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Volumn 353, Issue , 2016, Pages

Correction: External validation of clinical prediction models using big datasets from e-health records or IPD meta-Analysis: Opportunities and challenges (BMJ (Online) (2016) 353 DOI: 10.1136/bmj.i3140);External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: Opportunities and challenges

Author keywords

[No Author keywords available]

Indexed keywords

ACCESS TO INFORMATION; ARTICLE; CALIBRATION; CLINICAL RESEARCH; DATA BASE; ELECTRONIC MEDICAL RECORD; EXTERNAL VALIDATION; IMPACT EVALUATION; INDIVIDUAL PARTICIPANT DATA; METHODOLOGY; MODEL DEVELOPMENT; PRACTICE GUIDELINE; PREDICTION; PRIORITY JOURNAL; PROGNOSIS; REGISTER; STATISTICAL MODEL; VALIDATION STUDY; DECISION SUPPORT SYSTEM; ELECTRONIC HEALTH RECORD; HUMAN; INFORMATION PROCESSING; META ANALYSIS (TOPIC); PROCEDURES; RISK ASSESSMENT;

EID: 84976645528     PISSN: 09598146     EISSN: 17561833     Source Type: Journal    
DOI: 10.1136/bmj.l4379     Document Type: Erratum
Times cited : (343)

References (82)
  • 2
    • 67650045441 scopus 로고    scopus 로고
    • Prognosis and prognostic research: Developing a prognostic model
    • Royston P, Moons KGM, Altman DG, Vergouwe Y. Prognosis and prognostic research: Developing a prognostic model. BMJ 2009; 338: b604. doi: 10.1136/bmj.b604
    • (2009) BMJ , vol.338 , pp. b604
    • Royston, P.1    Kgm, M.2    Altman, D.G.3    Vergouwe, Y.4
  • 3
    • 84874505367 scopus 로고    scopus 로고
    • Prognosis Research Strategy (PROGRESS) 3: Prognostic model research
    • PROGRESS Group
    • Steyerberg EW, Moons KG, van der Windt DA, et al. PROGRESS Group. Prognosis Research Strategy (PROGRESS) 3: prognostic model research. PLoS Med 2013; 10: e1001381. doi: 10.1371/journal.pmed.1001381
    • (2013) PLoS Med , vol.10 , pp. e1001381
    • Steyerberg, E.W.1    Moons, K.G.2    Van Der Windt, D.A.3
  • 5
    • 47149088261 scopus 로고    scopus 로고
    • Predicting cardiovascular risk in England and Wales: Prospective derivation and validation of QRISK2
    • Hippisley-Cox J, Coupland C, Vinogradova Y, et al. Predicting cardiovascular risk in England and Wales: prospective derivation and validation of QRISK2. BMJ 2008; 336: 1475-82. doi: 10.1136/bmj.39609.449676.25
    • (2008) BMJ , vol.336 , pp. 1475-1482
    • Hippisley-Cox, J.1    Coupland, C.2    Vinogradova, Y.3
  • 6
    • 0020031067 scopus 로고
    • A prognostic index in primary breast cancer
    • Haybittle JL, Blamey RW, Elston CW, et al. A prognostic index in primary breast cancer. Br J Cancer 1982; 45: 361-6. doi: 10.1038/bjc.1982.62
    • (1982) Br J Cancer , vol.45 , pp. 361-366
    • Haybittle, J.L.1    Blamey, R.W.2    Elston, C.W.3
  • 8
    • 0034063321 scopus 로고    scopus 로고
    • Derivation of a simple clinical model to categorize patients probability of pulmonary embolism: Increasing the models utility with the SimpliRED D-dimer
    • Wells PS, Anderson DR, Rodger M, et al. Derivation of a simple clinical model to categorize patients probability of pulmonary embolism: increasing the models utility with the SimpliRED D-dimer. Thromb Haemost 2000; 83: 416-20
    • (2000) Thromb Haemost , vol.83 , pp. 416-420
    • Wells, P.S.1    Anderson, D.R.2    Rodger, M.3
  • 9
    • 0031588519 scopus 로고    scopus 로고
    • Value of assessment of pretest probability of deep-vein thrombosis in clinical management
    • Wells PS, Anderson DR, Bormanis J, et al. Value of assessment of pretest probability of deep-vein thrombosis in clinical management. Lancet 1997; 350: 1795-8. doi: 10.1016/S0140-6736(97)08140-3
    • (1997) Lancet , vol.350 , pp. 1795-1798
    • Wells, P.S.1    Anderson, D.R.2    Bormanis, J.3
  • 10
    • 67650082402 scopus 로고    scopus 로고
    • Prognosis and prognostic research: Validating a prognostic model
    • Altman DG, Vergouwe Y, Royston P, Moons KG. Prognosis and prognostic research: validating a prognostic model. BMJ 2009; 338: b605. doi: 10.1136/bmj.b605
    • (2009) BMJ , vol.338 , pp. b605
    • Altman, D.G.1    Vergouwe, Y.2    Royston, P.3    Moons, K.G.4
  • 11
    • 67650089602 scopus 로고    scopus 로고
    • Prognosis and prognostic research: Application and impact of prognostic models in clinical practice
    • Moons KG, Altman DG, Vergouwe Y, Royston P. Prognosis and prognostic research: application and impact of prognostic models in clinical practice. BMJ 2009; 338: b606. doi: 10.1136/bmj.b606
    • (2009) BMJ , vol.338 , pp. b606
    • Moons, K.G.1    Altman, D.G.2    Vergouwe, Y.3    Royston, P.4
  • 12
    • 67650022801 scopus 로고    scopus 로고
    • Prognosis and prognostic research: What why, and how?
