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Volumn 6, Issue 4, 2015, Pages 293-309

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

Author keywords

Cross design; Evidence synthesis; IPD; Meta analysis; Non randomized intervention studies; NRSI; RCT; Review

Indexed keywords

HUMAN; META ANALYSIS (TOPIC); PROPORTIONAL HAZARDS MODEL; RANDOMIZED CONTROLLED TRIAL (TOPIC); SOFTWARE; STATISTICAL ANALYSIS; STATISTICAL MODEL; STATISTICS AND NUMERICAL DATA; TREATMENT OUTCOME;

EID: 84956958671     PISSN: None     EISSN: 17592887     Source Type: Journal    
DOI: 10.1002/jrsm.1160     Document Type: Article
Times cited : (245)

References (118)
  • 2
    • 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 2012. Assessment of publication bias, selection bias, and unavailable data in meta-analyses using individual participant data: a database survey. British Medical Journal 344: d7762. DOI:10.1136/bmj.d7762.
    • (2012) British Medical Journal , vol.344 , pp. d7762
    • Ahmed, I.1    Sutton, A.J.2    Riley, R.D.3
  • 3
    • 84873470089 scopus 로고    scopus 로고
    • The use of individual patient-level data (IPD) to quantify the impact of pretreatment predictors of response to treatment in chronic hepatitis b patients
    • Ali S, Mealing S, Hawkins N, Lescrauwaet B, Bjork S, Mantovani L, Lampertico P 2013. The use of individual patient-level data (IPD) to quantify the impact of pretreatment predictors of response to treatment in chronic hepatitis b patients. BMJ Open 3: e001309. DOI:10.1136/bmjopen-2012-001309.
    • (2013) BMJ Open , vol.3 , pp. e001309
    • Ali, S.1    Mealing, S.2    Hawkins, N.3    Lescrauwaet, B.4    Bjork, S.5    Mantovani, L.6    Lampertico, P.7
  • 4
    • 84870942329 scopus 로고    scopus 로고
    • Two-stage meta-analysis of survival data from individual participants using percentile ratios
    • Barrett JK, Farewell VT, Siannis F, Tierney J, Higgins JPT 2012. Two-stage meta-analysis of survival data from individual participants using percentile ratios. Statistics in Medicine 31: 4296-4308. DOI:10.1002/sim.5516.
    • (2012) Statistics in Medicine , vol.31 , pp. 4296-4308
    • Barrett, J.K.1    Farewell, V.T.2    Siannis, F.3    Tierney, J.4    Higgins, J.P.T.5
  • 5
    • 84896367143 scopus 로고    scopus 로고
    • A questionnaire to assess the relevance and credibility of observational studies to inform health care decision making: an ISPOR-AMCP-NPC good practice task force report
    • Berger ML, Martin BC, Husereau D, Worley K, Allen JD, Yang W, Quon NC, Mullins CD, Kahler KH, Crown W 2014. A questionnaire to assess the relevance and credibility of observational studies to inform health care decision making: an ISPOR-AMCP-NPC good practice task force report. Value in Health 17: 143-156. DOI:10.1016/j.jval.2013.12.011.
    • (2014) Value in Health , vol.17 , pp. 143-156
    • Berger, M.L.1    Martin, B.C.2    Husereau, D.3    Worley, K.4    Allen, J.D.5    Yang, W.6    Quon, N.C.7    Mullins, C.D.8    Kahler, K.H.9    Crown, W.10
  • 6
    • 0037083255 scopus 로고    scopus 로고
    • Individual patient-versus group-level data meta-regressions for the investigation of treatment effect modifiers: ecological bias rears its ugly head
    • Berlin JA, Santanna J, Schmid CH, Szczech LA, Feldman HI, Anti-lymphocyte antibody induction therapy study group. 2002. Individual patient-versus group-level data meta-regressions for the investigation of treatment effect modifiers: ecological bias rears its ugly head. Statistics in Medicine 21: 371-387. DOI: 10.1002/sim.1023.
    • (2002) Statistics in Medicine , vol.21 , pp. 371-387
    • Berlin, J.A.1    Santanna, J.2    Schmid, C.H.3    Szczech, L.A.4    Feldman, H.I.5
  • 7
    • 84867048680 scopus 로고    scopus 로고
    • Individual patient data meta-analysis of time-to-event outcomes: one-stage versus two-stage approaches for estimating the hazard ratio under a random effects model
    • Bowden J, Tierney JF, Simmonds M, Copas AJ, Higgins JP 2011. Individual patient data meta-analysis of time-to-event outcomes: one-stage versus two-stage approaches for estimating the hazard ratio under a random effects model. Research Synthesis Methods 2: 150-162. DOI:10.1002/jrsm.45.
    • (2011) Research Synthesis Methods , vol.2 , pp. 150-162
    • Bowden, J.1    Tierney, J.F.2    Simmonds, M.3    Copas, A.J.4    Higgins, J.P.5
  • 8
    • 79961173550 scopus 로고    scopus 로고
    • Generalized linear models with clustered data: fixed and random effects models
    • Broström G, Holmberg H 2011. Generalized linear models with clustered data: fixed and random effects models. Computational Statistics & Data Analysis 55: 3123-3134. DOI:10.1016/j.csda.2011.06.011.
    • (2011) Computational Statistics & Data Analysis , vol.55 , pp. 3123-3134
    • Broström, G.1    Holmberg, H.2
  • 9
    • 84892585516 scopus 로고    scopus 로고
    • Use of Bayesian multivariate meta-analysis to estimate the HAQ for mapping onto the EQ-5D questionnaire in rheumatoid arthritis
    • Bujkiewicz S, Thompson JR, Sutton AJ, Cooper NJ, Harrison MJ, Symmons DPM, Abrams KR 2014. Use of Bayesian multivariate meta-analysis to estimate the HAQ for mapping onto the EQ-5D questionnaire in rheumatoid arthritis. Value in Health 17: 109-115. DOI:10.1016/j.jval.2013.11.005.
    • (2014) Value in Health , vol.17 , pp. 109-115
    • Bujkiewicz, S.1    Thompson, J.R.2    Sutton, A.J.3    Cooper, N.J.4    Harrison, M.J.5    Symmons, D.P.M.6    Abrams, K.R.7
  • 11
    • 84885418293 scopus 로고    scopus 로고
    • Combining multiple imputation and meta-analysis with individual participant data
    • Burgess S, White IR, Resche-Rigon M, Wood AM 2013. Combining multiple imputation and meta-analysis with individual participant data. Statistics in Medicine 32: 4499-4514. DOI:10.1002/sim.5844.
