| 1 | Using statistics and mathematical modelling to understand infectious disease outbreaks:COVID-19 as an example显示文摘During an infectious disease outbreak,biases in the data and complexities of the underlying dynamics pose significant challenges in mathematically modelling the outbreak and designing policy.Motivated by the ongoing response to COVID-19,we provide a toolkit of statistical and mathematical models beyond the simple SIR-type differential equation models for analysing the early stages of an outbreak and assessing interventions.In particular,we focus on parameter estimation in the presence of known biases in the data,and the effect of non-pharmaceutical interventions in enclosed subpopulations,such as households and care homes.We illustrate these methods by applying them to the COVID-19 pandemic. | Christopher E.Overton Helena B.Stage Shazaad Ahmad Jacob Curran-Sebastian Paul Dark Rajenki Das Elizabeth Fearon Timothy Felton Martyn Fyles Nick Gent Ian Hall Thomas House Hugo Lewkowicz Xiaoxi Pang Lorenzo Pellis Robert Sawko Andrew Ustianowski Bindu Vekaria Luke Webb | 2020 | Infectious Disease Modelling2020，5，1： | 1 |
| 4 | Identification of optimal contemporary antiemetic prophylaxis for doxorubicin-cyclophosphamide chemotherapy in Chinese cancer patients: post-hoc analysis of 3 prospective studies显示文摘Objective:Chemotherapy-induced nausea and vomiting(CINV)are common with doxorubicin-cyclophosphamide(AC)chemotherapy.Recommended antiemetic regimens incorporate neurokinin-1 receptor antagonist(NK1 RA),5-hydroxytryptamine type-3 receptor antagonist(5 HT3 RA),corticosteroid,and dopamine antagonists.This post-hoc analysis compared results of 3 prospective antiemetic studies conducted among Chinese breast cancer patients who received(neo)adjuvant AC,in order to identify optimal antiemetic prophylaxis.Methods:A total of 304 patients were included:Group 1,ondansetron/dexamethasone(D1);Group 2,aprepitant/ondansetron/dexamethasone(D1);Group 3,aprepitant/ondansetron/dexamethasone(D1–3);Group 4,aprepitant/ondansetron/dexamethasone(D1–3)/olanzapine;and Group 5,netupitant/palonosetron/dexamethasone(D1–3).Antiemetic efficacies of Groups 3,4,and 5 during cycle 1 of AC were individually compared with Group 1.In addition,emesis outcomes of patients in Groups 3 and 5,and those of Groups 2 and 3,were compared.Results:When comparing efficacies of a historical doublet(5 HT3 RA/dexamethasone)with triplet antiemetic regimens(NK1 RA/5 HT3 RA/dexamethasone)with/without olanzapine,complete response(CR)percentages and quality of life(QOL)in overall phase of cycle 1 AC were compared between Group 1 and the other groups:Group 1 vs.3,41.9%vs.38.3%(P=0.6849);Group 1 vs.4,41.9%vs.65.0%(P=0.0107);and Group 1 vs.5,41.9%vs.60.0%(P=0.0460).Groups 4 and 5 achieved a better QOL.When comparing netupitant-based(Group 3)with aprepitant-based(Group 5)triplet antiemetics,CR percentages were 38.3%vs.60.0%,respectively(P=0.0176);Group 5 achieved a better QOL.When comparing 1 day(Group 2)vs.3 day(Group 3)dexamethasone,CR percentages were 46.8%and 38.3%,respectively(P=0.3459);Group 3 had a worse QOL.Conclusions:Aprepitant-containing triplets were non-superior to doublet antiemetics.Netupitant-containing triplets and adding olanzapine to aprepitant-containing triplets were superior to doublets.Netupitant/palonosetron/dexamethasone was superior to aprepitant/ondansetron/dexamethasone.Protracted administration of dexamethasone provided limited additional benefit. | Winnie Yeo Leung Li Thomas KH Lau Kwai T Lai Vicky,TC Chan Kwan H Wong Christopher CH Yip Elizabeth Pang Maggie Cheung Vivian Chan Carol CH Kwok Joyce JS Suen Frankie KF Mo | 2021 | Cancer Biology & Medicine2021，18，3： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员