    • Moons KG, Royston P, Vergouwe Y, Grobbee DE, Altman DG. Prognosis and prognostic research: what, why, and how?BMJ 2009; 338: b375. doi: 10.1136/bmj.b375
    • (2009) BMJ , vol.338 , pp. b375
    • Moons, K.G.1    Royston, P.2    Vergouwe, Y.3    De, G.4    Altman, D.G.5
  • 13
    • 84874484807 scopus 로고    scopus 로고
    • Prognosis research strategy (PROGRESS) 1: A framework for researching clinical outcomes
    • PROGRESS Group
    • Hemingway H, Croft P, Perel P, et al. PROGRESS Group. Prognosis research strategy (PROGRESS) 1: a framework for researching clinical outcomes. BMJ 2013; 346: e5595. doi: 10.1136/bmj.e5595
    • (2013) BMJ , vol.346 , pp. e5595
    • Hemingway, H.1    Croft, P.2    Perel, P.3
  • 14
    • 84874455583 scopus 로고    scopus 로고
    • Prognosis Research Strategy (PROGRESS) 2: Prognostic Factor Research
    • PROGRESS Group
    • Riley RD, Hayden JA, Steyerberg EW, et al. PROGRESS Group. Prognosis Research Strategy (PROGRESS) 2: prognostic factor research. PLoS Med 2013; 10: e1001380. doi: 10.1371/journal. pmed.1001380
    • (2013) PLoS Med , vol.10 , pp. e1001380
    • Riley, R.D.1    Hayden, J.A.2    Steyerberg, E.W.3
  • 15
    • 84874455291 scopus 로고    scopus 로고
    • PROGRESS Group. Prognosis research strategy (PROGRESS) 4: Stratified medicine research
    • Hingorani AD, Windt DA, Riley RD, et al. PROGRESS Group. Prognosis research strategy (PROGRESS) 4: stratified medicine research. BMJ 2013; 346: e5793. doi: 10.1136/bmj.e5793
    • (2013) BMJ , vol.346 , pp. e5793
    • Hingorani, A.D.1    Windt, D.A.2    Riley, R.D.3
  • 16
    • 84942465542 scopus 로고    scopus 로고
    • How to develop a more accurate risk prediction model when there are few events [correction in BMJ 2016 353: I3235]
    • Pavlou M, Ambler G, Seaman SR, et al. How to develop a more accurate risk prediction model when there are few events [correction in BMJ 2016; 353: i3235]. BMJ 2015; 351: h3868. doi: 10.1136/bmj.h3868
    • (2015) BMJ , vol.351 , pp. h3868
    • Pavlou, M.1    Ambler, G.2    Seaman, S.R.3
  • 17
    • 84860113852 scopus 로고    scopus 로고
    • Risk prediction models i Development, internal validation, and assessing the incremental value of a new (bio)marker
    • Moons KG, Kengne AP, Woodward M, et al. Risk prediction models: I. Development, internal validation, and assessing the incremental value of a new (bio)marker. Heart 2012; 98: 683-90. doi: 10.1136/heartjnl-2011-301246
    • (2012) Heart , vol.98 , pp. 683-690
    • Moons, K.G.1    Kengne, A.P.2    Woodward, M.3
  • 19
    • 0141514712 scopus 로고    scopus 로고
    • External validation is necessary in prediction research: A clinical example
    • Bleeker SE, Moll HA, Steyerberg EW, et al. External validation is necessary in prediction research: a clinical example. J Clin Epidemiol 2003; 56: 826-32. doi: 10.1016/S0895-4356(03)00207-5
    • (2003) J Clin Epidemiol , vol.56 , pp. 826-832
    • Bleeker, S.E.1    Moll, H.A.2    Steyerberg, E.W.3
  • 20
    • 84923527988 scopus 로고    scopus 로고
    • 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. A new framework to enhance the interpretation of external validation studies of clinical prediction models. J Clin Epidemiol 2015; 68: 279-89. doi: 10.1016/j.jclinepi.2014.06.018
    • (2015) J Clin Epidemiol , vol.68 , pp. 279-289
    • Debray, T.P.1    Vergouwe, Y.2    Koffijberg, H.3    Nieboer, D.4    Steyerberg, E.W.5    Moons, K.G.6
  • 21
    • 77950389784 scopus 로고    scopus 로고
    • Reporting performance of prognostic models in cancer: A review
    • Mallett S, Royston P, Waters R, Dutton S, Altman DG. Reporting performance of prognostic models in cancer: a review. BMC Med 2010; 8: 21. doi: 10.1186/1741-7015-8-21
    • (2010) BMC Med , vol.8 , pp. 21
    • Mallett, S.1    Royston, P.2    Waters, R.3    Dutton, S.4    Altman, D.G.5
  • 22
    • 33644855006 scopus 로고    scopus 로고
    • Translating clinical research into clinical practice: Impact of using prediction rules to make decisions
    • Reilly BM, Evans AT. Translating clinical research into clinical practice: impact of using prediction rules to make decisions. Ann Intern Med 2006; 144: 201-9. doi: 10.7326/0003-4819-144-3-200602070-00009
    • (2006) Ann Intern Med , vol.144 , pp. 201-209
    • Reilly, B.M.1    Evans, A.T.2
  • 23
    • 84861556816 scopus 로고    scopus 로고
    • Reporting and methods in clinical prediction research: A systematic review
    • Bouwmeester W, Zuithoff NP, Mallett S, et al. Reporting and methods in clinical prediction research: a systematic review. PLoS Med 2012; 9: e1001221. doi: 10.1371/journal.pmed.1001221
    • (2012) PLoS Med , vol.9 , pp. e1001221
    • Bouwmeester, W.1    Zuithoff, N.P.2    Mallett, S.3
  • 25
    • 84872190358 scopus 로고    scopus 로고
    • Fracture risk assessment: State of the art methodologically unsound or poorly reported?