    • (2013) Statistics in Medicine , vol.32 , pp. 4499-4514
    • Burgess, S.1    White, I.R.2    Resche-Rigon, M.3    Wood, A.M.4
  • 12
    • 0030464206 scopus 로고    scopus 로고
    • On the relationship between response to treatment and survival time
    • Buyse M, Piedbois P 1996. On the relationship between response to treatment and survival time. Statistics in Medicine 15: 2797-2812. DOI:10.1002/(SICI)1097-0258(19961230)15:24<2797::AID-SIM290>3.0.CO;2-V.
    • (1996) Statistics in Medicine , vol.15 , pp. 2797-2812
    • Buyse, M.1    Piedbois, P.2
  • 14
    • 0027969191 scopus 로고
    • Obtaining data from randomised controlled trials: how much do we need for reliable and informative meta-analyses?
    • Clarke MJ, Stewart LA 1994. Obtaining data from randomised controlled trials: how much do we need for reliable and informative meta-analyses? British Medical Journal 309: 1007-1010. DOI:10.1136/bmj.309.6960.1007.
    • (1994) British Medical Journal , vol.309 , pp. 1007-1010
    • Clarke, M.J.1    Stewart, L.A.2
  • 15
    • 0034702175 scopus 로고    scopus 로고
    • Randomized, controlled trials, observational studies, and the hierarchy of research designs
    • Concato J, Shah N, Horwitz RI 2000. Randomized, controlled trials, observational studies, and the hierarchy of research designs. New England Journal of Medicine 342: 1887-1892. DOI:10.1056/NEJM200006223422507.
    • (2000) New England Journal of Medicine , vol.342 , pp. 1887-1892
    • Concato, J.1    Shah, N.2    Horwitz, R.I.3
  • 17
    • 84876028745 scopus 로고    scopus 로고
    • Individual participant data meta-analysis for a binary outcome: one-stage or two-stage?
    • 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.
    • (2013) PLoS One , vol.8 , pp. e60650
    • Debray, T.P.A.1    Moons, K.G.M.2    Abo-Zaid, G.M.A.3    Koffijberg, H.4    Riley, R.D.5
  • 19
    • 84873978476 scopus 로고    scopus 로고
    • Combining individual patient data and aggregate data in mixed treatment comparison meta-analysis: individual patient data may be beneficial if only for a subset of trials
    • 914-030
    • Donegan S, Williamson P, D'Alessandro U, Garner P, Smith CT 2013. Combining individual patient data and aggregate data in mixed treatment comparison meta-analysis: individual patient data may be beneficial if only for a subset of trials. Statistics in Medicine 32: 914-030. DOI:10.1002/sim.5584.
    • (2013) Statistics in Medicine , vol.32
    • Donegan, S.1    Williamson, P.2    D'Alessandro, U.3    Garner, P.4    Smith, C.T.5
  • 20
    • 84870064743 scopus 로고    scopus 로고
    • Assessing the consistency assumption by exploring treatment by covariate interactions in mixed treatment comparison meta-analysis: individual patient-level covariates versus aggregate trial-level covariates
    • Donegan S, Williamson P, D'Alessandro U, Tudur Smith C 2012. Assessing the consistency assumption by exploring treatment by covariate interactions in mixed treatment comparison meta-analysis: individual patient-level covariates versus aggregate trial-level covariates. Statistics in Medicine 31: 3840-3857. DOI:10.1002/sim.5470.
    • (2012) Statistics in Medicine , vol.31 , pp. 3840-3857
    • Donegan, S.1    Williamson, P.2    D'Alessandro, U.3    Tudur Smith, C.4
  • 21
    • 0027322604 scopus 로고
    • A new form of meta-analysis for combining results from randomized clinical trials and medical-practice databases
    • Droitcour J, Silberman G, Chelimsky E 1993. A new form of meta-analysis for combining results from randomized clinical trials and medical-practice databases. International Journal of Technology Assessment in Health Care 9: 440-449. DOI:10.1017/S0266462300004694.
    • (1993) International Journal of Technology Assessment in Health Care , vol.9 , pp. 440-449
    • Droitcour, J.1    Silberman, G.2    Chelimsky, E.3
  • 22
    • 0034816161 scopus 로고    scopus 로고
    • Individual patient-versus literature-based meta-analysis of survival data: time to event and event rate at a particular time can make a di_erence, an example based on head and neck cancer
    • Duchateau L, Pignon JP, Bijnens L, Bertin S, Bourhis J, Sylvester R 2001. Individual patient-versus literature-based meta-analysis of survival data: time to event and event rate at a particular time can make a di_erence, an example based on head and neck cancer. Controlled Clinical Trials 22: 538-547. DOI:10.1016/S0197-2456(01)00152-0.
    • (2001) Controlled Clinical Trials , vol.22 , pp. 538-547
    • Duchateau, L.1    Pignon, J.P.2    Bijnens, L.3    Bertin, S.4    Bourhis, J.5    Sylvester, R.6
  • 24
    • 79960838960 scopus 로고    scopus 로고
    • A critical review of methods for the assessment of patient-level interactions in individual participant data meta-analysis of randomized trials, and guidance for practitioners
    • Fisher DJ, Copas AJ, Tierney JF, Parmar MKB 2011. A critical review of methods for the assessment of patient-level interactions in individual participant data meta-analysis of randomized trials, and guidance for practitioners. Journal of Clinical Epidemiology 64: 949-967. DOI:10.1016/j.jclinepi.2010.11.016.
    • (2011) Journal of Clinical Epidemiology , vol.64 , pp. 949-967
    • Fisher, D.J.1    Copas, A.J.2    Tierney, J.F.3    Parmar, M.K.B.4
  • 25
    • 79957995000 scopus 로고    scopus 로고
    • Meta-analyses of adverse effects data derived from randomised controlled trials as compared to observational studies: methodological overview
    • Golder S, Loke YK, Bland M 2011. Meta-analyses of adverse effects data derived from randomised controlled trials as compared to observational studies: methodological overview. PLoS Medicine 8: e1001026. DOI:10.1371/journal.pmed.1001026.
    • (2011) PLoS Medicine , vol.8 , pp. e1001026
    • Golder, S.1    Loke, Y.K.2    Bland, M.3
  • 28
    • 84856397828 scopus 로고    scopus 로고
    • Enhanced secondary analysis of survival data: reconstructing the data from published Kaplan-Meier survival curves
    • Guyot P, Ades AE, Ouwens MJNM, Welton NJ 2012. Enhanced secondary analysis of survival data: reconstructing the data from published Kaplan-Meier survival curves. BMC Medical Research Methodology 12: 9. DOI:10.1186/1471-2288-12-9.
    • (2012) BMC Medical Research Methodology , vol.12 , pp. 9
    • Guyot, P.1    Ades, A.E.2    Ouwens, M.J.N.M.3    Welton, N.J.4
  • 29
    • 78650510868 scopus 로고    scopus 로고
    • Inconsistent results in meta-analyses for the prevention of falls are found between study-level data and patient-level data
    • Haines TP, Hill AM 2011. Inconsistent results in meta-analyses for the prevention of falls are found between study-level data and patient-level data. Journal of Clinical Epidemiology 64: 154-162. DOI:10.1016/j.jclinepi.2010.04.024.
