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Individual Participant Data (IPD) Meta-analyses of Diagnostic and Prognostic Modeling Studies: Guidance on Their Use


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Vyšlo v časopise: Individual Participant Data (IPD) Meta-analyses of Diagnostic and Prognostic Modeling Studies: Guidance on Their Use. PLoS Med 12(10): e32767. doi:10.1371/journal.pmed.1001886
Kategorie: Guidelines and Guidance
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pmed.1001886

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Zdroje

1. Moons KGM, Royston P, Vergouwe Y, Grobbee DE, Altman DG (2009) Prognosis and prognostic research: what, why, and how? British Medical Journal 338: b375. doi: 10.1136/bmj.b375 19237405

2. Steyerberg EW, Moons KGM, van der Windt DA, Hayden JA, Perel P, et al. (2013) Prognosis research strategy (PROGRESS) 3: Prognostic model research. PLoS Medicine 10: e1001381. doi: 10.1371/journal.pmed.1001381 23393430

3. Collins GS, Reitsma JB, Altman DG, Moons KGM (2015) Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. Annals of Internal Medicine 162: 55–63. doi: 10.7326/M14-0697 25560714

4. Moons KGM, Kengne AP, Grobbee DE, Royston P, Vergouwe Y, et al. (2012) Risk prediction models: II. External validation, model updating, and impact assessment. Heart 98: 691–698. doi: 10.1136/heartjnl-2011-301247 22397946

5. Altman DG, Vergouwe Y, Royston P, Moons KGM (2009) Prognosis and prognostic research: validating a prognostic model. British Medical Journal 338: b605. doi: 10.1136/bmj.b605 19477892

6. Steyerberg EW (2009) Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating. Statistics for Biology and Health. New York: Springer.

7. Harrell FE Jr (2001) Regression Modeling Strategies with applications to Linear Models, Logistic Regression and Survival Analysis. Springer Series in Statistics. New York: Springer.

8. Collins GS, Mallett S, Omar O, Yu LM (2011) Developing risk prediction models for type 2 diabetes: a systematic review of methodology and reporting. BMC Medicine 9: 103. doi: 10.1186/1741-7015-9-103 21902820

9. Altman DG (2009) Prognostic models: a methodological framework and review of models for breast cancer. Cancer Investigation 27: 235–243. doi: 10.1080/07357900802572110 19291527

10. Perel P, Edwards P, Wentz R, Roberts I (2006) Systematic review of prognostic models in traumatic brain injury. BMC Medical Informatics and Decision Making 6: 38. 17105661

11. Debray TPA, Moons KGM, Ahmed I, Koffijberg H, Riley RD (2013) A framework for developing, implementing, and evaluating clinical prediction models in an individual participant data meta-analysis. Statistics in Medicine 32: 3158–3180. doi: 10.1002/sim.5732 23307585

12. Cai T, Gerds TA, Zheng Y, Chen J (2011) Robust prediction of t-year survival with data from multiple studies. Biometrics 67: 436–444. doi: 10.1111/j.1541-0420.2010.01462.x 20670303

13. Royston P, Parmar MKB, Sylvester R (2004) Construction and validation of a prognostic model across several studies, with an application in superficial bladder cancer. Statistics in Medicine 23: 907–926. 15027080

14. Riley RD, Simmonds MC, Look MP (2007) Evidence synthesis combining individual patient data and aggregate data: a systematic review identified current practice and possible methods. Journal of Clinical Epidemiology 60: 431–439. 17419953

15. Ahmed I, Debray TPA, Moons KGM, Riley RD (2014) Developing and validating risk prediction models in an individual participant data meta-analysis. BMC Medical Research Methodology 14: 3. doi: 10.1186/1471-2288-14-3 24397587

16. Tierney J, Vale C, Riley R, Tudur Smith C, Stewart L, et al. (2015) Individual participant data (IPD) meta-analyses of randomised controlled trials: Guidance on their use. PLoS Medicine 12: e1001855. doi: 10.1371/journal.pmed.1001855 26196287