    • Collins GS, Michaëlsson K. Fracture risk assessment: state of the art, methodologically unsound, or poorly reported?Curr Osteoporos Rep 2012; 10: 199-207. doi: 10.1007/s11914-012-0108-1
    • (2012) Curr Osteoporos Rep , vol.10 , pp. 199-207
    • Collins, G.S.1    Michaëlsson, K.2
  • 26
    • 0034906866 scopus 로고    scopus 로고
    • Internal validation of predictive models: Efficiency of some procedures for logistic regression analysis
    • Steyerberg EW, Harrell FEJ Jr, , Borsboom GJ, Eijkemans MJ, Vergouwe Y, Habbema JD. Internal validation of predictive models: efficiency of some procedures for logistic regression analysis. J Clin Epidemiol 2001; 54: 774-81. doi: 10.1016/S0895-4356(01)00341-9
    • (2001) J Clin Epidemiol , vol.54 , pp. 774-781
    • Steyerberg, E.W.1    Harrell, F.E.J.2    Borsboom, G.J.3    Eijkemans, M.J.4    Vergouwe, Y.5    Habbema, J.D.6
  • 27
    • 77749306261 scopus 로고    scopus 로고
    • Meta-Analysis of individual participant data: Rationale, conduct, and reporting
    • Riley RD, Lambert PC, Abo-Zaid G. Meta-Analysis of individual participant data: rationale, conduct, and reporting. BMJ 2010; 340: c221. doi: 10.1136/bmj.c221
    • (2010) BMJ , vol.340 , pp. c221
    • Riley, R.D.1    Lambert, P.C.2    Abo-Zaid, G.3
  • 28
    • 84892152641 scopus 로고    scopus 로고
    • Developing and validating risk prediction models in an individual participant data meta-Analysis
    • Ahmed I, Debray TP, Moons KG, Riley RD. Developing and validating risk prediction models in an individual participant data meta-Analysis. BMC Med Res Methodol 2014; 14: 3. doi: 10.1186/1471-2288-14-3
    • (2014) BMC Med Res Methodol , vol.14 , pp. 3
    • Ahmed, I.1    Debray, T.P.2    Moons, K.G.3    Riley, R.D.4
  • 29
    • 84946072388 scopus 로고    scopus 로고
    • Cochrane IPD Meta-Analysis Methods group. Individual participant data (IPD) meta-Analyses of diagnostic and prognostic modeling studies: Guidance on their use
    • Debray TPA, Riley RD, Rovers MM, Reitsma JB, Moons KG. Cochrane IPD Meta-Analysis Methods group. Individual participant data (IPD) meta-Analyses of diagnostic and prognostic modeling studies: guidance on their use. PLoS Med 2015; 12: e1001886. doi: 10.1371/journal.pmed.1001886
    • (2015) PLoS Med , vol.12 , pp. e1001886
    • Debray, T.P.A.1    Riley, R.D.2    Rovers, M.M.3    Reitsma, J.B.4    Moons, K.G.5
  • 30
    • 84896692640 scopus 로고    scopus 로고
    • Emerging Risk Factors Collaboration. Assessing risk prediction models using individual participant data from multiple studies
    • Pennells L, Kaptoge S, White IR, Thompson SG, Wood AM. Emerging Risk Factors Collaboration. Assessing risk prediction models using individual participant data from multiple studies. Am J Epidemiol 2014; 179: 621-32. doi: 10.1093/aje/kwt298
    • (2014) Am J Epidemiol , vol.179 , pp. 621-632
    • Pennells, L.1    Kaptoge, S.2    White, I.R.3    Thompson, S.G.4    Wood, A.M.5
  • 31
    • 84923013798 scopus 로고    scopus 로고
    • The rise of big clinical databases
    • Cook JA, Collins GS. The rise of big clinical databases. Br J Surg 2015; 102: e93-101. doi: 10.1002/bjs.9723
    • (2015) Br J Surg , vol.102 , pp. e93-e101
    • Cook, J.A.1    Collins, G.S.2
  • 32
    • 77953707407 scopus 로고    scopus 로고
    • An independent and external validation of QRISK2 cardiovascular disease risk score: A prospective open cohort study
    • Collins GS, Altman DG. An independent and external validation of QRISK2 cardiovascular disease risk score: a prospective open cohort study. BMJ 2010; 340: c2442. doi: 10.1136/bmj.c2442
    • (2010) BMJ , vol.340 , pp. c2442
    • Collins, G.S.1    Altman, D.G.2
  • 33
    • 50949101668 scopus 로고    scopus 로고
    • Predicting outcome after traumatic brain injury: Development and international validation of prognostic scores based on admission characteristics
    • Steyerberg EW, Mushkudiani N, Perel P, et al. Predicting outcome after traumatic brain injury: development and international validation of prognostic scores based on admission characteristics. PLoS Med 2008; 5: e165. doi: 10.1371/journal.pmed.0050165
    • (2008) PLoS Med , vol.5 , pp. e165
    • Steyerberg, E.W.1    Mushkudiani, N.2    Perel, P.3
  • 34
    • 84923923813 scopus 로고    scopus 로고
    • Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): The TRIPOD statement
    • Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement. Ann Intern Med 2015; 162: 55-63. doi: 10.7326/M14-0697
    • (2015) Ann Intern Med , vol.162 , pp. 55-63
    • Collins, G.S.1    Reitsma, J.B.2    Altman, D.G.3    Moons, K.G.4
  • 35
    • 84920623458 scopus 로고    scopus 로고
    • Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): Explanation and elaboration
    • Moons KG, Altman DG, Reitsma JB, et al. Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med 2015; 162: W1-73. doi: 10.7326/M14-0698
    • (2015) Ann Intern Med , vol.162 , pp. W1-W73
    • Moons, K.G.1    Altman, D.G.2    Reitsma, J.B.3
  • 36
    • 1442351098 scopus 로고    scopus 로고
    • A new measure of prognostic separation in survival data
    • Royston P, Sauerbrei W. A new measure of prognostic separation in survival data. Stat Med 2004; 23: 723-48. doi: 10.1002/sim.1621
    • (2004) Stat Med , vol.23 , pp. 723-748
    • Royston, P.1    Sauerbrei, W.2
  • 37
    • 84957076656 scopus 로고    scopus 로고
    • A calibration hierarchy for risk models was defined: From utopia to empirical data
    • Van Calster B, Nieboer D, Vergouwe Y, De Cock B, Pencina MJ, Steyerberg EW. A calibration hierarchy for risk models was defined: from utopia to empirical data. J Clin Epidemiol 2016; S0895-4356(15)00581-8. doi: 10.1016/j.jclinepi.2015.12.005 PubMed
    • (2016) J Clin Epidemiol , vol.15 , pp. S0895-S4356
    • Van Calster, B.1    Nieboer, D.2    Vergouwe, Y.3    De Cock, B.4    Pencina, M.J.5    Steyerberg, E.W.6
  • 38
    • 84874529489 scopus 로고    scopus 로고
    • External validation of a Cox prognostic model: Principles and methods
    • Royston P, Altman DG. External validation of a Cox prognostic model: principles and methods. BMC Med Res Methodol 2013; 13: 33. doi: 10.1186/1471-2288-13-33
    • (2013) BMC Med Res Methodol , vol.13 , pp. 33
    • Royston, P.1    Altman, D.G.2
  • 39
    • 84899459258 scopus 로고    scopus 로고
    • External validation of multivariable prediction models: A systematic review of methodological conduct and reporting
    • Collins GS, de Groot JA, Dutton S, et al. External validation of multivariable prediction models: a systematic review of methodological conduct and reporting. BMC Med Res Methodol 2014; 14: 40. doi: 10.1186/1471-2288-14-40
    • (2014) BMC Med Res Methodol , vol.14 , pp. 40
    • Collins, G.S.1    De Groot, J.A.2    Dutton, S.3
  • 40
    • 17444420853 scopus 로고    scopus 로고
    • Substantial effective sample sizes were required for external validation studies of predictive logistic regression models
    • Vergouwe Y, Steyerberg EW, Eijkemans MJ, Habbema JD. Substantial effective sample sizes were required for external validation studies of predictive logistic regression models. J Clin Epidemiol 2005; 58: 475-83. doi: 10.1016/j.jclinepi.2004.06.017
    • (2005) J Clin Epidemiol , vol.58 , pp. 475-483
    • Vergouwe, Y.1    Steyerberg, E.W.2    Eijkemans, M.J.3    Habbema, J.D.4
  • 41
    • 84954405416 scopus 로고    scopus 로고
    • Sample size considerations for the external validation of a multivariable prognostic model: A resampling study
    • Collins GS, Ogundimu EO, Altman DG. Sample size considerations for the external validation of a multivariable prognostic model: a resampling study. Stat Med 2016; 35: 214-26. doi: 10.1002/sim.6787
    • (2016) Stat Med , vol.35 , pp. 214-226
    • Collins, G.S.1    Ogundimu, E.O.2    Altman, D.G.3
  • 42
    • 1542506052 scopus 로고    scopus 로고
    • Construction and validation of a prognostic model across several studies, with an application in superficial bladder cancer
    • Royston P, Parmar MKB, Sylvester R. Construction and validation of a prognostic model across several studies, with an application in superficial bladder cancer. Stat Med 2004; 23: 907-26. doi: 10.1002/sim.1691
    • (2004) Stat Med , vol.23 , pp. 907-926
    • Royston, P.1    Mkb, P.2    Sylvester, R.3
  • 43
    • 77958090625 scopus 로고    scopus 로고
    • External validity of risk models: Use of benchmark values to disentangle a case-mix effect from incorrect coefficients
