Sample sizes for clinical trials

Sample Sizes for Clinical Trials

Volumn , Issue , 2009, Pages 1-300

  Julious, Steven A ^a  

^a UNIVERSITY OF SHEFFIELD   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 85059716233     PISSN: None     EISSN: None     Source Type: Book    
DOI: None     Document Type: Book

References (293)

1. Agresti, A. (1993). Distribution free fitting of logit models with random effects for repeated categorical responses. Statistics in Medicine 12:1969–1987.
2. Agresti, A. (1999). Modelling ordered categorical data: recent advances and future challenges. Statistics in Medicine 18:2191–2207.
3. Agresti, A., and Coull, B.A. (1998). Approximate is better than exact for interval estimation of binomial proportions. The American Statistician 52:119–126.
4. Agresti, A., and Min, Y. (2001). On sample confidence intervals for parameters in discrete distributions. Biometrics 57:963–971.
5. Altman, D.G. (1980). Statistics and ethics in medical research III—how large a sample? British Medical Journal 281:1336–1338.
6. Armitage, P., and Berry, G. (1987). Statistical Methods in Medical Research, 2nd Ed. Blackwell Scientific, Oxford.
7. Bartlett, M.S. (1937). Properties of sufficiency and statistical tests. Proceedings of the Royal Statistical Society Series A 160:268–282.
8. Bauer, P., and Kieser, M. (1996). A unifying approach for confidence intervals and testing equivalence difference. Biometrika 83:934–937.
9. Berger, R.L., and Hsu, J.C. (1996). Bioequivalence trials, intersection-union tests and equivalence confidence sets. Statistical Science 11:283–319.
10. Biomarkers Definitions Working Group. (2001). Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clinical Pharmacology and Therapeutics 69:89–95.
11. Brush, G.G. (1988). How to Choose the Proper Sample Size. American Society for Quality Control, Milwaukee, Wisconsin, USA.
12. Bunker, J.P., Frazier, H.W., and Mosteller, F. (1994). Improving health: measuring the effects of health care. The Millbank Quarterly 72:225–258.
13. Campbell, M.J., Julious, S.A., and Altman, D.G. (1995). Estimating sample sizes for binary, ordered categorical, and continuous outcomes in two group comparisons. British Medical Journal 311:1145–1148.
14. Campbell, M.J., Julious, S.A., Walker, S.J., George, S.L., and Machin, D. (2007). A review of the use of the main quality of life measures, and sample size determination for quality of life measures, particularly in cancer trials. In: Advanced Handbook in Evidence-Based Healthcare, Stevens, A., Abrams, K.R., Brazier, J., Fitzpatrick, R., and Lilford, R.J. (Eds.). Sage, London, 338–351.
15. Chalmers, I. (1998). Unbiased relevant and reliable assessments in health care. British Medical Journal 317:1167–1168.
16. Chan, I.S.F. (2003). Proving non-inferiority or equivalence of two treatments with dichotomous endpoints using exact methods. Statistical Methods in Medical Research 12:37–58.
17. Chen, J.J., Tsong, Y., and Kang, S.H. (2000). Tests for equivalence or non-inferiority between two proportions. Drug Information Journal 34:569–578.
18. Chow, S.C., Shao, J., and Wang, H. (2002). A note on sample size calculations for mean comparisons based on noncentral t-statistics. Journal of Pharmaceutical Statistics 12:441–456.
19. Collett, D. (1994). Modelling Survival Data in Medical Research. Chapman and Hall, London.
20. Committee for Medicinal Products for Human Use (CHMP). (2005). Guideline on the Choice of Non-inferiority Margin. Doc. CPMP/EWP/2158/99.
21. Committee for Proprietary Medicinal Products (CPMP). (1997). Notes for Guidance on the Investigation of Drug Interactions. Doc. CPMP/EWP/560/95.
22. Committee for Proprietary Medicinal Products (CPMP). (1998). Notes for Guidance on the Investigation of Bioavailability and Bioequivalence. Doc. CPMP/EWP/QWP1401/98.
23. Committee for Proprietary Medicinal Products (CPMP). (1999). Concept Paper on the Development of a Committee for Proprietary Medicinal Products (CPMP) Points to Consider on Biostatistical Methodological Issues Arising from Recent CPMP Discussions on Licensing Applications: Choice of Delta.
24. Committee for Proprietary Medicinal Products (CPMP). (2000). Points to Consider on Switching between Superiority and Non-inferiority. Doc. CPMP/EWP/482/99.
25. Committee for Proprietary Medicinal Products (CPMP). (2002). Points to Consider on Multiplicity Issues in Clinical Trials. Doc. CPMP/EWP/908/99.
26. Committee for Proprietary Medicinal Products (CPMP). (2003). Points to Consider on Adjustment for Baseline Covariates. Doc CPMP/EWP/2863/99.
27. Connett, J.E., Smith, J.A., and McHugh, R.B. (1987). Sample size and power for pairedmatched case-control studies. Statistics in Medicine 6:53–59.
28. Crisp, A., and Curtis, P. (2008). Sample size estimation for non-inferiority trials of time to event data. Pharmaceutical Statistics 7:236–244.
29. D’Agostino, R.B., Massaro, J., and Sullivan, L.M. (2003). Non-inferiority trials: design concepts and issues—the encounters of academic consultants in statistics. Statistics in Medicine 22:169–186.
30. Day, S. (1988). Clinical trial numbers and confidence intervals of pre-specified size. The Lancet 2:1427.
31. Diletti, E., Hauschke, D., and Steinijans, V.W. (1991). Sample size determination for bioequivalence assessment by means of confidence intervals. International Journal of Clinical Pharmacology, Therapy and Toxicology 29:1–8.
32. Donner, A., and Klar, N. (2000). Design and Analysis of Cluster Randomization Trials in Health Research. Arnold, London.
33. Dunnett, C.W., and Gent, M. (1977). Significance testing to establish equivalence between treatments, with special reference to data in the form of 2×2 tables. Biometrics 33:593–602.