17. Geersing GJ, Zuithoff NPA, Kearon C, Anderson DR, Ten Cate-Hoek AJ, et al. (2014) Exclusion of deep vein thrombosis using the wells-rule in clinically important subgroups: Individual patient data meta-analysis. British Medical Journal 348: g1340. doi: 10.1136/bmj.g1340 24615063

18. Majed B, Tafflet M, Kee F, Haas B, Ferrieres J, et al. (2013) External validation of the 2008 Framingham cardiovascular risk equation for CHD and stroke events in a European population of middle-aged men. the PRIME study. Preventive Medicine 57: 49–54. doi: 10.1016/j.ypmed.2013.04.003 23603213

19. Den Ruijter HM, Peters SAE, Anderson TJ, Britton AR, Dekker JM, et al. (2012) Common carotid intima-media thickness measurements in cardiovascular risk prediction: a meta-analysis. The Journal of the American Medical Association 308: 796–803. doi: 10.1001/jama.2012.9630 22910757

20. Kengne AP, Beulens JWJ, Peelen LM, Moons KGM, van der Schouw YT, et al. (2014) Non-invasive risk scores for prediction of type 2 diabetes (EPIC-InterAct): a validation of existing models. The Lancet Diabetes & Endocrinology 2: 19–29. doi: 10.1016/S2213-8587(13)70103-7 24622666

21. Debray TPA, Koffijberg H, Nieboer D, Vergouwe Y, Steyerberg EW, et al. (2014) Meta-analysis and aggregation of multiple published prediction models. Statistics in Medicine 33: 2341–2362. doi: 10.1002/sim.6080 24752993

22. Greving JP, Wermer MJH, Brown RDJ, Morita A, Juvela S, et al. (2014) Development of the PHASES score for prediction of risk of rupture of intracranial aneurysms: a pooled analysis of six prospective cohort studies. Lancet Neurology 13: 59–66. doi: 10.1016/S1474-4422(13)70263-1 24290159

23. Peat G, Riley RD, Croft P, Morley KI, Kyzas PA, et al. (2014) Improving the transparency of prognosis research: the role of reporting, data sharing, registration, and protocols. PLoS Medicine 11: e1001671. doi: 10.1371/journal.pmed.1001671 25003600

24. Geersing GJ, Bouwmeester W, Zuithoff P, Spijker R, Leeflang M, et al. (2012) Search filters for finding prognostic and diagnostic prediction studies in medline to enhance systematic reviews. PLoS ONE 7: e32844. doi: 10.1371/journal.pone.0032844 22393453

25. Wilczynski NL, Haynes RB, the Hedges Team (2004) Developing optimal search strategies for detecting clinically sound prognostic studies in MEDLINE: an analytic survey. BMC Medical Research Methodology 2: 23. doi: 10.1186/1741-7015-2-23 15189561

26. Ingui BJ, Rogers MA (2001) Searching for clinical prediction rules in MEDLINE. Journal of the American Medical Informatics Association 8: 391–397. doi: 10.1136/jamia.2001.0080391 11418546

27. Debray TPA, Moons KGM, Abo-Zaid GMA, Koffijberg H, Riley RD (2013) Individual participant data meta-analysis for a binary outcome: one-stage or two-stage? PLoS ONE 8: e60650. doi: 10.1371/journal.pone.0060650 23585842

28. Blettner M, Sauerbrei W, Schlehofer B, Scheuchenpflug T, Friedenreich C (1999) Traditional reviews, meta-analyses and pooled analyses in epidemiology. International Journal of Epidemiology 28: 1–9. 10195657

29. Tudur Smith C, Dwan K, Altman DG, Clarke M, Riley R, et al. (2014) Sharing individual participant data from clinical trials: an opinion survey regarding the establishment of a central repository. PLoS ONE 9: e97886. doi: 10.1371/journal.pone.0097886 24874700

30. Clarke MJ, Stewart LA (1997) Meta-analyses using individual patient data. Journal of Evaluation in Clinical Practice 3: 207–212. 9406108

