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Articles 151 - 180 of 1108
Full-Text Articles in Statistics and Probability
Inferential Methods To Assess The Difference In The Area Under The Curve From Nested Binary Regression Models, Glenn Heller, Venkatraman E. Seshan, Chaya S. Moskowitz, Mithat Gonen
Inferential Methods To Assess The Difference In The Area Under The Curve From Nested Binary Regression Models, Glenn Heller, Venkatraman E. Seshan, Chaya S. Moskowitz, Mithat Gonen
Memorial Sloan-Kettering Cancer Center, Dept. of Epidemiology & Biostatistics Working Paper Series
The area under the curve (AUC) is the most common statistical approach to evaluate the discriminatory power of a set of factors in a binary regression model. A nested model framework is used to ascertain whether the AUC increases when new factors enter the model. Two statistical tests are proposed for the difference in the AUC parameters from these nested models. The asymptotic null distributions for the two test statistics are derived from the scenarios: (A) the difference in the AUC parameters is zero and the new factors are not associated with the binary outcome, (B) the difference in the …
Variable Selection With False Discovery Control, Kevin He, Yanming Li, Ji Zhu, Hongliang Liu, Jeffrey E. Lee, Christopher I. Amos, Terry Hyslop, Jiashun Jin, Qinyi Wei, Yi Li
Variable Selection With False Discovery Control, Kevin He, Yanming Li, Ji Zhu, Hongliang Liu, Jeffrey E. Lee, Christopher I. Amos, Terry Hyslop, Jiashun Jin, Qinyi Wei, Yi Li
The University of Michigan Department of Biostatistics Working Paper Series
Technological advances that allow routine identification of high-dimensional risk factors have led to high demand for statistical techniques that enable full utilization of these rich sources of information for genome-wide association studies (GWAS). Variable selection for censored outcome data as well as control of false discoveries (i.e. inclusion of irrelevant variables) in the presence of high-dimensional predictors present serious challenges. In the context of survival analysis with high-dimensional covariates, this paper develops a computationally feasible method for building general risk prediction models, while controlling false discoveries. We have proposed a high-dimensional variable selection method by incorporating stability selection to control …
Tests For Gene-Environment Interactions And Joint Effects With Exposure Misclassification, Philip S. Boonstra, Bhramar Mukherjee, Stephen B. Gruber, Jaeil Ahn, Stephanie L. Schmit, Nilanjan Chatterjee
Tests For Gene-Environment Interactions And Joint Effects With Exposure Misclassification, Philip S. Boonstra, Bhramar Mukherjee, Stephen B. Gruber, Jaeil Ahn, Stephanie L. Schmit, Nilanjan Chatterjee
The University of Michigan Department of Biostatistics Working Paper Series
The number of methods for genome-wide testing of gene-environment interactions (GEI) continues to increase with the hope of discovering new genetic risk factors and obtaining insight into the disease-gene-environment relationship. The relative performance of these methods based on family-wise type 1 error rate and power depends on underlying disease-gene-environment associations, estimates of which may be biased in the presence of exposure misclassification. This simulation study expands on a previously published simulation study of methods for detecting GEI by evaluating the impact of exposure misclassification. We consider seven single step and modular screening methods for identifying GEI at a genome-wide level …
Statistical Inference For The Mean Outcome Under A Possibly Non-Unique Optimal Treatment Strategy, Alexander R. Luedtke, Mark J. Van Der Laan
Statistical Inference For The Mean Outcome Under A Possibly Non-Unique Optimal Treatment Strategy, Alexander R. Luedtke, Mark J. Van Der Laan
U.C. Berkeley Division of Biostatistics Working Paper Series
We consider challenges that arise in the estimation of the value of an optimal individualized treatment strategy defined as the treatment rule that maximizes the population mean outcome, where the candidate treatment rules are restricted to depend on baseline covariates. We prove a necessary and sufficient condition for the pathwise differentiability of the optimal value, a key condition needed to develop a regular asymptotically linear (RAL) estimator of this parameter. The stated condition is slightly more general than the previous condition implied in the literature. We then describe an approach to obtain root-n rate confidence intervals for the optimal value …
Higher-Order Targeted Minimum Loss-Based Estimation, Marco Carone, Iván Díaz, Mark J. Van Der Laan
Higher-Order Targeted Minimum Loss-Based Estimation, Marco Carone, Iván Díaz, Mark J. Van Der Laan
U.C. Berkeley Division of Biostatistics Working Paper Series
Common approaches to parametric statistical inference often encounter difficulties in the context of infinite-dimensional models. The framework of targeted maximum likelihood estimation (TMLE), introduced in van der Laan & Rubin (2006), is a principled approach for constructing asymptotically linear and efficient substitution estimators in rich infinite-dimensional models. The mechanics of TMLE hinge upon first-order approximations of the parameter of interest as a mapping on the space of probability distributions. For such approximations to hold, a second-order remainder term must tend to zero sufficiently fast. In practice, this means an initial estimator of the underlying data-generating distribution with a sufficiently large …
Quantifying An Adherence Path-Specific Effect Of Antiretroviral Therapy In The Nigeria Pepfar Program, Caleb Miles, Ilya Shpitser, Phyllis Kanki, Seema Meloni, Eric J. Tchetgen Tchetgen
Quantifying An Adherence Path-Specific Effect Of Antiretroviral Therapy In The Nigeria Pepfar Program, Caleb Miles, Ilya Shpitser, Phyllis Kanki, Seema Meloni, Eric J. Tchetgen Tchetgen
Harvard University Biostatistics Working Paper Series
No abstract provided.