34. Farrington, C.P., and Manning, G. (1990). Test statistics and sample size formulae for comparative binomial trials with null hypothesis of non-zero risk difference or non-unity relative risk. Statistics in Medicine 9:1447–1454.
35. Fayers, P.M., and Machin, D. (2000). Quality of Life: Assessment, Analysis and Interpretation. Wiley, Chichester.
36. Fisher, R.A. (1935). The logic of inductive inference. Journal of the Royal Statistical Society, Series A 98:109–114.
37. Fleiss, J.L. (1981). Statistical Methods for Rates and Proportions, 2nd ed. Wiley, London.
38. Food and Drug Administration (FDA). (1992). Points to Consider. Clinical Evaluation of Anti-infective Drug Products.
39. Food and Drug Administration (FDA). (1997). Draft Guidance for Industry. Food-Effect Bioavailability and Bioequivalence Studies.
40. Food and Drug Administration (FDA). (1998). Guidance for Industry. Pharmacokinetics in Patients with Impaired Renal Function–Study Design, Data Analysis and Impact on Dosing and Labelling.
41. Food and Drug Administration (FDA). (1999a). Draft Guidance for Industry. Pharmacokinetics in Patients with Impaired Hepatic Function: Study Design, Data Analysis and Impact on Dosing and Labelling.
42. Food and Drug Administration (FDA). (1999b). Guidance for Industry. In Vivo Drug Metabolism/Drug Interaction Studies—Study Design, Data Analysis, and Recommendations for Dosing and Labelling.
43. Food and Drug Administration (FDA). (2000). Guidance for Industry. Bioavailability and Bioequivalence Studies for Orally Administered Drug Products—General Considerations.
44. Food and Drug Administration (FDA). (2001). Statistical Approaches to Establishing Bioequivalence.
45. Friendly, M. (1991). SAS System for Statistical Graphics. SAS, Cary, NC.
46. Frison, L.J., and Pocock, S.J. (1992). Repeated measures in clinical trials: analysis using mean summary statistics and its implication for design. Statistics in Medicine 11:1685–1704.
47. Garrett, A.D. (2003). Therapeutic equivalence: fallacies and falsification. Statistics in Medicine 22:741–762.
48. Grieve, A.P. (1989). Confidence intervals and trial sizes. Lancet February 11:337.
49. Grieve, A.P. (1990). Sample sizes and confidence intervals. The American Statistician 44(2):190.
50. Grieve, A.P. (1991). Confidence intervals and sample sizes. Biometrics 47:1597–1603.
51. Guenther, W.C. (1981). Sample size formulas for Normal theory t tests. The American Statistician 35:243–244.
52. Hamilton, M. (1960). Hamilton Depression Scale. Journal of Neurology, Neurosurgery and Psychiatry 23:56–62.
53. Hasselblad, V., and Kong, D.F. (2001). Statistical methods for comparison to placebo in active-control trials. Drug Information Journal 25:435–449.
54. Hauck, W.W., and Anderson, S. (1992). Types of bioequivalence and related statistical considerations. International Journal of Clinical Pharmacology, Therapy and Toxicology 30:181–187.
55. Hung, H.M.J., Wang, S.J., Lawrence, J., and O’Neil, R.T. (2003). Some fundamental issues with non-inferiority testing in active controlled trials. Statistics in Medicine 22:213–225.
56. Hutton, J.L. (2000). Number needed to treat: properties and problems (with comments). Journal of the Royal Statistical Society, Series A 63(3):403–419.
57. International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH) E3. (1996). Structure and Content of Clinical Study Reports.
58. International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH) E9. (1998). Statistical Principles for Clinical Trials.
59. International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH) E10. (2000). Choice of Control Group in Clinical Trials.
60. Johnson, W.O., Su, C.L., Gardner, I.A., and Christensen, R. (2004). Sample size calculations for surveys to substantiate freedom of populations from infectious agents. Biometrics 60:165–171.
61. Jones, B., Jarvis, P., Lewis, J.A., and Ebbutt, A.F. (1996). Trials to assess equivalence: the importance of rigorous methods. British Medical Journal 313:36–39.
62. Jones, B., and Kenward, M.J. (2003). Design and Analysis of Cross-Over Trials, 2nd ed. Wiley, Chichester.
63. Jones, D., and Whitehead, J. (1979). Sequential forms of the log rank and modified Wilcoxon tests for censored data. Biometrika 66:105–113.
64. Julious, S.A. (2000). Repeated measures in clinical trials: analysis using means summary statistics and its implications for design. Statistics in Medicine 19:3133–3135.
65. Julious, S.A. (2002a). Designing early phase trials with uncertain estimates of variability. Paper presented at International Society for Clinical Biostatisticians Conference, Dijon.
66. Julious, S.A. (2004a). Designing clinical trials with uncertain estimates of variability. The Journal of Pharmaceutical Statistics 3:261–268.
67. Julious, S.A. (2004c). Sample sizes for non-inferiority studies with binary data. Paper presented at International Society for Clinical Biostatisticians, Leiden.
68. Julious, S.A. (2004d). Tutorial in biostatistics: sample sizes for clinical trials with Normal data. Statistics in Medicine 23:1921–1986.
69. Julious, S.A. (2005b). Sample size of twelve per group rule of thumb for a pilot study. Journal of Pharmaceutical Statistics 4:287–291.
70. Julious, S.A. (2005d). Why do we use pooled variance analysis of variance? Journal of Pharmaceutical Statistics 4:3–5.
71. Julious, S.A., and Campbell, M.J. (1996). Sample size calculations for ordered categorical data. Statistics in Medicine 15:1065–1066.
72. Julious, S.A., and Campbell, M.J. (1998). Sample sizes for paired or matched ordinal data. Statistics in Medicine 17:1635–1642.
