| Year |
Citation |
Score |
| 2015 |
Bunouf P, Molenberghs G, Grouin JM, Thijs H. A SAS program combining R functionalities to implement pattern-mixture models Journal of Statistical Software. 68. DOI: 10.18637/Jss.V068.I08 |
0.497 |
|
| 2013 |
Mallinckrodt C, Roger J, Chuang-stein C, Molenberghs G, Lane PW, O'kelly M, Ratitch B, Xu L, Gilbert S, Mehrotra DV, Wolfinger R, Thijs H. Missing Data: Turning Guidance Into Action Statistics in Biopharmaceutical Research. 5: 369-382. DOI: 10.1080/19466315.2013.848822 |
0.517 |
|
| 2007 |
Beunckens C, Molenberghs G, Thijs H, Verbeke G. Incomplete hierarchical data. Statistical Methods in Medical Research. 16: 457-92. PMID 17656453 DOI: 10.1177/0962280206075310 |
0.534 |
|
| 2005 |
Hens N, Aerts M, Molenberghs G, Thijs H, Verbeke G. Kernel weighted influence measures Computational Statistics and Data Analysis. 48: 467-487. DOI: 10.1016/J.Csda.2004.02.010 |
0.57 |
|
| 2005 |
Van Steen K, Raby BA, Molenberghs G, Thijs H, De Wit M, Peeters M. An equivalence test for comparing DNA sequences Pharmaceutical Statistics. 4: 203-214. DOI: 10.1002/Pst.182 |
0.427 |
|
| 2004 |
Molenberghs G, Thijs H, Jansen I, Beunckens C, Kenward MG, Mallinckrodt C, Carroll RJ. Analyzing incomplete longitudinal clinical trial data. Biostatistics (Oxford, England). 5: 445-64. PMID 15208205 DOI: 10.1093/biostatistics/5.3.445 |
0.468 |
|
| 2004 |
Curran D, Molenberghs G, Thijs H, Verbeke G. Sensitivity analysis for pattern mixture models. Journal of Biopharmaceutical Statistics. 14: 125-43. PMID 15027504 DOI: 10.1081/Bip-120028510 |
0.533 |
|
| 2003 |
Jansen I, Molenberghs G, Aerts M, Thijs H, Van Steen K. A local influence approach applied to binary data from a psychiatric study. Biometrics. 59: 410-9. PMID 12926726 DOI: 10.1111/1541-0420.00048 |
0.579 |
|
| 2003 |
Molenberghs G, Thijs H, Kenward MG, Verbeke G. Sensitivity analysis of continuous incomplete longitudinal outcomes Statistica Neerlandica. 57: 112-135. DOI: 10.1111/1467-9574.00224 |
0.572 |
|
| 2003 |
Kenward MG, Molenberghs G, Thijs H. Pattern-mixture models with proper time dependence Biometrika. 90: 53-71. DOI: 10.1093/Biomet/90.1.53 |
0.546 |
|
| 2002 |
Thijs H, Molenberghs G, Michiels B, Verbeke G, Curran D. Strategies to fit pattern-mixture models. Biostatistics (Oxford, England). 3: 245-65. PMID 12933616 DOI: 10.1093/Biostatistics/3.2.245 |
0.534 |
|
| 2002 |
Michiels B, Molenberghs G, Bijnens L, Vangeneugden T, Thijs H. Selection models and pattern-mixture models to analyse longitudinal quality of life data subject to drop-out. Statistics in Medicine. 21: 1023-41. PMID 11933032 DOI: 10.1002/Sim.1064 |
0.547 |
|
| 2001 |
Verbeke G, Molenberghs G, Thijs H, Lesaffre E, Kenward MG. Sensitivity analysis for nonrandom dropout: a local influence approach. Biometrics. 57: 7-14. PMID 11252620 DOI: 10.1111/J.0006-341X.2001.00007.X |
0.578 |
|
| 2001 |
van Steen K, Molenberghs G, Verbeke G, Thijs H. A local influence approach to sensitivity analysis of incomplete longitudinal ordinal data Statistical Modelling: An International Journal. 1: 125-142. DOI: 10.1177/1471082X0100100203 |
0.566 |
|
| 2001 |
Molenberghs G, Verbeke G, Thijs H, Lesaffre E, Kenward MG. Influence analysis to assess sensitivity of the dropout process Computational Statistics and Data Analysis. 37: 93-113. DOI: 10.1016/S0167-9473(00)00065-7 |
0.562 |
|
| 2000 |
Thijs H, Molenberghs G, Verbeke G. The milk protein trial: Influence analysis of the dropout process Biometrical Journal. 42: 617-646. DOI: 10.1002/1521-4036(200009)42:5<617::Aid-Bimj617>3.0.Co;2-N |
0.564 |
|
| Show low-probability matches. |