메뉴 건너뛰기




Volumn 88, Issue , 2017, Pages 67-80

A systematic survey of the methods literature on the reporting quality and optimal methods of handling participants with missing outcome data for continuous outcomes in randomized controlled trials

(22)  Zhang, Yuqing a,b   Alyass, Akram a   Vanniyasingam, Thuva a   Sadeghirad, Behnam a,c   Flórez, Iván D a,d   Pichika, Sathish Chandra a   Kennedy, Sean Alexander e   Abdulkarimova, Ulviya a   Zhang, Yuan a   Iljon, Tzvia a   Morgano, Gian Paolo a   Colunga Lozano, Luis E f   Aloweni, Fazila Abu Bakar g   Lopes, Luciane C h,i   Yepes Nuñez, Juan José a,d   Fei, Yutong j   Wang, Li k   Kahale, Lara A l   Meyre, David a   Akl, Elie A l   more..


Author keywords

Continuous outcome; Missing participant data; MPD; Randomized controlled trials; Simulation; Statistical methods

Indexed keywords

ANALYTICAL ERROR; CHECKLIST; CLASSIFICATION; CONSENSUS; HUMAN; INFORMATION PROCESSING; MEDICAL INFORMATION; MISSING PARTICIPANT DATA HANDLING; PRIORITY JOURNAL; PUBLISHING; RANDOMIZED CONTROLLED TRIAL (TOPIC); RESEARCH SUBJECT; REVIEW; SAMPLE SIZE; SIMULATION; STATISTICAL ANALYSIS; COMPUTER SIMULATION; FOLLOW UP; MEASUREMENT ACCURACY; METHODOLOGY; PATIENT DROPOUT; QUESTIONNAIRE; STATISTICAL BIAS; STATISTICAL MODEL; STATISTICS AND NUMERICAL DATA;

EID: 85028729470     PISSN: 08954356     EISSN: 18785921     Source Type: Journal    
DOI: 10.1016/j.jclinepi.2017.05.016     Document Type: Review
Times cited : (31)

References (56)
  • 2
    • 84927950888 scopus 로고    scopus 로고
    • Impact of missing participant data for dichotomous outcomes on pooled effect estimates in systematic reviews: a protocol for a methodological study
    • Akl, E.A., Kahale, L.A., Agarwal, A., Al-Matari, N., Ebrahim, S., Alexander, P.E., et al. Impact of missing participant data for dichotomous outcomes on pooled effect estimates in systematic reviews: a protocol for a methodological study. Syst Rev, 3, 2014, 137.
    • (2014) Syst Rev , vol.3 , pp. 137
    • Akl, E.A.1    Kahale, L.A.2    Agarwal, A.3    Al-Matari, N.4    Ebrahim, S.5    Alexander, P.E.6
  • 3
    • 48749124902 scopus 로고    scopus 로고
    • Sampling-based approach to determining outcomes of patients lost to follow-up in antiretroviral therapy scale-up programs in Africa
    • Sampling-based approach to determining outcomes of patients lost to follow-up in antiretroviral therapy scale-up programs in Africa. JAMA 300 (2008), 506–507.
    • (2008) JAMA , vol.300 , pp. 506-507
  • 4
    • 85101444608 scopus 로고    scopus 로고
    • Statistical analysis with missing data
    • Wiley New York
    • Little, R.J.A., Rubin, D., Statistical analysis with missing data. 1987, Wiley, New York.
    • (1987)
    • Little, R.J.A.1    Rubin, D.2
  • 5
    • 84884486714 scopus 로고    scopus 로고
    • Strategies for dealing with missing data in clinical trials: from design to analysis
    • Dziura, J.D., Post, L.A., Zhao, Q., Fu, Z., Peduzzi, P., Strategies for dealing with missing data in clinical trials: from design to analysis. Yale J Biol Med 86 (2013), 343–358.
    • (2013) Yale J Biol Med , vol.86 , pp. 343-358
    • Dziura, J.D.1    Post, L.A.2    Zhao, Q.3    Fu, Z.4    Peduzzi, P.5
  • 6
    • 84880099876 scopus 로고    scopus 로고
    • A tutorial on sensitivity analyses in clinical trials: the what, why, when and how
    • Thabane, L., Mbuagbaw, L., Zhang, S., Samaan, Z., Marcucci, M., Ye, C., et al. A tutorial on sensitivity analyses in clinical trials: the what, why, when and how. BMC Med Res Methodol, 13, 2013, 92.
