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- 0causal inference; semi-parametric models; environmental exposure; limit of detection; population intervention model (1)
- Asymptotics; