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Items: 1 to 20 of 44

1.

Multivariable fractional polynomial interaction to investigate continuous effect modifiers in a meta-analysis on higher versus lower PEEP for patients with ARDS.

Kasenda B, Sauerbrei W, Royston P, Mercat A, Slutsky AS, Cook D, Guyatt GH, Brochard L, Richard JC, Stewart TE, Meade M, Briel M.

BMJ Open. 2016 Sep 8;6(9):e011148. doi: 10.1136/bmjopen-2016-011148.

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Discrimination-based sample size calculations for multivariable prognostic models for time-to-event data.

Jinks RC, Royston P, Parmar MK.

BMC Med Res Methodol. 2015 Oct 12;15:82. doi: 10.1186/s12874-015-0078-y.

4.

The extension of total gain (TG) statistic in survival models: properties and applications.

Choodari-Oskooei B, Royston P, Parmar MK.

BMC Med Res Methodol. 2015 Jul 1;15:50. doi: 10.1186/s12874-015-0042-x.

5.

Combining fractional polynomial model building with multiple imputation.

Morris TP, White IR, Carpenter JR, Stanworth SJ, Royston P.

Stat Med. 2015 Nov 10;34(25):3298-317. doi: 10.1002/sim.6553.

6.

Prognostic survival model for people diagnosed with invasive cutaneous melanoma.

Baade PD, Royston P, Youl PH, Weinstock MA, Geller A, Aitken JF.

BMC Cancer. 2015 Jan 31;15:27. doi: 10.1186/s12885-015-1024-4.

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An approach to trial design and analysis in the era of non-proportional hazards of the treatment effect.

Royston P, Parmar MK.

Trials. 2014 Aug 7;15:314. doi: 10.1186/1745-6215-15-314.

9.

Correcting for optimistic prediction in small data sets.

Smith GC, Seaman SR, Wood AM, Royston P, White IR.

Am J Epidemiol. 2014 Aug 1;180(3):318-24. doi: 10.1093/aje/kwu140.

10.

Tuning multiple imputation by predictive mean matching and local residual draws.

Morris TP, White IR, Royston P.

BMC Med Res Methodol. 2014 Jun 5;14:75. doi: 10.1186/1471-2288-14-75.

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Multiple imputation for an incomplete covariate that is a ratio.

Morris TP, White IR, Royston P, Seaman SR, Wood AM.

Stat Med. 2014 Jan 15;33(1):88-104. doi: 10.1002/sim.5935.

14.

External validation of a Cox prognostic model: principles and methods.

Royston P, Altman DG.

BMC Med Res Methodol. 2013 Mar 6;13:33. doi: 10.1186/1471-2288-13-33.

15.

Impact of lack-of-benefit stopping rules on treatment effect estimates of two-arm multi-stage (TAMS) trials with time to event outcome.

Choodari-Oskooei B, Parmar MK, Royston P, Bowden J.

Trials. 2013 Jan 23;14:23. doi: 10.1186/1745-6215-14-23.

16.

Prognostic model for survival in patients with metastatic renal cell carcinoma: results from the international kidney cancer working group.

Manola J, Royston P, Elson P, McCormack JB, Mazumdar M, NĂ©grier S, Escudier B, Eisen T, Dutcher J, Atkins M, Heng DY, Choueiri TK, Motzer R, Bukowski R; International Kidney Cancer Working Group..

Clin Cancer Res. 2011 Aug 15;17(16):5443-50. doi: 10.1158/1078-0432.CCR-11-0553.

17.

Designs for clinical trials with time-to-event outcomes based on stopping guidelines for lack of benefit.

Royston P, Barthel FM, Parmar MK, Choodari-Oskooei B, Isham V.

Trials. 2011 Mar 18;12:81. doi: 10.1186/1745-6215-12-81.

18.

Avoiding bias due to perfect prediction in multiple imputation of incomplete categorical variables.

White IR, Daniel R, Royston P.

Comput Stat Data Anal. 2010 Oct 1;54(10):2267-2275.

19.

Reporting performance of prognostic models in cancer: a review.

Mallett S, Royston P, Waters R, Dutton S, Altman DG.

BMC Med. 2010 Mar 30;8:21. doi: 10.1186/1741-7015-8-21. Review.

20.

Reporting methods in studies developing prognostic models in cancer: a review.

Mallett S, Royston P, Dutton S, Waters R, Altman DG.

BMC Med. 2010 Mar 30;8:20. doi: 10.1186/1741-7015-8-20. Review.

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