    • (2013) BMC Med Res Methodol , vol.13 , pp. 92
    • Thabane, L.1    Mbuagbaw, L.2    Zhang, S.3    Samaan, Z.4    Marcucci, M.5    Ye, C.6
  • 7
    • 0042066687 scopus 로고    scopus 로고
    • On the performance of random-coefficient pattern-mixture models for non-ignorable drop-out
    • Demirtas, H., Schafer, J.L., On the performance of random-coefficient pattern-mixture models for non-ignorable drop-out. Stat Med 22 (2003), 2553–2575.
    • (2003) Stat Med , vol.22 , pp. 2553-2575
    • Demirtas, H.1    Schafer, J.L.2
  • 8
    • 75749133269 scopus 로고    scopus 로고
    • An overview of practical approaches for handling missing data in clinical trials
    • DeSouza, C.M., Legedza, A.T., Sankoh, A.J., An overview of practical approaches for handling missing data in clinical trials. J Biopharm Stat 19 (2009), 1055–1073.
    • (2009) J Biopharm Stat , vol.19 , pp. 1055-1073
    • DeSouza, C.M.1    Legedza, A.T.2    Sankoh, A.J.3
  • 9
    • 84897051744 scopus 로고    scopus 로고
    • Missing data in alcohol clinical trials: a comparison of methods
    • Hallgren, K.A., Witkiewitz, K., Missing data in alcohol clinical trials: a comparison of methods. Alcohol Clin Exp Res 37 (2013), 2152–2160.
    • (2013) Alcohol Clin Exp Res , vol.37 , pp. 2152-2160
    • Hallgren, K.A.1    Witkiewitz, K.2
  • 10
    • 82955205812 scopus 로고    scopus 로고
    • Methods for using data abstracted from medical charts to impute longitudinal missing data in a clinical trial
    • Hebert, P.L., Taylor, L.T., Wang, J.J., Bergman, M.A., Methods for using data abstracted from medical charts to impute longitudinal missing data in a clinical trial. Value Health 14 (2011), 1085–1091.
    • (2011) Value Health , vol.14 , pp. 1085-1091
    • Hebert, P.L.1    Taylor, L.T.2    Wang, J.J.3    Bergman, M.A.4
  • 12
    • 77952832305 scopus 로고    scopus 로고
    • Semiparametric dimension reduction estimation for mean response with missing data
    • Hu, Z., Follmann, D.A., Qin, J., Semiparametric dimension reduction estimation for mean response with missing data. Biometrika 97 (2010), 305–319.
    • (2010) Biometrika , vol.97 , pp. 305-319
    • Hu, Z.1    Follmann, D.A.2    Qin, J.3
  • 13
    • 84880099876 scopus 로고    scopus 로고
    • A tutorial on sensitivity analyses in clinical trials: the what, why, when and how
    • A tutorial on sensitivity analyses in clinical trials: the what, why, when and how. BMC Med Res Methodol, 13, 2013, 92.
    • (2013) BMC Med Res Methodol , vol.13 , pp. 92
  • 15
    • 0037947306 scopus 로고    scopus 로고
    • Interpreting incomplete data in studies of diet and weight loss
    • Ware, J.H., Interpreting incomplete data in studies of diet and weight loss. N Engl J Med 348 (2003), 2136–2137.
    • (2003) N Engl J Med , vol.348 , pp. 2136-2137
    • Ware, J.H.1
  • 16
    • 54349127108 scopus 로고    scopus 로고
    • Does analysis using “last observation carried forward” introduce bias in dementia research?
    • Molnar, F.J., Hutton, B., Fergusson, D., Does analysis using “last observation carried forward” introduce bias in dementia research?. CMAJ 179 (2008), 751–753.
    • (2008) CMAJ , vol.179 , pp. 751-753
    • Molnar, F.J.1    Hutton, B.2    Fergusson, D.3
  • 17
    • 85101444608 scopus 로고    scopus 로고
    • Statistical analysis with missing data
    • 2nd ed. Wiley New York, NY
    • Little, R.J.A., Rubin, D., Statistical analysis with missing data. 2nd ed., 2002, Wiley, New York, NY.
    • (2002)
    • Little, R.J.A.1    Rubin, D.2
  • 18
    • 0003738155 scopus 로고
    • Multiple imputation for nonresponse in surveys
    • John Wiley & Sons, Inc. New York, NY
    • Rubin, D.B., Multiple imputation for nonresponse in surveys. 1987, John Wiley & Sons, Inc., New York, NY.
