-
1
-
-
35548934245
-
A simulation study comparing weighted estimating equations with multiple imputation based estimating equations for longitudinal binary data
-
Beunckens, C., C. Sotto, and G. Molenberghs. 2008. A simulation study comparing weighted estimating equations with multiple imputation based estimating equations for longitudinal binary data. Computational Statistics and Data Analysis 52: 1533–1548.
-
(2008)
Computational Statistics and Data Analysis
, vol.52
, pp. 1533-1548
-
-
Beunckens, C.1
Sotto, C.2
Molenberghs, G.3
-
2
-
-
79952598146
-
Doubly robust and multiple-imputation-based generalized estimating equations
-
Birhanu, T., G. Molenberghs, C. Sotto, and M. G. Kenward. 2011. Doubly robust and multiple-imputation-based generalized estimating equations. Journal of Biopharmaceutical Statistics 21: 202–225.
-
(2011)
Journal of Biopharmaceutical Statistics
, vol.21
, pp. 202-225
-
-
Birhanu, T.1
Molenberghs, G.2
Sotto, C.3
Kenward, M.G.4
-
3
-
-
84875577737
-
Cohort profile: The ‘Children of the 90s’—the index offspring of the Avon Longitudinal Study of Parents and Children
-
Boyd, A., J. Golding, J. Macleod, D. A. Lawlor, A. Fraser, J. Henderson, L. Molloy, A. Ness, S. Ring, and G. Davey Smith. 2013. Cohort profile: The ‘Children of the 90s’—the index offspring of the Avon Longitudinal Study of Parents and Children. International Journal of Epidemiology 42: 111–127.
-
(2013)
International Journal of Epidemiology
, vol.42
, pp. 111-127
-
-
Boyd, A.1
Golding, J.2
Macleod, J.3
Lawlor, D.A.4
Fraser, A.5
Henderson, J.6
Molloy, L.7
Ness, A.8
Ring, S.9
Davey Smith, G.10
-
4
-
-
0035873793
-
What can go wrong when you assume that correlated data are independent: An illustration from the evaluation of a childhood health intervention in Brazil
-
Cannon, M. J., L. Warner, J. A. Taddei, and D. G. Kleinbaum. 2001. What can go wrong when you assume that correlated data are independent: An illustration from the evaluation of a childhood health intervention in Brazil. Statistics in Medicine 20: 1461–1467.
-
(2001)
Statistics in Medicine
, vol.20
, pp. 1461-1467
-
-
Cannon, M.J.1
Warner, L.2
Taddei, J.A.3
Kleinbaum, D.G.4
-
5
-
-
80053980540
-
The impact of different sources of body mass index assessment on smoking onset: An application of multiple-source information models
-
Caria, M. P., R. Bellocco, M. R. Galanti, and N. J. Horton. 2011. The impact of different sources of body mass index assessment on smoking onset: An application of multiple-source information models. Stata Journal 11: 386–402.
-
(2011)
Stata Journal
, vol.11
, pp. 386-402
-
-
Caria, M.P.1
Bellocco, R.2
Galanti, M.R.3
Horton, N.J.4
-
6
-
-
0035755636
-
A comparison of inclusive and restrictive strategies in modern missing data procedures
-
Collins, L. M., J. L. Schafer, and C.-M. Kam. 2001. A comparison of inclusive and restrictive strategies in modern missing data procedures. Psychological Methods 6: 330–351.
-
(2001)
Psychological Methods
, vol.6
, pp. 330-351
-
-
Collins, L.M.1
Schafer, J.L.2
Kam, C.-M.3
-
7
-
-
75749133269
-
An overview of practical approaches for handling missing data in clinical trials
-
DeSouza, C. M., A. T. Legedza, and A. J. Sankoh. 2009. An overview of practical approaches for handling missing data in clinical trials. Journal of Biopharmaceutical Statistics 19: 1055–1073.
-
(2009)
Journal of Biopharmaceutical Statistics
, vol.19
, pp. 1055-1073
-
-
Desouza, C.M.1
Legedza, A.T.2
Sankoh, A.J.3
-
8
-
-
4243828610
-
Informative drop-out in longitudinal data analysis. Journal of the Royal Statistical Society
-
Diggle, P., and M. G. Kenward. 1994. Informative drop-out in longitudinal data analysis. Journal of the Royal Statistical Society, Series C 43: 49–93.
-
(1994)
Series C
, vol.43
, pp. 49-93
-
-
Diggle, P.1
Kenward, M.G.2
-
9
-
-
0028818449
-
Bivariate logistic regression analysis of childhood psychopathology ratings using multiple informants
-
Fitzmaurice, G. M., N. M. Laird, G. E. P. Zahner, and C. Daskalakis. 1995. Bivariate logistic regression analysis of childhood psychopathology ratings using multiple informants. American Journal of Epidemiology 142: 1194–1203.