    • Vergouwe Y, Moons KG, Steyerberg EW. External validity of risk models: Use of benchmark values to disentangle a case-mix effect from incorrect coefficients. Am J Epidemiol 2010; 172: 971-80. doi: 10.1093/aje/kwq223
    • (2010) Am J Epidemiol , vol.172 , pp. 971-980
    • Vergouwe, Y.1    Moons, K.G.2    Steyerberg, E.W.3
  • 44
    • 84864387127 scopus 로고    scopus 로고
    • Predicting the 10 year risk of cardiovascular disease in the United Kingdom: Independent and external validation of an updated version of QRISK2
    • Collins GS, Altman DG. Predicting the 10 year risk of cardiovascular disease in the United Kingdom: independent and external validation of an updated version of QRISK2. BMJ 2012; 344: e4181. doi: 10.1136/bmj.e4181
    • (2012) BMJ , vol.344 , pp. e4181
    • Collins, G.S.1    Altman, D.G.2
  • 45
    • 84880044696 scopus 로고    scopus 로고
    • 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. A framework for developing, implementing, and evaluating clinical prediction models in an individual participant data meta-Analysis. Stat Med 2013; 32: 3158-80. doi: 10.1002/sim.5732
    • (2013) Stat Med , vol.32 , pp. 3158-3180
    • Debray, T.P.1    Moons, K.G.2    Ahmed, I.3    Koffijberg, H.4    Riley, R.D.5
  • 46
    • 0036787049 scopus 로고    scopus 로고
    • Spectrum bias or spectrum effect? Subgroup variation in diagnostic test evaluation
    • Mulherin SA, Miller WC. Spectrum bias or spectrum effect?. Subgroup variation in diagnostic test evaluation. Ann Intern Med 2002; 137: 598-602. doi: 10.7326/0003-4819-137-7-200210010-00011
    • (2002) Ann Intern Med , vol.137 , pp. 598-602
    • Mulherin, S.A.1    Miller, W.C.2
  • 47
    • 0018178436 scopus 로고
    • Problems of spectrum and bias in evaluating the efficacy of diagnostic tests
    • Ransohoff DF, Feinstein AR. Problems of spectrum and bias in evaluating the efficacy of diagnostic tests. N Engl J Med 1978; 299: 926-30. doi: 10.1056/NEJM197810262991705
    • (1978) N Engl J Med , vol.299 , pp. 926-930
    • Ransohoff, D.F.1    Feinstein, A.R.2
  • 48
    • 0036959539 scopus 로고    scopus 로고
    • Between iatrotropic stimulus and interiatric referral: The domain of primary care research
    • Knottnerus JA. Between iatrotropic stimulus and interiatric referral: the domain of primary care research. J Clin Epidemiol 2002; 55: 1201-6. doi: 10.1016/S0895-4356(02)00528-0
    • (2002) J Clin Epidemiol , vol.55 , pp. 1201-1206
    • Knottnerus, J.A.1
  • 49
    • 22144482023 scopus 로고    scopus 로고
    • The Wells rule does not adequately rule out deep venous thrombosis in primary care patients
    • Oudega R, Hoes AW, Moons KG. The Wells rule does not adequately rule out deep venous thrombosis in primary care patients. Ann Intern Med 2005; 143: 100-7. doi: 10.7326/0003-4819-143-2-200507190-00008
    • (2005) Ann Intern Med , vol.143 , pp. 100-107
    • Oudega, R.1    Hoes, A.W.2    Moons, K.G.3
  • 50
    • 16644400192 scopus 로고    scopus 로고
    • Prognostic factors-confusion caused by bad quality of design, analysis and reporting of many studies
    • Bier H, ed. Karger
    • Sauerbrei W. Prognostic factors-confusion caused by bad quality of design, analysis and reporting of many studies. In: Bier H, ed. Current research in head and neck cancer advances in oto-rhinolaryngology. Karger, 2005: 184-200
    • (2005) Current Research in Head and Neck Cancer Advances in Oto-rhinolaryngology , pp. 184-200
    • Sauerbrei, W.1
  • 51
    • 36849089158 scopus 로고    scopus 로고
    • Updating methods improved the performance of a clinical prediction model in new patients
    • Janssen KJ, Moons KG, Kalkman CJ, Grobbee DE, Vergouwe Y. Updating methods improved the performance of a clinical prediction model in new patients. J Clin Epidemiol 2008; 61: 76-86. doi: 10.1016/j. jclinepi.2007.04.018
    • (2008) J Clin Epidemiol , vol.61 , pp. 76-86
    • Janssen, K.J.1    Moons, K.G.2    Kalkman, C.J.3    De, G.4    Vergouwe, Y.5
  • 52
    • 79955544813 scopus 로고    scopus 로고
    • Interpretation of random effects meta-Analyses
    • Riley RD, Higgins JP, Deeks JJ. Interpretation of random effects meta-Analyses. BMJ 2011; 342: d549. doi: 10.1136/bmj.d549
    • (2011) BMJ , vol.342 , pp. d549
    • Riley, R.D.1    Higgins, J.P.2    Deeks, J.J.3
  • 53
    • 84952630758 scopus 로고    scopus 로고