    • (2011) Journal of Clinical Epidemiology , vol.64 , pp. 154-162
    • Haines, T.P.1    Hill, A.M.2
  • 30
    • 0033635967 scopus 로고    scopus 로고
    • Zero-inflated Poisson and binomial regression with random effects: a case study
    • Hall DB 2000. Zero-inflated Poisson and binomial regression with random effects: a case study. Biometrics 56: 1030-1039.
    • (2000) Biometrics , vol.56 , pp. 1030-1039
    • Hall, D.B.1
  • 31
    • 84885849260 scopus 로고    scopus 로고
    • Issues relating to study design and risk of bias when including non-randomized studies in systematic reviews on the effects of interventions
    • Higgins JP, Ramsay C, Reeves BC, Deeks JJ, Shea B, Valentine JC, Tugwell P, Wells G 2013. Issues relating to study design and risk of bias when including non-randomized studies in systematic reviews on the effects of interventions. Research Synthesis Methods 4: 12-25. DOI:10.1002/jrsm.1056.
    • (2013) Research Synthesis Methods , vol.4 , pp. 12-25
    • Higgins, J.P.1    Ramsay, C.2    Reeves, B.C.3    Deeks, J.J.4    Shea, B.5    Valentine, J.C.6    Tugwell, P.7    Wells, G.8
  • 34
    • 33745453391 scopus 로고    scopus 로고
    • Improving ecological inference using individual-level data
    • Jackson C, Best N, Richardson S 2006. Improving ecological inference using individual-level data. Statistics in Medicine 25: 2136-2159. DOI:10.1002/sim.2370.
    • (2006) Statistics in Medicine , vol.25 , pp. 2136-2159
    • Jackson, C.1    Best, N.2    Richardson, S.3
  • 35
    • 38149102302 scopus 로고    scopus 로고
    • Hierarchical related regression for combining aggregate and individual data in studies of socio-economic disease risk factors
    • Jackson C, Best N, Richardson S 2008. Hierarchical related regression for combining aggregate and individual data in studies of socio-economic disease risk factors. Journal of the Royal Statistical Society: Series A (Statistics in Society) 171: 159-178. DOI:10.1111/j.1467-985X.2007.00500.x.
    • (2008) Journal of the Royal Statistical Society: Series A (Statistics in Society) , vol.171 , pp. 159-178
    • Jackson, C.1    Best, N.2    Richardson, S.3
  • 36
    • 84873707843 scopus 로고    scopus 로고
    • Network meta-analysis of individual and aggregate level data
    • Jansen JP 2012. Network meta-analysis of individual and aggregate level data. Research Synthesis Methods 3: 177-190. DOI:10.1002/jrsm.1048.
    • (2012) Research Synthesis Methods , vol.3 , pp. 177-190
    • Jansen, J.P.1
  • 37
    • 84896381773 scopus 로고    scopus 로고
    • Indirect treatment comparison/network meta-analysis study questionnaire to assess relevance and credibility to inform health care decision making: an ISPOR-AMCP-NPC Good Practice Task Force report
    • Jansen JP, Trikalinos T, Cappelleri JC, Daw J, Andes S, Eldessouki R, Salanti G 2014. Indirect treatment comparison/network meta-analysis study questionnaire to assess relevance and credibility to inform health care decision making: an ISPOR-AMCP-NPC Good Practice Task Force report. Value in Health 17: 157-173. DOI:10.1016/j.jval.2014.01.004.
    • (2014) Value in Health , vol.17 , pp. 157-173
    • Jansen, J.P.1    Trikalinos, T.2    Cappelleri, J.C.3    Daw, J.4    Andes, S.5    Eldessouki, R.6    Salanti, G.7
  • 38
    • 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 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.
    • (2015) Statistics in Medicine , vol.34 , pp. 1841-1863
    • Jolani, S.1    Debray, T.P.A.2    Koffijberg, H.3    van Buuren, S.4    Moons, K.G.M.5
  • 39
    • 62149129273 scopus 로고    scopus 로고
    • Meta-analysis of individual patient data versus aggregate data from longitudinal clinical trials
    • Jones AP, Riley RD, Williamson PR, Whitehead A 2009. Meta-analysis of individual patient data versus aggregate data from longitudinal clinical trials. Clinical Trials 6: 16-27. DOI:10.1177/1740774508100984.
    • (2009) Clinical Trials , vol.6 , pp. 16-27
    • Jones, A.P.1    Riley, R.D.2    Williamson, P.R.3    Whitehead, A.4
  • 40
    • 80054736097 scopus 로고    scopus 로고
    • Estimating treatment effect via simple cross design synthesis
    • Kaizar EE 2011. Estimating treatment effect via simple cross design synthesis. Statistics in Medicine 30: 2986-3009. DOI:10.1002/sim.4339.
    • (2011) Statistics in Medicine , vol.30 , pp. 2986-3009
    • Kaizar, E.E.1
  • 41
    • 39049119762 scopus 로고    scopus 로고
    • Practical methodology of meta-analysis of individual patient data using a survival outcome
    • Katsahian S, Latouche A, Mary JY, Chevret S, Porcher R 2008. Practical methodology of meta-analysis of individual patient data using a survival outcome. Contemporary Clinical Trials 29: 220-230. DOI:10.1016/j.cct.2007.08.002.
    • (2008) Contemporary Clinical Trials , vol.29 , pp. 220-230
    • Katsahian, S.1    Latouche, A.2    Mary, J.Y.3    Chevret, S.4    Porcher, R.5
  • 42
    • 84884690021 scopus 로고    scopus 로고
    • Bayesian inference for multivariate meta-analysis Box-Cox transformation models for individual patient data with applications to evaluation of cholesterol-lowering drugs
    • 39723990.
    • Kim S, Chen MH, Ibrahim JG, Shah AK, Lin J 2013. Bayesian inference for multivariate meta-analysis Box-Cox transformation models for individual patient data with applications to evaluation of cholesterol-lowering drugs. Statistics in Medicine 32: 39723990.
    • (2013) Statistics in Medicine , vol.32
    • Kim, S.1    Chen, M.H.2    Ibrahim, J.G.3    Shah, A.K.4    Lin, J.5
  • 44
    • 40049100643 scopus 로고    scopus 로고
    • Comparison of methods of handling missing data in individual patient data meta-analyses: an empirical example on antibiotics in children with acute otitis media
    • Koopman L, van der Heijden GJMG, Grobbee DE, Rovers MM 2008a. Comparison of methods of handling missing data in individual patient data meta-analyses: an empirical example on antibiotics in children with acute otitis media. American Journal of Epidemiology 167: 540-545. DOI:10.1093/aje/kwm341.