73. Julious, S.A., Campbell, M.J., and Altman, D.G. (1999). Estimating sample sizes for continuous, binary and ordinal outcomes in paired comparisons: practical hints. Journal of Biopharmaceutical Statistics 9(2):241–251.
74. Julious, S.A., and Debarnot, C.A.M. (2000). Why are pharmacokinetic data summarised as arithmetic means. Journal of Biopharmaceutical Statistics 10(1): 55–71.
75. Julious, S.A., George, S., and Campbell, M.J. (1995). Sample size for studies using the Short Form 36 (SF-36). Journal of Epidemiology and Community Health 49: 642–644.
76. Julious, S.A., George, S., Machin, D., and Stephens, R.J. (1997). Sample sizes for randomized trials measuring quality of life in cancer patients. Quality of Life Research 6:109–117.
77. Julious, S.A., and Owen, R.J. (2006). Sample size calculations for clinical studies allowing for uncertainty about the variance. Pharmaceutical Statistics 6(1): 29–37.
78. Julious, S.A., and Patterson, S.D. (2004). Sample sizes for estimation in clinical research. Journal of Pharmaceutical Statistics 3:213–215.
79. Julious, S.A., Walker, S., Campbell, M., George, S.L., and Machin, D. (2000). Determining sample sizes for cancer trials involving quality of life instruments. British Journal of Cancer 83(7):959–963.
80. Julious, S.A., and Zariffa, N. (2002). The ABC of pharmaceutical trial design: some basic principles. The Journal of Pharmaceutical Statistics 1:45–53.
81. Keene, O. (2002). Alternatives to the hazard ratio in summarising efficacy in time to event studies: an example from influenza trials. Statistics in Medicine 21: 3687–3700.
82. Kintz, J. (2007). NCSS, PASS and GESS. NCSS, Kaysville, UT.
83. Koch, G.G., and Gansky, S.A. (1996). Statistical considerations for multiplicity in confirmatory trials. Drug Information Journal 30:523–534.
84. Koopman, P.A.R. (1984). Confidence intervals for the ratio of two binomial proportions. Biometrics 40:513–517.
85. Kupper, L.L., and Hafner, K.B. (1989). How appropriate are popular sample size formulas? The American Statistician 43:101–105.
86. Lacey, J.M., Keene, O.N., and Pritchard, J.F., and Bye, A. (1997). Common non-compartmental pharmacokinetic variables: are they Normally or log-Normally distributed? Journal of Biopharmaceutical Statistics 7(1):171–178.
87. Lachin, J.M., and Foulkes, M.A. (1986). Evaluation of sample size and power for analyses of survival with allowance for non-uniform patient entry, losses to follow-up, non-compliance and stratification. Biometrics 42:507–519.
88. Lemeshow, S., Hosmer, D.W., Klar, J., and Lwanga, S.K. (1990). Adequacy of Sample Size in Health Studies. Wiley, Chichester.
89. Liu, J.P. (1995). Use of the repeated cross-over designs in assessing bioequivalence. Statistics in Medicine 14:1067–1078.
90. Lu, Y., and Bean, J.A. (1995). On the sample size for one-sided equivalence of sensitivities based upon the McNenar’s test. Statistics in Medicine 14:1831–1839.
91. Machin, D., Campbell, M.J., Fayers, P., and Pinol, A. (1997). Statistical Tables for the Design of Clinical Studies, 2nd ed. Blackwell Scientific, Oxford.
92. McCullagh, P. (1980). Regression models for ordinal data. Journal of the Royal Statistical Society, Series B 43:109–142.
93. Mclntyre, J. et al. (2005). Safety and efficacy of buccal midozolan versus rectal diazepam for emergency treatment of seizures in children: A randomised controlled trial. The Lancet 366(9481): 205–210.
94. Medical Research Council. (1948). Streptomycin treatment of pulmonary tuberculosis. British Medical Journal 2:769–782.
95. Medical Research Council Lung Cancer Working Party. (1996). Randomised trial of four-drug vs. less intensive two-drug chemotherapy in the palliative treatment of patients with small-cell lung cancer (SCLC) and poor prognosis. British Journal of Cancer 73:406–413.
96. Miettinen, O., and Nurminen, M. (1985). Comparative analysis of two rates. Statistics in Medicine 4:213–226.
97. Mood, A.M., and Snedecor, G.W. (1946). Query. Biometrics Bulletin 2(6):120–122.
98. Morikawa, T., and Yoshida, M. (1995). A useful testing strategy in phase III trials: combined test of superiority and test of equivalence. Journal of Biopharmaceutical Statistics 5(3):297–306.
99. Nam, J. (1997). Establishing equivalence of two treatments and sample size requirements in matched pairs design. Biometrics 50:1422–1430.
100. Newcombe, R.G. (1998b). Interval estimation for the difference between independent proportions: comparison of eleven methods. Statistics in Medicine 17:873–890.
101. Neyman, J., and Pearson, E.S. (1928). On the use and interpretation of test criteria. Biometrika 20(A):175–294.
102. Neyman, J., and Pearson, E.S. (1933a). On the problem of the most efficient tests of statistical hypotheses. Philosophical Transitions Royal Society (London) 231: 289–337.
103. Neyman, J., and Pearson, E.S. (1933b). The testing of statistical hypotheses in relation to the probabilities a priori. Proceeds of the Cambridge Philosophical Society 29:492–510.
104. Neyman, J., and Pearson, E.S. (1936a). Contributions to the theory of testing hypotheses. Journal Statistical Research Memoirs (University of London) 1:1–37.
105. Neyman, J., and Pearson, E.S. (1936b). Sufficient statistics and uniformly most powerful test of statistical hypothesis. Journal Statistical Research Memoirs (University of London) 1:113–137.
106. Neyman, J., and Pearson, E.S. (1938). Contributions to the theory of testing statistical hypotheses. Journal Statistical Research Memoirs (University of London) 2:25–57.
107. Noether, G.E. (1987). Sample size determination for some common nonparametric tests. Journal of the American Statistical Association 82:645–647.