On The Restricted Mean Survival Time Curve Survival Analysis, Lihui Zhao, Brian Claggett, Lu Tian, Hajime Uno, Marc A. Pfeffer, Scott D. Solomon, Lorenzo Trippa, L. J. Wei
On The Restricted Mean Survival Time Curve Survival Analysis, Lihui Zhao, Brian Claggett, Lu Tian, Hajime Uno, Marc A. Pfeffer, Scott D. Solomon, Lorenzo Trippa, L. J. Wei
Harvard University Biostatistics Working Paper Series
No abstract provided.
Constrained Bayesian Estimation Of Inverse Probability Weights For Nonmonotone Missing Data, Baoluo Sun, Eric J. Tchetgen Tchetgen
Constrained Bayesian Estimation Of Inverse Probability Weights For Nonmonotone Missing Data, Baoluo Sun, Eric J. Tchetgen Tchetgen
Harvard University Biostatistics Working Paper Series
No abstract provided.
Testing Gene-Environment Interactions In The Presence Of Measurement Error, Chongzhi Di, Li Hsu, Charles Kooperberg, Alex Reiner, Ross Prentice
Testing Gene-Environment Interactions In The Presence Of Measurement Error, Chongzhi Di, Li Hsu, Charles Kooperberg, Alex Reiner, Ross Prentice
UW Biostatistics Working Paper Series
Complex diseases result from an interplay between genetic and environmental risk factors, and it is of great interest to study the gene-environment interaction (GxE) to understand the etiology of complex diseases. Recent developments in genetics field allows one to study GxE systematically. However, one difficulty with GxE arises from the fact that environmental exposures are often measured with error. In this paper, we focus on testing GxE when the environmental exposure E is subject to measurement error. Surprisingly, contrast to the well-established results that the naive test ignoring measurement error is valid in testing the main effects, we find that …
Personalized Evaluation Of Biomarker Value: A Cost-Benefit Perspective, Ying Huang, Eric Laber
Personalized Evaluation Of Biomarker Value: A Cost-Benefit Perspective, Ying Huang, Eric Laber
UW Biostatistics Working Paper Series
For a patient who is facing a treatment decision, the added value of information provided by a biomarker depends on the individual patient’s expected response to treatment with and without the biomarker, as well as his/her tolerance of disease and treatment harm. However, individualized estimators of the value of a biomarker are lacking. We propose a new graphical tool named the subject-specific expected benefit curve for quantifying the personalized value of a biomarker in aiding a treatment decision. We develop semiparametric estimators for two general settings: i) when biomarker data are available from a randomized trial; and ii) when biomarker …
Cross-Design Synthesis For Extending The Applicability Of Trial Evidence When Treatment Effect Is Heterogeneous-I. Methodology, Ravi Varadhan, Carlos Weiss
Cross-Design Synthesis For Extending The Applicability Of Trial Evidence When Treatment Effect Is Heterogeneous-I. Methodology, Ravi Varadhan, Carlos Weiss
Johns Hopkins University, Dept. of Biostatistics Working Papers
Randomized controlled trials (RCTs) provide reliable evidence for approval of new treatments, informing clinical practice, and coverage decisions. The participants in RCTs are often not a representative sample of the larger at-risk population. Hence it is argued that the average treatment effect from the trial is not generalizable to the larger at-risk population. An essential premise of this argument is that there is significant heterogeneity in the treatment effect (HTE). We present a new method to extrapolate the treatment effect from a trial to a target group that is inadequately represented in the trial, when HTE is present. Our method …
Cross-Design Synthesis For Extending The Applicability Of Trial Evidence When Treatment Effect Is Heterogeneous. Part Ii. Application And External Validation, Carlos Weiss, Ravi Varadhan
Cross-Design Synthesis For Extending The Applicability Of Trial Evidence When Treatment Effect Is Heterogeneous. Part Ii. Application And External Validation, Carlos Weiss, Ravi Varadhan
Johns Hopkins University, Dept. of Biostatistics Working Papers