    • (2008) American Journal of Epidemiology , vol.167 , pp. 540-545
    • Koopman, L.1    van der Heijden, G.J.M.G.2    Grobbee, D.E.3    Rovers, M.M.4
  • 46
    • 0036139582 scopus 로고    scopus 로고
    • A comparison of summary patient-level covariates in meta-regression with individual patient data meta-analysis
    • Lambert PC, Sutton AJ, Abrams KR, Jones DR 2002. A comparison of summary patient-level covariates in meta-regression with individual patient data meta-analysis. Journal of Clinical Epidemiology 55: 86-94. DOI:10.1016/S0895-4356(01)00414-0.
    • (2002) Journal of Clinical Epidemiology , vol.55 , pp. 86-94
    • Lambert, P.C.1    Sutton, A.J.2    Abrams, K.R.3    Jones, D.R.4
  • 47
    • 0030864768 scopus 로고    scopus 로고
    • Grouped random effects models for Bayesian meta-analysis
    • Larose DT, Dey DK 1997. Grouped random effects models for Bayesian meta-analysis. Statistics in Medicine 16: 1817-1829. DOI:10.1002/(SICI)1097-0258(19970830)16:16<1817::AID-SIM621>3.0.CO;2-N.
    • (1997) Statistics in Medicine , vol.16 , pp. 1817-1829
    • Larose, D.T.1    Dey, D.K.2
  • 48
    • 0032501730 scopus 로고    scopus 로고
    • Summing up evidence: one answer is not always enough
    • Lau J, Ioannidis JP, Schmid CH 1998. Summing up evidence: one answer is not always enough. Lancet 351: 123-127. DOI:10.1016/S0140-6736(97)08468-7.
    • (1998) Lancet , vol.351 , pp. 123-127
    • Lau, J.1    Ioannidis, J.P.2    Schmid, C.H.3
  • 50
    • 79956202489 scopus 로고    scopus 로고
    • Logistic random effects regression models: a comparison of statistical packages for binary and ordinal outcomes
    • Li B, Lingsma HF, Steyerberg EW, Lesaffre E 2011. Logistic random effects regression models: a comparison of statistical packages for binary and ordinal outcomes. BMC Medical Research Methodology 11: 77. DOI:10.1186/1471-2288-11-77.
    • (2011) BMC Medical Research Methodology , vol.11 , pp. 77
    • Li, B.1    Lingsma, H.F.2    Steyerberg, E.W.3    Lesaffre, E.4
  • 51
    • 21844518225 scopus 로고
    • Random effects models for combining results from controlled and uncontrolled studies in a meta-analysis
    • Li Z, Begg B 1994. Random effects models for combining results from controlled and uncontrolled studies in a meta-analysis. Journal of the American Statistical Association 89: 1523-1527. DOI:10.2307/2291015.
    • (1994) Journal of the American Statistical Association , vol.89 , pp. 1523-1527
    • Li, Z.1    Begg, B.2
  • 52
    • 0141889807 scopus 로고    scopus 로고
    • Exploring the relationship between surrogates and clinical outcomes: analysis of individual patient data vs. meta-regression on group-level summary statistics
    • Li Z, Meredith MP 2003. Exploring the relationship between surrogates and clinical outcomes: analysis of individual patient data vs. meta-regression on group-level summary statistics. Journal of Biopharmaceutical Statistics 13: 777-792. DOI:10.1081/BIP-120024209.
    • (2003) Journal of Biopharmaceutical Statistics , vol.13 , pp. 777-792
    • Li, Z.1    Meredith, M.P.2
  • 54
    • 21444454737 scopus 로고    scopus 로고
    • The strengths and limitations of meta-analyses based on aggregate data
    • Lyman GH, Kuderer NM 2005. The strengths and limitations of meta-analyses based on aggregate data. BMC Medical Research Methodology 5: 14. DOI:10.1186/1471-2288-5-14.
    • (2005) BMC Medical Research Methodology , vol.5 , pp. 14
    • Lyman, G.H.1    Kuderer, N.M.2
  • 55
    • 77953255195 scopus 로고    scopus 로고
    • Comparison of one-step and two-step meta-analysis models using individual patient data
    • Mathew T, Nordström K 2010. Comparison of one-step and two-step meta-analysis models using individual patient data. Biometrical Journal 52: 271-287. DOI:10.1002/bimj.200900143.
    • (2010) Biometrical Journal , vol.52 , pp. 271-287
    • Mathew, T.1    Nordström, K.2
  • 56
    • 13844296849 scopus 로고    scopus 로고
    • Random effects survival models gave a better understanding of heterogeneity in individual patient data meta-analyses
    • Michiels S, Baujat B, Mahé C, Sargent D, Pignon J 2005. Random effects survival models gave a better understanding of heterogeneity in individual patient data meta-analyses. Journal of Clinical Epidemiology 58: 238-245. DOI:10.1016/j.jclinepi.2004.08.013.
    • (2005) Journal of Clinical Epidemiology , vol.58 , pp. 238-245
    • Michiels, S.1    Baujat, B.2    Mahé, C.3    Sargent, D.4    Pignon, J.5
  • 58
    • 84871665175 scopus 로고    scopus 로고
    • The use of two-way linear mixed models in multitreatment meta-analysis
    • Piepho HP, Williams ER, Madden LV 2012. The use of two-way linear mixed models in multitreatment meta-analysis. Biometrics 68: 1269-1277. DOI:10.1111/j.1541-0420.2012.01786.x.
    • (2012) Biometrics , vol.68 , pp. 1269-1277
    • Piepho, H.P.1    Williams, E.R.2    Madden, L.V.3
  • 59
    • 79953242702 scopus 로고    scopus 로고
    • Understanding differences in results from literature-based and individual patient meta-analyses: an example from meta-analyses of observational data
    • Poppe K, Doughty R, Yu C, Quintana M, Møller J, Klein A, Gamble G, Dini F, Whalley G, MeRGE collaborators 2011. Understanding differences in results from literature-based and individual patient meta-analyses: an example from meta-analyses of observational data. International Journal of Cardiology 148: 209-213. DOI:10.1016/j.ijcard.2009.09.566.
    • (2011) International Journal of Cardiology , vol.148 , pp. 209-213
    • Poppe, K.1    Doughty, R.2    Yu, C.3    Quintana, M.4    Møller, J.5    Klein, A.6    Gamble, G.7    Dini, F.8    Whalley, G.9
  • 60
    • 0034736553 scopus 로고    scopus 로고
    • Hierarchical models in generalized synthesis of evidence: an example based on studies of breast cancer screening
    • Prevost TC, Abrams KR, Jones DR 2000. Hierarchical models in generalized synthesis of evidence: an example based on studies of breast cancer screening. Statistics in Medicine 19: 3359-3376. DOI:10.1002/1097-0258(20001230)19:24<3359::AID-SIM710>3.0.CO;2-N.
    • (2000) Statistics in Medicine , vol.19 , pp. 3359-3376
    • Prevost, T.C.1    Abrams, K.R.2    Jones, D.R.3
  • 61
    • 84898059468 scopus 로고    scopus 로고
    • A linearization approach for the model-based analysis of combined aggregate and individual patient data
    • Ravva P, Karlsson MO, French JL 2014. A linearization approach for the model-based analysis of combined aggregate and individual patient data. Statistics in Medicine 33: 1460-1476. DOI:10.1002/sim.6045.
    • (2014) Statistics in Medicine , vol.33 , pp. 1460-1476
    • Ravva, P.1    Karlsson, M.O.2    French, J.L.3
  • 62
    • 84883349815 scopus 로고    scopus 로고
    • An introduction to methodological issues when including non-randomised studies in systematic reviews on the effects of interventions
    • Reeves BC, Higgins JPT, Ramsay C, Shea B, Tugwell P, Wells GA 2003. An introduction to methodological issues when including non-randomised studies in systematic reviews on the effects of interventions. Research Synthesis Methods 4: 1-11. DOI:10.1002/jrsm.1068.
    • (2003) Research Synthesis Methods , vol.4 , pp. 1-11
    • Reeves, B.C.1    Higgins, J.P.T.2    Ramsay, C.3    Shea, B.4    Tugwell, P.5    Wells, G.A.6
  • 63
    • 84887138473 scopus 로고    scopus 로고
    • Multiple imputation for handling systematically missing confounders in meta-analysis of individual participant data
    • 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.
    • (2013) Statistics in Medicine , vol.32 , pp. 4890-4905
    • Resche-Rigon, M.1    White, I.R.2    Bartlett, J.W.3    Peters, S.A.E.4    Thompson, S.G.5
  • 64
    • 84879169822 scopus 로고    scopus 로고
    • Meta-analysis of randomised trials with a continuous outcome according to baseline imbalance and availability of individual participant data
    • Riley RD, Kauser I, Bland M, Thijs L, Staessen JA, Wang J, Gueyffier F, Deeks JJ 2013. Meta-analysis of randomised trials with a continuous outcome according to baseline imbalance and availability of individual participant data. Statistics in Medicine 32: 2747-2766. DOI:10.1002/sim.5726.
    • (2013) Statistics in Medicine , vol.32 , pp. 2747-2766
    • Riley, R.D.1    Kauser, I.2    Bland, M.3    Thijs, L.4    Staessen, J.A.5    Wang, J.6    Gueyffier, F.7    Deeks, J.J.8
  • 65
    • 77749306261 scopus 로고    scopus 로고
    • Meta-analysis of individual participant data: rationale, conduct, and reporting
    • Riley RD, Lambert PC, Abo-Zaid G 2010. Meta-analysis of individual participant data: rationale, conduct, and reporting. British Medical Journal 340: c221. DOI:10.1136/bmj.c221.
    • (2010) British Medical Journal , vol.340 , pp. c221
    • Riley, R.D.1    Lambert, P.C.2    Abo-Zaid, G.3
  • 67
    • 34047170379 scopus 로고    scopus 로고
    • Evidence synthesis combining individual patient data and aggregate data: a systematic review identi_ed current practice and possible methods
    • Riley RD, Simmonds MC, Look MP 2007. Evidence synthesis combining individual patient data and aggregate data: a systematic review identi_ed current practice and possible methods. Journal of Clinical Epidemiology 60: 431-439. DOI:10.1016/j.jclinepi.2006.09.009.
    • (2007) Journal of Clinical Epidemiology , vol.60 , pp. 431-439
    • Riley, R.D.1    Simmonds, M.C.2    Look, M.P.3
  • 68
    • 78149266539 scopus 로고    scopus 로고
    • Meta-analysis of a binary outcome using individual participant data and aggregate data
    • Riley RD, Steyerberg EW 2010. Meta-analysis of a binary outcome using individual participant data and aggregate data. Research Synthesis Methods 1: 2-19. DOI:10.1002/jrsm.4.
    • (2010) Research Synthesis Methods , vol.1 , pp. 2-19
    • Riley, R.D.1    Steyerberg, E.W.2
  • 69
    • 42949128576 scopus 로고    scopus 로고
    • Investigating trial and treatment heterogeneity in an individual patient data meta-analysis of survival data by means of the penalized maximum likelihood approach
    • Rondeau V, Michiels S, Liquet B, Pignon JP 2008. Investigating trial and treatment heterogeneity in an individual patient data meta-analysis of survival data by means of the penalized maximum likelihood approach. Statistics in Medicine 27: 1894-1910. DOI:10.1002/sim.3161.
    • (2008) Statistics in Medicine , vol.27 , pp. 1894-1910
    • Rondeau, V.1    Michiels, S.2    Liquet, B.3    Pignon, J.P.4
  • 70
    • 0037102914 scopus 로고    scopus 로고
    • Flexible parametric proportional-hazards and proportional odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects
    • Royston P, Parmar MKB 2002. Flexible parametric proportional-hazards and proportional odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects. Statistics in Medicine 21: 2175-2197. DOI:10.1002/sim.1203.
    • (2002) Statistics in Medicine , vol.21 , pp. 2175-2197
    • Royston, P.1    Parmar, M.K.B.2
  • 71
    • 4344621625 scopus 로고    scopus 로고
    • A new approach to modelling interactions between treatment and continuous covariates in clinical trials by using fractional polynomials
    • PMID: 15287081.
    • Royston P, Sauerbrei W 2004. A new approach to modelling interactions between treatment and continuous covariates in clinical trials by using fractional polynomials. Statistics in Medicine 23: 2509-2525. PMID: 15287081.
    • (2004) Statistics in Medicine , vol.23 , pp. 2509-2525
    • Royston, P.1    Sauerbrei, W.2
  • 73
    • 84870254031 scopus 로고    scopus 로고
    • Mixed treatment comparisons using aggregate and individual participant level data
    • Saramago P, Sutton AJ, Cooper NJ, Manca A 2012. Mixed treatment comparisons using aggregate and individual participant level data. Statistics in Medicine 31: 3516-3536. DOI:10.1002/sim.5442.
    • (2012) Statistics in Medicine , vol.31 , pp. 3516-3536
    • Saramago, P.1    Sutton, A.J.2    Cooper, N.J.3    Manca, A.4
  • 74
    • 0032442347 scopus 로고    scopus 로고
    • A general framework for random effects survival analysis in the Cox proportional hazards setting
    • Sargent DJ 1998. A general framework for random effects survival analysis in the Cox proportional hazards setting. Biometrics 54: 1486-1497.
    • (1998) Biometrics , vol.54 , pp. 1486-1497
    • Sargent, D.J.1
  • 76
    • 4444333174 scopus 로고    scopus 로고
    • Meta-regression detected associations between heterogeneous treatment effects and study-level, but not patient-level, factors
    • Schmid CH, Stark PC, Berlin JA, Landais P, Lau J 2004. Meta-regression detected associations between heterogeneous treatment effects and study-level, but not patient-level, factors. Journal of Clinical Epidemiology 57: 683-697. DOI:10.1016/j.jclinepi.2003.12.001.