108. O’Brien, P.C., and Fleming, T.R. (1979). A multiple testing procedure for clinical trials. Biometrics 35:549–546.
109. Olkin, I. (1998). Odds ratios revisited. Evidence Based Medicine 3:71.
110. Owen, D.B. (1965). A special case of a bivariate non-central t-distribution. Biometrika 52:437–446.
111. Patel, K., Kay, R., and Rowell, L. (2006). Comparing proportional hazards and accelerated failure time models: and application in influenza. Pharmaceutical Statistics 5:213–224.
112. Pollock, M.A., Sturrock, A., Marshall, K., Davidson, K.M., Kelly, C.J.G., McMahaan, A.D., et al. (2005). Thyroxine treatment in patients with symptoms of hypothyroidism but thyroid function tests within the reference range: randomized double blind placebo controlled cross-over trial. British Medical Journal 323: 891–895.
113. Royston, P. (1993). Exact conditional and unconditional sample size for pair-matched studies with binary outcome: a practical guide. Statistics in Medicine 12: 699–712.
114. Schall, R., and Williams, R.L., for the Food and Drug Administration Individual Bioequivalence Working Group. (1996). Towards a practical strategy for assessing individual bioequivalence. Journal of Pharmacokinetics and Biopharmaceutics 24:133–149.
115. Schesselman, J.J. (1982). Case-Control Studies. Oxford University Press, New York.
116. Schouten, H.J.A. (1999). Sample size formula with a continuous outcome for unequal group sizes and unequal variances. Statistics in Medicine 18:87–91.
117. Senn, S. (1993). Cross-over Trials in Clinical Research. Wiley, Chichester.
118. Senn, S. (1997). Statistical Issues in Drug Development. Wiley, Chichester.
119. Senn, S. (1998). In the blood: proposed new requirements for the registering of generic drugs. The Lancet 352:85–86.
120. Senn, S. (2001a). Guest editorial. The misunderstood placebo. Applied Clinical Trials 5:40–46.
121. Senn, S. (2001b). Statistical issues in bioequivelance. Statistics in Medicine 20: 2787–2799.
122. Senn, S., Stevens, L., and Chaturvedi, N. (2000). Tutorial in biostatistics: repeated measures in clinical trials: simple measures for analysis using summary measures. Statistics in Medicine 19:861–877.
123. Sheiner, L.B. (1997). Learning versus confirming in clinical drug development. Clinical Pharmacology and Therapeutics 61:275–291.
124. Singer, J. (2001). A simple procedure to compute sample size needed to compared two independent groups when the population variances are unknown. Statistics in Medicine 20:1089–1095.
125. Tang, M.L. (2003). Matched-pair non-inferiority using rate ratio: a comparison of current methods and sample size refinement. Controlled Clinical Trials 24:364–377.
126. Tang, N.S., Tang, M.L., and Chan, S.F. (2003). On tests of equivalence via non-unity relative risk for matched pairs design. Statistics in Medicine 22:1217–1233.
127. Tango, T. (1998). Equivalence test and confidence interval for the difference in proportions for the paired sample design. Statistics in Medicine 17:891–908.
128. Tango, T. (1999). Improved confidence intervals for the difference between binomial proportions based on paired data. Statistics in Medicine 18:3511–3513.
129. Troendle, J.F., and Frank, J. (2001). Unbiased confidence intervals for the odds ratio of two independent samples with applications to case control data. Biometrics 57:484–489.
130. Tu, D. (1998). On the use of the ratio or the odds ratio of cure rates in therapeutic equivalence clinical trials with binary endpoints. Journal of Biopharmaceutical Statistics 8:135–176.
131. Walker, S. (1998). Odds ratios revisited. Evidence Based Medicine 3:71.
132. Whitehead, A., and Whitehead, J. (1991). A general parametric approach to the metaanalysis of randomised trials. Statistics in Medicine 10:1665–1677.
133. Whitehead, J. (1993). Sample size calculations for ordered categorical data. Statistics in Medicine 12:2257–2272.
134. Wiens, B.L. (2002). Choosing an equivalence limit for noninferiority and or equivalence studies. Controlled Clinical Trials 23:2–14.
135. Wood, J., and Lambert, M. (1999). Sample-size calculations for trials in health services research. Journal of Health Service Research and Policy 4:226–229.
136. Yardly, L., Donovan-Hall, M., Smith, H.E., Walsh, B.M, Mullee, M., and Bronstein, A.M. (2004). Effectiveness of primary care-based vestibular rehabilitation for chronic dizziness. Annals of Internal Medicine 141:598–605.
137. Zigmond, A.S., and Snaith, R.P. (1983). The Hospital Anxiety and Depression Scale. Acta Psychiatric Scandinavia 67:361–370.
138. Agresti, A. (2003). Dealing with discreteness: making ‘exact’ confidence intervals for proportions, differences of proportions and odds-ratios more exact. Statistical Methods in Medical Research 12:3–21.
139. Altman, D.G. (1996). Better reporting of randomised trials: the CONSORT statement. British Medical Journal 313:570–571.
140. Altman, D.G. (1998). Confidence intervals for the number needed to treat. British Medical Journal 317:1309–1312.
141. Altman, D.G., and Bland, J.M. (1999). Treatment allocation in controlled trials: why randomise? British Medical Journal 318:1209.
142. Altman, D.G., Deeks, J.L., and Sackett D. (1998). Odds-ratios should be avoided when events are common. British Medical Journal 317:1318.
143. Anderson, T.W., and Burnstein, H. (1967). Approximating the upper binomial confidence limit. Journal of the American Statistical Association 63:857–861.
144. Anderson, T.W., and Burnstein, H. (1968). Approximating the lower binomial confidence limit. Journal of the American Statistical Association 63:1413–1415.
145. Angus, J.E., and Shafer, R.E. (1934). Improved confidence statements for the binomial parameter. The American Statistician 38:189–191.
