Harvard University Biostatistics Working Paper SeriesCopyright (c) 2016 Harvard University All rights reserved.
http://biostats.bepress.com/harvardbiostat
Recent documents in Harvard University Biostatistics Working Paper Seriesen-usTue, 26 Jan 2016 11:29:03 PST3600Survival analysis with functions of mis-measured covariate histories: the case of chronic air pollution exposure in relation to mortality in the Nurses' Health Study
http://biostats.bepress.com/harvardbiostat/paper198
http://biostats.bepress.com/harvardbiostat/paper198Thu, 24 Sep 2015 07:42:59 PDT
Environmental epidemiologists are often interested in estimating the effect of functions of time-varying exposure histories, such as the 12-month moving average, in relation to chronic disease incidence or mortality. The individual exposure measurements that comprise such an exposure history are usually mis-measured, at least moderately, and, often, more substantially. To obtain unbiased estimates of Cox model hazard ratios for these complex mis-measured exposure functions, an extended risk set regression calibration (RRC) method for Cox models is developed and applied to a study of long-term exposure to the fine particulate matter ($PM_{2.5}$) component of air pollution in relation to all-cause mortality in the Nurses' Health Study. Simulation studies under several realistic assumptions about the measurement error model and about the correlation structure of the repeated exposure measurements were conducted to assess the finite sample properties of this new method, and found that the method has good performance in terms of finite sample bias reduction and nominal confidence interval coverage. User-friendly software has been developed and is available to the general public on the senior author's website.
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Xiaomei Liao et al.On Varieties of Doubly Robust Estimators Under Missing Not at Random With an Ancillary Variable
http://biostats.bepress.com/harvardbiostat/paper197
http://biostats.bepress.com/harvardbiostat/paper197Mon, 14 Sep 2015 07:34:26 PDTWang Miao et al.On Partial Identification of the Pure Direct Effect
http://biostats.bepress.com/harvardbiostat/paper196
http://biostats.bepress.com/harvardbiostat/paper196Tue, 08 Sep 2015 06:48:43 PDTCaleb Miles et al.Lepski's Method and Adaptive Estimation of Nonlinear Integral Functionals of Density
http://biostats.bepress.com/harvardbiostat/paper195
http://biostats.bepress.com/harvardbiostat/paper195Mon, 03 Aug 2015 12:21:37 PDTRajarshi Mukherjee et al.On Simple Relations Between Difference-in-differences and Negative Outcome Control of Unobserved Confounding
http://biostats.bepress.com/harvardbiostat/paper194
http://biostats.bepress.com/harvardbiostat/paper194Mon, 03 Aug 2015 06:40:01 PDTTamar Sofer et al.Accounting for Interactions and Complex Inter-Subject Dependency in Estimating Treatment Effect in Cluster Randomized Trials with Missing Outcomes
http://biostats.bepress.com/harvardbiostat/paper193
http://biostats.bepress.com/harvardbiostat/paper193Tue, 07 Jul 2015 08:17:58 PDTMelanie Prague et al.Negative Outcome Control for Unobserved Confounding Under a Cox Proportional Hazards Model
http://biostats.bepress.com/harvardbiostat/paper192
http://biostats.bepress.com/harvardbiostat/paper192Tue, 07 Jul 2015 06:12:17 PDTEric J. Tchetgen Tchetgen et al.Doubly Robust Estimation of a Marginal Average Effect of Treatment on the Treated With an Instrumental Variable
http://biostats.bepress.com/harvardbiostat/paper191
http://biostats.bepress.com/harvardbiostat/paper191Mon, 29 Jun 2015 06:00:09 PDTLan Liu et al.A general framework for diagnosing confounding of time-varying and other joint exposures
http://biostats.bepress.com/harvardbiostat/paper190
http://biostats.bepress.com/harvardbiostat/paper190Mon, 08 Jun 2015 08:03:39 PDTJohn W. JacksonIdentification and Doubly Robust Estimation of Data Missing Not at Random with an Ancillary Variable
http://biostats.bepress.com/harvardbiostat/paper189
http://biostats.bepress.com/harvardbiostat/paper189Fri, 05 Jun 2015 08:50:20 PDTWang Miao et al.Simulation of Semicompeting Risk Survival Data and Estimation Based on Multistate Frailty Model
http://biostats.bepress.com/harvardbiostat/paper188
http://biostats.bepress.com/harvardbiostat/paper188Wed, 04 Feb 2015 11:57:56 PST
We develop a simulation procedure to simulate the semicompeting risk survival data. In addition, we introduce an EM algorithm and a B–spline based estimation procedure to evaluate and implement Xu et al. (2010)’s nonparametric likelihood es- timation approach. The simulation procedure provides a route to simulate samples from the likelihood introduced in Xu et al. (2010)’s. Further, the EM algorithm and the B–spline methods stabilize the estimation and gives accurate estimation results. We illustrate the simulation and the estimation procedure with simluation examples and real data analysis.
