References
Araujo, A., Julious, S., & Senn, S. (2016). Understanding variation
in sets of N-of-1 trials. PLOS ONE,
11(12), e0167167.
Ard, M. C., & Edland, S. D. (2011). Design and analysis
considerations in alzheimer disease clinical trials. Clinical
Investigation, 1(8), 1091–1098.
Armitage, P. (1955). Tests for linear trends in proportions and
frequencies. Biometrics, 11(3), 375–386.
Armitage, P., McPherson, C. K., & Rowe, B. C. (1969). Repeated
significance tests on accumulating data. Journal of the Royal
Statistical Society: Series A, 132(2), 235–244.
Asakura, K., Evans, S. R., & Hamasaki, T. (2020). Interim monitoring
for futility in clinical trials with two co-primary endpoints using
prediction. Statistics in Biopharmaceutical Research.
Azher, R. A., Wason, J. M. S., & Grayling, M. J. (2024). A
comparison of randomization methods for multi-arm clinical trials.
Statistics in Medicine.
Babb, J., Rogatko, A., & Zacks, S. (1998). Cancer phase
I clinical trials: Efficient dose escalation with overdose
control. Statistics in Medicine, 17(10), 1103–1120.
Barnard, G. A. (1947). Significance tests for 2 × 2 tables. Biometrika,
34(1/2), 123–138.
Berger, V. W., Rezvani, A., & Makarewicz, V. A. (2003). Direct
effect on validity of response run-in selection in clinical trials.
Controlled Clinical Trials, 24(2), 156–166.
Berry, S. M., Carlin, B. P., Lee, J. J., & Müller, P. (2010).
Bayesian adaptive methods for clinical trials. Chapman;
Hall/CRC.
Boschloo, R. D. (1970). Raised conditional level of significance for the
2 × 2-table when testing the equality
of two probabilities. Statistica Neerlandica, 24(1),
1–9.
Bugni, F. A., Canay, I. A., & Shaikh, A. M. (2018). Inference under
covariate-adaptive randomization. Journal of the American
Statistical Association, 113(524), 1784–1796.
Buuren, S. van, & Groothuis-Oudshoorn, K. (2011). mice: Multivariate imputation by chained equations
in R. Journal of Statistical Software,
45(3), 1–67.
Callegaro, A., Shree, B. S. H., & Karkada, N. (2021). Inference
under covariate-adaptive randomization: A simulation study.
Statistical Methods in Medical Research, 30(5),
1273–1284.
Candel, M. J. J. M., & Van Breukelen, G. J. P. (2023). Best (but oft
forgotten) practices: Efficient sample sizes for commonly used trial
designs. American Journal of Clinical Nutrition.
Carpenter, J. R., Roger, J. H., & Kenward, M. G. (2013). Analysis of
longitudinal trials with protocol deviation: A framework for relevant,
accessible assumptions, and inference via multiple imputation.
Journal of Biopharmaceutical Statistics, 23(6),
1352–1371.
Chan, A.-W., Tetzlaff, J. M., Altman, D. G., et
al. (2013). SPIRIT 2013 statement: Defining standard
protocol items for clinical trials. Annals of Internal
Medicine, 158(3), 200–207.
Cheung, Y. K. (2011). Dose finding by the continual reassessment
method. Chapman; Hall/CRC.
Chow, S.-C., & Chang, M. (2008). Adaptive design methods in clinical
trials: A review. Orphanet Journal of Rare Diseases,
3, 11.
Coart, E., Bamps, P., Quinaux, E., Sturbois, G., Saad, E. D.,
Burzykowski, T., & Buyse, M. (2023). Minimization in randomized
clinical trials. Statistics in Medicine.
Cochran, W. G. (1954). Some methods for strengthening the common χ2 tests.
Biometrics, 10(4), 417–451.
Crans, G. G., & Shuster, J. J. (2008). How conservative is
Fisher’s exact test? A quantitative evaluation of the
two-sample comparative binomial trial. Statistics in Medicine,
27(18), 3598–3611.
De Silva, A. P., Moreno-Betancur, M., De Livera, A. M., Lee, K. J.,
& Simpson, J. A. (2017). A comparison of multiple imputation methods
for handling missing values in longitudinal data in the presence of a
time-varying covariate with a non-linear association with time. BMC
Medical Research Methodology, 17.