146. Beale, S.L. (1989). Sample size determination for confidence intervals on the population mean and on the difference between two population means. Biometrics 45:969–977.
147. Begg, C., Cho, M., Eastwood, S., Horton, R., Moher, R., Olkin, I., et al. (1996). Improving the quality of reporting of randomised controlled trials: the CONSORT statement. Journal of the American Medical Association 276:637–639.
148. Bender, R. (2001). Calculating confidence intervals for the number needed to treat. Controlled Clinical Trials 22:102–110.
149. Birkett, M.A., and Day, S.J. (1994). Internal pilot studies for estimating sample size. Statistics in Medicine 13:2455–2463.
150. Biswas, A. (2001). Adaptive designs for binary treatment responses in phase III clinical trials: controversies and progress. Statistical Methods in Medical Research 10:353–364.
151. Blyth, C.R., and Still, H.A. (1983). Binomial confidence intervals. Journal of the American Statistical Association 78:108–116.
152. Bradburn, M.J., Clark, T.G., Love, S.B., and Altman, D.G. (2003a). Survival analysis part II: multivariate data analysis—an introduction to concepts and methods. British Journal of Cancer 2003; 89:431–436.
153. Bradburn, M.J., Clark, T.G., Love, S.B., and Altman, D.G. (2003b). Survival analysis part III: multivariate data analysis—choosing a model and assessing its adequacy and fit. British Journal of Cancer 89:605–611.
154. Bradford Hill, A. (1990). Memories of the British streptomycin trial: the first randomized clinical trial. Controlled Clinical Trials 11:77–79.
155. Browne, R.H. (1995). On the use of a pilot sample for sample size determination. Statistics in Medicine 14:1933–1940.
156. Bunke, O., and Droge, B. (1984). Bootstrap and cross-validation estimates of the prediction error for linear regression models. The Annals of Statistics 12: 1400–1424.
157. Burman, C.F., and Senn, S. (2003). Examples of options in drug development. Pharmaceutical Statistics 2:113–125.
158. Campbell, M., Grimshaw, J., and Steen, N. (2000). Sample size calculations for cluster randomised trials. Journal of Health Services Research and Policy 5:12–16.
159. Campbell, M.J. (2000). Cluster randomised trials in general (family) practice research. Statistical Methods in Medical Research 9:81–94.
160. Campbell, M.K., and Grimshaw, J.M. (1998). Cluster randomised trials: time for improvement. The implications of adopting a cluster design are still largely ignored. British Medical Journal 317:1171–1172.
161. Casella, G. (1986). Refining binomial confidence intervals. The Canadian Journal of Statistics 14:113–129.
162. Chalmers, T.C., Celano, P., Sacks, H.S., and Smith, H. (1983). Bias in treatment assignment in controlled clinical trials. New England Journal of Medicine 309: 1358–1361.
163. Charig, C.R., Webb, D.R., Payne, S.R., and Wickham, O.E. (1986). Comparison of treatment of renal calculi by operative surgery, percutaneous nephrolithotomy and extracoporeal shock wave lithotripsy. British Medical Journal 292: 879–892.
164. Clark, T.G., Bradburn, M.J., Love, S.B., and Altman, D.G. (2003). Survival analysis part I: basic concepts and first analyses. British Journal of Cancer 2003; 89:232–238.
165. Clayton, D., and Hills, M. (1993). Statistical Models in Epidemiology. Oxford University Press, Oxford.
166. Clopper, C.J., and Pearson, E.S. (1934). The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26:404–413.
167. Committee for Proprietary Medicinal Products (CPMP). (2004a). Notes for Guidance on the Evaluation of Medicinal Products Indicated for the Treatment of Bacterial Infections. Doc. CPMP/EWP/558/95.
168. Committee for Proprietary Medicinal Products (CPMP). (2004b). Points to Consider on the Choice of Non-Inferiority Margin (Draft). Doc. CPMP/EWP/2158/99 draft.
169. Conner, R.J. (1987). Sample size for testing differences in proportions for the pairedsample design. Biometrics 43:207–211.
170. Cook, R.A., and Sackett, D.L. (1995). The number needed to treat: clinically useful measure of treatment effect. British Medical Journal 310:452–454.
171. Cowell, R.G., Dawid, A.P., Hutchinson, T.A., Roden, S., and Spiegelhalter, D.J. (1993). Bayesian networks for the analysis of drug safety. The Statistician 42(4): 369–384.
172. Crow, E.L. (1956). Confidence intervals for a proportion. Biometrika 43:423–435.
173. Daly, L. (1992). Simple SAS macros for the calculation of exact binomial and Poisson confidence limits. Computational and Biological Medicine 22:351–361.
174. Dark, R., Bolland, K., and Whitehead, J. (2003). Statistical methods for ordered data based on a constrained odds model. Biometrical Journal 45(4):453–470.
175. Davies, H.T.O., Crombie, I.K., and Tavakoli, M. (1998). When can odds-ratios mislead? British Medical Journal 316:989–991.
176. Day, S. (2000). Operational difficulties with internal pilot studies to update sample size. Drug Information Journal 34:461–468.
177. Day, S.J., and Altman, D.G. (2000). Blinding in clinical trials and other studies. British Medical Journal 321:504.
178. Deeks, J. (1998). Odds-ratios should be used only in case-control studies and logistic regression studies. British Medical Journal 317:1155–1156.
179. de Haes, J.C.J.M., and van Knippenberg, F.C.E. (1985). The quality of life of cancer patients—a review of the literature. Social Science and Medicine 20: 809–817.
180. de Haes, J.C.J.M., van Knippenberg, F.C.E., and Neijt, J.P. (1990). Measuring psychological and physical distress in cancer patients: structure and application of the Rotterdam Symptom Checklist. British Journal of Cancer 62:1034–1038.
181. Desu, M.M., and Raghavarao, D. (1990). Sample Size Methodology. Academic Press, London.
182. Diggle, P.J., Liang, K.Y., and Zeger, S.L. (1996). Analysis of Longitudinal Data. Oxford University Press, Oxford. .
