scholarly journals Bootstrapping complex time‐to‐event data without individual patient data, with a view toward time‐dependent exposures

2019 ◽  
Vol 38 (20) ◽  
pp. 3747-3763 ◽  
Author(s):  
Tobias Bluhmki ◽  
Hein Putter ◽  
Arthur Allignol ◽  
Jan Beyersmann ◽  
Keyword(s):  
Individual Patient Data ◽  
Patient Data ◽  
Time Dependent ◽  
Event Data ◽  
Time To Event ◽  
Complex Time ◽  
Time To Event Data

Estimating the Cutoff Points of Time-Dependent Risk Factors by Using Joint Modeling of Longitudinal and Time-to-Event Data: A 14-Year Follow-up Study—Tehran Lipid and Glucose Study

2019 ◽  
Vol 31 (8) ◽  
pp. 728-736
Author(s):  
Nezhat Shakeri ◽  
Fereidoun Azizi
Keyword(s):  
Risk Factors ◽  
Cox Model ◽  
Joint Modeling ◽  
Time Dependent ◽  
Estimation Methods ◽  
Event Data ◽  
Time To Event ◽  
Time To Event Data ◽  
Cutoff Points

Diagnostic accuracy and optimal cutoff points of risk factors is one of the important issues in medical decisions. In order to reassess the cutoff points of markers, longitudinal and time-to-event data of elderly individuals were collected repeatedly through 3 follow-up stages in the Tehran Lipid and Glucose Study. Time-dependent area under the ROC (receiver operating characteristic) curves (AUCs) based on the joint modeling of longitudinal and time-to-event data technique were measured. AUCs were considered to evaluate the discriminative potential of the models. The joint model produced higher AUC values than the Cox model; therefore, accuracy was improved although it is computationally complicated. The results had some differences with the thresholds reported in guidelines due to specificity to the population and/or the means of estimation methods. The estimated cutoff points with regard to sex can be used as a guideline for the Iranian elderly population.

Approximate Bayesian inference for joint linear and partially linear modeling of longitudinal zero-inflated count and time to event data

2021 ◽  
pp. 096228022110028
Author(s):  
T Baghfalaki ◽  
M Ganjali
Keyword(s):  
Linear Model ◽  
Joint Modeling ◽  
Partially Linear Model ◽  
Event Data ◽  
Time To Event ◽  
Data Set ◽  
Partially Linear ◽  
Time To Event Data ◽  
Count Response ◽  
Approximate Bayesian

Joint modeling of zero-inflated count and time-to-event data is usually performed by applying the shared random effect model. This kind of joint modeling can be considered as a latent Gaussian model. In this paper, the approach of integrated nested Laplace approximation (INLA) is used to perform approximate Bayesian approach for the joint modeling. We propose a zero-inflated hurdle model under Poisson or negative binomial distributional assumption as sub-model for count data. Also, a Weibull model is used as survival time sub-model. In addition to the usual joint linear model, a joint partially linear model is also considered to take into account the non-linear effect of time on the longitudinal count response. The performance of the method is investigated using some simulation studies and its achievement is compared with the usual approach via the Bayesian paradigm of Monte Carlo Markov Chain (MCMC). Also, we apply the proposed method to analyze two real data sets. The first one is the data about a longitudinal study of pregnancy and the second one is a data set obtained of a HIV study.

scholarly journals Mitigation of biases in estimating hazard ratios under non-sensitive and non-specific observation of outcomes–applications to influenza vaccine effectiveness

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Ulrike Baum ◽  
Sangita Kulathinal ◽  
Kari Auranen
Keyword(s):  
False Positive ◽  
False Positive Rate ◽  
Vaccine Effectiveness ◽  
Partial Likelihood ◽  
Event Data ◽  
Time To Event ◽  
Positive Events ◽  
Time To Event Data ◽  
Hazard Ratios ◽  
Positive Rate

Abstract Background Non-sensitive and non-specific observation of outcomes in time-to-event data affects event counts as well as the risk sets, thus, biasing the estimation of hazard ratios. We investigate how imperfect observation of incident events affects the estimation of vaccine effectiveness based on hazard ratios. Methods Imperfect time-to-event data contain two classes of events: a portion of the true events of interest; and false-positive events mistakenly recorded as events of interest. We develop an estimation method utilising a weighted partial likelihood and probabilistic deletion of false-positive events and assuming the sensitivity and the false-positive rate are known. The performance of the method is evaluated using simulated and Finnish register data. Results The novel method enables unbiased semiparametric estimation of hazard ratios from imperfect time-to-event data. False-positive rates that are small can be approximated to be zero without inducing bias. The method is robust to misspecification of the sensitivity as long as the ratio of the sensitivity in the vaccinated and the unvaccinated is specified correctly and the cumulative risk of the true event is small. Conclusions The weighted partial likelihood can be used to adjust for outcome measurement errors in the estimation of hazard ratios and effectiveness but requires specifying the sensitivity and the false-positive rate. In absence of exact information about these parameters, the method works as a tool for assessing the potential magnitude of bias given a range of likely parameter values.

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