Bias Induced by Self-reported Smoking on Periodontitis-Systemic Disease                Associations

Non-causal associations between periodontitis and systemic diseases may be spuriously induced by smoking because of its strong relationship to both. The goal of this study was to evaluate whether adjustment for self-reported smoking removes tobacco-related confounding and eliminated such spurious confounding. Using NHANES III data, we evaluated associations between attachment loss and serum cotinine after adjustment by self-reported number of cigarettes smoked. Cotinine, a metabolite of nicotine, should not be related to attachment loss, if self-reported smoking captures the effect of tobacco on attachment levels. Adjustment for self-reported cigarette smoking did not completely remove the correlation between attachment loss and serum-cotinine level (r = 0.075, n= 1507, p = 0.003). Simulation studies indicated similar results for time-to-event data. These findings demonstrate the difficulty in distinguishing the effects of periodontitis from those of smoking with respect to a smoking-related outcome. Future studies should report results of analyses on separate subcohorts of never-smokers and smokers.

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Cox-sMBPLS: An Algorithm for Disease Survival Prediction and Multi-Omics Module Discovery Incorporating Cis-Regulatory Quantitative Effects

Frontiers in Genetics ◽

10.3389/fgene.2021.701405 ◽

2021 ◽

Vol 12 ◽

Author(s):

Nasim Vahabi ◽

Caitrin W. McDonough ◽

Ankit A. Desai ◽

Larisa H. Cavallari ◽

Julio D. Duarte ◽

...

Keyword(s):

Feature Selection ◽

Disease Onset ◽

Prediction Performance ◽

Biological Information ◽

Survival Prediction ◽

Simulation Studies ◽

Time To Event ◽

Time To Event Data ◽

Proposed Model ◽

Module Discovery

BackgroundThe development of high-throughput techniques has enabled profiling a large number of biomolecules across a number of molecular compartments. The challenge then becomes to integrate such multimodal Omics data to gain insights into biological processes and disease onset and progression mechanisms. Further, given the high dimensionality of such data, incorporating prior biological information on interactions between molecular compartments when developing statistical models for data integration is beneficial, especially in settings involving a small number of samples.ResultsWe develop a supervised model for time to event data (e.g., death, biochemical recurrence) that simultaneously accounts for redundant information within Omics profiles and leverages prior biological associations between them through a multi-block PLS framework. The interactions between data from different molecular compartments (e.g., epigenome, transcriptome, methylome, etc.) were captured by using cis-regulatory quantitative effects in the proposed model. The model, coined Cox-sMBPLS, exhibits superior prediction performance and improved feature selection based on both simulation studies and analysis of data from heart failure patients.ConclusionThe proposed supervised Cox-sMBPLS model can effectively incorporate prior biological information in the survival prediction system, leading to improved prediction performance and feature selection. It also enables the identification of multi-Omics modules of biomolecules that impact the patients’ survival probability and also provides insights into potential relevant risk factors that merit further investigation.

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Prognostic review and time-to-event data meta-analysis of low skeletal muscle mass in patients with peripheral arterial disease of the lower limbs undergoing revascularization

International Angiology ◽

10.23736/s0392-9590.19.04248-2 ◽

2020 ◽

Vol 39 (1) ◽

Author(s):

Madeleine Sharpe ◽

Emeka Okoye ◽

George A. Antoniou

Keyword(s):

Skeletal Muscle ◽

Peripheral Arterial Disease ◽

Skeletal Muscle Mass ◽

Meta Analysis ◽

Arterial Disease ◽

Lower Limbs ◽

Event Data ◽

Time To Event ◽

Time To Event Data ◽

Peripheral Arterial

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

Statistical Methods in Medical Research ◽

10.1177/09622802211002868 ◽

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.

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

Emerging Themes in Epidemiology ◽

10.1186/s12982-020-00091-z ◽

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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