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Items: 1 to 50 of 53

1.

On the aggregation of published prognostic scores for causal inference in observational studies.

Nguyen TL, Collins GS, Pellegrini F, Moons KGM, Debray TPA.

Stat Med. 2020 Feb 5. doi: 10.1002/sim.8489. [Epub ahead of print]

PMID:
32022311
2.

Individual participant data meta-analysis of intervention studies with time-to-event outcomes: A review of the methodology and an applied example.

de Jong VMT, Moons KGM, Riley RD, Tudur Smith C, Marson AG, Eijkemans MJC, Debray TPA.

Res Synth Methods. 2020 Mar;11(2):148-168. doi: 10.1002/jrsm.1384. Epub 2020 Feb 6. Review.

3.

Predicting disability progression in multiple sclerosis: Insights from advanced statistical modeling.

Pellegrini F, Copetti M, Sormani MP, Bovis F, de Moor C, Debray TP, Kieseier BC.

Mult Scler. 2019 Nov 5:1352458519887343. doi: 10.1177/1352458519887343. [Epub ahead of print]

PMID:
31686590
4.

Statistical approaches to identify subgroups in meta-analysis of individual participant data: a simulation study.

Belias M, Rovers MM, Reitsma JB, Debray TPA, IntHout J.

BMC Med Res Methodol. 2019 Sep 2;19(1):183. doi: 10.1186/s12874-019-0817-6.

5.

Assessment of heterogeneity in an individual participant data meta-analysis of prediction models: An overview and illustration.

Steyerberg EW, Nieboer D, Debray TPA, van Houwelingen HC.

Stat Med. 2019 Sep 30;38(22):4290-4309. doi: 10.1002/sim.8296. Epub 2019 Aug 2.

6.

Evidence synthesis in prognosis research.

Debray TPA, de Jong VMT, Moons KGM, Riley RD.

Diagn Progn Res. 2019 Jul 11;3:13. doi: 10.1186/s41512-019-0059-4. eCollection 2019.

7.

Development and validation of a novel prediction model to identify patients in need of specialized trauma care during field triage: design and rationale of the GOAT study.

van der Sluijs R, Debray TPA, Poeze M, Leenen LPH, van Heijl M.

Diagn Progn Res. 2019 Jun 20;3:12. doi: 10.1186/s41512-019-0058-5. eCollection 2019.

8.

Understanding the relation between Zika virus infection during pregnancy and adverse fetal, infant and child outcomes: a protocol for a systematic review and individual participant data meta-analysis of longitudinal studies of pregnant women and their infants and children.

Wilder-Smith A, Wei Y, Araújo TVB, VanKerkhove M, Turchi Martelli CM, Turchi MD, Teixeira M, Tami A, Souza J, Sousa P, Soriano-Arandes A, Soria-Segarra C, Sanchez Clemente N, Rosenberger KD, Reveiz L, Prata-Barbosa A, Pomar L, Pelá Rosado LE, Perez F, Passos SD, Nogueira M, Noel TP, Moura da Silva A, Moreira ME, Morales I, Miranda Montoya MC, Miranda-Filho DB, Maxwell L, Macpherson CNL, Low N, Lan Z, LaBeaud AD, Koopmans M, Kim C, João E, Jaenisch T, Hofer CB, Gustafson P, Gérardin P, Ganz JS, Dias ACF, Elias V, Duarte G, Debray TPA, Cafferata ML, Buekens P, Broutet N, Brickley EB, Brasil P, Brant F, Bethencourt S, Benedetti A, Avelino-Silva VL, Ximenes RAA, Alves da Cunha A, Alger J; Zika Virus Individual Participant Data Consortium.

BMJ Open. 2019 Jun 18;9(6):e026092. doi: 10.1136/bmjopen-2018-026092.

9.

Performance of the Framingham risk models and pooled cohort equations for predicting 10-year risk of cardiovascular disease: a systematic review and meta-analysis.

Damen JA, Pajouheshnia R, Heus P, Moons KGM, Reitsma JB, Scholten RJPM, Hooft L, Debray TPA.

BMC Med. 2019 Jun 13;17(1):109. doi: 10.1186/s12916-019-1340-7.