Diggle, P. J., Heagerty, P., Liang, K.-Y., & Zeger, S. L. (2002).
Analysis of longitudinal data. Oxford Statistical Science
Series.
Diggle, P., & Kenward, M. G. (1994). Informative drop-out in
longitudinal data analysis. Journal of the Royal Statistical
Society: Series C, 43(1), 49–93.
Dmitrienko, A., Tamhane, A. C., & Bretz, F. (2009). Multiple
testing problems in pharmaceutical statistics. Chapman; Hall/CRC.
Donohue, M. C., Jacqmin-Gadda, H., Le Goff, M., et
al. (2014). Estimating long-term multivariate progression from
short-term data. Alzheimer’s and Dementia, 10(5),
S400–S410.
Duan, N., Kravitz, R. L., & Schmid, C. H. (2013). Single-patient
(N-of-1) trials: A pragmatic clinical
decision methodology for patient-centered comparative effectiveness
research. Journal of Clinical Epidemiology, 66(8),
S21–S28.
Edwards, L. J., Muller, K. E., Wolfinger, R. D., Qaqish, B. F., &
Schabenberger, O. (2008). An R2 statistic for
fixed effects in the linear mixed model. Statistics in
Medicine, 27(29), 6137–6157.
Efron, B. (1971). Forcing a sequential experiment to be balanced.
Biometrika, 58(3), 403–417.
Ellenberg, S. S., Fleming, T. R., & DeMets, D. L. (2019). Data
monitoring committees in clinical trials: A practical perspective
(2nd ed.). Wiley.
European Medicines Agency. (2019). Guideline on the investigation of
subgroups in confirmatory clinical trials. EMA.
Fay, M. P., & Hunsberger, S. A. (2021). Practical valid inferences
for the two-sample binomial problem. Statistics Surveys,
15, 72–110.
Feaster, D. J., Mikulich-Gilbertson, S., & Brincks, A. M. (2011).
Modeling site effects in the design and analysis of multi-site trials.
American Journal of Drug and Alcohol Abuse, 37(5),
383–391.
Fitzmaurice, G. M., Laird, N. M., & Ware, J. H. (2011). Applied
longitudinal analysis (2nd ed.). Wiley.
Fleming, T. R., & Harrington, D. P. (1991). Counting processes
and survival analysis. Wiley.
Freedman, D. A. (2008). On regression adjustments to experimental data.
Advances in Applied Mathematics, 40(2), 180–193.
Friede, T., & Kieser, M. (2006). Sample size recalculation in
internal pilot study designs: A review. Biometrical Journal,
48(4), 537–555.
Friedman, L. M., Furberg, C. D., DeMets, D. L., Reboussin, D. M., &
Granger, C. B. (2015). Fundamentals of clinical trials (5th
ed.). Springer.
Frost, C., Kenward, M. G., & Fox, N. C. (2008). Optimizing the
design of clinical trials where the outcome is a rate: Can estimating a
baseline rate in a run-in period increase efficiency? Statistics in
Medicine, 27(19), 3717–3731.
Greevy, R., Lu, B., Silber, J. H., & Rosenbaum, P. (2004). Optimal
multivariate matching before randomization. Biostatistics,
5(2), 263–275.
Halperin, M., Lan, K. K. G., Ware, J. H., Johnson, N. J., & DeMets,
D. L. (1982). An aid to data monitoring in long-term clinical trials.
Controlled Clinical Trials, 3(4), 311–323.
Harrall, K. K., Muller, K. E., Starling, A. P., Adgate, J. L., Dabelea,
D., & Magzamen, S. (2023). Power and sample size analysis for
longitudinal mixed models of health in populations exposed to
environmental contaminants: A tutorial. BMC Medical Research
Methodology, 23.
Hayes, R. J., & Moulton, L. H. (2017). Cluster randomised
trials (2nd ed.). Chapman; Hall/CRC.
Hendrickson, R. C., Thomas, R. G., Schork, N. J., & Raskind, M. A.
(2020). Optimizing aggregated N-of-1 trial
designs for predictive biomarker validation: Statistical methods and
theoretical findings. Frontiers in Digital Health, 2.
Hilgers, R.-D., Manolov, M., Heussen, N., & Rosenberger, W. F.