    • (2004) Journal of Clinical Epidemiology , vol.57 , pp. 683-697
    • Schmid, C.H.1    Stark, P.C.2    Berlin, J.A.3    Landais, P.4    Lau, J.5
  • 77
    • 84876907682 scopus 로고    scopus 로고
    • Differences in interaction and subgroup-specific effects were observed between randomized and nonrandomized studies in three empirical examples
    • Schmidt AF, Rovers MM, Klungel OH, Hoes AW, Knol MJ, Nielen M, de Boer A, Groenwold RHH 2013. Differences in interaction and subgroup-specific effects were observed between randomized and nonrandomized studies in three empirical examples. Journal of Clinical Epidemiology 66: 599-607. DOI:10.1016/j.jclinepi.2012.08.008.
    • (2013) Journal of Clinical Epidemiology , vol.66 , pp. 599-607
    • Schmidt, A.F.1    Rovers, M.M.2    Klungel, O.H.3    Hoes, A.W.4    Knol, M.J.5    Nielen, M.6    de Boer, A.7    Groenwold, R.H.H.8
  • 78
    • 78649677374 scopus 로고    scopus 로고
    • One-stage parametric meta-analysis of time-to-event outcomes
    • Siannis F, Barrett J, Farewell V, Tierney J 2010. One-stage parametric meta-analysis of time-to-event outcomes. Statistics in Medicine 29: 3030-3045. DOI:10.1002/sim.4086.
    • (2010) Statistics in Medicine , vol.29 , pp. 3030-3045
    • Siannis, F.1    Barrett, J.2    Farewell, V.3    Tierney, J.4
  • 80
    • 77956524623 scopus 로고    scopus 로고
    • Comparative effectiveness without head-to-head trials: a method for matching adjusted indirect comparisons applied to psoriasis treatment with adalimumab or etanercept
    • Signorovitch JE, Wu EQ, Yu AP, Gerrits CM, Kantor E, Bao Y, Gupta SR, Mulani PM 2010. Comparative effectiveness without head-to-head trials: a method for matching adjusted indirect comparisons applied to psoriasis treatment with adalimumab or etanercept. PharmacoEconomics 28: 935-945. DOI:10.2165/11538370-000000000-00000.
    • (2010) PharmacoEconomics , vol.28 , pp. 935-945
    • Signorovitch, J.E.1    Wu, E.Q.2    Yu, A.P.3    Gerrits, C.M.4    Kantor, E.5    Bao, Y.6    Gupta, S.R.7    Mulani, P.M.8
  • 81
    • 34250620135 scopus 로고    scopus 로고
    • Covariate heterogeneity in meta-analysis: criteria for deciding between meta-regression and individual patient data
    • Simmonds MC, Higgins JPT 2007. Covariate heterogeneity in meta-analysis: criteria for deciding between meta-regression and individual patient data. Statistics in Medicine 26: 2982-2999. DOI:10.1002/sim.2768.
    • (2007) Statistics in Medicine , vol.26 , pp. 2982-2999
    • Simmonds, M.C.1    Higgins, J.P.T.2
  • 82
    • 21344439360 scopus 로고    scopus 로고
    • Meta-analysis of individual patient data from randomized trials: a review of methods used in practice
    • Simmonds MC, Higgins JPT, Stewart LA, Tierney JF, Clarke MJ, Thompson SG 2005. Meta-analysis of individual patient data from randomized trials: a review of methods used in practice. Clinical Trials 2: 209-217. DOI:10.1191/1740774505cn087oa.
    • (2005) Clinical Trials , vol.2 , pp. 209-217
    • Simmonds, M.C.1    Higgins, J.P.T.2    Stewart, L.A.3    Tierney, J.F.4    Clarke, M.J.5    Thompson, S.G.6
  • 83
    • 0030919503 scopus 로고    scopus 로고
    • Comparison of effect estimates from a meta-analysis of summary data from published studies and from a meta-analysis using individual patient data for ovarian cancer studies
    • Steinberg KK, Smith SJ, Stroup DF, Olkin I, Lee NC, Williamson GD, Thacker SB 1997. Comparison of effect estimates from a meta-analysis of summary data from published studies and from a meta-analysis using individual patient data for ovarian cancer studies. American Journal of Epidemiology 145: 917-925.
    • (1997) American Journal of Epidemiology , vol.145 , pp. 917-925
    • Steinberg, K.K.1    Smith, S.J.2    Stroup, D.F.3    Olkin, I.4    Lee, N.C.5    Williamson, G.D.6    Thacker, S.B.7
  • 84
    • 84867036811 scopus 로고    scopus 로고
    • Statistical analysis of individual participant data meta-analyses: a comparison of methods and recommendations for practice
    • Stewart GB, Altman DD, Askie L, Duley L, Simmonds M, Stewart LA 2012. Statistical analysis of individual participant data meta-analyses: a comparison of methods and recommendations for practice. PLoS One 7: e46042. DOI:10.1371/journal.pone.0046042.
    • (2012) PLoS One , vol.7 , pp. e46042
    • Stewart, G.B.1    Altman, D.D.2    Askie, L.3    Duley, L.4    Simmonds, M.5    Stewart, L.A.6
  • 85
    • 0036167948 scopus 로고    scopus 로고
    • To IPD or not to IPD? Advantages and disadvantages of systematic reviews using individual patient data
    • Stewart LA, Tierney JF 2002. To IPD or not to IPD? Advantages and disadvantages of systematic reviews using individual patient data. Evaluation & the Health Professions 25: 76-97. DOI:10.1177/0163278702025001006.
    • (2002) Evaluation & the Health Professions , vol.25 , pp. 76-97
    • Stewart, L.A.1    Tierney, J.F.2
  • 86
    • 78649672029 scopus 로고    scopus 로고
    • Random effects meta-analysis of event outcome in the framework of the generalized linear mixed model with applications in sparse data
    • Stijnen T, Hamza TH, Özdemir P 2010. Random effects meta-analysis of event outcome in the framework of the generalized linear mixed model with applications in sparse data. Statistics in Medicine 29: 3046-3067. DOI:10.1002/sim.4040.
    • (2010) Statistics in Medicine , vol.29 , pp. 3046-3067
    • Stijnen, T.1    Hamza, T.H.2    Özdemir, P.3
  • 87
    • 79952586276 scopus 로고    scopus 로고
    • The use of propensity scores to assess the generalizability of results from randomized trials: use of propensity scores to assess generalizability
    • Stuart EA, Cole SR, Bradshaw CP, Leaf PJ 2011. The use of propensity scores to assess the generalizability of results from randomized trials: use of propensity scores to assess generalizability. Journal of the Royal Statistical Society: Series A (Statistics in Society) 174: 369-386. DOI:10.1111/j.1467-985X.2010.00673.x.
