13,089 research outputs found
A proportional hazards model for time-to-event data with epidemiological bias
In hepatitis C virus (HCV) epidemiological studies, the estimation of progression to cirrhosis and prognostic effects of associated risk factors is of particular importance when projecting national disease burden. However, the progression estimates obtained from conventional methods could be distorted due to a referral bias (Fu et al., 2007). In recent years, several approaches have been developed to handle this epidemiological bias in analyzing time-to-event data. This paper proposes a new estimation approach for this problem under a semiparametric proportional hazards framework. The new method uses a martingale approach based on the mean rate function, rather than the traditional hazard rate function, and develops an iterative algorithm to estimate the Cox regression parameter and baseline hazard rate simultaneously. The consistency and asymptotic properties of the proposed estimators are derived theoretically and evaluated via simulation studies. The new method is also applied to a real HCV cohort study
Bayesian correction for covariate measurement error: a frequentist evaluation and comparison with regression calibration
Bayesian approaches for handling covariate measurement error are well
established, and yet arguably are still relatively little used by researchers.
For some this is likely due to unfamiliarity or disagreement with the Bayesian
inferential paradigm. For others a contributory factor is the inability of
standard statistical packages to perform such Bayesian analyses. In this paper
we first give an overview of the Bayesian approach to handling covariate
measurement error, and contrast it with regression calibration (RC), arguably
the most commonly adopted approach. We then argue why the Bayesian approach has
a number of statistical advantages compared to RC, and demonstrate that
implementing the Bayesian approach is usually quite feasible for the analyst.
Next we describe the closely related maximum likelihood and multiple imputation
approaches, and explain why we believe the Bayesian approach to generally be
preferable. We then empirically compare the frequentist properties of RC and
the Bayesian approach through simulation studies. The flexibility of the
Bayesian approach to handle both measurement error and missing data is then
illustrated through an analysis of data from the Third National Health and
Nutrition Examination Survey
Analysis of time-to-event for observational studies: Guidance to the use of intensity models
This paper provides guidance for researchers with some mathematical
background on the conduct of time-to-event analysis in observational studies
based on intensity (hazard) models. Discussions of basic concepts like time
axis, event definition and censoring are given. Hazard models are introduced,
with special emphasis on the Cox proportional hazards regression model. We
provide check lists that may be useful both when fitting the model and
assessing its goodness of fit and when interpreting the results. Special
attention is paid to how to avoid problems with immortal time bias by
introducing time-dependent covariates. We discuss prediction based on hazard
models and difficulties when attempting to draw proper causal conclusions from
such models. Finally, we present a series of examples where the methods and
check lists are exemplified. Computational details and implementation using the
freely available R software are documented in Supplementary Material. The paper
was prepared as part of the STRATOS initiative.Comment: 28 pages, 12 figures. For associated Supplementary material, see
http://publicifsv.sund.ku.dk/~pka/STRATOSTG8
Bias of Maximum-Likelihood estimates in logistic and Cox regression models: A comparative simulation study
Parameter estimates of logistic and Cox regression models are biased for finite samples. In a simulation study we investigated for both models the behaviour of the bias in relation to sample size and further parameters. In the case of a dichotomous explanatory variable x the magnitude of the bias is strongly influenced by the baseline risk defined by the constants of the models and the risk resulting for the high risk group. To conduct a direct comparison of the bias of the two models analyses were based on the same simulated data. Overall, the bias of the two models appear to be similar, however, the Cox model has less bias in situations where the baseline risk is high
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