scholarly journals 464Basics of survival analysis: age is not appropriate as time scale in Cox regression model

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Yangyang Liu ◽  
Jingjing Zhang ◽  
Toshiharu Mitsuhashi ◽  
Toshihiko Matsuo ◽  
Takashi Yorifuji ◽  
...  
Keyword(s):  
Regression Model ◽  
Time Scale ◽  
Cox Regression ◽  
Cox Model ◽  
Survival Function ◽  
Likelihood Estimation ◽  
Continuous Variable ◽  
Study Time ◽  
Right Censoring ◽  
Cox Regression Model

Abstract Background Many previous methodological studies suggested to use age as time scale in Cox regression model, and some statistical analyses directly applied this conclusion. In the present study, we explain why age is not a more appropriate time scale compared to the time-on-study time scale. Methods We address this argument based on five aspects: Cox regression model, conditional likelihood estimation, dataset of left-truncation or right-censoring, algorithms and software for Cox model, and inferring survival function. Furthermore, logical and algorithmic errors arise in the procedure of parameter inference with age time scale, and that certain evaluation indicators proposed by previous studies are inappropriate. Results The function of time scale is mainly a sampling method for maximum likelihood estimation to infer coefficient of Cox regression model, and the method defined by the age time scale is incorrect in logics and algorithms. Furthermore, age as time scale creates new problems, such as the omission of covariates, loss of information as a continuous variable, increase in dropout, and inability to obtain the survival function. Conclusions For the Cox regression model, the classic time-on-study time scale is more appropriate compared to age as time scale. Key messages It is an important discussion because using age as time scale was first proposed decades ago, meaning that lots of turnovers in researchers, newbies tend to accept the assumptions of their predecessors, but the suitability has never been rigorously verified.

scholarly journals Cox proportion hazard model for patients with hepatitis disease in Iraq

2009 ◽  
Vol 6 (3) ◽  
pp. 612-617
Author(s):  
Baghdad Science Journal
Keyword(s):  
Regression Model ◽  
Survival Time ◽  
Cox Regression ◽  
Survival Function ◽  
Hazard Model ◽  
Cox Regression Model ◽  
Hepatic Diseases ◽  
Kaplan Meier

Cox regression model have been used to estimate proportion hazard model for patients with hepatitis disease recorded in Gastrointestinal and Hepatic diseases Hospital in Iraq for (2002 -2005). Data consists of (age, gender, survival time terminal stat). A Kaplan-Meier method has been applied to estimate survival function and hazerd function.

scholarly journals P1253ASSOCIATION OF PREDIALYSIS PULSE PRESSURE (PP) AND ITS INTRADIALYTIC VARIATIONS IN 5 YEAR MULTICENTER COHORT STUDY

2020 ◽  
Vol 35 (Supplement_3) ◽  
Author(s):  
Valeriy Shilo ◽  
Ivan Drachev
Keyword(s):  
Regression Model ◽  
Cox Regression ◽  
Cox Model ◽  
Surrogate Marker ◽  
Maintenance Hemodialysis ◽  
Intradialytic Hypotension ◽  
Multicenter Cohort Study ◽  
Dialysis Dose ◽  
Cox Regression Model ◽  
Total Cohort

