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- Keyword
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- Bayesian hierarchical models; Correlation; Medical error; Voluntary error reports (1)
- Biomarkers; Variable Importance; Targeted Maximum Likelihood; Standard Method (1)
- Cardiovascular diseases; Cox's model; nonparametric functional estimation; risk index; ROC analysis; survival analysis (1)
- Causal inference; direct effect; intermediate variables; marginal structural models; time-dependent confounding (1)
- Coronary heart disease; nonparametric functional estimation; risk factors/markers; pointwise and simultaneous confidence interval; subgroup analysis (1)
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- Direct effect; indirect effect; instability; inverse probability; weighting; pathway; structural nested model; surrogate marker (1)
- Ecological inference; ecological regression; ecological fallacy; double robustness; missing data; marginal structural models (1)
- Statistical diagnosis; Likelihood ratio; Array CGH; Second primary cancer; Cancer metastasis (1)
- Publication
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- Harvard University Biostatistics Working Paper Series (7)
- Johns Hopkins University, Dept. of Biostatistics Working Papers (4)
- U.C. Berkeley Division of Biostatistics Working Paper Series (3)
- COBRA Preprint Series (1)
- Memorial Sloan-Kettering Cancer Center, Dept. of Epidemiology & Biostatistics Working Paper Series (1)
Articles 1 - 17 of 17
Full-Text Articles in Medicine and Health Sciences
Learning From Near Misses In Medication Errors: A Bayesian Approach, Jessica A. Myers, Francesca Dominici, Laura Morlock
Learning From Near Misses In Medication Errors: A Bayesian Approach, Jessica A. Myers, Francesca Dominici, Laura Morlock
Johns Hopkins University, Dept. of Biostatistics Working Papers
Medical errors originating in health care facilities are a significant source of preventable morbidity, mortality, and healthcare costs. Voluntary error report systems that collect information on the causes and contributing factors of medi- cal errors regardless of the resulting harm may be useful for developing effective harm prevention strategies. Some patient safety experts question the utility of data from errors that did not lead to harm to the patient, also called near misses. A near miss (a.k.a. close call) is an unplanned event that did not result in injury to the patient. Only a fortunate break in the chain of …
A Small Sample Correction For Estimating Attributable Risk In Case-Control Studies, Daniel B. Rubin
A Small Sample Correction For Estimating Attributable Risk In Case-Control Studies, Daniel B. Rubin
U.C. Berkeley Division of Biostatistics Working Paper Series
The attributable risk, often called the population attributable risk, is in many epidemiological contexts a more relevant measure of exposure-disease association than the excess risk, relative risk, or odds ratio. When estimating attributable risk with case-control data and a rare disease, we present a simple correction to the standard approach making it essentially unbiased, and also less noisy. As with analogous corrections given in Jewell (1986) for other measures of association, the adjustment often won't make a substantial difference unless the sample size is very small or point estimates are desired within fine strata, but we discuss the possible utility …
Spatial Misalignment In Time Series Studies Of Air Pollution And Health Data, Roger D. Peng, Michelle L. Bell
Spatial Misalignment In Time Series Studies Of Air Pollution And Health Data, Roger D. Peng, Michelle L. Bell
Johns Hopkins University, Dept. of Biostatistics Working Papers
Time series studies of environmental exposures often involve comparing daily changes in a toxicant measured at a point in space with daily changes in an aggregate measure of health. Spatial misalignment of the exposure and response variables can bias the estimation of health risk and the magnitude of this bias depends on the spatial variation of the exposure of interest. In air pollution epidemiology, there is an increasing focus on estimating the health effects of the chemical components of particulate matter. One issue that is raised by this new focus is the spatial misalignment error introduced by the lack of …
Calibrating Parametric Subject-Specific Risk Estimation, Tianxi Cai, Lu Tian, Hajime Uno, Scott D. Solomon, L. J. Wei
Calibrating Parametric Subject-Specific Risk Estimation, Tianxi Cai, Lu Tian, Hajime Uno, Scott D. Solomon, L. J. Wei
Harvard University Biostatistics Working Paper Series
No abstract provided.
Evaluating Subject-Level Incremental Values Of New Markers For Risk Classification Rule, Tianxi Cai, Lu Tian, Donald M. Lloyd-Jones, L. J. Wei
Evaluating Subject-Level Incremental Values Of New Markers For Risk Classification Rule, Tianxi Cai, Lu Tian, Donald M. Lloyd-Jones, L. J. Wei
Harvard University Biostatistics Working Paper Series
No abstract provided.
