Impact of the model-building strategy on inference about nonlinear and time-dependent covariate effects in survival analysis

2014 ◽  
Vol 33 (19) ◽  
pp. 3318-3337 ◽  
Author(s):  
Willy Wynant ◽  
Michal Abrahamowicz
Keyword(s):  
Survival Analysis ◽  
Model Building ◽  
Time Dependent ◽  
Covariate Effects ◽  
Model Building Strategy

Auxiliary Modeling Procedures

2019 ◽  
pp. 136-153
Author(s):  
David McDowall ◽  
Richard McCleary ◽  
Bradley J. Bartos
Keyword(s):  
Time Series ◽  
Error Correction ◽  
Unit Root ◽  
Model Building ◽  
Causal Effect ◽  
Information Criteria ◽  
Unit Root Tests ◽  
Error Correction Models ◽  
Control Time ◽  
Model Building Strategy

Chapter 5 describes three sets of auxiliary methods that have emerged as add-on supplements to the traditional ARIMA model-building strategy. First, Bayesian information criteria (BIC) can be used to inform incremental modeling decisions. BICs are also the basis for the Bayesian hypothesis tests introduced in Chapter 6. Second, unit root tests can be used to inform differencing decisions. Used appropriately, unit root tests guard against over-differencing. Finally, co-integration and error correction models have become a popular way of representing the behavior of two time series that follow a shared path. We use the principle of co-integration to define the ideal control time series. Put simply, a time series and its ideal counterfactual control time series are co-integrated up the time of the intervention. At that point, if the two time series diverge, the magnitude of their divergence is taken as the causal effect of the intervention.

scholarly journals Model-based optimization of subgroup weights for survival analysis

2019 ◽  
Vol 35 (14) ◽  
pp. i484-i491
Author(s):  
Jakob Richter ◽  
Katrin Madjar ◽  
Jörg Rahnenführer
Keyword(s):  
Survival Analysis ◽  
Prediction Model ◽  
Model Building ◽  
Prediction Models ◽  
Survival Prediction ◽  
Good Prediction ◽  
Similar Data ◽  
Weighted Likelihood ◽  
Model Based ◽  
Specific Subgroup

AbstractMotivationTo obtain a reliable prediction model for a specific cancer subgroup or cohort is often difficult due to limited sample size and, in survival analysis, due to potentially high censoring rates. Sometimes similar data from other patient subgroups are available, e.g. from other clinical centers. Simple pooling of all subgroups can decrease the variance of the predicted parameters of the prediction models, but also increase the bias due to heterogeneity between the cohorts. A promising compromise is to identify those subgroups with a similar relationship between covariates and target variable and then include only these for model building.ResultsWe propose a subgroup-based weighted likelihood approach for survival prediction with high-dimensional genetic covariates. When predicting survival for a specific subgroup, for every other subgroup an individual weight determines the strength with which its observations enter into model building. MBO (model-based optimization) can be used to quickly find a good prediction model in the presence of a large number of hyperparameters. We use MBO to identify the best model for survival prediction of a specific subgroup by optimizing the weights for additional subgroups for a Cox model. The approach is evaluated on a set of lung cancer cohorts with gene expression measurements. The resulting models have competitive prediction quality, and they reflect the similarity of the corresponding cancer subgroups, with both weights close to 0 and close to 1 and medium weights.Availability and implementationmlrMBO is implemented as an R-package and is freely available at http://github.com/mlr-org/mlrMBO.

Survival analysis with change-points in covariate effects

2020 ◽  
Vol 29 (11) ◽  
pp. 3235-3248
Author(s):  
Chun Yin Lee ◽  
KF Lam
Keyword(s):  
Survival Analysis ◽  
Change Point ◽  
Cox Regression ◽  
Bootstrap Method ◽  
Bernstein Polynomials ◽  
Change Points ◽  
Ratio Test ◽  
Covariate Effect ◽  
Sequential Approach ◽  
Covariate Effects

We apply a maximal likelihood ratio test for the presence of multiple change-points in the covariate effects based on the Cox regression model. The covariate effect is assumed to change smoothly at one or more unknown change-points. The number of change-points is inferred by a sequential approach. Confidence intervals for the regression and change-point parameters are constructed by a bootstrap method based on Bernstein polynomials conditionally on the number of change-points. The methods are assessed by simulations and are applied to two datasets.