-
(1995)
American Journal of Epidemiology
, vol.142
, pp. 1194-1203
-
-
Fitzmaurice, G.M.1
Laird, N.M.2
Zahner, G.E.P.3
Daskalakis, C.4
-
11
-
-
79953212978
-
Comparisons of methods for analysis of repeated binary responses with missing data
-
Frank Liu, G., and X. Zhan. 2011. Comparisons of methods for analysis of repeated binary responses with missing data. Journal of Biopharmaceutical Statistics 21: 371–392.
-
(2011)
Journal of Biopharmaceutical Statistics
, vol.21
, pp. 371-392
-
-
Frank Liu, G.1
Zhan, X.2
-
12
-
-
0035130957
-
ALSPAC—the Avon longitudinal study of parents and children. I. Study methodology
-
the ALSPAC Study Team
-
Golding, J., M. Pembrey, R. Jones, and the ALSPAC Study Team. 2001. ALSPAC—the Avon longitudinal study of parents and children. I. Study methodology. Paediatric and Perinatal Epidemiology 15: 74–87.
-
(2001)
Paediatric and Perinatal Epidemiology
, vol.15
, pp. 74-87
-
-
Golding, J.1
Pembrey, M.2
Jones, R.3
-
13
-
-
0033943101
-
The Development and Well-Being Assessment: Description and initial validation of an integrated assessment of child and adolescent psychopathology
-
Goodman, R., T. Ford, H. Richards, R. Gatward, and H. Meltzer. 2000. The Development and Well-Being Assessment: Description and initial validation of an integrated assessment of child and adolescent psychopathology. Journal of Child Psychology and Psychiatry 41: 645–655.
-
(2000)
Journal of Child Psychology and Psychiatry
, vol.41
, pp. 645-655
-
-
Goodman, R.1
Ford, T.2
Richards, H.3
Gatward, R.4
Meltzer, H.5
-
14
-
-
4444253801
-
Regression analysis of multiple source and multiple informant data from complex survey samples
-
Horton, N. J., and G. M. Fitzmaurice. 2004. Regression analysis of multiple source and multiple informant data from complex survey samples. Statistics in Medicine 23: 2911–2933.
-
(2004)
Statistics in Medicine
, vol.23
, pp. 2911-2933
-
-
Horton, N.J.1
Fitzmaurice, G.M.2
-
15
-
-
0035479298
-
Multiple informants: Mortality associated with psychiatric disorders in the Stirling County Study
-
Horton, N. J., N. M. Laird, J. M. Murphy, R. R. Monson, A. M. Sobol, and A. H. Leighton. 2001. Multiple informants: Mortality associated with psychiatric disorders in the Stirling County Study. American Journal of Epidemiology 154: 649–656.
-
(2001)
American Journal of Epidemiology
, vol.154
, pp. 649-656
-
-
Horton, N.J.1
Laird, N.M.2
Murphy, J.M.3
Monson, R.R.4
Sobol, A.M.5
Leighton, A.H.6
-
16
-
-
23044525261
-
Multiple imputation in practice: Comparison of software packages for regression models with missing variables
-
Horton, N. J., and S. R. Lipsitz. 2001. Multiple imputation in practice: Comparison of software packages for regression models with missing variables. American Statistician 55: 244–254.
-
(2001)
American Statistician
, vol.55
, pp. 244-254
-
-
Horton, N.J.1
Lipsitz, S.R.2
-
17
-
-
0242710940
-
A potential for bias when rounding in multiple imputation
-
Horton, N. J., S. R. Lipsitz, and M. Parzen. 2003. A potential for bias when rounding in multiple imputation. American Statistician 57: 229–232.
-
(2003)
American Statistician
, vol.57
, pp. 229-232
-
-
Horton, N.J.1
Lipsitz, S.R.2
Parzen, M.3
-
18
-
-
0030332693
-
Youth risk behavior surveillance—United States, 1995
-
Kann, L., C. W. Warren, W. A. Harris, J. L. Collins, B. I. Williams, J. G. Ross, and L. J. Kolbe. 1996. Youth risk behavior surveillance—United States, 1995. Journal of School Health 66: 365–377.
-
(1996)
Journal of School Health
, vol.66
, pp. 365-377
-
-
Kann, L.1
Warren, C.W.2
Harris, W.A.3
Collins, J.L.4
Williams, B.I.5
Ross, J.G.6
Kolbe, L.J.7
-
19
-
-
84874501270
-
On weighting approaches for missing data
-
Li, L., C. Shen, X. Li, and J. M. Robins. 2011. On weighting approaches for missing data. Statistical Methods in Medical Research 22: 14–30.
