scholarly journals Score and deviance residuals based on the full likelihood approach in survival analysis

2020 ◽  
Vol 19 (6) ◽  
pp. 940-954
Author(s):  
Susan Halabi ◽  
Sandipan Dutta ◽  
Yuan Wu ◽  
Aiyi Liu
Keyword(s):  
Survival Analysis ◽  
Full Likelihood ◽  
Likelihood Approach ◽  
Deviance Residuals

Performance of methods for estimating the effect of covariates on group membership probabilities in group-based trajectory models

2017 ◽  
Vol 27 (10) ◽  
pp. 2918-2932 ◽  
Author(s):  
Christopher E Davies ◽  
Lynne C Giles ◽  
Gary FV Glonek
Keyword(s):  
Group Membership ◽  
Outcome Variable ◽  
Covariate Effect ◽  
Trajectory Model ◽  
Full Likelihood ◽  
Regression Approach ◽  
Likelihood Approach ◽  
Trajectory Models ◽  
Modelling Methods ◽  
Trajectory Modelling

One purpose of a longitudinal study is to gain insight of how characteristics at earlier points in time can impact on subsequent outcomes. Typically, the outcome variable varies over time and the data for each individual can be used to form a discrete path of measurements, that is a trajectory. Group-based trajectory modelling methods seek to identify subgroups of individuals within a population with trajectories that are more similar to each other than to trajectories in distinct groups. An approach to modelling the influence of covariates measured at earlier time points in the group-based setting is to consider models wherein these covariates affect the group membership probabilities. Models in which prior covariates impact the trajectories directly are also possible but are not considered here. In the present study, we compared six different methods for estimating the effect of covariates on the group membership probabilities, which have different approaches to account for the uncertainty in the group membership assignment. We found that when investigating the effect of one or several covariates on a group-based trajectory model, the full likelihood approach minimized the bias in the estimate of the covariate effect. In this ‘1-step’ approach, the estimation of the effect of covariates and the trajectory model are carried out simultaneously. Of the ‘3-step’ approaches, where the effect of the covariates is assessed subsequent to the estimation of the group-based trajectory model, only Vermunt’s improved 3 step resulted in bias estimates similar in size to the full likelihood approach. The remaining methods considered resulted in considerably higher bias in the covariate effect estimates and should not be used. In addition to the bias empirically demonstrated for the probability regression approach, we have shown analytically that it is biased in general.

A composite likelihood approach to predict the sex of the baby

2017 ◽  
Vol 27 (11) ◽  
pp. 3386-3396
Author(s):  
Simone Tiberi ◽  
Bruno Scarpa ◽  
Nicola Sartori
Keyword(s):  
Composite Likelihood ◽  
The Other ◽  
Selective Sampling ◽  
Likelihood Functions ◽  
Pseudo Likelihood ◽  
Significant Dependence ◽  
Full Likelihood ◽  
Likelihood Approach

Couples with diseases associated with the sexual chromosomes, as well as families in countries where the desire for a male is extreme, are interested in influencing the sex of the baby. We propose an original composite likelihood approach to analyse the relation between sex of the newborn and timing of the intercourse which leads to conception. Although there exist numerous works on this relation, only few studies have been carried out on independent datasets to validate the existing theories. Since the sex of the newborn is only known in case of conception, the full likelihood of the data is not easily defined without strong assumptions. A composite likelihood is a pseudo likelihood defined as the product of likelihood functions relative to subsets of the data. In particular, we consider two such likelihoods, one modelling the day-specific probabilities of conception and the other modelling the sex of the newborn given a conception has occurred. The methodology is applied to a dataset from a European fecundability study. The results show no significant dependence of the sex of the newborn on the time of intercourse. The method developed may be applied to other situations when data are affected by selective sampling.

Risk for psychotherapy drop-out in survival analysis: The influence of general change mechanisms and symptom severity.

2020 ◽  
Vol 67 (6) ◽  
pp. 712-722
Author(s):  
Sebastian Gmeinwieser ◽  
Kai Sebastian Schneider ◽  
Maximilian Bardo ◽  
Timo Brockmeyer ◽  
York Hagmayer
Keyword(s):  
Survival Analysis ◽  
Symptom Severity ◽  
Drop Out ◽  
General Change ◽  
Change Mechanisms
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