Velocity model building strategy for multi‐azimuth surveys

2010 ◽  
Author(s):  
Junru Jiao ◽  
Chaoguang Zhou ◽  
Sonny Lin ◽  
Dennis van der Burg ◽  
Sverre Brandsberg‐Dahl
Keyword(s):  
Model Building ◽  
Velocity Model ◽  
Velocity Model Building ◽  
Model Building Strategy

8. Mover-Stayer Models for Analyzing Event Nonoccurrence and Event Timing with Time-Dependent Covariates: An Application to an Analysis of Remarriage

1998 ◽  
Vol 28 (1) ◽  
pp. 327-361 ◽  
Author(s):  
Kazuo Yamaguchi
Keyword(s):  
Black Women ◽  
White Women ◽  
Time Dependent ◽  
Regression Equations ◽  
New Model ◽  
American Women ◽  
Duration Data ◽  
Covariate Effects ◽  
Event Timing ◽  
Time Dependent Covariates

This paper introduces a novel extension of mover-stayer models for duration data that allows time-dependent covariates to be used for both a pair of regression equations, one that identifies the determinants of event timing and one that identifies the determinants of the probability of ultimate event nonoccurrence. Existing models intended to distinguish covariate effects on event timing from those on event nonoccurrence cannot use time-dependent covariates in the equation for the probability of ultimate event nonoccurrence. This paper applies the new model to an analysis of remarriage among American women. The analysis generally demonstrates that some covariates effect remarriage timing while others affect the probability of ultimate remarriage nonoccurrence. Some differences in patterns of remarriage between black women and white women are also reported. Theoretical implications of these findings are discussed.

scholarly journals SURVIVAL MODELS IN THE CONTEXT OF MULTIPLICATIVE AND ADDITIVE HAZARDS

2019 ◽  
Vol 37 (3) ◽  
pp. 306
Author(s):  
Suely Ruiz GIOLO ◽  
Jaqueline Aparecida RAMINELLI
Keyword(s):  
Survival Analysis ◽  
Primary Biliary Cirrhosis ◽  
Goodness Of Fit ◽  
Operating Characteristic ◽  
Graphical Representation ◽  
Predictive Accuracy ◽  
Time Dependent ◽  
Survival Models ◽  
Biliary Cirrhosis ◽  
Additive Hazards

In survival analysis, multiplicative and additive hazards models provide the two principal frameworks to study the association between the hazard and covariates. When these models are considered for analyzing a given survival dataset, it becomes relevant to evaluate the overall goodness-of-fit and how well each model can predict those subjects who subsequently will or will not experience the event. In this paper, this evaluation is based on a graphical representation of the Cox-Snell residuals and also on a time-dependent version of the area under the receiver operating characteristic (ROC) curve, denoted by AUC(t). A simulation study is carried out to evaluate the performance of the AUC(t) as a tool for comparing the predictive accuracy of survival models. A dataset from the Mayo Clinic trial in primary biliary cirrhosis  (PBC) of the liver is also considered to illustrate the usefulness of these tools to compare survival models formulated under distinct hazards frameworks.

Setting the Stage

2021 ◽  
pp. 1-26
Author(s):  
Neal M. Krause
Keyword(s):  
Conceptual Model ◽  
Conceptual Models ◽  
Model Building ◽  
Social Phenomenon ◽  
Religion And Health ◽  
Theoretical Frameworks ◽  
New Model ◽  
The Core ◽  
Critical Overview ◽  
Model Building Strategy

This chapter presents a detailed rationale for why this volume is needed. The discussion is divided into five sections: (1) a critical overview of the religion-and-health literature is provided; (2) some preliminary observations are made on the state of current theoretical frameworks and conceptual models in the field; (3) a new conceptual model is introduced—this model is based on the premise that religion is, in essence, a social phenomenon that serves as a key conduit for the transmission of core religious virtues; (4) a new model-building strategy is illustrated by showing how submodels (i.e., brief supplementary models) can be used to expand the conceptual scope of the core model; and (5) an overview is provided of the chapters that follow.

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