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Lingmin Zeng

Chinese Academy of Sciences

Publishes on Statistical Methods in Clinical Trials, Advanced Statistical Process Monitoring, Lung Cancer Treatments and Mutations. 31 papers and 2.2k citations.

31Publications
2.2kTotal Citations

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Top publicationsby citations

Osimertinib in Resected <i>EGFR</i> -Mutated Non–Small-Cell Lung Cancer
Yi‐Long Wu, Masahiro Tsuboi, Jie He et al.|New England Journal of Medicine|2020
Cited by 1.7kOpen Access

BACKGROUND: ) mutation-positive advanced non-small-cell lung cancer (NSCLC). The efficacy and safety of osimertinib as adjuvant therapy are unknown. METHODS: mutation-positive NSCLC in a 1:1 ratio to receive either osimertinib (80 mg once daily) or placebo for 3 years. The primary end point was disease-free survival among patients with stage II to IIIA disease (according to investigator assessment). The secondary end points included disease-free survival in the overall population of patients with stage IB to IIIA disease, overall survival, and safety. RESULTS: A total of 682 patients underwent randomization (339 to the osimertinib group and 343 to the placebo group). At 24 months, 90% of the patients with stage II to IIIA disease in the osimertinib group (95% confidence interval [CI], 84 to 93) and 44% of those in the placebo group (95% CI, 37 to 51) were alive and disease-free (overall hazard ratio for disease recurrence or death, 0.17; 99.06% CI, 0.11 to 0.26; P<0.001). In the overall population, 89% of the patients in the osimertinib group (95% CI, 85 to 92) and 52% of those in the placebo group (95% CI, 46 to 58) were alive and disease-free at 24 months (overall hazard ratio for disease recurrence or death, 0.20; 99.12% CI, 0.14 to 0.30; P<0.001). At 24 months, 98% of the patients in the osimertinib group (95% CI, 95 to 99) and 85% of those in the placebo group (95% CI, 80 to 89) were alive and did not have central nervous system disease (overall hazard ratio for disease recurrence or death, 0.18; 95% CI, 0.10 to 0.33). Overall survival data were immature; 29 patients died (9 in the osimertinib group and 20 in the placebo group). No new safety concerns were noted. CONCLUSIONS: mutation-positive NSCLC, disease-free survival was significantly longer among those who received osimertinib than among those who received placebo. (Funded by AstraZeneca; ADAURA ClinicalTrials.gov number, NCT02511106.).

Neoadjuvant Osimertinib With/Without Chemotherapy Versus Chemotherapy Alone for <i>EGFR</i> -Mutated Resectable Non-Small-Cell Lung Cancer: NeoADAURA
Masahiro Tsuboi, Walter Weder, Carles Escriu et al.|Future Oncology|2021
Cited by 188Open Access

Osimertinib is a third-generation, irreversible oral EGFR-tyrosine kinase inhibitor), that potently inhibits EGFR-tyrosine kinase inhibitor-sensitizing mutations and T790M resistance mutations together with efficacy in CNS metastases in patients with non-small-cell lung cancer (NSCLC). Here we describe the rationale and design for the Phase III NeoADAURA study (NCT04351555), which will evaluate neoadjuvant osimertinib with or without chemotherapy versus chemotherapy alone prior to surgery, in patients with resectable stage II–IIIB N2 EGFR mutation-positive NSCLC. The primary end point is centrally assessed major pathological response at the time of resection. Secondary end points include event-free survival, pathological complete response, nodal downstaging at the time of surgery, disease-free survival, overall survival and health-related quality of life. Safety and tolerability will also be assessed. Trial Registration number: NCT04351555 (ClinicalTrials.gov)

Group variable selection via SCAD-<i>L</i><sub>2</sub>
Lingmin Zeng, Jun Xie|Statistics|2012
Cited by 50

We propose a penalized regression method SCAD-L2 using a penalty function called SCAD (smoothly clipped absolute deviation) combined with an L2 penalty. The new method inherits good features of SCAD, namely unbiasedness, continuity, and sparsity. In addition, it favours another important property that highly correlated variables are in or out a model together. SCAD-L2 derives its power by focusing on group variable selection. For data with dependent structures, where traditional variable selection methods are unstable, SCAD-L2 can select variable groups and preserve small prediction errors.