    • (2011) Journal of the Royal Statistical Society: Series A (Statistics in Society) , vol.174 , pp. 369-386
    • Stuart, E.A.1    Cole, S.R.2    Bradshaw, C.P.3    Leaf, P.J.4
  • 88
    • 68149156091 scopus 로고    scopus 로고
    • The devil is in the details...or not? A primer on individual patient data meta-analysis
    • Sud S, Douketis J 2009. The devil is in the details...or not? A primer on individual patient data meta-analysis. Evidence-Based Medicine 14: 100-101. DOI:10.1136/ebm.14.4.100.
    • (2009) Evidence-Based Medicine , vol.14 , pp. 100-101
    • Sud, S.1    Douketis, J.2
  • 90
    • 65649088642 scopus 로고    scopus 로고
    • Evidence synthesis as the key to more coherent and efficient research
    • Sutton AJ, Cooper NJ, Jones DR 2009. Evidence synthesis as the key to more coherent and efficient research. BMC Medical Research Methodology 9: 29. DOI:10.1186/1471-2288-9-29.
    • (2009) BMC Medical Research Methodology , vol.9 , pp. 29
    • Sutton, A.J.1    Cooper, N.J.2    Jones, D.R.3
  • 91
    • 39549093107 scopus 로고    scopus 로고
    • Recent developments in meta-analysis
    • Sutton AJ, Higgins JPT 2008. Recent developments in meta-analysis. Statistics in Medicine 27: 625-650. DOI:10.1002/sim.2934.
    • (2008) Statistics in Medicine , vol.27 , pp. 625-650
    • Sutton, A.J.1    Higgins, J.P.T.2
  • 92
    • 39549122751 scopus 로고    scopus 로고
    • Meta-analysis of individual- and aggregate-level data
    • Sutton AJ, Kendrick D, Coupland C 2008. Meta-analysis of individual- and aggregate-level data. Statistics in Medicine 27: 651-669. DOI:10.1002/sim.2916.
    • (2008) Statistics in Medicine , vol.27 , pp. 651-669
    • Sutton, A.J.1    Kendrick, D.2    Coupland, C.3
  • 93
    • 14544299743 scopus 로고    scopus 로고
    • Individual patient-level and study-level meta-analysis for investigating modifiers of treatment effect
    • Teramukai S, Matsuyama Y, Mizuno S, Sakamoto J 2004. Individual patient-level and study-level meta-analysis for investigating modifiers of treatment effect. Japanese Journal of Clinical Oncology 34: 717-721. DOI:10.1093/jjco/hyh138.
    • (2004) Japanese Journal of Clinical Oncology , vol.34 , pp. 717-721
    • Teramukai, S.1    Matsuyama, Y.2    Mizuno, S.3    Sakamoto, J.4
  • 94
    • 84902756973 scopus 로고    scopus 로고
    • Systematic review of methods for individual patient data meta-analysis with binary outcomes
    • Thomas D, Radji S, Benedetti A 2014. Systematic review of methods for individual patient data meta-analysis with binary outcomes. BMC Medical Research Methodology 14: 79. DOI:10.1186/1471-2288-14-79.
    • (2014) BMC Medical Research Methodology , vol.14 , pp. 79
    • Thomas, D.1    Radji, S.2    Benedetti, A.3
  • 95
    • 73449107423 scopus 로고    scopus 로고
    • Thinking big: large-scale collaborative research in observational epidemiology
    • Thompson A 2009. Thinking big: large-scale collaborative research in observational epidemiology. European Journal of Epidemiology 24: 727-731. DOI:10.1007/s10654-009-9412-1.
    • (2009) European Journal of Epidemiology , vol.24 , pp. 727-731
    • Thompson, A.1
  • 96
    • 78149260793 scopus 로고    scopus 로고
    • Statistical methods for the time-to-event analysis of individual participant data from multiple epidemiological studies
    • Thompson S, Kaptoge S, White I, Wood A, Perry P, Danesh J, The Emerging Risk Factors Collaboration 2010. Statistical methods for the time-to-event analysis of individual participant data from multiple epidemiological studies. International Journal of Epidemiology 39: 1345-1359. DOI:10.1093/ije/dyq063.
    • (2010) International Journal of Epidemiology , vol.39 , pp. 1345-1359
    • Thompson, S.1    Kaptoge, S.2    White, I.3    Wood, A.4    Perry, P.5    Danesh, J.6
  • 97
    • 0035183210 scopus 로고    scopus 로고
    • Multilevel models for meta-analysis, and their application to absolute risk differences
    • Thompson SG, Turner RM, Warn DE 2001. Multilevel models for meta-analysis, and their application to absolute risk differences. Statistical Methods in Medical Research 10: 375-392. DOI:10.1177/096228020101000602.
    • (2001) Statistical Methods in Medical Research , vol.10 , pp. 375-392
    • Thompson, S.G.1    Turner, R.M.2    Warn, D.E.3
  • 100
    • 0035133211 scopus 로고    scopus 로고
    • Predictive modeling and heterogeneity of baseline risk in meta-analysis of individual patient data
    • Trikalinos TA, Ioannidis JP 2001. Predictive modeling and heterogeneity of baseline risk in meta-analysis of individual patient data. Journal of Clinical Epidemiology 54: 245-252. DOI:10.1016/S0895-4356(00)00311-5.
    • (2001) Journal of Clinical Epidemiology , vol.54 , pp. 245-252
    • Trikalinos, T.A.1    Ioannidis, J.P.2
  • 101
    • 40949140242 scopus 로고    scopus 로고
    • A comparison of methods for fixed effects meta-analysis of individual patient data with time to event outcomes
    • Tudur Smith C, Williamson PR 2007. A comparison of methods for fixed effects meta-analysis of individual patient data with time to event outcomes. Clinical Trials 4: 621-630. DOI:10.1177/1740774507085276.
    • (2007) Clinical Trials , vol.4 , pp. 621-630
    • Tudur Smith, C.1    Williamson, P.R.2
  • 102
    • 17844371350 scopus 로고    scopus 로고
    • Investigating heterogeneity in an individual patient data meta-analysis of time to event outcomes
    • Tudur Smith C, Williamson PR, Marson AG 2005a. Investigating heterogeneity in an individual patient data meta-analysis of time to event outcomes. Statistics in Medicine 24: 1307-1319. DOI:10.1002/sim.2050.
    • (2005) Statistics in Medicine , vol.24 , pp. 1307-1319
    • Tudur Smith, C.1    Williamson, P.R.2    Marson, A.G.3
  • 103
    • 25444433144 scopus 로고    scopus 로고
    • An overview of methods and empirical comparison of aggregate data and individual patient data results for investigating heterogeneity in meta-analysis of time-to-event outcomes
    • Tudur Smith C, Williamson PR, Marson AG 2005b. An overview of methods and empirical comparison of aggregate data and individual patient data results for investigating heterogeneity in meta-analysis of time-to-event outcomes. Journal of Evaluation in Clinical Practice 11: 468-478. DOI:10.1111/j.1365-2753.2005.00559.x.