A Metastasis Or A Second Independent Cancer? Evaluating The Clonal Origin Of Tumors Using Array-Cgh Data, Irina Ostrovnaya, Adam Olshen, Venkatraman E. Seshan, Irene Orlow, D G. Albertson, Colin B. Begg
A Metastasis Or A Second Independent Cancer? Evaluating The Clonal Origin Of Tumors Using Array-Cgh Data, Irina Ostrovnaya, Adam Olshen, Venkatraman E. Seshan, Irene Orlow, D G. Albertson, Colin B. Begg
Memorial Sloan-Kettering Cancer Center, Dept. of Epidemiology & Biostatistics Working Paper Series
When a cancer patient develops a new tumor it is necessary to determine if this is a recurrence (metastasis) of the original cancer, or an entirely new occurrence of the disease. This is accomplished by assessing the histo-pathology of the lesions, and it is frequently relatively straightforward. However, there are many clinical scenarios in which this pathological diagnosis is difficult. Since each tumor is characterized by a genetic fingerprint of somatic mutations, a more definitive diagnosis is possible in principle in these difficult clinical scenarios by comparing the fingerprints. In this article we develop and evaluate a statistical strategy for …
Properties Of Monotonic Effects On Directed Acyclic Graphs, Tyler J. Vanderweele, James M. Robins
Properties Of Monotonic Effects On Directed Acyclic Graphs, Tyler J. Vanderweele, James M. Robins
COBRA Preprint Series
Various relationships are shown hold between monotonic effects and weak monotonic effects and the monotonicity of certain conditional expectations. Counterexamples are provided to show that the results do not hold under less restrictive conditions. Monotonic effects are furthermore used to relate signed edges on a causal directed acyclic graph to qualitative effect modification. The theory is applied to an example concerning the direct effect of smoking on cardiovascular disease controlling for hypercholesterolemia. Monotonicity assumptions are used to construct a test for whether there is a variable that confounds the relationship between the mediator, hypercholesterolemia, and the outcome, cardiovascular disease.
Influence Of Prediction Approaches For Spatially-Dependent Air Pollution Exposure On Health Effect Estimation, Sun-Young Kim, Lianne Sheppard, Ho Kim
Influence Of Prediction Approaches For Spatially-Dependent Air Pollution Exposure On Health Effect Estimation, Sun-Young Kim, Lianne Sheppard, Ho Kim
UW Biostatistics Working Paper Series
Background: Air pollution studies increasingly estimate individual-level exposures from area-based measurements by using exposure prediction methods such as nearest monitor and kriging predictions. However, little is known about the properties of these methods for health effects estimation. This simulation study explores how two common prediction approaches for fine particulate matter (PM2.5) affect relative risk estimates for cardiovascular events in a single geographic area.
Methods: We estimated two sets of parameters to define correlation structures from 2002 PM2.5 data in the Los Angeles (LA) area and selected additional parameters to evaluate different correlation features. For each structure, annual average PM2.5 was …
Nonparametric Regression Using Local Kernel Estimating Equations For Correlated Failure Time Data, Zhangsheng Yu, Xihong Lin
Nonparametric Regression Using Local Kernel Estimating Equations For Correlated Failure Time Data, Zhangsheng Yu, Xihong Lin
Harvard University Biostatistics Working Paper Series
No abstract provided.
A Comparison Of Methods For Estimating The Causal Effect Of A Treatment In Randomized Clinical Trials Subject To Noncompliance, Rod Little, Qi Long, Xihong Lin
A Comparison Of Methods For Estimating The Causal Effect Of A Treatment In Randomized Clinical Trials Subject To Noncompliance, Rod Little, Qi Long, Xihong Lin
Harvard University Biostatistics Working Paper Series
No abstract provided.
Semiparametric Maximum Likelihood Estimation In Normal Transformation Models For Bivariate Survival Data, Yi Li, Ross L. Prentice, Xihong Lin
Semiparametric Maximum Likelihood Estimation In Normal Transformation Models For Bivariate Survival Data, Yi Li, Ross L. Prentice, Xihong Lin
Harvard University Biostatistics Working Paper Series
No abstract provided.