    • Multivariate meta-Analysis of individual participant data helped externally validate the performance and implementation of a prediction model
    • Snell KI, Hua H, Debray TP, et al. Multivariate meta-Analysis of individual participant data helped externally validate the performance and implementation of a prediction model. J Clin Epidemiol 2016; 69: 40-50. doi: 10.1016/j.jclinepi.2015.05.009
    • (2016) J Clin Epidemiol , vol.69 , pp. 40-50
    • Snell, K.I.1    Hua, H.2    Debray, T.P.3
  • 56
    • 84896930969 scopus 로고    scopus 로고
    • Exclusion of deep vein thrombosis using the Wells rule in clinically important subgroups: Individual patient data meta-Analysis
    • Geersing GJ, Zuithoff NP, Kearon C, et al. Exclusion of deep vein thrombosis using the Wells rule in clinically important subgroups: individual patient data meta-Analysis. BMJ 2014; 348: g1340. doi: 10.1136/bmj.g1340
    • (2014) BMJ , vol.348 , pp. g1340
    • Geersing, G.J.1    Zuithoff, N.P.2    Kearon, C.3
  • 57
    • 43949083703 scopus 로고    scopus 로고
    • Comparison of non-parametric confidence intervals for the area under the ROC curve of a continuous-scale diagnostic test
    • Gengsheng Qin , Hotilovac L. Comparison of non-parametric confidence intervals for the area under the ROC curve of a continuous-scale diagnostic test. Stat Methods Med Res 2008; 17: 207-21. doi: 10.1177/0962280207087173
    • (2008) Stat Methods Med Res , vol.17 , pp. 207-221
    • Qin, G.1    Hotilovac, L.2
  • 58
    • 84896732451 scopus 로고    scopus 로고
    • QRISK2 validation by ethnic group
    • Tillin T, Hughes AD, Whincup P, et al. QRISK2 validation by ethnic group. Heart 2014; 100: 437. doi: 10.1136/heartjnl-2013-305333
    • (2014) Heart , vol.100 , pp. 437
    • Tillin, T.1    Hughes, A.D.2    Whincup, P.3
  • 59
    • 84900855393 scopus 로고    scopus 로고
    • Ethnic group differences in cardiovascular risk assessment scores: National cross-sectional study
    • Dalton AR, Bottle A, Soljak M, Majeed A, Millett C. Ethnic group differences in cardiovascular risk assessment scores: national cross-sectional study. Ethn Health 2014; 19: 367-84. doi: 10.1080/13557858.2013.797568
    • (2014) Ethn Health , vol.19 , pp. 367-384
    • Dalton, A.R.1    Bottle, A.2    Soljak, M.3    Majeed, A.4    Millett, C.5
  • 61
    • 84928762608 scopus 로고    scopus 로고
    • Summarising and validating test accuracy results across multiple studies for use in clinical practice
    • Riley RD, Ahmed I, Debray TP, et al. Summarising and validating test accuracy results across multiple studies for use in clinical practice. Stat Med 2015; 34: 2081-103. doi: 10.1002/sim.6471
    • (2015) Stat Med , vol.34 , pp. 2081-2103
    • Riley, R.D.1    Ahmed, I.2    Debray, T.P.3
  • 62
    • 84897537279 scopus 로고    scopus 로고
    • Estimating a test's accuracy using tailored meta-Analysis-How setting-specific data may aid study selection
    • Willis BH, Hyde CJ. Estimating a test's accuracy using tailored meta-Analysis-How setting-specific data may aid study selection. J Clin Epidemiol 2014; 67: 538-46. doi: 10.1016/j.jclinepi.2013.10.016
    • (2014) J Clin Epidemiol , vol.67 , pp. 538-546
    • Willis, B.H.1    Hyde, C.J.2
  • 63
    • 84881493693 scopus 로고    scopus 로고
    • Variation of a test's sensitivity and specificity with disease prevalence
    • Leeflang MM, Rutjes AW, Reitsma JB, Hooft L, Bossuyt PM. Variation of a test's sensitivity and specificity with disease prevalence. CMAJ 2013; 185: E537-44. doi: 10.1503/cmaj.121286
    • (2013) CMAJ , vol.185 , pp. E537-E544
    • Leeflang, M.M.1    Rutjes, A.W.2    Reitsma, J.B.3    Hooft, L.4    Bossuyt, P.M.5
  • 64
    • 56349139109 scopus 로고    scopus 로고
    • Predicting mortality with pneumonia severity scores: Importance of model recalibration to local settings
    • Schuetz P, Koller M, Christ-Crain M, et al. Predicting mortality with pneumonia severity scores: importance of model recalibration to local settings. Epidemiol Infect 2008; 136: 1628-37. doi: 10.1017/S0950268808000435
    • (2008) Epidemiol Infect , vol.136 , pp. 1628-1637
    • Schuetz, P.1    Koller, M.2    Christ-Crain, M.3
  • 65
    • 84859928513 scopus 로고    scopus 로고
    • Individual participant data meta-Analysis of prognostic factor studies: State of the art?
