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Items: 1 to 20 of 89

1.

Power and sample size determination for the group comparison of patient-reported outcomes with Rasch family models.

Blanchin M, Hardouin JB, Guillemin F, Falissard B, Sébille V.

PLoS One. 2013;8(2):e57279. doi: 10.1371/journal.pone.0057279. Epub 2013 Feb 28.

2.

Towards power and sample size calculations for the comparison of two groups of patients with item response theory models.

Hardouin JB, Amri S, Feddag ML, Sébille V.

Stat Med. 2012 May 20;31(11-12):1277-90. doi: 10.1002/sim.4387. Epub 2011 Nov 8.

PMID:
22069169
3.

Power and sample size determination for the group comparison of patient-reported outcomes using the Rasch model: impact of a misspecification of the parameters.

Blanchin M, Guilleux A, Perrot B, Bonnaud-Antignac A, Hardouin JB, Sébille V.

BMC Med Res Methodol. 2015 Mar 15;15:21. doi: 10.1186/s12874-015-0011-4.

4.

Power and sample size determination in the Rasch model: evaluation of the robustness of a numerical method to non-normality of the latent trait.

Guilleux A, Blanchin M, Hardouin JB, Sébille V.

PLoS One. 2014 Jan 10;9(1):e83652. doi: 10.1371/journal.pone.0083652. eCollection 2014.

5.

A simple ratio-based approach for power and sample size determination for 2-group comparison using Rasch models.

Sébille V, Blanchin M, Guillemin F, Falissard B, Hardouin JB.

BMC Med Res Methodol. 2014 Jul 5;14:87. doi: 10.1186/1471-2288-14-87.

6.

The Cramér-Rao Bounds and Sensor Selection for Nonlinear Systems with Uncertain Observations.

Wang Z, Shen X, Wang P, Zhu Y.

Sensors (Basel). 2018 Apr 5;18(4). pii: E1103. doi: 10.3390/s18041103.

7.

Biases and power for groups comparison on subjective health measurements.

Hamel JF, Hardouin JB, Le Neel T, Kubis G, Roquelaure Y, Sébille V.

PLoS One. 2012;7(10):e44695. doi: 10.1371/journal.pone.0044695. Epub 2012 Oct 24.

8.

Comparison of CTT and Rasch-based approaches for the analysis of longitudinal Patient Reported Outcomes.

Blanchin M, Hardouin JB, Le Neel T, Kubis G, Blanchard C, Mirallié E, Sébille V.

Stat Med. 2011 Apr 15;30(8):825-38. doi: 10.1002/sim.4153. Epub 2010 Dec 28.

PMID:
21432877
9.

What are the appropriate methods for analyzing patient-reported outcomes in randomized trials when data are missing?

Hamel JF, Sebille V, Le Neel T, Kubis G, Boyer FC, Hardouin JB.

Stat Methods Med Res. 2017 Dec;26(6):2897-2908. doi: 10.1177/0962280215615158. Epub 2015 Nov 6.

PMID:
26546257
10.

Power and Sample Size Calculations in Clinical Trials with Patient-Reported Outcomes under Equal and Unequal Group Sizes Based on Graded Response Model: A Simulation Study.

Doostfatemeh M, Taghi Ayatollah SM, Jafari P.

Value Health. 2016 Jul-Aug;19(5):639-47. doi: 10.1016/j.jval.2016.03.1857. Epub 2016 Jul 29.

11.

Power and sample size determination for group comparison of patient-reported outcomes using polytomous Rasch models.

Hardouin JB, Blanchin M, Feddag ML, Le Néel T, Perrot B, Sébille V.

Stat Med. 2015 Jul 20;34(16):2444-55. doi: 10.1002/sim.6478. Epub 2015 Mar 18.

PMID:
25787270
12.

Power analysis in randomized clinical trials based on item response theory.

Holman R, Glas CA, de Haan RJ.

Control Clin Trials. 2003 Aug;24(4):390-410.

PMID:
12865034
13.

Power analysis on the time effect for the longitudinal Rasch model.

Feddag ML, Blanchin M, Hardouin JB, Sebille V.

J Appl Meas. 2014;15(3):292-301.

PMID:
24992252
14.

Overview of classical test theory and item response theory for the quantitative assessment of items in developing patient-reported outcomes measures.

Cappelleri JC, Jason Lundy J, Hays RD.

Clin Ther. 2014 May;36(5):648-62. doi: 10.1016/j.clinthera.2014.04.006. Epub 2014 May 5. Review.

15.

Assessment of score- and Rasch-based methods for group comparison of longitudinal patient-reported outcomes with intermittent missing data (informative and non-informative).

de Bock É, Hardouin JB, Blanchin M, Le Neel T, Kubis G, Sébille V.

Qual Life Res. 2015 Jan;24(1):19-29. doi: 10.1007/s11136-014-0648-1. Epub 2014 Feb 23.

PMID:
24563110
16.

Selection of optimal AR spectral estimation method for EEG signals using Cramer-Rao bound.

Subasi A.

Comput Biol Med. 2007 Feb;37(2):183-94. Epub 2006 Feb 14.

PMID:
16476421
17.

Methodological issues regarding power of classical test theory (CTT) and item response theory (IRT)-based approaches for the comparison of patient-reported outcomes in two groups of patients--a simulation study.

Sébille V, Hardouin JB, Le Néel T, Kubis G, Boyer F, Guillemin F, Falissard B.

BMC Med Res Methodol. 2010 Mar 25;10:24. doi: 10.1186/1471-2288-10-24.

18.

Why item response theory should be used for longitudinal questionnaire data analysis in medical research.

Gorter R, Fox JP, Twisk JW.

BMC Med Res Methodol. 2015 Jul 30;15:55. doi: 10.1186/s12874-015-0050-x.

19.

Subgroup analyses in randomised controlled trials: quantifying the risks of false-positives and false-negatives.

Brookes ST, Whitley E, Peters TJ, Mulheran PA, Egger M, Davey Smith G.

Health Technol Assess. 2001;5(33):1-56. Review.

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