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1 Supplementary information Network Meta-Analysis of The Effectiveness of Neoadjuvant Endocrine Therapy for postmenopausal, HR Positive Breast Cancer Wei Wang 1*, Chenghao Liu *, Wenbin Zhou 1, Tiansong Xia 2, Hui Xie 3, Shui Wang Department of Breast Surgery, The First Affiliated Hospital with Nanjing Medical University, 300 Guangzhou Road, Nanjing, China Supplementary Figure 1 Risk of bias tool, developed by the Cochrane Collaboration, to assess the potential limitations in randomized trials separates a judgment about risk of bias from a description of the support for that judgment, for a series of items covering different domains of bias + = low risk of bias; - = high risk of bias;? = unclear risk of bias
2 CPalmieri,2014 Subtotal (I-squared = %, p = ) Exe(<20 wks) vs Exe( 20 wks) Takashi Hojo,2013 Subtotal (I-squared = %, p = ) Let vs Exe(<20 wks) Matthew JWllis,2011 Subtotal (I-squared = 00%, p = 0642) Ana vs Exe(<20 wks) Matthew JWllis,2011 Subtotal (I-squared = %, p = ) Matthew JWllis,2011 Subtotal (I-squared = %, p = ) Let vs Let+Eve Jose Baselga,2009 Subtotal (I-squared = %, p = ) Exe(<20 wks) vs Exe(<20 wks)+cel Subtotal (I-squared = %, p = ) Let vs Exe(<20 wks)+cel Subtotal (I-squared = %, p = ) Ian ESmith,2007 Subtotal (I-squared = %, p = ) Subtotal (I-squared = 00%, p = 0320) Subtotal (I-squared = %, p = ) Tam vs Ana+Tam Subtotal (I-squared = %, p = ) WEiermann,2001 Subtotal (I-squared = %, p = ) 294 (050, 1714) 294 (050, 1714) 079 (026, 240) 079 (026, 240) 175 (102, 301) 131 (043, 395) 165 (102, 269) 132 (078, 224) 132 (078, 224) 133 (076, 231) 133 (076, 231) 068 (041, 111) 068 (041, 111) 079 (027, 233) 079 (027, 233) 103 (036, 295) 103 (036, 295) 170 (094, 305) 170 (094, 305) 150 (096, 234) 105 (061, 181) 130 (092, 183) 091 (053, 156) 091 (053, 156) 087 (050, 150) 087 (050, 150) 220 (141, 344) 220 (141, 344) Supplementary Figure 2 Forest plots of COR
3 CPalmieri,2014 Subtotal (I-squared = %, p = ) Exe(<20 wks) vs Exe( 20 wks) Takashi Hojo,2013 Subtotal (I-squared = %, p = ) Let vs Exe(<20 wks) Matthew JWllis,2011 Subtotal (I-squared = 00%, p = 0441) Ana vs Exe(<20 wks) Matthew JWllis,2011 Subtotal (I-squared = %, p = ) Matthew JWllis,2011 Subtotal (I-squared = %, p = ) Let vs Let+Eve Jose Baselga,2009 Subtotal (I-squared = %, p = ) Exe(<20 wks) vs Exe(<20 wks)+cel Subtotal (I-squared = %, p = ) Let vs Exe(<20 wks)+cel Subtotal (I-squared = %, p = ) Ian ESmith,2007 Subtotal (I-squared = %, p = ) Subtotal (I-squared = 00%, p = 0739) Subtotal (I-squared = %, p = ) Tam vs Ana+Tam Subtotal (I-squared = %, p = ) WEiermann,2001 Subtotal (I-squared = %, p = ) 100 (013, 781) 100 (013, 781) 312 (012, 8012) 312 (012, 8012) 177 (062, 502) 633 (029, 13867) 209 (079, 554) 111 (044, 284) 111 (044, 284) 159 (055, 461) 159 (055, 461) 050 (028, 087) 050 (028, 087) 038 (003, 446) 038 (003, 446) 290 (011, 7413) 290 (011, 7413) 184 (085, 397) 184 (085, 397) 085 (047, 155) 105 (036, 310) 089 (053, 151) 136 (049, 380) 136 (049, 380) 130 (047, 362) 130 (047, 362) 091 (006, 1460) 091 (006, 1460) Supplementary Figure 3 Forest plots of TC
4 Let vs Exe(<20wks) Matthew JEllis,2011 Subtotal (I-squared = 179%, p = 0270) Let vs Exe(<20wks)+Cel Subtotal (I-squared = %, p = ) Let vs Let+Eve Jose Baselga,2009 Subtotal (I-squared = %, p = ) CPalmieri,2014 Subtotal (I-squared = %, p = ) WEiermann,2001 Subtotal (I-squared = %, p = ) Matthew JEllis,2011 Subtotal (I-squared = %, p = ) Ian ESmith,2007 Subtotal (I-squared = %, p = ) Subtotal (I-squared = %, p = ) Subtotal (I-squared = 00%, p = 0336) Ana vs Exe(<20wks) Matthew JEllis,2011 Subtotal (I-squared = %, p = ) Tam vs Tam+Ana Subtotal (I-squared = %, p = ) Exe(<20wks) vs Exe(<20wks)+Cel Subtotal (I-squared = %, p = ) 383 (040, 3691) 097 (039, 243) 123 (054, 281) 150 (030, 739) 150 (030, 739) 034 (013, 089) 034 (013, 089) 179 (052, 610) 179 (052, 610) 111 (041, 303) 111 (041, 303) 108 (042, 276) 108 (042, 276) 241 (076, 764) 241 (076, 764) 071 (024, 211) 071 (024, 211) 033 (012, 095) 070 (023, 209) 047 (022, 098) 090 (035, 230) 090 (035, 230) 101 (036, 280) 101 (036, 280) 039 (004, 402) 039 (004, 402) Supplementary Figure 4 Forest plots of fatigue