Randomized controlled trials (RCTs) generally provide the most reliable evidence. When participants in RCTs are selected with respect to characteristics that are potential treatment effect modifiers, the average treatment effect from the trials may not be applicable to a specific target population. We present a new method to project the treatment effect from a RCT to a target group that is inadequately represented in the trial when there is heterogeneity in the treatment effect (HTE). The method integrates RCT and observational data through cross-design synthesis. An essential component is to identify HTE and a calibration factor for unmeasured confounding for …
Enhanced Precision In The Analysis Of Randomized Trials With Ordinal Outcomes, Iván Díaz, Elizabeth Colantuoni, Michael Rosenblum
Enhanced Precision In The Analysis Of Randomized Trials With Ordinal Outcomes, Iván Díaz, Elizabeth Colantuoni, Michael Rosenblum
Johns Hopkins University, Dept. of Biostatistics Working Papers
We present a general method for estimating the effect of a treatment on an ordinal outcome in randomized trials. The method is robust in that it does not rely on the proportional odds assumption. Our estimator leverages information in prognostic baseline variables, and has all of the following properties: (i) it is consistent; (ii) it is locally efficient; (iii) it is guaranteed to match or improve the precision of the standard, unadjusted estimator. To the best of our knowledge, this is the first estimator of the causal relation between a treatment and an ordinal outcome to satisfy these properties. We …
Optimal Bayesian Adaptive Trials When Treatment Efficacy Depends On Biomarkers, Yifan Zhang, Lorenzo Trippa, Giovanni Parmigiani
Optimal Bayesian Adaptive Trials When Treatment Efficacy Depends On Biomarkers, Yifan Zhang, Lorenzo Trippa, Giovanni Parmigiani
Harvard University Biostatistics Working Paper Series
No abstract provided.
Generalized Quantile Treatment Effect, Sergio Venturini, Francesca Dominici, Giovanni Parmigiani
Generalized Quantile Treatment Effect, Sergio Venturini, Francesca Dominici, Giovanni Parmigiani
Harvard University Biostatistics Working Paper Series
No abstract provided.
A Bayesian Approach To Joint Modeling Of Menstrual Cycle Length And Fecundity, Kirsten J. Lum, Rajeshwari Sundaram, Germaine M. Buck-Louis, Thomas A. Louis
A Bayesian Approach To Joint Modeling Of Menstrual Cycle Length And Fecundity, Kirsten J. Lum, Rajeshwari Sundaram, Germaine M. Buck-Louis, Thomas A. Louis
Johns Hopkins University, Dept. of Biostatistics Working Papers
Female menstrual cycle length is thought to play an important role in couple fecundity, or the biologic capacity for reproduction irrespective of pregnancy intentions. A complete assessment of the association between menstrual cycle length and fecundity requires a model that accounts for multiple risk factors (both male and female) and the couple's intercourse pattern relative to ovulation. We employ a Bayesian joint model consisting of a mixed effects accelerated failure time model for longitudinal menstrual cycle lengths and a hierarchical model for the conditional probability of pregnancy in a menstrual cycle given no pregnancy in previous cycles of trying, in …
Online Targeted Learning, Mark J. Van Der Laan, Samuel D. Lendle
Online Targeted Learning, Mark J. Van Der Laan, Samuel D. Lendle
U.C. Berkeley Division of Biostatistics Working Paper Series
We consider the case that the data comes in sequentially and can be viewed as sample of independent and identically distributed observations from a fixed data generating distribution. The goal is to estimate a particular path wise target parameter of this data generating distribution that is known to be an element of a particular semi-parametric statistical model. We want our estimator to be asymptotically efficient, but we also want that our estimator can be calculated by updating the current estimator based on the new block of data without having to revisit the past data, so that it is computationally much …
Estimation Of The Overall Treatment Effect In The Presence Of Interference In Cluster-Randomized Trials Of Infectious Disease Prevention, Nicole Bohme Carnegie, Rui Wang, Victor De Gruttola
Estimation Of The Overall Treatment Effect In The Presence Of Interference In Cluster-Randomized Trials Of Infectious Disease Prevention, Nicole Bohme Carnegie, Rui Wang, Victor De Gruttola
Harvard University Biostatistics Working Paper Series
No abstract provided.