Abstract Background and Aims Elevated PP, as a surrogate marker of elastic properties the arteries vessel wall, is important characteristics of the cardiovascular system and may be associated with poor survival both in the general eldery population with arterial hypertension and in patients on maintenance hemodialysis (HD). It has been shown, that increase predialysis PP in HD patients was associated with a higher risk of hospitalization or death, but the relationship between PP changes during HD was not well investigated in large prospective cohort studies. The aim of the study was to assess the effect of elevated predialisis PP and its intradialytic PP variations on survival in the Kaplan-Meir curves and in the Cox regression models. Method The retrospective cohort included patients who underwent maintenance HD in the large chain of B. Braun Avitum free standing HD units in Russia from 2011 to 2016 years (n = 3704). The mean age of the patients was 54,8±13,7 years, 45% were women and 55% men. All patients were on B. Braun Dialog+ Evolution dialysis machines with Adimea option for on-line KT/V measurement and synthetic alpha-polysulphone Xevonta series dialyzers (surface area 1,8, 2,0 and 2,3 sq. m.) The delivered dialysis dose, according to the Daugirdas 2nd generation formula was 1,6 ± 0.23 (spKt/V). Statistical analysis in a Kaplan-Meier curves and proportional Cox regression model were performed. The study used averaged BP data measured over the entire observation period and PP calculation. Patients were divided into subgroups according PP calculation <35, 35-55, 55-75 and more than 75 mm Hg. Variations in intradialytic PP (ΔPP) were divided into groups according to PP average change during HD procedure: -25 and lower decrease, -25 - -10, -10 - 0, 0 - 10, and 10 - 25 increase, mm Hg. Results From total cohort of 3704 patients, 207 (5,6%) has highly elevated PP (> 75 mmHg) and another 1549 (41,8%) has slightly elevated PP (55-75 mm hg). During the study, 393 deaths occurred. The Kaplan-Meyer survival curves clearly demonstrate that the worst survival rate occurs in the subgroup of patients with the markedly elevated predialysis PP (n=207, 35 deaths; HR = 1,7 CI = 1,3 – 2,6; p <0,001; Pic. 1). Then we analyze association of intradialytic PP changes and mortality in total cohort (n=3704) and the subgroups with elevated PP (n=1756). Both marked PP drop down and PP increase during HD worsen survival: the most poor demonstrate patients with highest decrease in PP (-25 mm Hg and more) and then with highest increase in PP (+10-25 mm hg) within HD procedure (Pic. 2). In unadjusted Cox model predialytic PP and survival remain significant (p=0,01), but not PP changes (p=0,3). After multivariable adjustments in the Cox regression model with main demographic factors (age, treatment duration) and key laboratory indices (spKT/V, urea, creatinine, hemoglobin, albumin, Ca, PO4, PTH) there were no association between both predialysis PP and PP changes and mortality (p=0,9 and 0,1, respectively). Among the independent risk factors in our model, highest hazard ratio affecting survival has for ultrafiltration (UF) speed both for predialytic PP and PP variations (tabl. 1 and table 2). Conclusion In our study PP and its intradialytic variations show statistical significant association with mortality in single factor survival analysis and in unadjusted Cox regression model, but were not independent factor in adjusted multivariate HR model. UF speed has the highest impact on mortality in our model. We can hypothesize, that patients with elevated PP are vulnerable for high UF rate and prone for intradialytic hypotension and higher mortality on maintenance HD.

scholarly journals Comparison of deep learning with regression analysis in creating predictive models for SARS-CoV-2 outcomes

2020 ◽  
Author(s):  
Ahmed Abdulaal ◽  
Aatish Patel ◽  
Esmita Charani ◽  
Sarah Denny ◽  
Saleh A Alqahtani ◽  
...  
Keyword(s):  
Deep Learning ◽  
Regression Model ◽  
Predictive Models ◽  
Cox Regression ◽  
Cox Model ◽  
Machine Learning Algorithms ◽  
Prognostic Models ◽  
Ann Model ◽  
Hospital Data ◽  
Cox Regression Model

Abstract Background: Accurately predicting patient outcomes in SARS-CoV-2 could aid patient management and allocation of healthcare resources. There are a variety of methods which can be used to develop prognostic models, ranging from logistic regression and survival analysis to more complex machine learning algorithms and deep learning. Despite several models having been created for SARS-CoV-2, most of these have been found to be highly susceptible to bias. We aimed to develop and compare two separate predictive models for death during admission with SARS-CoV-2. Method: Between March 1 - April 24, 2020, 398 patients were identified with laboratory confirmed SARS-CoV-2 in a London teaching hospital. Data from electronic health records were extracted and used to create two predictive models using: 1) a Cox regression model and 2) an artificial neural network (ANN). Model performance profiles were assessed by validation, discrimination, and calibration. Results: Both the Cox regression and ANN models achieved high accuracy (83.8%, 95% confidence interval (CI): 73.8 - 91.1 and 90.0%, 95% CI: 81.2 - 95.6, respectively). The area under the receiver operator curve (AUROC) for the ANN (92.6%, 95% CI: 91.1 - 94.1) was significantly greater than that of the Cox regression model (86.9%, 95% CI: 85.7 - 88.2), p=0.0136. Both models achieved acceptable calibration with Brier scores of 0.13 and 0.11 for the Cox model and ANN, respectively. Conclusion: We demonstrate an ANN which is non-inferior to a Cox regression model but with potential for further development such that it can learn as new data becomes available. Deep learning techniques are particularly suited to complex datasets with non-linear solutions, which make them appropriate for use in conditions with a paucity of prior knowledge. Accurate prognostic models for SARS-CoV-2 can provide benefits at the patient, departmental and organisational level.