    • (1987)
    • Rubin, D.B.1
  • 19
    • 0003398574 scopus 로고    scopus 로고
    • Analysis of incomplete multivariate data
    • Chapman and Hall New York
    • Schafer, J.L., Analysis of incomplete multivariate data. 1997, Chapman and Hall, New York.
    • (1997)
    • Schafer, J.L.1
  • 20
    • 23044525261 scopus 로고    scopus 로고
    • Multiple imputation in practice: comparison of software packages for regression models with missing variables
    • Horton, N.J., Lipsitz, S., Multiple imputation in practice: comparison of software packages for regression models with missing variables. Am Stat 55 (2001), 244–254.
    • (2001) Am Stat , vol.55 , pp. 244-254
    • Horton, N.J.1    Lipsitz, S.2
  • 21
    • 0043198343 scopus 로고    scopus 로고
    • Modern statistical methods for handling missing repeated measurements in obesity trial data: beyond LOCF
    • Gadbury, G.L., Coffey, C.S., Allison, D.B., Modern statistical methods for handling missing repeated measurements in obesity trial data: beyond LOCF. Obes Reb 4 (2003), 175–184.
    • (2003) Obes Reb , vol.4 , pp. 175-184
    • Gadbury, G.L.1    Coffey, C.S.2    Allison, D.B.3
  • 22
    • 84881000671 scopus 로고    scopus 로고
    • A two-step multiple imputation for analysis of repeated measures with left-censored and missing data
    • Liu, G.F., Hu, P., Mehrotra, D.V., A two-step multiple imputation for analysis of repeated measures with left-censored and missing data. Stat Biopharm Res 5 (2013), 116–125.
    • (2013) Stat Biopharm Res , vol.5 , pp. 116-125
    • Liu, G.F.1    Hu, P.2    Mehrotra, D.V.3
  • 23
    • 84871651612 scopus 로고    scopus 로고
    • Analysis of longitudinal clinical trials with missing data using multiple imputation in conjunction with robust regression
    • Mehrotra, D.V., Li, X., Liu, J., Lu, K., Analysis of longitudinal clinical trials with missing data using multiple imputation in conjunction with robust regression. Biometrics 68 (2012), 1250–1259.
    • (2012) Biometrics , vol.68 , pp. 1250-1259
    • Mehrotra, D.V.1    Li, X.2    Liu, J.3    Lu, K.4
  • 24
    • 33845891920 scopus 로고    scopus 로고
    • The design of simulation studies in medical statistics
    • Burton, A., Altman, D., Royston, P., Holder, R.L., The design of simulation studies in medical statistics. Stat Med 25 (2006), 4279–4292.
    • (2006) Stat Med , vol.25 , pp. 4279-4292
    • Burton, A.1    Altman, D.2    Royston, P.3    Holder, R.L.4
  • 25
    • 0346102882 scopus 로고    scopus 로고
    • Maximum likelihood methods for nonignorable missing responses and covariates in random effects models
    • Stubbendick, A.L., Ibrahim, J.G., Maximum likelihood methods for nonignorable missing responses and covariates in random effects models. Biometrics 59 (2003), 1140–1150.
    • (2003) Biometrics , vol.59 , pp. 1140-1150
    • Stubbendick, A.L.1    Ibrahim, J.G.2
  • 26
    • 62749191476 scopus 로고    scopus 로고
    • A semi-parametric shared parameter model to handle nonmonotone nonignorable missingness
    • Tsonaka, R., Verbeke, G., Lesaffre, E., A semi-parametric shared parameter model to handle nonmonotone nonignorable missingness. Biometrics 65 (2009), 81–87.
    • (2009) Biometrics , vol.65 , pp. 81-87
    • Tsonaka, R.1    Verbeke, G.2    Lesaffre, E.3
  • 27
    • 79951553309 scopus 로고    scopus 로고
    • Empirical likelihood for semiparametric regression model with missing response data
    • Xue, L., Xue, D., Empirical likelihood for semiparametric regression model with missing response data. J Multivar Anal 102 (2011), 723–740.
    • (2011) J Multivar Anal , vol.102 , pp. 723-740
    • Xue, L.1    Xue, D.2
  • 28
    • 77949724484 scopus 로고    scopus 로고
    • Bayesian quantile regression for longitudinal studies with nonignorable missing data
    • Yuan, Y., Yin, G., Bayesian quantile regression for longitudinal studies with nonignorable missing data. Biometrics 66 (2010), 105–114.
