Harvard University Biostatistics Working Paper SeriesCopyright (c) 2014 Harvard University All rights reserved.
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Recent documents in Harvard University Biostatistics Working Paper Seriesen-usWed, 26 Nov 2014 01:48:01 PST3600On 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.Likelihood Based Estimation of Logistic Structural Nested Mean Models with an Instrumental Variable
http://biostats.bepress.com/harvardbiostat/paper178
http://biostats.bepress.com/harvardbiostat/paper178Mon, 04 Aug 2014 07:13:37 PDTRoland A. Matsouaka et al.A General Approach to Detect Gene (G)-environment (E) Additive Interaction Leveraging G-E Independence in Case-control Studies
http://biostats.bepress.com/harvardbiostat/paper177
http://biostats.bepress.com/harvardbiostat/paper177Wed, 30 Jul 2014 09:55:20 PDTEric Tchetgen Tchetgen et al.A Simple Regression-based Approach to Account for Survival Bias in Birth Outcomes Research
http://biostats.bepress.com/harvardbiostat/paper176
http://biostats.bepress.com/harvardbiostat/paper176Mon, 21 Jul 2014 06:49:06 PDTEric J. Tchetgen Tchetgen et al.A Note on the Control Function Approach with an Instrumental Variable and a Binary Outcome
http://biostats.bepress.com/harvardbiostat/paper175
http://biostats.bepress.com/harvardbiostat/paper175Mon, 21 Jul 2014 06:49:02 PDTEric Tchetgen TchetgenControl Function Assisted IPW Estimation with a Secondary Outcome in Case-Control Studies
http://biostats.bepress.com/harvardbiostat/paper174
http://biostats.bepress.com/harvardbiostat/paper174Wed, 16 Jul 2014 07:45:51 PDTTamar Sofer et al.Predicting the Future Subject's Outcome via an Optimal Stratification Procedure with Baseline Information
http://biostats.bepress.com/harvardbiostat/paper173
http://biostats.bepress.com/harvardbiostat/paper173Tue, 01 Jul 2014 07:17:06 PDTFlorence H. Yong et al.Adjustment for Mismeasured Exposure using Validation Data and Propensity Scores
http://biostats.bepress.com/harvardbiostat/paper172
http://biostats.bepress.com/harvardbiostat/paper172Tue, 27 May 2014 05:48:01 PDT
Propensity score methods are widely used to analyze observational studies in which patient characteristics might not be balanced by treatment group. These methods assume that exposure, or treatment assignment, is error-free, but in reality these variables can be subject to measurement error. This arises in the context of comparative effectiveness research, using observational administrative claims data in which accurate procedural codes are not always available. When using propensity score based methods, this error affects both the exposure variable directly, as well as the propensity score. We propose a two step maximum likelihood approach using validation data to adjust for the measurement error. First, we use a likelihood approach to estimate an adjusted propensity score. Using the adjusted propensity score, we then use a likelihood approach on the outcome model to adjust for measurement error in the exposure variable directly. In addition, we show the bias introduced when using error-prone treatment in the inverse probability weighting (IPW) estimator and propose an approach to eliminate this bias. Simulations show our proposed approaches reduce the bias and mean squared error (MSE) of the treatment effect estimator compared to using the error-prone treatment assignment.
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Danielle Braun et al.Bounds to Evaluate the Pure/natural Direct Effect without Cross-world Counterfactual Independence
http://biostats.bepress.com/harvardbiostat/paper171
http://biostats.bepress.com/harvardbiostat/paper171Mon, 31 Mar 2014 10:52:13 PDTEric Tchetgen Tchetgen et al.A unification of mediation and interaction: a four-way decomposition
http://biostats.bepress.com/harvardbiostat/paper170
http://biostats.bepress.com/harvardbiostat/paper170Tue, 25 Mar 2014 12:13:39 PDT
It is shown that the overall effect of an exposure on an outcome, in the presence of a mediator with which the exposure may interact, can be decomposed into four components: (i) the effect of the exposure in the absence of the mediator, (ii) the interactive effect when the mediator is left to what it would be in the absence of exposure, (iii) a mediated interaction, and (iv) a pure mediated effect. These four components, respectively, correspond to the portion of the effect that is due to neither mediation nor interaction, to just interaction (but not mediation), to both mediation and interaction, and to just mediation (but not interaction). This four-way decomposition unites methods that attribute effects to interactions and methods that assess mediation. Certain combinations of these four components correspond to measures for mediation, while other combinations correspond to measures of interaction previously proposed in the literature. Prior decompositions in the literature are in essence special cases of this four-way decomposition. The four-way decomposition can be carried out using standard statistical models, and software is provided to estimate each of the four components. The four-way decomposition provides maximum insight into how much of an effect is mediated, how much is due to interaction, how much is due to both mediation and interaction together, and how much is due to neither.
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Tyler J. VanderWeeleA Predictive Enrichment Procedure to Identify Potential Responders to a New Therapy for Randomized, Comparative, Controlled Clinical Studies
http://biostats.bepress.com/harvardbiostat/paper169
http://biostats.bepress.com/harvardbiostat/paper169Tue, 11 Mar 2014 07:48:15 PDTJunlong Li et al.Mediation Analysis with Time-Varying Exposures and Mediators
http://biostats.bepress.com/harvardbiostat/paper168
http://biostats.bepress.com/harvardbiostat/paper168Tue, 04 Mar 2014 10:38:34 PST
In this paper we consider mediation analysis when exposures and mediators vary over time. We give non-parametric identification results, discuss parametric implementation, and also provide a weighting approach to direct and indirect effects based on combining the results of two marginal structural models. We also discuss how our results give rise to a causal interpretation of the effect estimates produced from longitudinal structural equation models. When there are no time-varying confounders affected by prior exposure and mediator values, identification of direct and indirect effects is achieved by a longitudinal version of Pearl's mediation formula. When there are time-varying confounders affected by prior exposure and mediator, natural direct and indirect effects are not identified. We define a randomized interventional analogue of natural direct and indirect effects that are identified in this setting. The formula that identifies these effects we refer to as the "mediational g-formula." When there is no mediation, the mediational g-formula reduces to Robins' regular g-formula for longitudinal data. When there are no time-varying confouders affected by prior exposure and mediator values, then the mediational g-formula reduces to a longitudinal version of Pearl's mediation formula. However, the mediational g-formula itself can accomodate both mediation and time-varying confounders.
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Tyler J. VanderWeele et al.