(2020). Design and analysis of stratified clinical trials in the
presence of bias. Statistical Methods in Medical Research,
29(6), 1715–1727.
Hochberg, Y., & Tamhane, A. C. (1987). Multiple comparison
procedures. Wiley.
Huque, M. H., Carlin, J. B., Simpson, J. A., & Lee, K. J. (2018). A
comparison of multiple imputation methods for missing data in
longitudinal studies. BMC Medical Research Methodology,
18.
Hussey, M. A., & Hughes, J. P. (2007). Design and analysis of
stepped wedge cluster randomized trials. Contemporary Clinical
Trials, 28(2), 182–191.
Iddi, S., & Donohue, M. C. (2022). Power and sample size for
longitudinal models in R: The
longpower
package and Shiny app. The R Journal,
14(1), 264–282.
International Council for Harmonisation. (2019). ICH
E9(R1) addendum on estimands and sensitivity analysis in
clinical trials. ICH Harmonised Guideline.
Jennison, C., & Turnbull, B. W. (2000). Group sequential methods
with applications to clinical trials. Chapman; Hall/CRC.
Kahan, B. C., Jairath, V., Doré, C. J., & Morris, T. P. (2014). The
risks and rewards of covariate adjustment in randomized trials: An
assessment of 12 outcomes from 8 studies. Trials, 15,
139.
Kahan, B. C., & Morris, T. P. (2012). Improper analysis of trials
randomised using stratified blocks or minimisation. Statistics in
Medicine, 31(4), 328–340.
Kim, J., Troxel, A. B., Halpern, S. D., Volpp, K. G., Kahan, B. C.,
Morris, T. P., & Harhay, M. O. (2020). Analysis of multicenter
clinical trials with very low event rates. Trials, 21.
Kravitz, R. L., Duan, N., & Braslow, J. (2004). Evidence-based
medicine, heterogeneity of treatment effects, and the trouble with
averages. Milbank Quarterly, 82(4), 661–687.
Lachin, J. M. (1981). Introduction to sample size determination and
power analysis for clinical trials. Controlled Clinical Trials,
2(2), 93–113.
Lachin, J. M. (1988). Properties of simple randomization in clinical
trials. Controlled Clinical Trials, 9(4), 312–326.
Laird, N. M., & Wang, F. (1990). Estimating rates of change in
randomized clinical trials. Controlled Clinical Trials,
11(6), 405–419.
Lan, K. K. G., & DeMets, D. L. (1983). Discrete sequential
boundaries for clinical trials. Biometrika, 70(3),
659–663.
Lan, K. K. G., Simon, R., & Halperin, M. (1982). Stochastically
curtailed tests in long-term clinical trials. Communications in
Statistics: Sequential Analysis, 1(3), 207–219.
Lin, W. (2013). Agnostic notes on regression adjustments to experimental
data: Reexamining freedman’s critique. Annals of Applied
Statistics, 7(1), 295–318.
Lipkovich, I., Ratitch, B., & Mallinckrodt, C. H. (2020). Causal
inference and estimands in clinical trials. Statistics in
Biopharmaceutical Research, 12(1), 54–67.
Little, R. J. A., & Rubin, D. B. (2019). Statistical analysis
with missing data (3rd ed.). Wiley.
Liu, S., & Yuan, Y. (2015). Bayesian optimal interval designs for
phase I clinical trials. Journal of the Royal
Statistical Society: Series C, 64(3), 507–523.
Localio, A. R., Berlin, J. A., Ten Have, T. R., & Kimmel, S. E.
(2001). Adjustments for center in multicenter studies: An overview.
Annals of Internal Medicine, 135(2), 112–123.
Lydersen, S., & Laake, P. (2003). Power comparison of two-sided
exact tests for association in 2 × 2
contingency tables using standard, mid-p and randomized test versions.
Statistics in Medicine, 22(24), 3859–3871.
Mallinckrodt, C. H., Lane, P. W., Schnell, D., Peng, Y., & Mancuso,
J. P. (2008). Recommendations for the primary analysis of continuous
endpoints in longitudinal clinical trials. Drug Information
Journal, 42(4), 303–319.
Mehrotra, D. V., Chan, I. S. F., & Berger, R. L. (2003). A
cautionary note on exact unconditional inference for a difference
between two independent binomial proportions. Biometrics,
59(2), 441–450.