    • (2010) Biometrics , vol.66 , pp. 105-114
    • Yuan, Y.1    Yin, G.2
  • 29
    • 79953209006 scopus 로고    scopus 로고
    • MMRM versus MI in dealing with missing data—a comparison based on 25 NDA data sets
    • Siddiqui, O., MMRM versus MI in dealing with missing data—a comparison based on 25 NDA data sets. J Biopharm Stat 21 (2011), 423–436.
    • (2011) J Biopharm Stat , vol.21 , pp. 423-436
    • Siddiqui, O.1
  • 30
    • 68249114452 scopus 로고    scopus 로고
    • Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls
    • Sterne, J.A., White, I.R., Carlin, J.B., Spratt, M., Royston, P., Kenward, M.G., et al. Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. BMJ, 338, 2009, b2393.
    • (2009) BMJ , vol.338 , pp. b2393
    • Sterne, J.A.1    White, I.R.2    Carlin, J.B.3    Spratt, M.4    Royston, P.5    Kenward, M.G.6
  • 31
    • 33847704498 scopus 로고    scopus 로고
    • Missing data on the Center for Epidemiologic Studies Depression Scale: a comparison of 4 imputation techniques
    • Bono, C., Ried, L.D., Kimberlin, C., Vogel, B., Missing data on the Center for Epidemiologic Studies Depression Scale: a comparison of 4 imputation techniques. Res Social Adm Pharm 3 (2007), 1–27.
    • (2007) Res Social Adm Pharm , vol.3 , pp. 1-27
    • Bono, C.1    Ried, L.D.2    Kimberlin, C.3    Vogel, B.4
  • 32
    • 29344475979 scopus 로고    scopus 로고
    • Simple adjustments for randomized trials with nonrandomly missing or censored outcomes arising from informative covariates
    • Baker, S.G., Fitzmaurice, G.M., Freedman, L.S., Kramer, B.S., Simple adjustments for randomized trials with nonrandomly missing or censored outcomes arising from informative covariates. Biostatistics 7 (2006), 29–40.
    • (2006) Biostatistics , vol.7 , pp. 29-40
    • Baker, S.G.1    Fitzmaurice, G.M.2    Freedman, L.S.3    Kramer, B.S.4
  • 33
    • 33845891920 scopus 로고    scopus 로고
    • The design of simulation studies in medical statistics
    • Burton, A., Altman, D.G., Royston, P., Holder, R.L., The design of simulation studies in medical statistics. Stat Med 25 (2006), 4279–4292.
    • (2006) Stat Med , vol.25 , pp. 4279-4292
    • Burton, A.1    Altman, D.G.2    Royston, P.3    Holder, R.L.4
  • 35
    • 10844288743 scopus 로고    scopus 로고
    • A random pattern-mixture model for longitudinal data with dropouts
    • Guo, W., Ratcliffe, S.J., Have, T.T.T., A random pattern-mixture model for longitudinal data with dropouts. J Am Stat Assoc 99 (2004), 929–937.
    • (2004) J Am Stat Assoc , vol.99 , pp. 929-937
    • Guo, W.1    Ratcliffe, S.J.2    Have, T.T.T.3
  • 36
    • 33745054916 scopus 로고    scopus 로고
    • Analysis of a long-term study of neurotic disorder, with insights into the process of non-response
    • Longford, N.T., Tyrer, P., Nur, U.A.M., Seivewright, H., Analysis of a long-term study of neurotic disorder, with insights into the process of non-response. J R Statist Soc A 169 (2006), 507–523.
    • (2006) J R Statist Soc A , vol.169 , pp. 507-523
    • Longford, N.T.1    Tyrer, P.2    Nur, U.A.M.3    Seivewright, H.4
  • 37
    • 84871659058 scopus 로고    scopus 로고
    • Bayesian model selection for incomplete data using the posterior predictive distribution
    • Daniels, M.J., Chatterjee, A.S., Wang, C., Bayesian model selection for incomplete data using the posterior predictive distribution. Biometrics 68 (2012), 1055–1063.
    • (2012) Biometrics , vol.68 , pp. 1055-1063
    • Daniels, M.J.1    Chatterjee, A.S.2    Wang, C.3
  • 38
    • 0035977410 scopus 로고    scopus 로고
    • Intention-to-treat: methods for dealing with missing values in clinical trials of progressively deteriorating diseases
    • Unnebrink, K., Windeler, J., Intention-to-treat: methods for dealing with missing values in clinical trials of progressively deteriorating diseases. Stat Med 20 (2001), 3931–3946.