5 Let vs Exe(<20wks) Matthew JEllis,2011 Subtotal (I-squared = 00%, p = 0743) Let vs Exe(<20wks)+Cel Subtotal (I-squared = %, p = ) Let vs Let+Eve Jose Baselga,2009 Subtotal (I-squared = %, p = ) CPalmieri,2014 Subtotal (I-squared = %, p = ) WEiermann,2001 Subtotal (I-squared = %, p = ) Matthew JEllis,2011 Subtotal (I-squared = %, p = ) Ian ESmith,2007 Subtotal (I-squared = %, p = ) Subtotal (I-squared = %, p = ) Subtotal (I-squared = 446%, p = 0179) Ana vs Exe(<20wks) Matthew JEllis,2011 Subtotal (I-squared = %, p = ) Tam vs Tam+Ana Subtotal (I-squared = %, p = ) Exe(<20wks) vsexe(<20wks)+cel Subtotal (I-squared = %, p = ) 266 (121, 582) 211 (068, 651) 247 (130, 470) 844 (255, 2791) 844 (255, 2791) 164 (081, 332) 164 (081, 332) 692 (129, 3729) 692 (129, 3729) 079 (047, 134) 079 (047, 134) 136 (070, 265) 136 (070, 265) 242 (090, 653) 242 (090, 653) 057 (030, 107) 057 (030, 107) 061 (032, 117) 118 (059, 235) 083 (052, 133) 195 (086, 442) 195 (086, 442) 092 (051, 168) 092 (051, 168) 400 (120, 1328) 400 (120, 1328) Supplementary Figure 5 Forest plots of hot flash
6 Let vs Exe(<20wks) Matthew JEllis,2011 Subtotal (I-squared = %, p = ) CPalmieri,2014 Subtotal (I-squared = %, p = ) WEiermann,2001 Subtotal (I-squared = %, p = ) Matthew JEllis,2011 Subtotal (I-squared = %, p = ) Ian ESmith,2007 Subtotal (I-squared = %, p = ) Subtotal (I-squared = %, p = ) Subtotal (I-squared = 50%, p = 0305) Ana vs Exe(<20wks) Matthew JEllis,2011 Subtotal (I-squared = %, p = ) Tam vs Tam+Ana Subtotal (I-squared = %, p = ) Exe(<20wks) vs Exe( 20wks) Takashi Hojo,2013 Subtotal (I-squared = %, p = ) 060 (027, 133) 060 (027, 133) 467 (048, 4562) 467 (048, 4562) 149 (095, 233) 149 (095, 233) 039 (018, 084) 039 (018, 084) 064 (036, 114) 064 (036, 114) 203 (093, 445) 203 (093, 445) 285 (120, 676) 169 (101, 281) 195 (126, 302) 153 (077, 301) 153 (077, 301) 071 (027, 185) 071 (027, 185) 436 (045, 4208) 436 (045, 4208) Supplementary Figure 6 Forest plots of BCS
7 Supplementary Figure 7-10 Ranking for clinical objective response (A), treatment completion (B), complete response (C), partial response (D) Rank 1 represented best and rank N represented worst 1=Anastrozole, 2=Anastrozole+Gefitinib, 3=Anastrozole+Tamoxifen, 4=chemotherapy, 5=Everolimus+Letrozole, 6=Exemestane ( 20wks), 7=Exemestane (<20wks), 8=Exemestane (< 20wks)+Celecoxib, 9=Letrozole, 10=Tamoxifen
8 Supplementary Figure 11,12 Ranking for fatigue (A) and hot flash (B) Rank 1 represented worst and rank N represented best 1=Anastrozole, 2=Anastrozole+Gefitinib, 3=Anastrozole+Tamoxifen, 4=chemotherapy, 5=Everolimus+Letrozole, 6=Exemestane (<20wks), 7=Exemestane (<20wks)+Celecoxib, 8=Letrozole, 9=Tamoxifen Supplementary Figure 13 Ranking for breast conserving surgery Rank 1 represented best and rank N represented worst 1=Anastrozole, 2=Anastrozole+Gefitinib, 3=Anastrozole+Tamoxifen, 4=chemotherapy, 5=Exemestane ( 20wks), 6=Exemestane (<20wks),7=Letrozole, 8=Tamoxifen
9 Supplementary Figure 14 Ranking for SMAA benefit-risk analysis based on synthesizing COR and TC The bars indicate the probability that each treatment is the best, second best, etc (Rank1 is best,rank N is worst) 1=Anastrozole, 2=Anastrozole+Gefitinib, 3=Anastrozole+Tamoxifen, 4=CT, 5=Everolimus+Letrozole, 6=Exemestane ( 20wks), 7=Exemestane (<20wks), 8=Exemestane (< 20wks)+Celecoxib, 9=Letrozole, 10=Tamoxifen
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