Cox Regression Models With Functional Covariates For Survival Data, Jonathan E. Gellar, Elizabeth Colantuoni, Dale M. Needham, Ciprian M. Crainiceanu
Cox Regression Models With Functional Covariates For Survival Data, Jonathan E. Gellar, Elizabeth Colantuoni, Dale M. Needham, Ciprian M. Crainiceanu
Johns Hopkins University, Dept. of Biostatistics Working Papers
We extend the Cox proportional hazards model to cases when the exposure is a densely sampled functional process, measured at baseline. The fundamental idea is to combine penalized signal regression with methods developed for mixed effects proportional hazards models. The model is fit by maximizing the penalized partial likelihood, with smoothing parameters estimated by a likelihood-based criterion such as AIC or EPIC. The model may be extended to allow for multiple functional predictors, time varying coefficients, and missing or unequally-spaced data. Methods were inspired by and applied to a study of the association between time to death after hospital discharge …
Targeted Learning Of An Optimal Dynamic Treatment, And Statistical Inference For Its Mean Outcome, Mark J. Van Der Laan, Alexander R. Luedtke
Targeted Learning Of An Optimal Dynamic Treatment, And Statistical Inference For Its Mean Outcome, Mark J. Van Der Laan, Alexander R. Luedtke
U.C. Berkeley Division of Biostatistics Working Paper Series
Suppose we observe n independent and identically distributed observations of a time-dependent random variable consisting of baseline covariates, initial treatment and censoring indicator, intermediate covariates, subsequent treatment and censoring indicator, and a final outcome. For example, this could be data generated by a sequentially randomized controlled trial, where subjects are sequentially randomized to a first line and second line treatment, possibly assigned in response to an intermediate biomarker, and are subject to right-censoring. In this article we consider estimation of an optimal dynamic multiple time-point treatment rule defined as the rule that maximizes the mean outcome under the dynamic treatment, …
Instrumental Variable Estimation In A Survival Context, Eric J. Tchetgen Tchetgen, Stefan Walter, Stijn Vansteelandt, Torben Martinussen, Maria Glymour
Instrumental Variable Estimation In A Survival Context, Eric J. Tchetgen Tchetgen, Stefan Walter, Stijn Vansteelandt, Torben Martinussen, Maria Glymour
Harvard University Biostatistics Working Paper Series
No abstract provided.
Likelihood Based Estimation Of Logistic Structural Nested Mean Models With An Instrumental Variable, Roland A. Matsouaka, Eric J. Tchetgen Tchetgen
Likelihood Based Estimation Of Logistic Structural Nested Mean Models With An Instrumental Variable, Roland A. Matsouaka, Eric J. Tchetgen Tchetgen
Harvard University Biostatistics Working Paper Series
No abstract provided.
A General Approach To Detect Gene (G)-Environment (E) Additive Interaction Leveraging G-E Independence In Case-Control Studies, Eric Tchetgen Tchetgen, Tamar Sofer, Benedict H.W. Wong
A General Approach To Detect Gene (G)-Environment (E) Additive Interaction Leveraging G-E Independence In Case-Control Studies, Eric Tchetgen Tchetgen, Tamar Sofer, Benedict H.W. Wong
Harvard University Biostatistics Working Paper Series
No abstract provided.