-
(2011)
Statistical Methods in Medical Research
, vol.22
, pp. 14-30
-
-
Li, L.1
Shen, C.2
Li, X.3
Robins, J.M.4
-
20
-
-
77649173768
-
Longitudinal data analysis using generalized linear models
-
Liang, K.-Y., and S. L. Zeger. 1986. Longitudinal data analysis using generalized linear models. Biometrika 73: 13–22.
-
(1986)
Biometrika
, vol.73
, pp. 13-22
-
-
Liang, K.-Y.1
Zeger, S.L.2
-
22
-
-
84883162373
-
JMASM 32: Multiple imputation of missing multilevel, longitudinal data: A case when practical considerations trump best practices?
-
Lloyd, J. E. V., J. Obradović, R. M. Carpiano, and F. Motti-Stefanidi. 2013. JMASM 32: Multiple imputation of missing multilevel, longitudinal data: A case when practical considerations trump best practices? Journal of Modern Applied Statistical Methods 12: 261–275.
-
(2013)
Journal of Modern Applied Statistical Methods
, vol.12
, pp. 261-275
-
-
Lloyd, J.E.V.1
Obradović, J.2
Carpiano, R.M.3
Motti-Stefanidi, F.4
-
24
-
-
33748709502
-
Using the outcome for imputation of missing predictor values was preferred
-
Moons, K. G., R. A. Donders, T. Stijnen, and F. E. Harrell, Jr. 2006. Using the outcome for imputation of missing predictor values was preferred. Journal of Clinical Epidemiology 59: 1092–1101.
-
(2006)
Journal of Clinical Epidemiology
, vol.59
, pp. 1092-1101
-
-
Moons, K.G.1
Donders, R.A.2
Stijnen, T.3
Harrell, F.E.4
-
25
-
-
0002344593
-
A multivariate technique for multiply imputing missing values using a sequence of regression models
-
Raghunathan, T. E., J. M. Lepkowski, J. V. Hoewyk, and P. Solenberger. 2001. A multivariate technique for multiply imputing missing values using a sequence of regression models. Survey Methodology 27: 85–95.
-
(2001)
Survey Methodology
, vol.27
, pp. 85-95
-
-
Raghunathan, T.E.1
Lepkowski, J.M.2
Hoewyk, J.V.3
Solenberger, P.4
-
26
-
-
33646501982
-
Multiple imputation of missing values: Update of ice
-
Royston, P. 2005. Multiple imputation of missing values: Update of ice. Stata Journal 5: 527–536.
-
(2005)
Stata Journal
, vol.5
, pp. 527-536
-
-
Royston, P.1
-
27
-
-
0017133178
-
Inference and missing data
-
Rubin, D. B. 1976. Inference and missing data. Biometrika 63: 581–592.
-
(1976)
Biometrika
, vol.63
, pp. 581-592
-
-
Rubin, D.B.1
-
30
-
-
84902370661
-
Assessing eating disorder symptoms in adolescence: Is there a role for multiple informants?
-
Swanson, S. A., K. M. Aloisio, N. J. Horton, K. R. Sonneville, R. D. Crosby, K. T. Eddy, A. E. Field, and N. Micali. 2014. Assessing eating disorder symptoms in adolescence: Is there a role for multiple informants? International Journal of Eating Disorders 47: 475–482.
-
(2014)
International Journal of Eating Disorders
, vol.47
, pp. 475-482
-
-
Swanson, S.A.1
Aloisio, K.M.2
Horton, N.J.3
Sonneville, K.R.4
Crosby, R.D.5
Eddy, K.T.6
Field, A.E.7
Micali, N.8
-
32
-
-
78651256743
-
Multiple imputation using chained equations: Issues and guidance for practice
-
White, I. R., P. Royston, and A. M. Wood. 2011. Multiple imputation using chained equations: Issues and guidance for practice. Statistics in Medicine 30: 377–399.
-
(2011)
Statistics in Medicine
, vol.30
, pp. 377-399
-
-
White, I.R.1
Royston, P.2
Wood, A.M.3
-
33
-
-
0031452790
-
Generalized estimating equation model for binary outcomes with missing covariates
-
Xie, F., and M. C. Paik. 1997. Generalized estimating equation model for binary outcomes with missing covariates. Biometrics 53: 1458–1466.
-
(1997)
Biometrics
, vol.53
, pp. 1458-1466
-
-
Xie, F.1
Paik, M.C.2
-
34
-
-
78650664527
-
The impact of dichotomization in longitudinal data analysis: A simulation study
-
Yoo, B. 2010. The impact of dichotomization in longitudinal data analysis: a simulation study. Pharmaceutical Statistics 9: 298–312.
-
(2010)
Pharmaceutical Statistics
, vol.9
, pp. 298-312
-
-
Yoo, B.1
|