DISTRIBUTION-FREE ESTIMATION OF THE BOX–COX REGRESSION MODEL WITH CENSORING

2011 ◽  
Vol 28 (3) ◽  
pp. 680-695 ◽  
Author(s):  
Songnian Chen
Keyword(s):  
Panel Data ◽  
Regression Model ◽  
Fixed Effects ◽  
Cox Regression ◽  
Cox Model ◽  
Parametric Estimation ◽  
Cross Sectional ◽  
Cox Regression Model ◽  
Distribution Free ◽  
The Cross

The Box–Cox regression model has been widely used in applied economics. However, there has been very limited discussion when data are censored. The focus has been on parametric estimation in the cross-sectional case, and there has been no discussion at all for the panel data model with fixed effects. This paper fills these important gaps by proposing distribution-free estimators for the Box–Cox model with censoring in both the cross-sectional and panel data settings. The proposed methods are easy to implement by combining a convex minimization problem with a one-dimensional search. The procedures are applicable to other transformation models.

scholarly journals Comparison of deep learning with regression analysis in creating predictive models for SARS-CoV-2 outcomes

2020 ◽  
Author(s):  
Ahmed Abdulaal ◽  
Aatish Patel ◽  
Esmita Charani ◽  
Sarah Denny ◽  
Saleh Alqahtani ◽  
...  
Keyword(s):  
Deep Learning ◽  
Regression Model ◽  
Predictive Models ◽  
Cox Regression ◽  
Cox Model ◽  
Machine Learning Algorithms ◽  
Prognostic Models ◽  
Ann Model ◽  
Hospital Data ◽  
Cox Regression Model

Abstract Background Accurately predicting patient outcomes in Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) could aid patient management and allocation of healthcare resources. There are a variety of methods which can be used to develop prognostic models, ranging from logistic regression and survival analysis to more complex machine learning algorithms and deep learning. Despite several models having been created for SARS-CoV-2, most of these have been found to be highly susceptible to bias. We aimed to develop and compare two separate predictive models for death during admission with SARS-CoV-2.MethodBetween March 1 - April 24, 2020, 398 patients were identified with laboratory confirmed SARS-CoV-2 in a London teaching hospital. Data from electronic health records were extracted and used to create two predictive models using: 1) a Cox regression model and 2) an artificial neural network (ANN). Model performance profiles were assessed by validation, discrimination, and calibration.Results Both the Cox regression and ANN models achieved high accuracy (83.8%, 95% confidence interval (CI): 73.8 - 91.1 and 90.0%, 95% CI: 81.2 - 95.6, respectively). The area under the receiver operator curve (AUROC) for the ANN (92.6%, 95% CI: 91.1 - 94.1) was significantly greater than that of the Cox regression model (86.9%, 95% CI: 85.7 - 88.2), p=0.0136. Both models achieved acceptable calibration with Brier scores of 0.13 and 0.11 for the Cox model and ANN, respectively. ConclusionWe demonstrate an ANN which is non-inferior to a Cox regression model but with potential for further development such that it can learn as new data becomes available. Deep learning techniques are particularly suited to complex datasets with non-linear solutions, which make them appropriate for use in conditions with a paucity of prior knowledge. Accurate prognostic models for SARS-CoV-2 can provide benefits at the patient, departmental and organisational level.

scholarly journals Comparison of deep learning with regression analysis in creating predictive models for SARS-CoV-2 outcomes

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Ahmed Abdulaal ◽  
Aatish Patel ◽  
Esmita Charani ◽  
Sarah Denny ◽  
Saleh A. Alqahtani ◽  
...  
Keyword(s):  
Deep Learning ◽  
Regression Model ◽  
Predictive Models ◽  
Cox Regression ◽  
Cox Model ◽  
Machine Learning Algorithms ◽  
Prognostic Models ◽  
Ann Model ◽  
Hospital Data ◽  
Cox Regression Model

Abstract Background Accurately predicting patient outcomes in Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) could aid patient management and allocation of healthcare resources. There are a variety of methods which can be used to develop prognostic models, ranging from logistic regression and survival analysis to more complex machine learning algorithms and deep learning. Despite several models having been created for SARS-CoV-2, most of these have been found to be highly susceptible to bias. We aimed to develop and compare two separate predictive models for death during admission with SARS-CoV-2. Method Between March 1 and April 24, 2020, 398 patients were identified with laboratory confirmed SARS-CoV-2 in a London teaching hospital. Data from electronic health records were extracted and used to create two predictive models using: (1) a Cox regression model and (2) an artificial neural network (ANN). Model performance profiles were assessed by validation, discrimination, and calibration. Results Both the Cox regression and ANN models achieved high accuracy (83.8%, 95% confidence interval (CI) 73.8–91.1 and 90.0%, 95% CI 81.2–95.6, respectively). The area under the receiver operator curve (AUROC) for the ANN (92.6%, 95% CI 91.1–94.1) was significantly greater than that of the Cox regression model (86.9%, 95% CI 85.7–88.2), p = 0.0136. Both models achieved acceptable calibration with Brier scores of 0.13 and 0.11 for the Cox model and ANN, respectively. Conclusion We demonstrate an ANN which is non-inferior to a Cox regression model but with potential for further development such that it can learn as new data becomes available. Deep learning techniques are particularly suited to complex datasets with non-linear solutions, which make them appropriate for use in conditions with a paucity of prior knowledge. Accurate prognostic models for SARS-CoV-2 can provide benefits at the patient, departmental and organisational level.