10.

Empirical evidence of the impact of study characteristics on the performance of prediction models: a meta-epidemiological study.

Damen JAAG, Debray TPA, Pajouheshnia R, Reitsma JB, Scholten RJPM, Moons KGM, Hooft L.

BMJ Open. 2019 Apr 1;9(4):e026160. doi: 10.1136/bmjopen-2018-026160.

11.

A guide to systematic review and meta-analysis of prognostic factor studies.

Riley RD, Moons KGM, Snell KIE, Ensor J, Hooft L, Altman DG, Hayden J, Collins GS, Debray TPA.

BMJ. 2019 Jan 30;364:k4597. doi: 10.1136/bmj.k4597. No abstract available.

PMID:
30700442
12.

The use of prognostic scores for causal inference with general treatment regimes.

Nguyen TL, Debray TPA.

Stat Med. 2019 May 20;38(11):2013-2029. doi: 10.1002/sim.8084. Epub 2019 Jan 16.

13.

Cardiovascular risk prediction models for women in the general population: A systematic review.

Baart SJ, Dam V, Scheres LJJ, Damen JAAG, Spijker R, Schuit E, Debray TPA, Fauser BCJM, Boersma E, Moons KGM, van der Schouw YT; CREW consortium.

PLoS One. 2019 Jan 8;14(1):e0210329. doi: 10.1371/journal.pone.0210329. eCollection 2019.

14.

Erratum.

Kers J, Peters-Sengers H, Heemskerk MBA, Berger SP, Betjes MGH, van Zuilen AD, Hilbrands LB, de Fijter JW, Nurmohamed AS, Christiaans MH, Homan van der Heide JJ, Debray TPA, Bemelman FJ.

Nephrol Dial Transplant. 2018 Oct 29. doi: 10.1093/ndt/gfy353. [Epub ahead of print] No abstract available.

PMID:
30376111
15.

The life expectancy of Stephen Hawking, according to the ENCALS model.

Westeneng HJ, Al-Chalabi A, Hardiman O, Debray TP, van den Berg LH.

Lancet Neurol. 2018 Aug;17(8):662-663. doi: 10.1016/S1474-4422(18)30241-2. Epub 2018 Jul 17. No abstract available.

PMID:
30033055
16.

A framework for meta-analysis of prediction model studies with binary and time-to-event outcomes.

Debray TP, Damen JA, Riley RD, Snell K, Reitsma JB, Hooft L, Collins GS, Moons KG.

Stat Methods Med Res. 2019 Sep;28(9):2768-2786. doi: 10.1177/0962280218785504. Epub 2018 Jul 23.

17.

Prognosis for patients with amyotrophic lateral sclerosis: development and validation of a personalised prediction model.

Westeneng HJ, Debray TPA, Visser AE, van Eijk RPA, Rooney JPK, Calvo A, Martin S, McDermott CJ, Thompson AG, Pinto S, Kobeleva X, Rosenbohm A, Stubendorff B, Sommer H, Middelkoop BM, Dekker AM, van Vugt JJFA, van Rheenen W, Vajda A, Heverin M, Kazoka M, Hollinger H, Gromicho M, Körner S, Ringer TM, Rödiger A, Gunkel A, Shaw CE, Bredenoord AL, van Es MA, Corcia P, Couratier P, Weber M, Grosskreutz J, Ludolph AC, Petri S, de Carvalho M, Van Damme P, Talbot K, Turner MR, Shaw PJ, Al-Chalabi A, Chiò A, Hardiman O, Moons KGM, Veldink JH, van den Berg LH.

Lancet Neurol. 2018 May;17(5):423-433. doi: 10.1016/S1474-4422(18)30089-9. Epub 2018 Mar 26.

18.

Predicition models for delayed graft function: external validation on The Dutch Prospective Renal Transplantation Registry.

Kers J, Peters-Sengers H, Heemskerk MBA, Berger SP, Betjes MGH, van Zuilen AD, Hilbrands LB, de Fijter JW, Nurmohamed AS, Christiaans MH, Homan van der Heide JJ, Debray TPA, Bemelman FJ.