    • Abo-Zaid G, Sauerbrei W, Riley RD. Individual participant data meta-Analysis of prognostic factor studies: state of the art?BMC Med Res Methodol 2012; 12: 56. doi: 10.1186/1471-2288-12-56
    • (2012) BMC Med Res Methodol , vol.12 , pp. 56
    • Abo-Zaid, G.1    Sauerbrei, W.2    Riley, R.D.3
  • 66
    • 84926444242 scopus 로고    scopus 로고
    • 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. Imputation of systematically missing predictors in an individual participant data meta-Analysis: a generalized approach using MICE. Stat Med 2015; 34: 1841-63. doi: 10.1002/sim.6451
    • (2015) Stat Med , vol.34 , pp. 1841-1863
    • Jolani, S.1    Debray, T.P.2    Koffijberg, H.3    Van Buuren, S.4    Moons, K.G.5
  • 67
    • 84887138473 scopus 로고    scopus 로고
    • Multiple imputation for handling systematically missing confounders in meta-Analysis of individual participant data
    • PROG-IMT Study Group
    • Resche-Rigon M, White IR, Bartlett JW, Peters SA, Thompson SG. PROG-IMT Study Group. Multiple imputation for handling systematically missing confounders in meta-Analysis of individual participant data. Stat Med 2013; 32: 4890-905. doi: 10.1002/sim.5894
    • (2013) Stat Med , vol.32 , pp. 4890-4905
    • Resche-Rigon, M.1    White, I.R.2    Bartlett, J.W.3    Peters, S.A.4    Thompson, S.G.5
  • 68
    • 84939607655 scopus 로고    scopus 로고
    • Data Resource Profile: Clinical Practice Research Datalink (CPRD
    • Herrett E, Gallagher AM, Bhaskaran K, et al. Data Resource Profile: Clinical Practice Research Datalink (CPRD). Int J Epidemiol 2015; 44: 827-36. doi: 10.1093/ije/dyv098
    • (2015) Int J Epidemiol , vol.44 , pp. 827-836
    • Herrett, E.1    Gallagher, A.M.2    Bhaskaran, K.3
  • 69
    • 84938702916 scopus 로고    scopus 로고
    • Individual Participant Data (IPD) Meta-Analyses of Randomised Controlled Trials: Guidance on Their Use
    • Tierney JF, Vale C, Riley R, et al. Individual Participant Data (IPD) Meta-Analyses of Randomised Controlled Trials: Guidance on Their Use. PLoS Med 2015; 12: e1001855. doi: 10.1371/journal. pmed.1001855
    • (2015) PLoS Med , vol.12 , pp. e1001855
    • Tierney, J.F.1    Vale, C.2    Riley, R.3
  • 70
    • 33747170479 scopus 로고    scopus 로고
    • Systematic review of multiple studies of prognosis: The feasibility of obtaining individual patient data
    • Auget J-L, Balakrishnan N, Mesbah M, et al, eds Birkhäuser
    • Altman DG, Trivella M, Pezzella F, et al. Systematic review of multiple studies of prognosis: the feasibility of obtaining individual patient data. In: Auget J-L, Balakrishnan N, Mesbah M, et al, eds. Advances in statistical methods for the health sciences. Birkhäuser, 2006: 3-18
    • (2006) Advances in Statistical Methods for the Health Sciences , pp. 3-18
    • Altman, D.G.1    Trivella, M.2    Pezzella, F.3
  • 71
    • 84857135675 scopus 로고    scopus 로고
    • Assessment of publication bias, selection bias, and unavailable data in meta-Analyses using individual participant data: A database survey
    • Ahmed I, Sutton AJ, Riley RD. Assessment of publication bias, selection bias, and unavailable data in meta-Analyses using individual participant data: a database survey. BMJ 2012; 344: d7762. doi: 10.1136/bmj.d7762
    • (2012) BMJ , vol.344 , pp. d7762
    • Ahmed, I.1    Sutton, A.J.2    Riley, R.D.3
  • 72
    • 0030474271 scopus 로고    scopus 로고
    • A simulation study of the number of events per variable in logistic regression analysis
    • Peduzzi P, Concato J, Kemper E, Holford TR, Feinstein AR. A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol 1996; 49: 1373-9. doi: 10.1016/S0895-4356(96)00236-3
    • (1996) J Clin Epidemiol , vol.49 , pp. 1373-1379
    • Peduzzi, P.1    Concato, J.2    Kemper, E.3    Holford, T.R.4    Feinstein, A.R.5
  • 73
    • 84944065233 scopus 로고    scopus 로고
    • Discrimination-based sample size calculations for multivariable prognostic models for time-to-event data
    • Jinks RC, Royston P, Parmar MK. Discrimination-based sample size calculations for multivariable prognostic models for time-to-event data. BMC Med Res Methodol 2015; 15: 82. doi: 10.1186/s12874-015-0078-y.
