:"References." Institute of Medicine. 2001. Small Clinical Trials: Issues and Challenges. Washington, DC: The National Academies Press. doi: 10.17226/10078.
×
Page 94
Suggested Citation:"References." Institute of Medicine. 2001. Small Clinical Trials: Issues and Challenges. Washington, DC: The National Academies Press. doi: 10.17226/10078.
×
Page 95
Suggested Citation:"References." Institute of Medicine. 2001. Small Clinical Trials: Issues and Challenges. Washington, DC: The National Academies Press. doi: 10.17226/10078.
×
Page 96
Suggested Citation:"References." Institute of Medicine. 2001. Small Clinical Trials: Issues and Challenges. Washington, DC: The National Academies Press. doi: 10.17226/10078.
×
Page 97
Suggested Citation:"References." Institute of Medicine. 2001. Small Clinical Trials: Issues and Challenges. Washington, DC: The National Academies Press. doi: 10.17226/10078.
×
Page 98
Suggested Citation:"References." Institute of Medicine. 2001. Small Clinical Trials: Issues and Challenges. Washington, DC: The National Academies Press. doi: 10.17226/10078.
×
Page 99
Suggested Citation:"References." Institute of Medicine. 2001. Small Clinical Trials: Issues and Challenges. Washington, DC: The National Academies Press. doi: 10.17226/10078.
×
Page 100
Suggested Citation:"References." Institute of Medicine. 2001. Small Clinical Trials: Issues and Challenges. Washington, DC: The National Academies Press. doi: 10.17226/10078.
×
Page 101
Suggested Citation:"References." Institute of Medicine. 2001. Small Clinical Trials: Issues and Challenges. Washington, DC: The National Academies Press. doi: 10.17226/10078.
×
Page 102
Next: Appendix A Study Methods »
Small Clinical Trials: Issues and Challenges Get This Book
×
Buy Paperback | $50.00 Buy Ebook | $40.99
MyNAP members save 10% online.
Login or Register to save!
Download Free PDF

Clinical trials are used to elucidate the most appropriate preventive, diagnostic, or treatment options for individuals with a given medical condition. Perhaps the most essential feature of a clinical trial is that it aims to use results based on a limited sample of research participants to see if the intervention is safe and effective or if it is comparable to a comparison treatment. Sample size is a crucial component of any clinical trial. A trial with a small number of research participants is more prone to variability and carries a considerable risk of failing to demonstrate the effectiveness of a given intervention when one really is present. This may occur in phase I (safety and pharmacologic profiles), II (pilot efficacy evaluation), and III (extensive assessment of safety and efficacy) trials. Although phase I and II studies may have smaller sample sizes, they usually have adequate statistical power, which is the committee's definition of a "large" trial. Sometimes a trial with eight participants may have adequate statistical power, statistical power being the probability of rejecting the null hypothesis when the hypothesis is false.

Small Clinical Trials assesses the current methodologies and the appropriate situations for the conduct of clinical trials with small sample sizes. This report assesses the published literature on various strategies such as (1) meta-analysis to combine disparate information from several studies including Bayesian techniques as in the confidence profile method and (2) other alternatives such as assessing therapeutic results in a single treated population (e.g., astronauts) by sequentially measuring whether the intervention is falling above or below a preestablished probability outcome range and meeting predesigned specifications as opposed to incremental improvement.

  1. ×

    Welcome to OpenBook!

    You're looking at OpenBook, NAP.edu's online reading room since 1999. Based on feedback from you, our users, we've made some improvements that make it easier than ever to read thousands of publications on our website.

    Do you want to take a quick tour of the OpenBook's features?

    No Thanks Take a Tour »
  2. ×

    Show this book's table of contents, where you can jump to any chapter by name.

    « Back Next »
  3. ×

    ...or use these buttons to go back to the previous chapter or skip to the next one.

    « Back Next »
  4. ×

    Jump up to the previous page or down to the next one. Also, you can type in a page number and press Enter to go directly to that page in the book.

    « Back Next »
  5. ×

    Switch between the Original Pages, where you can read the report as it appeared in print, and Text Pages for the web version, where you can highlight and search the text.

    « Back Next »
  6. ×

    To search the entire text of this book, type in your search term here and press Enter.

    « Back Next »
  7. ×

    Share a link to this book page on your preferred social network or via email.

    « Back Next »
  8. ×

    View our suggested citation for this chapter.

    « Back Next »
  9. ×

    Ready to take your reading offline? Click here to buy this book in print or download it as a free PDF, if available.

    « Back Next »
Stay Connected!