Doubly Robust Ecological Inference, Daniel B. Rubin, Mark J. Van Der Laan
Doubly Robust Ecological Inference, Daniel B. Rubin, Mark J. Van Der Laan
U.C. Berkeley Division of Biostatistics Working Paper Series
The ecological inference problem is a famous longstanding puzzle that arises in many disciplines. The usual formulation in epidemiology is that we would like to quantify an exposure-disease association by obtaining disease rates among the exposed and unexposed, but only have access to exposure rates and disease rates for several regions. The problem is generally intractable, but can be attacked under the assumptions of King's (1997) extended technique if we can correctly specify a model for a certain conditional distribution. We introduce a procedure that it is a valid approach if either this original model is correct or if we …
An Overview Of Observational Sleep Research With Application To Sleep Stage Transitioning, Brian S. Caffo, B. Swihart, A. Laffan, C. Crainiceanu, N. Punjabi
An Overview Of Observational Sleep Research With Application To Sleep Stage Transitioning, Brian S. Caffo, B. Swihart, A. Laffan, C. Crainiceanu, N. Punjabi
Johns Hopkins University, Dept. of Biostatistics Working Papers
In this manuscript we give an overview of observational sleep research with a particular emphasis on sleep stage transitions. Sleep states represent a categorization of sleep electroencephalogram behavior over the night. We postulate that the rate of transitioning between sleep states is an important predictor of health. This claim is evaluated by comparing subjects with sleep disordered breathing to matched controls.
Targeted Methods For Biomarker Discovery, The Search For A Standard, Catherine Tuglus, Mark J. Van Der Laan
Targeted Methods For Biomarker Discovery, The Search For A Standard, Catherine Tuglus, Mark J. Van Der Laan
U.C. Berkeley Division of Biostatistics Working Paper Series
More often than not biomarker studies analyze large quantities of variables with complicated and generally unknown correlation structure. There are numerous statistical methods which attempt to unravel these variables and determine the underlying mechanism through identification of causally related biomarkers. Results from these methods are generally difficult to interpret and nearly impossible to compare across studies. The FDA has currently called for a standardization of methods and protocol for biomarker detection. In response, we propose targeted variable importance (tVIM) as a standardized method for biomarker discovery. Through the use of targeted Maximum Likelihood, tVIM provides double robust estimates of variable …
Marginal Structural Models For Partial Exposure Regimes, Stijn Vansteelandt, Karl Mertens, Carl Suetens, Els Goetghebeur
Marginal Structural Models For Partial Exposure Regimes, Stijn Vansteelandt, Karl Mertens, Carl Suetens, Els Goetghebeur
Harvard University Biostatistics Working Paper Series
Intensive care unit (ICU) patients are ell known to be highly susceptible for nosocomial (i.e. hospital-acquired) infections due to their poor health and many invasive therapeutic treatments. The effects of acquiring such infections in ICU on mortality are however ill understood. Our goal is to quantify these effects using data from the National Surveillance Study of Nosocomial Infections in Intensive Care
Units (Belgium). This is a challenging problem because of the presence of time-dependent confounders (such as exposure to mechanical ventilation)which lie on the causal path from infection to mortality. Standard statistical analyses may be severely misleading in such settings …
Model Selection And Health Effect Estimation In Environmental Epidemiology, Francesca Dominici, Chi Wang, Ciprian Crainiceanu, Giovanni Parmigiani
Model Selection And Health Effect Estimation In Environmental Epidemiology, Francesca Dominici, Chi Wang, Ciprian Crainiceanu, Giovanni Parmigiani
Johns Hopkins University, Dept. of Biostatistics Working Papers
In air pollution epidemiology, improvements in statistical analysis tools can translate into significant scientific advances, because of the unfavorable signal-to-noise ratios, and large correlations between exposures and confounders. Therefore, the use of a novel model selection approach in identifying time windows of exposure to pollutants that lead to adverse health effects is important and welcome. However, previous literature has raised concerns about approaches that select a model based on a given data set, and then estimate health effects in the same data assuming that the chosen model is correct. Problems can be particularly severe when: 1) the sample size is …
Estimation Of Controlled Direct Effects, Sylvie Goetgeluk, Stijn Vansteelandt, Els Goetghebeur
Estimation Of Controlled Direct Effects, Sylvie Goetgeluk, Stijn Vansteelandt, Els Goetghebeur
Harvard University Biostatistics Working Paper Series
No abstract provided.