Nephrol Dial Transplant. 2018 Jul 1;33(7):1259-1268. doi: 10.1093/ndt/gfy019.

PMID:
29462353
19.

Exacerbations in Adults with Asthma: A Systematic Review and External Validation of Prediction Models.

Loymans RJB, Debray TPA, Honkoop PJ, Termeer EH, Snoeck-Stroband JB, Schermer TRJ, Assendelft WJJ, Timp M, Chung KF, Sousa AR, Sont JK, Sterk PJ, Reddel HK, Ter Riet G.

J Allergy Clin Immunol Pract. 2018 Nov - Dec;6(6):1942-1952.e15. doi: 10.1016/j.jaip.2018.02.004. Epub 2018 Feb 15.

20.

Validation of an imaging based cardiovascular risk score in a Scottish population.

Kockelkoren R, Jairam PM, Murchison JT, Debray TPA, Mirsadraee S, van der Graaf Y, Jong PA, van Beek EJR.

Eur J Radiol. 2018 Jan;98:143-149. doi: 10.1016/j.ejrad.2017.11.016. Epub 2017 Nov 23.

21.

The development of CHAMP: a checklist for the appraisal of moderators and predictors.

van Hoorn R, Tummers M, Booth A, Gerhardus A, Rehfuess E, Hind D, Bossuyt PM, Welch V, Debray TPA, Underwood M, Cuijpers P, Kraemer H, van der Wilt GJ, Kievit W.

BMC Med Res Methodol. 2017 Dec 21;17(1):173. doi: 10.1186/s12874-017-0451-0.

22.

Detecting small-study effects and funnel plot asymmetry in meta-analysis of survival data: A comparison of new and existing tests.

Debray TPA, Moons KGM, Riley RD.

Res Synth Methods. 2018 Mar;9(1):41-50. doi: 10.1002/jrsm.1266. Epub 2017 Nov 28.

23.

Practical implications of using real-world evidence (RWE) in comparative effectiveness research: learnings from IMI-GetReal.

Makady A, Stegenga H, Ciaglia A, Debray TP, Lees M, Happich M, Ryll B, Abrams K, Thwaites R, Garner S, Jonsson P, Goettsch W.

J Comp Eff Res. 2017 Sep;6(6):485-490. doi: 10.2217/cer-2017-0044. Epub 2017 Aug 31.

24.

Meta-analysis of prediction model performance across multiple studies: Which scale helps ensure between-study normality for the C-statistic and calibration measures?

Snell KI, Ensor J, Debray TP, Moons KG, Riley RD.

Stat Methods Med Res. 2018 Nov;27(11):3505-3522. doi: 10.1177/0962280217705678. Epub 2017 May 8.

25.

Reporting of Bayesian analysis in epidemiologic research should become more transparent.

Rietbergen C, Debray TPA, Klugkist I, Janssen KJM, Moons KGM.

J Clin Epidemiol. 2017 Jun;86:51-58.e2. doi: 10.1016/j.jclinepi.2017.04.008. Epub 2017 Apr 18. Review.

PMID:
28428139
26.

Predictive performance of the CHA2DS2-VASc rule in atrial fibrillation: a systematic review and meta-analysis.

van Doorn S, Debray TPA, Kaasenbrood F, Hoes AW, Rutten FH, Moons KGM, Geersing GJ.

J Thromb Haemost. 2017 Jun;15(6):1065-1077. doi: 10.1111/jth.13690. Epub 2017 May 9. Review.

27.

Erratum to: Methods for evaluating medical tests and biomarkers.