    • (2001) Stat Med , vol.20 , pp. 3931-3946
    • Unnebrink, K.1    Windeler, J.2
  • 39
    • 0141939440 scopus 로고    scopus 로고
    • Semiparametric regression analysis of longitudinal data with informative drop-outs
    • Lin, D.Y., Ying, Z., Semiparametric regression analysis of longitudinal data with informative drop-outs. Biostatistics 4 (2003), 385–398.
    • (2003) Biostatistics , vol.4 , pp. 385-398
    • Lin, D.Y.1    Ying, Z.2
  • 40
    • 0032499928 scopus 로고    scopus 로고
    • A practical approach to adjusting for attrition bias in HIV clinical trials with serial marker responses
    • Cozzi Lepri, A., Smith, G.D., Mocroft, A., Sabin, C.A., Morris, R.W., Phillips, A.N., A practical approach to adjusting for attrition bias in HIV clinical trials with serial marker responses. AIDS 12 (1998), 1155–1161.
    • (1998) AIDS , vol.12 , pp. 1155-1161
    • Cozzi Lepri, A.1    Smith, G.D.2    Mocroft, A.3    Sabin, C.A.4    Morris, R.W.5    Phillips, A.N.6
  • 41
    • 10944241763 scopus 로고    scopus 로고
    • Mixtures of varying coefficient models for longitudinal data with discrete or continuous nonignorable dropout
    • Hogan, J.W., Lin, X., Herman, B., Mixtures of varying coefficient models for longitudinal data with discrete or continuous nonignorable dropout. Biometrics 60 (2004), 854–864.
    • (2004) Biometrics , vol.60 , pp. 854-864
    • Hogan, J.W.1    Lin, X.2    Herman, B.3
  • 42
    • 33644905302 scopus 로고    scopus 로고
    • Review of guidelines and literature for handling missing data in longitudinal clinical trials with a case study
    • Liu, M., Wei, L., Zhang, J., Review of guidelines and literature for handling missing data in longitudinal clinical trials with a case study. Pharm Stat 5 (2006), 7–18.
    • (2006) Pharm Stat , vol.5 , pp. 7-18
    • Liu, M.1    Wei, L.2    Zhang, J.3
  • 43
    • 0030681257 scopus 로고    scopus 로고
    • An imputation method for non-ignorable missing data in studies of blood pressure
    • Cook, N.R., An imputation method for non-ignorable missing data in studies of blood pressure. Stat Med 16 (1997), 2713–2728.
    • (1997) Stat Med , vol.16 , pp. 2713-2728
    • Cook, N.R.1
  • 44
    • 21844466220 scopus 로고    scopus 로고
    • A comparison of imputation methods in a longitudinal randomized clinical trial
    • Tang, L., Song, J., Belin, T.R., Unutzer, J., A comparison of imputation methods in a longitudinal randomized clinical trial. Stat Med 24 (2005), 2111–2128.
    • (2005) Stat Med , vol.24 , pp. 2111-2128
    • Tang, L.1    Song, J.2    Belin, T.R.3    Unutzer, J.4
  • 46
    • 4444287351 scopus 로고    scopus 로고
    • An extended general location model for causal inferences from data subject to noncompliance and missing values
    • Peng, Y., Little, R.J., Raghunathan, T.E., An extended general location model for causal inferences from data subject to noncompliance and missing values. Biometrics 60 (2004), 598–607.
    • (2004) Biometrics , vol.60 , pp. 598-607
    • Peng, Y.1    Little, R.J.2    Raghunathan, T.E.3
  • 47
    • 84950421496 scopus 로고
    • Analysis of semiparametric regression models for repeated outcomes in the presence of missing data
    • Robins, J.M., Rotnitzky, A., Zhao, L.P., Analysis of semiparametric regression models for repeated outcomes in the presence of missing data. J Am Stat Assoc 90 (1995), 106–121.
    • (1995) J Am Stat Assoc , vol.90 , pp. 106-121
    • Robins, J.M.1    Rotnitzky, A.2    Zhao, L.P.3
  • 48
    • 84860208846 scopus 로고    scopus 로고
    • Accurate mean comparisons for paired samples with missing data: an application to a smoking-cessation trial
    • Xu, J., Harrar, S.W., Accurate mean comparisons for paired samples with missing data: an application to a smoking-cessation trial. Biom J 54 (2012), 281–295.