A Novel Targeted Learning Method For Quantitative Trait Loci Mapping, Hui Wang, Zhongyang Zhang, Sherri Rose, Mark J. Van Der Laan
A Novel Targeted Learning Method For Quantitative Trait Loci Mapping, Hui Wang, Zhongyang Zhang, Sherri Rose, Mark J. Van Der Laan
U.C. Berkeley Division of Biostatistics Working Paper Series
We present a novel semiparametric method for quantitative trait loci (QTL) mapping in experimental crosses. Conventional genetic mapping methods typically assume parametric models with Gaussian errors and obtain parameter estimates through maximum likelihood estimation. In contrast with univariate regression and interval mapping methods, our model requires fewer assumptions and also accommodates various machine learning algorithms. Estimation is performed with targeted maximum likelihood learning methods. We demonstrate our semiparametric targeted learning approach in a simulation study and a well-studied barley dataset.
A Note On The Control Function Approach With An Instrumental Variable And A Binary Outcome, Eric Tchetgen Tchetgen
A Note On The Control Function Approach With An Instrumental Variable And A Binary Outcome, Eric Tchetgen Tchetgen
Harvard University Biostatistics Working Paper Series
No abstract provided.
A Simple Regression-Based Approach To Account For Survival Bias In Birth Outcomes Research, Eric J. Tchetgen Tchetgen, Kelesitse Phiri, Roger Shapiro
A Simple Regression-Based Approach To Account For Survival Bias In Birth Outcomes Research, Eric J. Tchetgen Tchetgen, Kelesitse Phiri, Roger Shapiro
Harvard University Biostatistics Working Paper Series
No abstract provided.
Entering The Era Of Data Science: Targeted Learning And The Integration Of Statistics And Computational Data Analysis, Mark J. Van Der Laan, Richard J.C.M. Starmans
Entering The Era Of Data Science: Targeted Learning And The Integration Of Statistics And Computational Data Analysis, Mark J. Van Der Laan, Richard J.C.M. Starmans
U.C. Berkeley Division of Biostatistics Working Paper Series
This outlook article will appear in Advances in Statistics and it reviews the research of Dr. van der Laan's group on Targeted Learning, a subfield of statistics that is concerned with the construction of data adaptive estimators of user-supplied target parameters of the probability distribution of the data and corresponding confidence intervals, aiming to only rely on realistic statistical assumptions. Targeted Learning fully utilizes the state of the art in machine learning tools, while still preserving the important identity of statistics as a field that is concerned with both accurate estimation of the true target parameter value and assessment of …
Control Function Assisted Ipw Estimation With A Secondary Outcome In Case-Control Studies, Tamar Sofer, Marilyn C. Cornelis, Peter Kraft, Eric J. Tchetgen Tchetgen
Control Function Assisted Ipw Estimation With A Secondary Outcome In Case-Control Studies, Tamar Sofer, Marilyn C. Cornelis, Peter Kraft, Eric J. Tchetgen Tchetgen
Harvard University Biostatistics Working Paper Series
No abstract provided.
Super-Learning Of An Optimal Dynamic Treatment Rule, Alexander R. Luedtke, Mark J. Van Der Laan
Super-Learning Of An Optimal Dynamic Treatment Rule, Alexander R. Luedtke, Mark J. Van Der Laan
U.C. Berkeley Division of Biostatistics Working Paper Series
We consider the estimation of an optimal dynamic two time-point treatment rule defined as the rule that maximizes the mean outcome under the dynamic treatment, where the candidate rules are restricted to depend only on a user-supplied subset of the baseline and intermediate covariates. This estimation problem is addressed in a statistical model for the data distribution that is nonparametric, beyond possible knowledge about the treatment and censoring mechanisms. We propose data adaptive estimators of this optimal dynamic regime which are defined by sequential loss-based learning under both the blip function and weighted classification frameworks. Rather than \textit{a priori} selecting …
Targeted Learning Of The Mean Outcome Under An Optimal Dynamic Treatment Rule, Mark J. Van Der Laan, Alexander R. Luedtke
Targeted Learning Of The Mean Outcome Under An Optimal Dynamic Treatment Rule, Mark J. Van Der Laan, Alexander R. Luedtke
U.C. Berkeley Division of Biostatistics Working Paper Series
We consider estimation of and inference for the mean outcome under the optimal dynamic two time-point treatment rule defined as the rule that maximizes the mean outcome under the dynamic treatment, where the candidate rules are restricted to depend only on a user-supplied subset of the baseline and intermediate covariates. This estimation problem is addressed in a statistical model for the data distribution that is nonparametric beyond possible knowledge about the treatment and censoring mechanism. This contrasts from the current literature that relies on parametric assumptions. We establish that the mean of the counterfactual outcome under the optimal dynamic treatment …