Gopalakrishna G, Langendam M, Scholten R, Bossuyt P, Leeflang M, Noel-Storr A, Thomas J, Marshall I, Wallace B, Whiting P, Davenport C, Leeflang M, GopalaKrishna G, de Salis I, Mallett S, Wolff R, Whiting P, Riley R, Westwood M, Kleinen J, Collins G, Reitsma H, Moons K, Zapf A, Hoyer A, Kramer K, Kuss O, Ensor J, Deeks JJ, Martin EC, Riley RD, Rücker G, Steinhauser S, Schumacher M, Riley R, Ensor J, Snell K, Willis B, Debray T, Moons K, Deeks J, Collins G, di Ruffano LF, Willis B, Davenport C, Mallett S, Taylor-Phillips S, Hyde C, Deeks J, Mallett S, Taylor SA, Batnagar G; STREAMLINE COLON Investigators; STREAMLINE LUNG Investigators; METRIC Investigators, Taylor-Phillips S, Di Ruffano LF, Seedat F, Clarke A, Deeks J, Byron S, Nixon F, Albrow R, Walker T, Deakin C, Hyde C, Zhelev Z, Hunt H, di Ruffano LF, Yang Y, Abel L, Buchanan J, Fanshawe T, Shinkins B, Wynants L, Verbakel J, Van Huffel S, Timmerman D, Van Calster B, Leeflang M, Zwinderman A, Bossuyt P, Oke J, O'Sullivan J, Perera R, Nicholson B, Bromley HL, Roberts TE, Francis A, Petrie D, Mann GB, Malottki K, Smith H, Deeks J, Billingham L, Sitch A, Mallett S, Deeks J, Gerke O, Holm-Vilstrup M, Segtnan EA, Halekoh U, Høilund-Carlsen PF, Francq BG, Deeks J, Sitch A, Dinnes J, Parkes J, Gregory W, Hewison J, Altman D, Rosenberg W, Selby P, Asselineau J, Perez P, Paye A, Bessede E, Proust-Lima C, Naaktgeboren C, de Groot J, Rutjes A, Bossuyt P, Reitsma J, Moons K, Collins G, Ogundimu E, Cook J, Le Manach Y, Altman D, Wynants L, Vergouwe Y, Van Huffel S, Timmerman D, Van Calster B, Pajouheshnia R, Groenwold R, Moons K, Reitsma J, Peelen L, Van Calster B, Nieboer D, Vergouwe Y, De Cock B, Pencina MJ, Steyerberg EW, Cooper J, Taylor-Phillips S, Parsons N, Stinton C, Smith S, Dickens A, Jordan R, Enocson A, Fitzmaurice D, Sitch A, Adab P, Francq BG, Boachie C, Vidmar G, Freeman K, Connock M, Taylor-Phillips S, Court R, Clarke A, de Groot J, Naaktgeboren C, Reitsma H, Moons C, Harris J, Mumford A, Plummer Z, Lee K, Reeves B, Rogers C, Verheyden V, Angelini GD, Murphy GJ, Huddy J, Ni M, Good K, Cooke G, Bossuyt P, Hanna G, Ma J, Altman D, Collins G, Moons KGMC, de Groot JAH, Mallett S, Altman DG, Reitsma JB, Collins GS, Moons KGM, Altman DG, Reitsma JB, Collins GS, Kamarudin AN, Kolamunnage-Dona R, Cox T, Ni M, Huddy J, Borsci S, Hanna G, Pérez T, Pardo MC, Candela-Toha A, Muriel A, Zamora J, Sanghera S, Mohiuddin S, Martin R, Donovan J, Coast J, Seo MK, Cairns J, Mitchell E, Smith A, Wright J, Hall P, Messenger M, Calder N, Wickramasekera N, Vinall-Collier K, Lewington A, Pajouheshnia R, Damen J, Groenwold R, Moons K, Peelen L, Messenger M, Cairns D, Smith A, Hutchinson M, Wright J, Hall P, Calder N, Sturgeon C, Mitchel L, Kift R, Christakoudi S, Rungall M, Mobillo P, Montero