    • (2005) Journal of Evaluation in Clinical Practice , vol.11 , pp. 468-478
    • Tudur Smith, C.1    Williamson, P.R.2    Marson, A.G.3
  • 104
    • 0034736559 scopus 로고    scopus 로고
    • A multilevel model framework for meta-analysis of clinical trials with binary outcomes
    • Turner RM, Omar RZ, Yang M, Goldstein H, Thompson SG 2000. A multilevel model framework for meta-analysis of clinical trials with binary outcomes. Statistics in Medicine 19: 3417-3432. DOI:10.1002/1097-0258(20001230)19:24<3417::AID-SIM614>3.0.CO;2-L.
    • (2000) Statistics in Medicine , vol.19 , pp. 3417-3432
    • Turner, R.M.1    Omar, R.Z.2    Yang, M.3    Goldstein, H.4    Thompson, S.G.5
  • 105
    • 0034736561 scopus 로고    scopus 로고
    • Proportional hazards model with random effects
    • Vaida F, Xu R 2000. Proportional hazards model with random effects. Statistics in Medicine 19: 3309-3324. DOI:10.1002/1097-0258(20001230)19:24<3309::AID-SIM825>3.0.CO;2-9.
    • (2000) Statistics in Medicine , vol.19 , pp. 3309-3324
    • Vaida, F.1    Xu, R.2
  • 106
    • 84884991169 scopus 로고    scopus 로고
    • Issues relating to confounding and meta-analysis when including non-randomized studies in systematic reviews on the effects of interventions
    • Valentine JC, Thompson SG 2013. Issues relating to confounding and meta-analysis when including non-randomized studies in systematic reviews on the effects of interventions. Research Synthesis Methods 4: 26-35. DOI:10.1002/jrsm.1064.
    • (2013) Research Synthesis Methods , vol.4 , pp. 26-35
    • Valentine, J.C.1    Thompson, S.G.2
  • 109
    • 75749150931 scopus 로고    scopus 로고
    • Individual patient meta-analysis - rewards and challenges
    • van Walraven C 2010. Individual patient meta-analysis - rewards and challenges. Journal of Clinical Epidemiology 63: 235-237. DOI:10.1016/j.jclinepi.2009.04.001.
    • (2010) Journal of Clinical Epidemiology , vol.63 , pp. 235-237
    • van Walraven, C.1
  • 110
    • 84893603578 scopus 로고    scopus 로고
    • Checklists of methodological issues for review authors to consider when including non-randomized studies in systematic reviews
    • Wells GA, Shea B, Higgins JP, Sterne J, Tugwell P, Reeves BC 2013. Checklists of methodological issues for review authors to consider when including non-randomized studies in systematic reviews. Research Synthesis Methods 4: 63-77. DOI:10.1002/jrsm.1077.
    • (2013) Research Synthesis Methods , vol.4 , pp. 63-77
    • Wells, G.A.1    Shea, B.2    Higgins, J.P.3    Sterne, J.4    Tugwell, P.5    Reeves, B.C.6
  • 111
    • 39549086989 scopus 로고    scopus 로고
    • Allowing for uncertainty due to missing data in meta-analysis-part 2: hierarchical models
    • White IR, Welton NJ, Wood AM, Ades AE, Higgins JPT 2008. Allowing for uncertainty due to missing data in meta-analysis-part 2: hierarchical models. Statistics in Medicine 27: 728-745. DOI:10.1002/sim.3007.
    • (2008) Statistics in Medicine , vol.27 , pp. 728-745
    • White, I.R.1    Welton, N.J.2    Wood, A.M.3    Ades, A.E.4    Higgins, J.P.T.5
  • 113
    • 0033566653 scopus 로고    scopus 로고
    • Investigating centre effects in a multi-centre clinical trial of superficial bladder cancer
    • Yamaguchi T, Ohashi Y 1999. Investigating centre effects in a multi-centre clinical trial of superficial bladder cancer. Statistics in Medicine 18: 1961-1971.
    • (1999) Statistics in Medicine , vol.18 , pp. 1961-1971
    • Yamaguchi, T.1    Ohashi, Y.2
  • 114
    • 0035987873 scopus 로고    scopus 로고
    • Proportional hazards models with random effects to examine centre effects in multicentre cancer clinical trials
    • Yamaguchi T, Ohashi Y, Matsuyama Y 2002. Proportional hazards models with random effects to examine centre effects in multicentre cancer clinical trials. Statistical Methods in Medical Research 11: 221-236. DOI:10.1191/0962280202sm284ra.
    • (2002) Statistical Methods in Medical Research , vol.11 , pp. 221-236
    • Yamaguchi, T.1    Ohashi, Y.2    Matsuyama, Y.3
  • 115
    • 0035888098 scopus 로고    scopus 로고
    • Zero-inflated Poisson regression with random effects to evaluate an occupational injury prevention programme
    • Yau KK, Lee AH 2001. Zero-inflated Poisson regression with random effects to evaluate an occupational injury prevention programme. Statistics in Medicine 20: 2907-2920. DOI:10.1002/sim.860.
    • (2001) Statistics in Medicine , vol.20 , pp. 2907-2920
    • Yau, K.K.1    Lee, A.H.2
  • 116
    • 0032525798 scopus 로고    scopus 로고
    • ML and REML estimation in survival analysis with time dependent correlated frailty
    • Yau KK, McGilchrist CA 1998. ML and REML estimation in survival analysis with time dependent correlated frailty. Statistics in Medicine 17: 1201-1213. DOI:10.1002/(SICI)1097-0258(19980615)17:11<1201::AID-SIM845>3.0.CO;2-7.
    • (1998) Statistics in Medicine , vol.17 , pp. 1201-1213
    • Yau, K.K.1    McGilchrist, C.A.2
  • 117
    • 45749108295 scopus 로고    scopus 로고
    • Multiple imputation inference for multivariate multilevel continuous data with ignorable non-response
    • Yucel RM 2008. Multiple imputation inference for multivariate multilevel continuous data with ignorable non-response. Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences 366: 2389-2403. DOI:10.1098/rsta.2008.0038.
    • (2008) Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences , vol.366 , pp. 2389-2403
    • Yucel, R.M.1
  • 118
    • 80051757583 scopus 로고    scopus 로고
    • Random-covariances and mixed-effects models for imputing multivariate multilevel continuous data
    • Yucel RM 2011. Random-covariances and mixed-effects models for imputing multivariate multilevel continuous data. Statistical Modelling 11: 351-370. DOI:10.1177/1471082X1001100404.
    • (2011) Statistical Modelling , vol.11 , pp. 351-370
    • Yucel, R.M.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.