    • (2015) BMC Med Res Methodol , vol.15 , pp. 82
    • Jinks, R.C.1    Royston, P.2    Parmar, M.K.3
  • 74
    • 84962074698 scopus 로고    scopus 로고
    • Adequate sample size for developing prediction models is not simply related to events per variable
    • 30011-30017
    • Ogundimu EO, Altman DG, Collins GS. Adequate sample size for developing prediction models is not simply related to events per variable. J Clin Epidemiol 2016; S0895-4356(16)30011-7
    • (2016) J Clin Epidemiol , vol.16 , pp. S0895-S4356
    • Ogundimu, E.O.1    Altman, D.G.2    Collins, G.S.3
  • 75
    • 84885970943 scopus 로고    scopus 로고
    • Screening for data clustering in multicenter studies: The residual intraclass correlation
    • Wynants L, Timmerman D, Bourne T, Van Huffel S, Van Calster B. Screening for data clustering in multicenter studies: the residual intraclass correlation. BMC Med Res Methodol 2013; 13: 128. doi: 10.1186/1471-2288-13-128
    • (2013) BMC Med Res Methodol , vol.13 , pp. 128
    • Wynants, L.1    Timmerman, D.2    Bourne, T.3    Van Huffel, S.4    Van Calster, B.5
  • 76
    • 84928753669 scopus 로고    scopus 로고
    • Preferred Reporting Items for Systematic Review and Meta-Analyses of individual participant data: The PRISMA-IPD Statement
    • PRISMA-IPD Development Group
    • Stewart LA, Clarke M, Rovers M, et al. PRISMA-IPD Development Group. Preferred Reporting Items for Systematic Review and Meta-Analyses of individual participant data: the PRISMA-IPD Statement. JAMA 2015; 313: 1657-65. doi: 10.1001/jama.2015.3656
    • (2015) JAMA , vol.313 , pp. 1657-1665
    • Stewart, L.A.1    Clarke, M.2    Rovers, M.3
  • 77
    • 66849095444 scopus 로고    scopus 로고
    • Predicting risk of type 2 diabetes in England and Wales: Prospective derivation and validation of QDScore
    • b880
    • Hippisley-Cox J, Coupland C, Robson J, Sheikh A, Brindle P. Predicting risk of type 2 diabetes in England and Wales: prospective derivation and validation of QDScore. BMJ 2009; 338: b880. doi: 10.1136/bmj. b880
    • (2009) BMJ , vol.338
    • Hippisley-Cox, J.1    Coupland, C.2    Robson, J.3    Sheikh, A.4    Brindle, P.5
  • 78
    • 84866440887 scopus 로고    scopus 로고
    • Aggregating published prediction models with individual participant data: A comparison of different approaches
    • Debray TP, Koffijberg H, Vergouwe Y, Moons KG, Steyerberg EW. Aggregating published prediction models with individual participant data: a comparison of different approaches. Stat Med 2012; 31: 2697-712. doi: 10.1002/sim.5412
    • (2012) Stat Med , vol.31 , pp. 2697-2712
    • Debray, T.P.1    Koffijberg, H.2    Vergouwe, Y.3    Moons, K.G.4    Steyerberg, E.W.5
  • 79
    • 84861872921 scopus 로고    scopus 로고
    • Comparing risk prediction models
    • Collins GS, Moons KG. Comparing risk prediction models. BMJ 2012; 344: e3186. doi: 10.1136/bmj.e3186
    • (2012) BMJ , vol.344 , pp. e3186
    • Collins, G.S.1    Moons, K.G.2
  • 80
    • 33750905259 scopus 로고    scopus 로고
    • Decision curve analysis: A novel method for evaluating prediction models
    • Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making 2006; 26: 565-74. doi: 10.1177/0272989X06295361
    • (2006) Med Decis Making , vol.26 , pp. 565-574
    • Vickers, A.J.1    Elkin, E.B.2
  • 81
    • 84928713036 scopus 로고    scopus 로고
    • Why data sharing should be the expected norm
    • Krumholz HM. Why data sharing should be the expected norm. BMJ 2015; 350: h599. doi: 10.1136/bmj.h599
    • (2015) BMJ , vol.350 , pp. h599
    • Krumholz, H.M.1
  • 82
    • 84925764176 scopus 로고    scopus 로고
    • Uptake of systematic reviews and meta-Analyses based on individual participant data in clinical practice guidelines: Descriptive study
    • Cochrane IPD Meta-Analysis Methods Group
    • Vale CL, Rydzewska LH, Rovers MM, Emberson JR, Gueyffier F, Stewart LA. Cochrane IPD Meta-Analysis Methods Group. Uptake of systematic reviews and meta-Analyses based on individual participant data in clinical practice guidelines: descriptive study. BMJ 2015; 350: h1088. doi: 10.1136/bmj.h1088
    • (2015) BMJ , vol.350 , pp. h1088
    • Vale, C.L.1    Rydzewska, L.H.2    Rovers, M.M.3    Emberson, J.R.4    Gueyffier, F.5    Stewart, L.A.6


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