R, Tsui TL, Kon SP, Tucker B, Sacks S, Farmer C, Strom T, Chowdhury P, Rebollo-Mesa I, Hernandez-Fuentes M, Damen JAAG, Debray TPA, Heus P, Hooft L, Moons KGM, Pajouheshnia R, Reitsma JB, Scholten RJPM, Damen JAAG, Hooft L, Schuit E, Debray TPA, Collins GS, Tzoulaki I, Lassale CM, Siontis GCM, Chiocchia V, Roberts C, Schlüssel MM, Gerry S, Black JA, Heus P, van der Schouw YT, Peelen LM, Moons KGM, Damen JAAG, Debray TPA, Heus P, Hooft L, Moons KGM, Pajouheshnia R, Reitsma JB, Scholten RJPM, Ma J, Altman D, Collins G, Spence G, McCartney D, van den Bruel A, Lasserson D, Hayward G, Vach W, de Jong A, Burggraaff C, Hoekstra O, Zijlstra J, de Vet H, Hunt H, Hyde C, Graziadio S, Allen J, Johnston L, O'Leary R, Power M, Allen J, Graziadio S, Johnson L, O'Leary R, Power M, Waters R, Simpson J, Johnston L, Allen J, Graziadio S, O'Leary R, Waters R, Power M, Mallett S, Fanshawe TR, Phillips P, Plumb A, Helbren E, Halligan S, Taylor SA, Gale A, Mallett S, Sekula P, Altman DG, Sauerbrei W, Mallett S, Fanshawe TR, Forman JR, Dutton SJ, Takwoingi Y, Hensor EM, Nichols TE, Shinkins B, Yang Y, Abel L, Di Ruffano LF, Fanshawe T, Kempf E, Porcher R, de Beyer J, Moons K, Altman D, Reitsma H, Hopewell S, Sauerbrei W, Collins G, Dennis J, Shields B, Jones A, Henley W, Pearson E, Hattersley A; MASTERMIND consortium, Heus P, Damen JAAG, Pajouheshnia R, Scholten RJPM, Reitsma JB, Collins GS, Altman DG, Moons KGM, Hooft L, Shields B, Dennis J, Jones A, Henley W, Pearson E, Hattersley A; MASTERMIND consortium, Scheibler F, Rummer A, Sturtz S, Großelfinger R, Banister K, Ramsay C, Azuara-Blanco A, Cook J, Boachie C, Burr J, Kumarasamy M, Bourne R, Uchegbu I, Borsci S, Murphy J, Hanna G, Uchegbu I, Carter A, Murphy J, Ni M, Marti J, Eatock J, Uchegbu I, Robotham J, Dudareva M, Gilchrist M, Holmes A, Uchegbu I, Borsci S, Monaghan P, Lord S, StJohn A, Sandberg S, Cobbaert C, Lennartz L, Verhagen-Kamerbeek W, Ebert C, Bossuyt P, Horvath A; Test Evaluation Working Group of the European Federation of Clinical Chemistry and Laboratory Medicine, Jenniskens K, Naaktgeboren C, Reitsma J, Moons K, de Groot J, Hyde C, Peters J, Grigore B, Peters J, Hyde C, Hyde C, Ukoumunne O, Peters J, Zhelev Z, Levis B, Benedetti A, Levis AW, Ioannidis JPA, Shrier I, Cuijpers P, Gilbody S, Kloda LA, McMillan D, Patten SB, Steele RJ, Ziegelstein RC, Bombardier CH, Osório FL, Fann JR, Gjerdingen D, Lamers F, Lotrakul M, Loureiro SR, Löwe B, Shaaban J, Stafford L, van Weert HCPM, Whooley MA, Williams LS, Wittkampf KA, Yeung AS, Thombs BD, Peters J, Cooper C, Buchanan J, Nieto T, Smith C, Tucker O, Dretzke J, Beggs A, Rai N, Davenport C, Bayliss S, Stevens S, Snell K, Mallet S, Deeks J, Sundar S, Hall E, Porta N, Estelles DL, de Bono J; CTC-STOP protocol development group.

