scholarly journals Parametric survival analysis using R: Illustration with lung cancer data

2019 ◽  
Vol 3 (4) ◽  
Author(s):  
Mukesh Kumar ◽  
Prashant Kr. Sonker ◽  
Agni Saroj ◽  
Aanchal Jain ◽  
Atanu Bhattacharjee ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Survival Analysis ◽  
Cancer Data ◽  
Lung Cancer Data ◽  
Parametric Survival ◽  
Parametric Survival Analysis

scholarly journals Statistical learning methods as a preprocessing step for survival analysis: evaluation of concept using lung cancer data

2011 ◽  
Vol 10 (1) ◽  
pp. 97 ◽  
Author(s):  
Madhusmita Behera ◽  
Erin E Fowler ◽  
Taofeek K Owonikoko ◽  
Walker H Land ◽  
William Mayfield ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Survival Analysis ◽  
Statistical Learning ◽  
Learning Methods ◽  
Cancer Data ◽  
Lung Cancer Data

scholarly journals Comparison of Multi Layered Percepton and Radial Basis Function Classification Performance of Lung Cancer Data

2020 ◽  
Vol 1471 ◽  
pp. 012043
Author(s):  
Yessi Jusman ◽  
Zul Indra ◽  
Roni Salambue ◽  
Siti Nurul Aqmariah Mohd Kanafiah ◽  
Muhammad Ahdan Fawwaz Nurkholid
Keyword(s):  
Lung Cancer ◽  
Radial Basis Function ◽  
Basis Function ◽  
Classification Performance ◽  
Cancer Data ◽  
Lung Cancer Data ◽  
Radial Basis

scholarly journals Simulation of parametric model towards the fixed covariate of right censored lung cancer data

2017 ◽  
Vol 890 ◽  
pp. 012172
Author(s):  
Siti Afiqah Muhamad Jamil ◽  
M. Asrul Affendi Abdullah ◽  
Sie Long Kek ◽  
Oyebayo Ridwan Olaniran ◽  
Syahila Enera Amran
Keyword(s):  
Lung Cancer ◽  
Parametric Model ◽  
Cancer Data ◽  
Lung Cancer Data

scholarly journals P103 Apples and pears? a comparison of two sources of lung cancer data in england

Thorax ◽  
2016 ◽  
Vol 71 (Suppl 3) ◽  
pp. A139.1-A139
Author(s):  
A Khakwani ◽  
R Hubbard ◽  
R Jack ◽  
N Wood ◽  
S Vernon ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Data ◽  
Lung Cancer Data

scholarly journals Pitfalls and perils of survival analysis under incorrect assumptions: the case of Covid-19 data

Biomédica ◽  
2021 ◽  
Vol 41 (Sp. 2) ◽  
Author(s):  
Daniele Piovani ◽  
Georgios K. Nikolopoulos ◽  
Stefanos Bonovas
Keyword(s):  
Decision Making ◽  
Survival Analysis ◽  
Preventive Measures ◽  
Clinical Course ◽  
Virus Disease ◽  
Current Medical Research ◽  
Basic Assumptions ◽  
Parametric Survival ◽  
Parametric Survival Analysis ◽  
Non Parametric

Non-parametric survival analysis has become a very popular statistical method in current medical research. Employing, however, survival methodology when its fundamental assumptions are not fulfilled can severely bias the results. Currently, hundreds of clinical studies are using survival methods to investigate factors potentially associated with the prognosis of Corona Virus Disease 2019 (Covid-19), and test new preventive and therapeutic strategies. In the pandemic era, it is more critical than ever that decision-making is evidence-based and relies on solid statistical methods. However, this is not always the case. Serious methodologic errors have been identified in recent seminal studies about Covid-19: one reporting outcomes of patients treated with remdesivir, and another one on the epidemiology, clinical course and outcomes of critically-ill patients. High-quality evidence is essential to inform clinicians about optimal Covid-19 therapies, and policymakers about the true effect of preventive measures aiming to tackle the pandemic. Though timely evidence is needed, we should encourage the appropriate application of survival analysis methods and careful peer-review to avoid publishing flawed results, which could affect decision-making. In this paper, we recapitulate the basic assumptions underlying non-parametric survival analysis and frequent errors in its application, and discuss how to handle data of Covid-19.

scholarly journals ANALISIS SINTASAN PARAMETRIK PADA PASIEN STROKE DENGAN PENDEKATAN DISTRIBUSI WEIBULL

2019 ◽  
Vol 8 (1) ◽  
pp. 55
Author(s):  
NI MADE SRI WAHYUNI ◽  
I WAYAN SUMARJAYA ◽  
NI LUH PUTU SUCIPTAWATI
Keyword(s):  
Body Mass Index ◽  
Survival Analysis ◽  
Weibull Distribution ◽  
Survival Data ◽  
The Body ◽  
Predictor Variables ◽  
Survival Models ◽  
Stroke Patients ◽  
Parametric Survival ◽  
Parametric Survival Analysis

Parametric survival analysis is one of the survival analysis that has a distribution of survival data that follows a certain distribution. Weibull distribution is a distribution that is often used in parametric survival analysis. The purpose of this study is to determine parametric survival models using the Weibull distribution and to determine  the factors that can influence the recovery of stroke patients. This study uses data on stroke patients in the Wangaya hospital, Denpasar in 2017. The best model obtained in this study is a model that consists of two predictor variables, namely the age and the body mass index (BMI).Therefore the  factors that can influence the recovery of stroke patients are age and BMI.

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