183. Donner, A. (1983). Approaches to sample size estimation in the design of clinical trials— a review. Statistics in Medicine 3:199–214.
184. Donner, A., and Wells, G. (1986). A comparison of confidence interval methods for the intra-class correlation coefficient. Biometrics 42(2):401–412.
185. Ederer, F., and Mantel, N. (1974). Confidence limits on the ratio of two Poisson variables. American Journal of Epidemiology 100(3):165–167.
186. Edwardes, M.D. (1998). The evaluation of confidence sets with application to binomial intervals. Statistica Sinica 8:393–409.
187. Efron, B., and Gong, G. (1983). A leisurely look at the bootstrap, the jacknife and the cross-validation. American Statistician, 37:36–48.
188. Efron, B., and Tibshirani, R.J. (1993). An introduction to the bootstrap. Chapman and Hall: New York.
189. Eldridge, S.M. (2005). Assessing, understanding an improving the efficiency of cluster randomized trials in primary care. Unpublished PhD dissertation, University of London.
190. Ellenberg, S.S., and Temple, R. (2000). Placebo-controlled trials and active control trials in the evaluation of new treatments. Part 2: practical issues and specific cases. Annals of Internal Medicine 133:464–470.
191. Ellison, B.E. (1964). Two theorems for inferences about the Normal distribution with applications in acceptance sampling. Journal of the American Statistical Association 59:89–95.
192. Enas, G., and Andersen, J.S. (2001). Enhancing the value delivered by the statistician throughout drug discovery and development: putting statistical science into regulated pharmaceutical innovation. Statistics in Medicine 20:2697–2708.
193. Escobar, M.D. (1994). Estimating means with dirichlet process priors. Journal of the American Statistical Association 89:268–277.
194. Escobar, M.D., and West, M. (1995). Bayesian density estimation and inference using mixtures. Journal of the American Statistical Association 90:570–588.
195. Fayers, P., Ashby, D., and Parmar, M. (1997). Tutorial in biostatistics: Bayesian data monitoring in clinical trials. Statistics in Medicine 16:1413–1430.
196. Fayers, P., and Machin, D. (1995). Sample size: how many patients are necessary? British Journal of Cancer 72:1–9.
197. Feng, Z., and Grizzle, J.E. (1992). Correlated binomial variates: properties of estimator of intraclass correlation and its effect on sample size calculation. Statistics in Medicine 11:1607–1614.
198. Ferguson, T.S. (1973). A Bayesian analysis of some nonparamatric problems. The Annals of Statistics 1:209–230.
199. Ferguson, T.S. (1974). Prior distributions on spaces of probability measures. The Annals of Statistics 2:615–629.
200. Fisher, R.A. (1925). Statistical Methods for Research Workers. Oliver and Boyd, Edinburgh.
201. Fleiss, J.L., and Levin, B. (1988). Sample size determination in studies with matched pairs. Journal of Clinical Epidemiology 41:727–730.
202. Friede, T., and Kieser, M. (2001). A comparison of methods for adaptive sample size adjustment. Statistics in Medicine 20:3861–3873.
203. Ghosh, B.K. (1979). A comparison of some approximate confidence limits for the binomial parameter. Journal of the American Statistical Association 74: 894–900.
204. Gould, A.L. (1992). Interim analyses for monitoring clinical trials that do not materially affect the Type I error rate. Statistics in Medicine 11:55–66.
205. Gould, A.L. (1995a). Group sequential extensions of a standard bioequivalence testing procedure. Journal of Pharmacokinetics and Biopharmaceutics 23:5–86.
206. Gould, A.L. (1995b). Planning and revising the sample size for a trial. Statistics in Medicine 14:1039–1051.
207. Gould, A.L. (2001). Sample size re-estimation: recent developments and practical considerations. Statistics in Medicine 20:2625–2643.
208. Gould, A.L., and Shih, W.J. (1992). Sample size re-estimation without unblinding for Normally distributed data with unknown variance. Communications in Statistics—Theory and Methods 21:2833–2853.
209. Gould, A.L., and Shih, W.J. (1998). Modifying the design of ongoing trials without unblinding. Statistics in Medicine 17:89–100.
210. Graham, P.L., Mengersen, K., and Morton, A.P. (2003). Confidence limits for the ratio of two rates based on likelihood scores: noniterative method. Statistics in Medicine 22:2071–2083.
211. Greenberg, R.P., and Fisher, S. (1994). Suspended judgement—seeing through the double masked design: a commentary. Controlled Clinical Trials 15:244–246.
212. Greenland, S. (1988). On sample-size and power calculations for studies using confidence intervals. American Journal of Epidemiology 128(1):231–237.
213. Grieve, A.P. (2003). The number needed to treat: a useful clinical measure or a case of the emperor’s new clothes? Journal of Pharmaceutical Statistics 2:87–102.
214. Guyatt, G.H., Juniper, E.F., Walter, S.D., Griffith, L.E., and Goldstein, R.S. (1998). Interpreting treatment effects in randomised trials. British Medical Journal 316:690–693.
215. Hall, P. (1992). Bootstrap and Edgeworth Expansion. Springer-Verlag, New York.
216. Hilton, J.F., and Mehta C.R. (1993). Power and sample size calculations for exact conditional tests with ordered data. Biometrics 49:609–616.
217. Hinkley, D.V. (1988). Bootstrap methods. Journal of the Royal Statistical Society, Series B 50:321–337.
218. Hinkley, D.V., and Schechtman, E. (1987). Conditional bootstrap methods in the mean shift model. Biometrika 74:85–94.
219. Johnson, N.L., and Kotz, S. (1994a). Distributions in Statistics: Continuous Univariate Distributions—1. Wiley, Chichester.
220. Johnson, N.L., and Kotz, S. (1994b). Distributions in Statistics: Continuous Univariate Distributions—2. Wiley, Chichester.