Diagn Progn Res. 2017 Mar 30;1:11. doi: 10.1186/s41512-017-0011-4. eCollection 2017.

28.

Combining randomized and non-randomized evidence in network meta-analysis.

Efthimiou O, Mavridis D, Debray TP, Samara M, Belger M, Siontis GC, Leucht S, Salanti G; GetReal Work Package 4.

Stat Med. 2017 Apr 15;36(8):1210-1226. doi: 10.1002/sim.7223. Epub 2017 Jan 12.

PMID:
28083901
29.

A guide to systematic review and meta-analysis of prediction model performance.

Debray TP, Damen JA, Snell KI, Ensor J, Hooft L, Reitsma JB, Riley RD, Moons KG.

BMJ. 2017 Jan 5;356:i6460. doi: 10.1136/bmj.i6460. No abstract available.

PMID:
28057641
30.

A closed testing procedure to select an appropriate method for updating prediction models.

Vergouwe Y, Nieboer D, Oostenbrink R, Debray TPA, Murray GD, Kattan MW, Koffijberg H, Moons KGM, Steyerberg EW.

Stat Med. 2017 Dec 10;36(28):4529-4539. doi: 10.1002/sim.7179. Epub 2016 Nov 28.

PMID:
27891652
31.

GetReal in mathematical modelling: a review of studies predicting drug effectiveness in the real world.

Panayidou K, Gsteiger S, Egger M, Kilcher G, Carreras M, Efthimiou O, Debray TP, Trelle S, Hummel N; GetReal methods review group.

Res Synth Methods. 2016 Sep;7(3):264-77. doi: 10.1002/jrsm.1202. Epub 2016 Aug 16. Review.

32.

An overview of methods for network meta-analysis using individual participant data: when do benefits arise?

Debray TP, Schuit E, Efthimiou O, Reitsma JB, Ioannidis JP, Salanti G, Moons KG; GetReal Workpackage.

Stat Methods Med Res. 2018 May;27(5):1351-1364. doi: 10.1177/0962280216660741. Epub 2016 Aug 11.

PMID:
27487843
33.

External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges.

Riley RD, Ensor J, Snell KI, Debray TP, Altman DG, Moons KG, Collins GS.

BMJ. 2016 Jun 22;353:i3140. doi: 10.1136/bmj.i3140. No abstract available. Erratum in: BMJ. 2019 Jun 25;365:l4379.

34.

Prediction models for cardiovascular disease risk in the general population: systematic review.

Damen JA, Hooft L, Schuit E, Debray TP, Collins GS, Tzoulaki I, Lassale CM, Siontis GC, Chiocchia V, Roberts C, Schlüssel MM, Gerry S, Black JA, Heus P, van der Schouw YT, Peelen LM, Moons KG.

BMJ. 2016 May 16;353:i2416. doi: 10.1136/bmj.i2416. Review.

35.

Explicit inclusion of treatment in prognostic modeling was recommended in observational and randomized settings.

Groenwold RH, Moons KG, Pajouheshnia R, Altman DG, Collins GS, Debray TP, Reitsma JB, Riley RD, Peelen LM.

J Clin Epidemiol. 2016 Oct;78:90-100. doi: 10.1016/j.jclinepi.2016.03.017. Epub 2016 Apr 1.

PMID:
27045189
36.

GetReal in network meta-analysis: a review of the methodology.

Efthimiou O, Debray TP, van Valkenhoef G, Trelle S, Panayidou K, Moons KG, Reitsma JB, Shang A, Salanti G; GetReal Methods Review Group.

Res Synth Methods. 2016 Sep;7(3):236-63. doi: 10.1002/jrsm.1195. Epub 2016 Jan 11. Review.

PMID:
26754852
37.

Isoniazid Prophylactic Therapy for the Prevention of Tuberculosis in HIV Infected Adults: A Systematic Review and Meta-Analysis of Randomized Trials.

Ayele HT, Mourik MS, Debray TP, Bonten MJ.

PLoS One. 2015 Nov 9;10(11):e0142290. doi: 10.1371/journal.pone.0142290. eCollection 2015. Review.

38.

Individual participant data (IPD) meta-analyses of diagnostic and prognostic modeling studies: guidance on their use.

Debray TP, Riley RD, Rovers MM, Reitsma JB, Moons KG; Cochrane IPD Meta-analysis Methods group.

PLoS Med. 2015 Oct 13;12(10):e1001886. doi: 10.1371/journal.pmed.1001886. eCollection 2015 Oct. No abstract available.

39.

Get real in individual participant data (IPD) meta-analysis: a review of the methodology.

Debray TP, Moons KG, van Valkenhoef G, Efthimiou O, Hummel N, Groenwold RH, Reitsma JB; GetReal Methods Review Group.

Res Synth Methods. 2015 Dec;6(4):293-309. doi: 10.1002/jrsm.1160. Epub 2015 Aug 19. Review.