Mehta, C. R., & Patel, N. R. (1983). A network algorithm for
performing fisher’s exact test in r × c contingency tables.
Journal of the American Statistical Association,
78(382), 427–434.
Mehta, C. R., & Pocock, S. J. (2011). Adaptive increase in sample
size when interim results are promising: A practical guide with
examples. Statistics in Medicine, 30(28), 3267–3284.
Molenberghs, G., & Kenward, M. G. (2007). Missing data in
clinical studies. Wiley.
Moore, K. L., & Laan, M. J. van der. (2009). Covariate adjustment in
randomized trials with binary outcomes: Targeted maximum likelihood
estimation. Statistics in Medicine, 28(1), 39–64.
Nakagawa, S., & Schielzeth, H. (2013). A general and simple method
for obtaining R2
from generalized linear mixed-effects models. Methods in Ecology and
Evolution, 4(2), 133–142.
National Research Council. (2010). The prevention and treatment of
missing data in clinical trials. National Academies Press.
O’Brien, P. C., & Fleming, T. R. (1979). A multiple testing
procedure for clinical trials. Biometrics, 35(3),
549–556.
O’Quigley, J., Pepe, M., & Fisher, L. (1990). Continual reassessment
method: A practical design for phase 1 clinical trials in cancer.
Biometrics, 46(1), 33–48.
Ouwens, M. J. N. M., Tan, F. E. S., & Berger, M. P. F. (2002).
Maximin D-optimal designs for longitudinal mixed effects
models. Biometrics, 58(4), 735–741.
Overall, J. E., Tonidandel, S., & Starbuck, R. R. (2006).
Rule-of-thumb adjustment of sample sizes to accommodate dropouts in a
two-stage analysis of repeated measurements. International Journal
of Methods in Psychiatric Research, 15(1), 1–11.
Pablos-Mendez, A., Barr, R. G., & Shea, S. (1998). The use of run-in
periods in randomized trials. JAMA, 279(3), 222–225.
Piantadosi, S. (2017). Clinical trials: A methodologic
perspective (3rd ed.). Wiley.
Pocock, S. J. (1976). The combination of randomized and historical
controls in clinical trials. Journal of Chronic Diseases,
29(3), 175–188.
Pocock, S. J. (1977). Group sequential methods in the design and
analysis of clinical trials. Biometrika, 64(2),
191–199.
Pocock, S. J., Assmann, S. E., Enos, L. E., & Kasten, L. E. (2002).
Subgroup analysis, covariate adjustment and baseline comparisons in
clinical trial reporting. Statistics in Medicine,
21(19), 2917–2930.
Pocock, S. J., & Simon, R. (1975). Sequential treatment assignment
with balancing for prognostic factors in the controlled clinical trial.
Biometrics, 31(1), 103–115.
Proschan, M. A. (2005). Two-stage sample size re-estimation based on a
nuisance parameter: A review. Journal of Biopharmaceutical
Statistics, 15(4), 559–574.
Raab, G. M., Day, S., & Sales, J. (2000). How to select covariates
to include in the analysis of a clinical trial. Controlled Clinical
Trials, 21(4), 330–342.
Rosenberger, W. F., & Lachin, J. M. (2016). Randomization in
clinical trials: Theory and practice (2nd ed.). Wiley.
Schmid, C. H., & Staudenmayer, J. (2021). Bayesian models for N-of-1 trials. Harvard Data Science
Review.
Schork, N. J. (2015). Personalized medicine: Time for one-person trials.
Nature, 520(7549), 609–611.
Schulz, K. F., Altman, D. G., & Moher, D. (2010).
CONSORT 2010 statement: Updated guidelines for reporting
parallel group randomised trials. BMJ, 340, c332.
Senn, S. (1994). Testing for baseline balance in clinical trials.
Statistics in Medicine, 13(17), 1715–1726.
Senn, S. (2002). Cross-over trials in clinical research (2nd
ed.). Wiley.
Senn, S. (2007). Statistical issues in drug development (2nd
ed.). Wiley.
Senn, S. (2019). Sample size considerations for n-of-1 trials. Statistical
Methods in Medical Research, 28(2), 372–383.