221. Johnson, N.L., and Kotz, S. (1994c). Distributions in Statistics: Discrete Distributions. Wiley, Chichester.
222. Joseph, L., du Berger, R., and B’elisle, P. (1997). Bayesian and mixed Bayesian/likelihood criteria for sample size determination. Statistics in Medicine 16(7): 769–781.
223. Julious, S.A. (2004b). Sample size re-determination for repeated measures studies. Biometrics 60:284–285.
224. Julious, S.A. (2004e). Using confidence intervals around individual means to assess statistical significance between two means. The Journal of Pharmaceutical Statistics 3:217–222.
225. Julious, S.A. (2005a). Issues with number needed to treat [letter]. Statistics in Medicine 24:3233–3235.
226. Julious, S.A. (2005c). Two-sided confidence intervals for the single proportion: comparison of seven methods [letter]. Statistics in Medicine 24:3383–3384.
227. Julious, S.A., and Mullee, M.A. (1994). Confounding and Simpon’s paradox. British Medical Journal 308:1480–1481.
228. Julious, S.A., and Mullee, M.A. (2000). Crude rates of outcome. British Journal of Surgery 87:8–9.
229. Julious, S.A., and Swank, D. (2005). Moving statistics beyond the individual clinical trial—applying decision science to optimise a clinical development plan. Journal of Pharmaceutical Statistics 4:37–46.
230. Kendall, M., and Stuart, A. (1977). Distribution Theory. Vol. 1 of The Advanced Theory of Statistics, 4th ed. Griffin, London.
231. Kieser, M., and Friede, T. (2000). Re-calculating the sample size in internal pilot study designs with control of the Type I error rate. Statistics in Medicine 19:901–911.
232. Korn, E.L. (1986) Sample size tables for bounding small proportions. Biometrics 42:213–216.
233. Kunz, R., and Oxman, A.D. (1998). The unpredictability paradox: review of empirical comparisons of randomised trials and non-randomised clinical trials. British Medical Journal 317:1185–1190.
234. Kwang, G.P.S., and Hutton, J.L. (2003). Choice of parametric models in survival analysis: applications to monotherapy for epilepsy and cerebral palsy. Applied Statistics 52(2):153–168.
235. Lachin, J.M. (1977). Sample size determination for r x c comparative trials. Biometrics 33:315–324.
236. Lake, S., Kammann, E., Klar, N., and Betensky, R. (2002). Samples size re-estimation in cluster randomisation trials. Statistics in Medicine 21:1337–1350.
237. Laster, L.L., and Johnson, M.F. (2003). Non-inferiority trials: the ‘at least as good as’ criterion. Statistics in Medicine 22:187–200.
238. Lee, M.K., Song, H.H., Kang, S.H., and Ahn, C.W. (2002). The determination of sample sizes in the comparison of two multinomial proportions from ordered categories. Biometrical Journal 44(4):395–409.
239. Lesaffre, E., and Pledger, G. (1999). A note on the number needed to treat. Controlled Clinical Trials 20:439–447.
240. Liu, K.J. (1991). Sample sizes for repeated measurements in dichotomous data. Statistics in Medicine 10:463–472.
241. Liu, Q., Proschan, M.A., and Pledger, G.W. (2002). A unified theory of two-stage adaptive designs. Journal of the American Statistical Association 97:1034–1041.
242. Lui, K.J. (2001). Interval estimation of simple difference with dichotomous data with repeated measurements. Biometrical Journal 43:845–861.
243. Lunn, D.J., Wakefield, J., and Racine-Poon, R. (2001). Cumulative logic models for ordinal data: a case study involving allergic rhinitis severity scores. Statistics in Medicine 20:2261–2285.
244. Matthews, J.N.S. (2000). An Introduction to Randomised Controlled Trials. Arnold, London.
245. May, W.L., and Johnson, W.D. (1997). The validity and power for tests of two correlation proportions. Statistics in Medicine 16:1081–1096.
246. Miettinen, O.S. (1968). The matched pairs design in the case of all-or-none responses. Biometrics 24:339–353.
247. Mood, A.M., Graybill, F.A., and Boes, D.C. (1974). Introduction to the Theory of Statistics. McGraw-Hill, London.
248. Moorey, S., Greer, S., Watsonm, M., Gormann, C., Rowdenn, L., Tunmore, R., et al. (1991). The factor structure and factor stability of the Hospital Anxiety and Depression Scale in patients with cancer. British Journal of Psychiatry 158: 255–259.
249. Newcombe, R.G. (1998a). Improved confidence intervals for the difference between binomial proportions based on paired data. Statistics in Medicine 17: 2633–2650.
250. Newcombe, R.G. (1998c). Two-sided confidence intervals for the single proportion: comparison of seven methods. Statistics in Medicine 17:857–872.
251. Nixon, R.M., and Thompson, S.G. (2003). Baseline adjustments for binary data in repeated cross-sectional cluster randomised trials. Statistics in Medicine 22: 2673–2692.
252. O’Brien, R.G., and Lohr, V.I. (1984). Power analysis for linear models: the time has come. SUGI Conference Proceedings.
253. O’Quigley, J., Pepe, M., and Fisher, L. (1990). Continual reassessment method: a practical guide for phase I clinical trials in cancer. Biometrics 46:33–48.
254. O’Quigley, J., and Shen, L.Z. (1996). Continuous reassessment methods: a likelihood approach. Biometrics 52:673–684.
255. Pham-Gia, T., and Turkkan, N. (1992). Sample size determination in Bayesian analysis. The Statistician 41:389–392.
256. Pocock, S.J. (1983). Clinical Trials: A Practical Approach. Wiley, Chichester.
257. Posch, M., and Bauer, P. (2000). Interim analysis and sample size reassessment. Biometrics 56:1170–1176.
258. Proschan, M.A., Liu, Q., and Hunsberger, S. (2003). Practical midcourse sample modification in clinical trials. Controlled Clinical Trials 23:4–15.