    • (2012) Biom J , vol.54 , pp. 281-295
    • Xu, J.1    Harrar, S.W.2
  • 49
    • 0032499928 scopus 로고    scopus 로고
    • A practical approach to adjusting for attrition bias in HIV clinical trials with serial marker responses
    • Cozzi Lepri, A., Smith, G.D., Mocroft, A., Sabin, C.A., Morris, R.W., Phillips, A.N., A practical approach to adjusting for attrition bias in HIV clinical trials with serial marker responses. AIDS 12 (1998), 1155–1161.
    • (1998) AIDS , vol.12 , pp. 1155-1161
    • Cozzi Lepri, A.1    Smith, G.D.2    Mocroft, A.3    Sabin, C.A.4    Morris, R.W.5    Phillips, A.N.6
  • 50
    • 84876335926 scopus 로고    scopus 로고
    • Robust inference for longitudinal data analysis with non-ignorable and non-monotonic missing values
    • Tseng, C.-H., Elashoff, R., Li, N., Li, G., Robust inference for longitudinal data analysis with non-ignorable and non-monotonic missing values. Stat Interface 5 (2012), 479–490.
    • (2012) Stat Interface , vol.5 , pp. 479-490
    • Tseng, C.-H.1    Elashoff, R.2    Li, N.3    Li, G.4
  • 51
    • 37548999814 scopus 로고    scopus 로고
    • Using hierarchical likelihood for missing data problems
    • Yun, S.C., Lee, Y., Kenward, M.G., Using hierarchical likelihood for missing data problems. Biometrika 94 (2007), 905–919.
    • (2007) Biometrika , vol.94 , pp. 905-919
    • Yun, S.C.1    Lee, Y.2    Kenward, M.G.3
  • 52
    • 7944229074 scopus 로고    scopus 로고
    • Conditional mixed models adjusting for non-ignorable drop-out with administrative censoring in longitudinal studies
    • Li, J., Schluchter, M.D., Conditional mixed models adjusting for non-ignorable drop-out with administrative censoring in longitudinal studies. Stat Med 23 (2004), 3489–3503.
    • (2004) Stat Med , vol.23 , pp. 3489-3503
    • Li, J.1    Schluchter, M.D.2
  • 53
    • 63049121747 scopus 로고    scopus 로고
    • Mixed-effect hybrid models for longitudinal data with nonignorable dropout
    • Yuan, Y., Little, R.J., Mixed-effect hybrid models for longitudinal data with nonignorable dropout. Biometrics 65 (2009), 478–486.
    • (2009) Biometrics , vol.65 , pp. 478-486
    • Yuan, Y.1    Little, R.J.2
  • 54
    • 84856206744 scopus 로고    scopus 로고
    • Dealing with missing outcome data in randomized trials and observational studies
    • Groenwold, R.H., Donders, A.R., Roes, K.C., Harrell, F.E. Jr., Moons, K.G., Dealing with missing outcome data in randomized trials and observational studies. Am J Epidemiol 175 (2012), 210–217.
    • (2012) Am J Epidemiol , vol.175 , pp. 210-217
    • Groenwold, R.H.1    Donders, A.R.2    Roes, K.C.3    Harrell, F.E.4    Moons, K.G.5
  • 55
    • 0036224591 scopus 로고    scopus 로고
    • Impact of missing data due to selective dropouts in cohort studies and clinical trials
    • Touloumi, G., Pocock, S.J., Babiker, A.G., Darbyshire, J.H., Impact of missing data due to selective dropouts in cohort studies and clinical trials. Epidemiology 13 (2002), 347–355.
    • (2002) Epidemiology , vol.13 , pp. 347-355
    • Touloumi, G.1    Pocock, S.J.2    Babiker, A.G.3    Darbyshire, J.H.4
  • 56
    • 84882918433 scopus 로고    scopus 로고
    • Multiple imputation of missing values was not necessary before performing a longitudinal mixed-model analysis
    • Twisk, J., de Boer, M., de Vente, W., Heymans, M., Multiple imputation of missing values was not necessary before performing a longitudinal mixed-model analysis. J Clin Epidemiol 66 (2013), 1022–1028.
    • (2013) J Clin Epidemiol , vol.66 , pp. 1022-1028
    • Twisk, J.1    de Boer, M.2    de Vente, W.3    Heymans, M.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.