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Fei Jiang et al.On the Restricted Mean Survival Time Curve Survival Analysis
http://biostats.bepress.com/harvardbiostat/paper187
http://biostats.bepress.com/harvardbiostat/paper187Mon, 24 Nov 2014 07:52:46 PSTLihui Zhao et al.Quantifying an Adherence Path-Specific Effect of Antiretroviral Therapy in the Nigeria PEPFAR Program
http://biostats.bepress.com/harvardbiostat/paper186
http://biostats.bepress.com/harvardbiostat/paper186Mon, 24 Nov 2014 07:38:34 PSTCaleb Miles et al.Constrained Bayesian Estimation of Inverse Probability Weights for Nonmonotone Missing Data
http://biostats.bepress.com/harvardbiostat/paper185
http://biostats.bepress.com/harvardbiostat/paper185Wed, 19 Nov 2014 09:45:07 PSTBaoLuo Sun et al.Nonparametric Adjustment for Measurement Error in Time to Event Data
http://biostats.bepress.com/harvardbiostat/paper184
http://biostats.bepress.com/harvardbiostat/paper184Wed, 22 Oct 2014 09:37:50 PDT
Measurement error in time to event data used as a predictor will lead to inaccurate predictions. This arises in the context of self-reported family history, a time to event predictor often measured with error, used in Mendelian risk prediction models. Using a validation data set, we propose a method to adjust for this type of measurement error. We estimate the measurement error process using a nonparametric smoothed Kaplan-Meier estimator, and use Monte Carlo integration to implement the adjustment. We apply our method to simulated data in the context of both Mendelian risk prediction models and multivariate survival prediction models, as well as illustrate our method using a data application for Mendelian risk prediction models. Results from simulations are evaluated using measures of mean squared error of prediction (MSEP), area under the response operating characteristics curve (ROC-AUC), and the ratio of observed to expected number of events. These results show that our adjusted method mitigates the effects of measurement error mainly by improving calibration and by improving total accuracy. In some scenarios discrimination is also improved.
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Danielle Braun et al.Extending Mendelian Risk Prediction Models to Handle Misreported Family History
http://biostats.bepress.com/harvardbiostat/paper183
http://biostats.bepress.com/harvardbiostat/paper183Wed, 22 Oct 2014 09:37:48 PDT
Mendelian risk prediction models calculate the probability of a proband being a mutation carrier based on family history and known mutation prevalence and penetrance. Family history in this setting, is self-reported and is often reported with error. Various studies in the literature have evaluated misreporting of family history. Using a validation data set which includes both error-prone self-reported family history and error-free validated family history, we propose a method to adjust for misreporting of family history. We estimate the measurement error process in a validation data set (from University of California at Irvine (UCI)) using nonparametric smoothed Kaplan-Meier estimators, and use Monte Carlo integration to implement the adjustment. In this paper, we extend BRCAPRO, a Mendelian risk prediction model for breast and ovarian cancers, to adjust for misreporting in family history. We apply the extended model to data from the Cancer Genetics Network (CGN).
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Danielle Braun et al.Optimal Bayesian Adaptive Trials when Treatment Efficacy Depends on Biomarkers
http://biostats.bepress.com/harvardbiostat/paper182
http://biostats.bepress.com/harvardbiostat/paper182Tue, 14 Oct 2014 05:47:50 PDTYifan Zhang et al.Generalized Quantile Treatment Effect
http://biostats.bepress.com/harvardbiostat/paper181
http://biostats.bepress.com/harvardbiostat/paper181Tue, 07 Oct 2014 12:26:00 PDTSergio Venturini et al.Estimation of the Overall Treatment Effect in the Presence of Interference in Cluster-randomized Trials of Infectious Disease Prevention
http://biostats.bepress.com/harvardbiostat/paper180
http://biostats.bepress.com/harvardbiostat/paper180Fri, 19 Sep 2014 07:16:14 PDTNicole Bohme Carnegie et al.Instrumental Variable Estimation in a Survival Context
http://biostats.bepress.com/harvardbiostat/paper179
http://biostats.bepress.com/harvardbiostat/paper179Tue, 12 Aug 2014 07:33:27 PDTEric J. Tchetgen Tchetgen et al.