259. Rabbee, N., Mehta, C., Patel, N., and Senchaudhuri, P. (2003). Power and sample for ordered data. Statistical Methods in Medical Research 12:73–84.
260. Rao, C.R. (1965). Linear Statistical Inference and Its Applications. Wiley, Chichester.
261. Rasch, D., and Horrendorfer, G. (1986). Experimental Design: Sample Size Determination and Block Designs. Reidel, Lancaster.
262. Reiczigel, J. (2003). Confidence intervals for the binomial parameter: some new considerations. Statistics in Medicine 22:611–621.
263. Robinson, L.D., and Jewell, N.P. (1991). Some surprising results about covariate adjustment in logistic regression models. International Statistical Review 58(2): 227–240.
264. Sackett, D.L., Deeks, J.J., and Altman, D.G. (1996). Down with odds-ratios! Evidence- Based Medicine 1(6):164–166.
265. Santer, T.J., and Snell, M.K. (1980). Small sample confidence intervals for p1-p2 and p1/p2 in 2x2 contingency tables. Journal of the American Statistical Association 75:386–394.
266. Schulzer, M., and Mancini, G.B. (1996). ‘Unqualified success’ and ‘unmitigated failure’: number-needed-to-treat-related concepts for assessing treatment efficacy in the presence of treatment-induced adverse events. International Journal of Epidemiology 25(4):704–712.
267. Seber, G.A.F. (1977) Linear Regression Analysis. Wiley, Chichester.
268. Senn, S. (2000). Consensus and controversy in pharmaceutical statistics (with discussion). Journal of the Royal Statistical Society, Series D 49:135–176.
269. Shaban, S.A. (1980). Change-point problem and two-phase regression: an annotated bibliography. International Statistical Review 48:83–93.
270. Simpson, E.H. (1951). The interpretation of interaction in contingency tables. Journal of the Royal Statistical Society, Series B 2:238–241.
271. Smeeth, L., Haines, A., and Ebrahim, S. (1999). Numbers needed to treat derived from meta analyses—sometimes informative, usually misleading. British Medical Journal 318:1548–1551.
272. Spiegelhalter, D., Abrams, K.R., and Myles, J.P. (2004). Bayesian Approaches to Clinical Trials and Health-Care Evaluation. Wiley, Chichester.
273. Spiegelhalter, D., Freedman, L., and Parmer, M. (1995). Bayesian approaches to randomized trials. Journal of the Royal Statistical Society, Series A 157:387–416.
274. Spiegelhalter, D.J. (2001). Bayesian methods for cluster randomised trials with continuous responses. Statistical in Medicine 20:435–452.
275. Spiegelhalter, D.J., Myles, J.P., Jones, D.R., and Abrams, K.R. (1999). Methods in health service research: an introduction to Bayesian methods in health service research. British Medical Journal 319:508–512.
276. Sterne, T.E. (1945). Some remarks on confidence or fiducial limits. Biometrika 41: 275–278.
277. Swiger, L.A., Harvey, W.R., Everson, D.O., and Gregory, K.E. (1964). The variance of intraclass correlation involving groups with one observation. Biometrics 20:818–826.
278. Thompson, S.G., Warn, D.E., and Turner, R.M. (2004). Bayesian methods for analysis of binary data in cluster randomised trials on the absolute risk scale. Statistics in Medicine 23:389–410.
279. Turner, R.M., Omar, R.Z., and Thompson, S.G. (2001). Bayesian methods of analysis for cluster randomised trials with binary outcome data. Statistics in Medicine 20:453–471.
280. Turner, R.M., Omar, R.Z., and Thompson, S.G. (2006). Constructing intervals for the intra-cluster correlation coefficient using Bayesian modelling and application in cluster randomised trials. Statistics in Medicine 25:1443–1456.
281. Turner, R.M., Prevost, A.T., and Thompson, S.G. (2004). Allowing for imprecision of the intracluster correlation coefficient in the design of cluster randomised trials. Statistics in Medicine 23:1195–1214.
282. Ukoummunne, O.C. (2003a). A comparison of confidence intervals methods for the intraclass correlation coefficient in cluster randomised trials. Statistics in Medicine 21:3757–3774.
283. Ukoummunne, O.C., Davison, A.C., Gulliford, M.C., and Chinn, C. (2003b). Nonparametric bootstrap confidence intervals for the intraclass correlation coefficient. Statistics in Medicine 22:3805–3821.
284. Vollset, S.E. (1993). Confidence intervals for a binomial proportion. Statistics in Medicine 12:809–824.
285. Whitehead, A., and Jones, N.M.B. (1994). A meta analysis of clinical trials involving different classifications of response into ordered categories. Statistics in Medicine 13:2503–2515.
286. Whitehead, J., Zhou, Y., Patterson, S., Webber, D., and Francis, S. (2001). Easy to implement Bayesian methods for dose escalation studies in healthy volunteers. Biostatistics 2:47–61.
287. Wilson, E.B. (1927). Probably inference, the law of succession and statistical inference. Journal of the American Statistical Association 22:202–212.
288. Wittes, J., and Brittain, E. (1990). The role of internal pilot studies in increasing the efficacy of clinical trials. Statistics in Medicine 9:65–72.
289. Wu, C.F.J. (1986). Jackknife, bootstrap and other re-sampling methods in regression analysis. Annals of Statistics 14:1261–1295.
290. Yoshioka, A. (1998). Use of randomisation in the Medical Research Council’s clinical trials of streptomycin in pulmonary tuberculosis in the 1940s. British Medical Journal 317:1220–1223.
291. Zucker, D.M. (2004). Sample size re-determination for repeated measures studies. Biometrics 60:284–285.
292. Zucker, D.M., and Denne, J. (2002). Sample size re-determination for repeated measures studies. Biometrics 48(3):548–559.
293. Zucker, D.M., Wittes, J.T., Schabenberger, O., and Brittain E. (1999). Internal pilot studies II. Comparison of various procedures. Statistics in Medicine 18: 3493–3509.