scholarly journals Study of survival time in pulp export

CERNE ◽  
2012 ◽  
Vol 18 (4) ◽  
pp. 547-555
Author(s):  
Luiz Moreira Coelho Junior ◽  
José Luiz Pereira de Rezende ◽  
Mario Javier Ferrua Vivanco ◽  
Antônio Donizette de Oliveira ◽  
Luís Antônio Coimbra Borges
Keyword(s):  
European Union ◽  
Regression Model ◽  
Cox Regression ◽  
Cox Model ◽  
Geographic Location ◽  
Forest Products ◽  
The European Union ◽  
Cox Regression Model ◽  
International Trade Agreements ◽  
Economic Markets

This study analyzed the time for a country to survive exporting pulp, using a Cox regression model. Covariates being used included data about population, Gross Domestic Product, total exports of forest products as an aggregate, pulp production and balance of trade for pulp, economic markets and blocks, and geographic regions. To select and check the most significant covariates, a proposal formulated by Collet (1994) was used. It was concluded that survival analysis via the Cox regression model proved to be a powerful tool for predicting the survival of a country exporting pulp; around 80% of countries that have pulp in their list of exports continue to export the commodity; out of the fifteen covariates selected for fitting the Cox model, four explain the model and two were found significant in explaining the survival of a country exporting pulp; international trade agreements were more significant in the Cox regression model than classes of macroeconomic forest indicators and geographic location; covariates explaining the odds of a country exporting pulp to survive, according to the hazard ratio, were, in descending order, integration between ECLAC and European Union, be a member of the European Union (V07) and be a member of ECLAC (V6); Brazil has 3.5 times as much chance of survival exporting pulp through an integration between ECLAC and the European Union than a country that is not a part of such integration; the probability that Brazil will survive exporting pulp is greater than the probability that Asian countries will.

scholarly journals Comparison of deep learning with regression analysis in creating predictive models for SARS-CoV-2 outcomes

2020 ◽  
Author(s):  
Ahmed Abdulaal ◽  
Aatish Patel ◽  
Esmita Charani ◽  
Sarah Denny ◽  
Saleh A Alqahtani ◽  
...  
Keyword(s):  
Deep Learning ◽  
Regression Model ◽  
Predictive Models ◽  
Cox Regression ◽  
Cox Model ◽  
Machine Learning Algorithms ◽  
Prognostic Models ◽  
Ann Model ◽  
Hospital Data ◽  
Cox Regression Model

Abstract Background Accurately predicting patient outcomes in Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) could aid patient management and allocation of healthcare resources. There are a variety of methods which can be used to develop prognostic models, ranging from logistic regression and survival analysis to more complex machine learning algorithms and deep learning. Despite several models having been created for SARS-CoV-2, most of these have been found to be highly susceptible to bias. We aimed to develop and compare two separate predictive models for death during admission with SARS-CoV-2.Method Between March 1 - April 24, 2020, 398 patients were identified with laboratory confirmed SARS-CoV-2 in a London teaching hospital. Data from electronic health records were extracted and used to create two predictive models using: 1) a Cox regression model and 2) an artificial neural network (ANN). Model performance profiles were assessed by validation, discrimination, and calibration.Results Both the Cox regression and ANN models achieved high accuracy (83.8%, 95% confidence interval (CI): 73.8 - 91.1 and 90.0%, 95% CI: 81.2 - 95.6, respectively). The area under the receiver operator curve (AUROC) for the ANN (92.6%, 95% CI: 91.1 - 94.1) was significantly greater than that of the Cox regression model (86.9%, 95% CI: 85.7 - 88.2), p=0.0136. Both models achieved acceptable calibration with Brier scores of 0.13 and 0.11 for the Cox model and ANN, respectively. Conclusion We demonstrate an ANN which is non-inferior to a Cox regression model but with potential for further development such that it can learn as new data becomes available. Deep learning techniques are particularly suited to complex datasets with non-linear solutions, which make them appropriate for use in conditions with a paucity of prior knowledge. Accurate prognostic models for SARS-CoV-2 can provide benefits at the patient, departmental and organisational level.

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