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Relevant bibliographies by topics / Blinded sample size re-estimation

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Journal articles

Academic literature on the topic 'Blinded sample size re-estimation'

Author: Grafiati

Published: 4 June 2021

Last updated: 7 February 2022

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Journal articles on the topic "Blinded sample size re-estimation"

1

Sobel, Marc, and Ibrahim Turkoz. "Bayesian blinded sample size re-estimation." Communications in Statistics - Theory and Methods 47, no. 24 (2017): 5916–33. http://dx.doi.org/10.1080/03610926.2017.1404097.

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2

Lawrence Gould, A. "Issues in blinded sample size re-estimation." Communications in Statistics - Simulation and Computation 26, no. 3 (1997): 1229–39. http://dx.doi.org/10.1080/03610919708813436.

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3

Golkowski, Daniel, Tim Friede, and Meinhard Kieser. "Blinded sample size re-estimation in crossover bioequivalence trials." Pharmaceutical Statistics 13, no. 3 (2014): 157–62. http://dx.doi.org/10.1002/pst.1617.

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4

Lu, Kaifeng. "Distribution of the two-samplet-test statistic following blinded sample size re-estimation." Pharmaceutical Statistics 15, no. 3 (2016): 208–15. http://dx.doi.org/10.1002/pst.1737.

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5

Govindarajulu, Z. "Blinded sample size re-estimation in clinical trials comparing several treatments." Statistics 45, no. 6 (2011): 575–91. http://dx.doi.org/10.1080/02331881003675971.

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6

Posch, Martin, Florian Klinglmueller, Franz König, and Frank Miller. "Estimation after blinded sample size reassessment." Statistical Methods in Medical Research 27, no. 6 (2016): 1830–46. http://dx.doi.org/10.1177/0962280216670424.

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Abstract:

Blinded sample size reassessment is a popular means to control the power in clinical trials if no reliable information on nuisance parameters is available in the planning phase. We investigate how sample size reassessment based on blinded interim data affects the properties of point estimates and confidence intervals for parallel group superiority trials comparing the means of a normal endpoint. We evaluate the properties of two standard reassessment rules that are based on the sample size formula of the z-test, derive the worst case reassessment rule that maximizes the absolute mean bias and obtain an upper bound for the mean bias of the treatment effect estimate.

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7

Stark, Maria, and Antonia Zapf. "Sample size calculation and re-estimation based on the prevalence in a single-arm confirmatory diagnostic accuracy study." Statistical Methods in Medical Research 29, no. 10 (2020): 2958–71. http://dx.doi.org/10.1177/0962280220913588.

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Introduction In a confirmatory diagnostic accuracy study, sensitivity and specificity are considered as co-primary endpoints. For the sample size calculation, the prevalence of the target population must be taken into account to obtain a representative sample. In this context, a general problem arises. With a low or high prevalence, the study may be overpowered in one subpopulation. One further issue is the correct pre-specification of the true prevalence. With an incorrect assumption about the prevalence, an over- or underestimated sample size will result. Methods To obtain the desired power independent of the prevalence, a method for an optimal sample size calculation for the comparison of a diagnostic experimental test with a prespecified minimum sensitivity and specificity is proposed. To face the problem of an incorrectly pre-specified prevalence, a blinded one-time re-estimation design of the sample size based on the prevalence and a blinded repeated re-estimation design of the sample size based on the prevalence are evaluated by a simulation study. Both designs are compared to a fixed design and additionally among each other. Results The type I error rates of both blinded re-estimation designs are not inflated. Their empirical overall power equals the desired theoretical power and both designs offer unbiased estimates of the prevalence. The repeated re-estimation design reveals no advantages concerning the mean squared error of the re-estimated prevalence or sample size compared to the one-time re-estimation design. The appropriate size of the internal pilot study in the one-time re-estimation design is 50% of the initially calculated sample size. Conclusions A one-time re-estimation design of the prevalence based on the optimal sample size calculation is recommended in single-arm diagnostic accuracy studies.

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8

Schneider, S., H. Schmidli, and T. Friede. "Blinded sample size re-estimation for recurrent event data with time trends." Statistics in Medicine 32, no. 30 (2013): 5448–57. http://dx.doi.org/10.1002/sim.5977.

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9

Schneider, Simon, Heinz Schmidli, and Tim Friede. "Robustness of methods for blinded sample size re-estimation with overdispersed count data." Statistics in Medicine 32, no. 21 (2013): 3623–35. http://dx.doi.org/10.1002/sim.5800.

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10

Mütze, Tobias, and Tim Friede. "Blinded sample size re-estimation in three-arm trials with ‘gold standard’ design." Statistics in Medicine 36, no. 23 (2017): 3636–53. http://dx.doi.org/10.1002/sim.7356.

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