Senn, S., & Julious, S. (2024). The analysis of continuous data from
n-of-1 trials using paired
cycles: A simple tutorial. Trials, 25.
Shan, G., Li, Y., Lu, X., Zhang, Y., & Wu, S. S. (2024). Comparison
of Pocock and Simon’s covariate-adaptive
randomization procedures in clinical trials. BMC Medical Research
Methodology, 24.
Shao, J., Yu, X., & Zhong, B. (2010). A theory for testing
hypotheses under covariate-adaptive randomization. Biometrika,
97(2), 347–360.
Simon, R. (1989). Optimal two-stage designs for phase II
clinical trials. Controlled Clinical Trials, 10(1),
1–10.
Stekhoven, D. J., & Bühlmann, P. (2012). MissForest:
Non-parametric missing value imputation for mixed-type data.
Bioinformatics, 28(1), 112–118.
Suissa, S., & Shuster, J. J. (1985). Exact unconditional sample
sizes for the 2 × 2 binomial trial.
Journal of the Royal Statistical Society: Series A,
148(4), 317–327.
Sverdlov, O., Ryeznik, Y., Anisimov, V., Kuznetsova, O. M., Knight, R.,
Carter, K., Drescher, S., & Zhao, W. (2024). Selecting a
randomization method for a multi-center clinical trial with stochastic
recruitment considerations. BMC Medical Research Methodology.
Taves, D. R. (2010). The use of minimization in clinical trials.
Contemporary Clinical Trials, 31(2), 180–184.
Tsiatis, A. A., Davidian, M., Zhang, M., & Lu, X. (2008). Covariate
adjustment for two-sample treatment comparisons in randomized clinical
trials: A principled yet flexible approach. Statistics in
Medicine, 27(23), 4658–4677.
US Food and Drug Administration. (2019). Adaptive designs for
clinical trials of drugs and biologics: Guidance for industry. FDA.
US Food and Drug Administration. (2021). Adjusting for covariates in
randomized clinical trials for drugs and biological products: Guidance
for industry. FDA.
Van Lancker, K., Bretz, F., & Dukes, O. (2024). Covariate adjustment
in randomized controlled trials: General concepts and practical
considerations. Clinical Trials.
Vohra, S., Shamseer, L., Sampson, M., et al.
(2015). CONSORT extension for reporting N-of-1 trials (CENT) 2015 statement.
BMJ, 350, h1738.
Wald, A. (1945). Sequential tests of statistical hypotheses. Annals
of Mathematical Statistics, 16(2), 117–186.
Wang, B., Ogburn, E. L., & Rosenblum, M. (2019). Analysis of
covariance in randomized trials: More precision and valid confidence
intervals, without model assumptions. Biometrics,
75(4), 1391–1400.
Wang, Y., Rosenberger, W. F., & Uschner, D. (2020). Randomization
tests for multiarmed randomized clinical trials. Statistics in
Medicine.
Whitehead, J. (1997). The design and analysis of sequential clinical
trials (2nd ed.). Wiley.
Winkens, B., Schouten, H. J. A., Breukelen, G. J. P. van, & Berger,
M. P. F. (2005). Optimal time-points in clinical trials with linearly
divergent treatment effects. Statistics in Medicine,
24(24), 3743–3756.
Winkens, B., Schouten, H. J. A., Breukelen, G. J. P. van, & Berger,
M. P. F. (2006). Optimal number of repeated measures and group sizes in
clinical trials with linearly divergent treatment effects.
Contemporary Clinical Trials, 27(1), 57–69.
Ye, T., Shao, J., Yi, Y., & Zhao, Q. (2023). Toward better practice
of covariate adjustment in analyzing randomized clinical trials.
Journal of the American Statistical Association.
Yusuf, S., Collins, R., & Peto, R. (1991). Factorial designs in
clinical trials. Statistics in Medicine, 10(6),
867–878.
Zhao, Y., & Edland, S. D. (2022). Power formulas for mixed effects
models with random slope and intercept comparing rate of change across
groups. Biometrical Journal.
Zucker, D. R., Ruthazer, R., & Schmid, C. H. (2010). Individual
(N-of-1) trials can be combined to give
population comparative treatment effect estimates: Methodologic
considerations. Journal of Clinical Epidemiology,
63(12), 1312–1323.