40.

Multivariate meta-analysis of individual participant data helped externally validate the performance and implementation of a prediction model.

Snell KI, Hua H, Debray TP, Ensor J, Look MP, Moons KG, Riley RD.

J Clin Epidemiol. 2016 Jan;69:40-50. doi: 10.1016/j.jclinepi.2015.05.009. Epub 2015 May 16.

41.

Summarising and validating test accuracy results across multiple studies for use in clinical practice.

Riley RD, Ahmed I, Debray TP, Willis BH, Noordzij JP, Higgins JP, Deeks JJ.

Stat Med. 2015 Jun 15;34(13):2081-103. doi: 10.1002/sim.6471. Epub 2015 Mar 20.

42.

Imputation of systematically missing predictors in an individual participant data meta-analysis: a generalized approach using MICE.

Jolani S, Debray TP, Koffijberg H, van Buuren S, Moons KG.

Stat Med. 2015 May 20;34(11):1841-63. doi: 10.1002/sim.6451. Epub 2015 Feb 9.

PMID:
25663182
43.

Qualitative elastography can replace thyroid nodule fine-needle aspiration in patients with soft thyroid nodules. A systematic review and meta-analysis.

Nell S, Kist JW, Debray TP, de Keizer B, van Oostenbrugge TJ, Borel Rinkes IH, Valk GD, Vriens MR.

Eur J Radiol. 2015 Apr;84(4):652-61. doi: 10.1016/j.ejrad.2015.01.003. Epub 2015 Jan 16. Review.

PMID:
25638577
44.

A new framework to enhance the interpretation of external validation studies of clinical prediction models.

Debray TP, Vergouwe Y, Koffijberg H, Nieboer D, Steyerberg EW, Moons KG.

J Clin Epidemiol. 2015 Mar;68(3):279-89. doi: 10.1016/j.jclinepi.2014.06.018. Epub 2014 Aug 30.

45.

Meta-analysis and aggregation of multiple published prediction models.

Debray TP, Koffijberg H, Nieboer D, Vergouwe Y, Steyerberg EW, Moons KG.

Stat Med. 2014 Jun 30;33(14):2341-62. doi: 10.1002/sim.6080. Epub 2014 Jan 14.

PMID:
24752993
46.

Developing and validating risk prediction models in an individual participant data meta-analysis.

Ahmed I, Debray TP, Moons KG, Riley RD.

BMC Med Res Methodol. 2014 Jan 8;14:3. doi: 10.1186/1471-2288-14-3. Review.

47.

Clinical prediction models for bronchopulmonary dysplasia: a systematic review and external validation study.

Onland W, Debray TP, Laughon MM, Miedema M, Cools F, Askie LM, Asselin JM, Calvert SA, Courtney SE, Dani C, Durand DJ, Marlow N, Peacock JL, Pillow JJ, Soll RF, Thome UH, Truffert P, Schreiber MD, Van Reempts P, Vendettuoli V, Vento G, van Kaam AH, Moons KG, Offringa M.

BMC Pediatr. 2013 Dec 17;13:207. doi: 10.1186/1471-2431-13-207. Review.

48.

Individual participant data meta-analyses should not ignore clustering.

Abo-Zaid G, Guo B, Deeks JJ, Debray TP, Steyerberg EW, Moons KG, Riley RD.

J Clin Epidemiol. 2013 Aug;66(8):865-873.e4. doi: 10.1016/j.jclinepi.2012.12.017. Epub 2013 May 4.

49.

Individual participant data meta-analysis for a binary outcome: one-stage or two-stage?

Debray TP, Moons KG, Abo-Zaid GM, Koffijberg H, Riley RD.

PLoS One. 2013 Apr 9;8(4):e60650. doi: 10.1371/journal.pone.0060650. Print 2013.

50.

A framework for developing, implementing, and evaluating clinical prediction models in an individual participant data meta-analysis.

Debray TP, Moons KG, Ahmed I, Koffijberg H, Riley RD.

Stat Med. 2013 Aug 15;32(18):3158-80. doi: 10.1002/sim.5732. Epub 2013 Jan 11.

PMID:
23307585

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