31. Riboli E, Hunt KJ, Slimani N, Ferrari P, Norat T, et al. (2002) European prospective investigation into cancer and nutrition (EPIC): study populations and data collection. Public Health Nutrition 5: 1113–1124. doi: 10.1079/PHN2002394 12639222

32. The Emerging Risk Factors Collaboration (2007) The emerging risk factors collaboration: analysis of individual data on lipid, inflammatory and other markers in over 1.1 million participants in 104 prospective studies of cardiovascular diseases. European Journal of Epidemiology 22: 839–869. 17876711

33. Debray TPA, Koffijberg H, Lu D, Vergouwe Y, Steyerberg EW, et al. (2012) Incorporating published univariable associations in diagnostic and prognostic modeling. BMC Medical Research Methodology 12: 121. doi: 10.1186/1471-2288-12-121 22883206

34. Debray TPA, Koffijberg H, Vergouwe Y, Moons KG, Steyerberg EW (2012) Aggregating published prediction models with individual participant data: a comparison of different approaches. Statistics in Medicine 31: 2697–2712. doi: 10.1002/sim.5412 22733546

35. Moons KGM, de Groot JAH, Bouwmeester W, Vergouwe Y, Mallett S, et al. (2014) Critical appraisal and data extraction for systematic reviews of clinical prediction modelling studies: The CHARMS checklist. PLoS Medicine 11: e1001744. doi: 10.1371/journal.pmed.1001744 25314315

36. Steyerberg EW, Eijkemans MJ, Van Houwelingen JC, Lee KL, Habbema JD (2000) Prognostic models based on literature and individual patient data in logistic regression analysis. Statistics in Medicine 19: 141–160. doi: 10.1002/(SICI)1097-0258(20000130)19:2<141::AID-SIM334>3.0.CO;2-O 10641021

37. Jolani S, Debray TPA, Koffijberg H, van Buuren S, Moons KGM (2015) Imputation of systematically missing predictors in an individual participant data meta-analysis: a generalized approach using MICE. Statistics in Medicine 34: 1841–1863. doi: 10.1002/sim.6451 25663182

38. Resche-Rigon M, White IR, Bartlett JW, Peters SAE, Thompson SG (2013) Multiple imputation for handling systematically missing confounders in meta-analysis of individual participant data. Statistics in Medicine 32: 4890–4905. doi: 10.1002/sim.5894 23857554

39. The Fibrinogen Studies Collaboration (2009) Systematically missing confounders in individual participant data meta-analysis of observational cohort studies. Statistics in Medicine 28: 1218–1237. doi: 10.1002/sim.3540 19222087

40. Snell K, Hui H, Debray T, Ensor J, Look M, et al. (2015) Multivariate meta-analysis of individual participant data helped externally validate the performance and implementation of a prediction model. Journal of Clinical Epidemiology. E-pub ahead of print. doi: 10.1016/j.jclinepi.2015.05.009

41. Debray TPA, Vergouwe Y, Koffijberg H, Nieboer D, Steyerberg EW, et al. (2015) A new framework to enhance the interpretation of external validation studies of clinical prediction models. Journal of Clinical Epidemiology 68: 279–289. doi: 10.1016/j.jclinepi.2014.06.018 25179855

42. Steyerberg EW, Harrell FEJ (2015) Prediction models need appropriate internal, internal-external, and external validation. Journal of Clinical Epidemiology. E-pub ahead of print. doi: 10.1016/j.jclinepi.2015.04.005

43. Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gøtzsche PC, et al. (2009) The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration. British Medical Journal 339: b2700. doi: 10.1136/bmj.b2700 19622552

44. Stewart LA, Clarke M, Rovers M, Riley RD, Simmonds M, et al. (2015) Preferred reporting items for a systematic review and meta-analysis of individual participant data: The PRISMA-IPD statement. Journal of the American Medical Association 313: 1657–1665. doi: 10.1001/jama.2015.3656 25919529

45. Moons KGM, Altman DG, Reitsma JB, Ioannidis JPA, Macaskill P, et al. (2015) Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): Explanation and elaboration. Annals of Internal Medicine 162: W1–W73. doi: 10.7326/M14-0698 25560730

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