scholarly journals Population pharmacokinetic model with time‐varying clearance for lorlatinib using pooled data from patients with non‐small cell lung cancer and healthy participants

2021 ◽  
Vol 10 (2) ◽  
pp. 148-160
Author(s):  
Joseph Chen ◽  
Brett Houk ◽  
Yazdi K. Pithavala ◽  
Ana Ruiz‐Garcia
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Population Pharmacokinetic Model ◽  
Pharmacokinetic Model ◽  
Small Cell ◽  
Population Pharmacokinetic ◽  
Time Varying ◽  
Small Cell Lung ◽  
Pooled Data ◽  
Healthy Participants

scholarly journals The Effect of Time-dependent Prognostic Factors on Survival of Non-Small Cell Lung Cancer using Bayesian Extended Cox Model

2020 ◽  
Author(s):  
Vida Pahlevani ◽  
Hossein Fallahzadeh ◽  
Nima Pahlevani ◽  
Abolfazl Nikpour ◽  
Morteza Mohammadzadeh
Keyword(s):  
Lung Cancer ◽  
Prognostic Factors ◽  
Small Cell Lung Cancer ◽  
Risk Ratio ◽  
Cell Lung Cancer ◽  
Cox Model ◽  
Small Cell ◽  
Time Varying ◽  
Small Cell Lung ◽  
Extended Cox Model

Background: Lung cancer is one of the most common cancers around the world. The aim of this study was to use Extended Cox Model (ECM) with Bayesian approach to survey the behavior of potential time-varying prognostic factors of Non-small cell lung cancer. Materials and Methods: Survival status of all 190 patients diagnosed with Non-Small Cell lung cancer referring to hospitals in Yazd were recorded from 2009 to 2013 by phone call. We fitted conventional Cox proportional hazards (Cox PH) as well as Bayesian ECM. Inference for estimated risk ratios was based on 90% credible intervals. Log pseudo marginal likelihood criteria (LMPL) was used for model comparison. Statistical computations were based on R language. Results: In this study, 190 patients with non-small cell lung cancer were followed, of whom 160 died because of the disease (84.2%). Median of survival time was 8 ± 0.076 month. After fitting the Cox PH Model, it was determined that the PH assumption was not satisfied for the type of treatment, the disease stage, and pathology status variables (p <0.001). LPML for Cox PH and Bayesian ECM was -431.593 and -401.01, respectively. Estimated hazard ratio curves based on Bayesian ECM showed that the risk ratio for these variables exhibited significant time varying behavior on hazard of lung cancer through follow up time. Conclusion: Based on LMPL, Bayesian ECM was found to have a better fit than Cox PH Model which declares, results from Cox PH should be interpreted with care. Especially, from beginning of the study to about 20 month after, very high risk ratio was estimated for variables whose PH was not satisfying for them.

Efficacy and safety of alectinib in ALK+ non-small-cell lung cancer (NSCLC): Pooled data from two pivotal phase II studies (NP28673 and NP28761).

2016 ◽  
Vol 34 (15_suppl) ◽  
pp. e20507-e20507 ◽  
Author(s):  
James Chih-Hsin Yang ◽  
Sai-Hong Ignatius Ou ◽  
Luigi De Petris ◽  
Shirish M. Gadgeel ◽  
Leena Gandhi ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Phase Ii ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Efficacy And Safety ◽  
Small Cell Lung ◽  
Pivotal Phase ◽  
Pooled Data ◽  
Phase Ii Studies

scholarly journals Utility of red cell distribution width as a diagnostic and prognostic marker in non-small cell lung cancer

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Bin Song ◽  
Pengchong Shi ◽  
Jianhong Xiao ◽  
Yanfang Song ◽  
Menglu Zeng ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Cell Distribution ◽  
Small Cell Lung ◽  
Distribution Width ◽  
Diagnosis And Prognosis ◽  
Nsclc Patients ◽  
Healthy Participants

Abstract An increasing number of studies have indicated that red blood cell distribution width (RDW) may be a novel biomarker for the diagnosis and prognosis of various malignancies. However, to date, data on the association of RDW with non-small cell lung cancer (NSCLC) are unclear. Our present study aimed to explore the value of RDW in NSCLC patients. A total of 338 NSCLC patients, 109 small cell lung cancer (SCLC) patients, and 302 healthy participants were retrospectively analyzed between January 2016 and December 2018. In the present study, we found that RDW was significantly increased in NSCLC patients. Receiver-operating characteristic (ROC) analysis showed that the area under the ROC curve (AUC) of RDW was 0.753 in discriminating NSCLC patients from healthy participants, the optimal cut-off value of RDW was 12.95, and the specificity and sensitivity were 76.33% and 76.16%, respectively. Further analysis found that RDW can enhance the diagnostic performance of Cyfra21-1 and NSE in discriminating NSCLC patients from healthy participants or SCLC patients. Among NSCLC patients, RDW was significantly correlated with TNM stage, T stage, N stage, M stage, and Cyfra21-1, indicating that RDW may be helpful for predicting the prognosis of NSCLC patients. Our findings suggest that RDW can be used as an auxiliary marker for the diagnosis and prognosis of NSCLC.

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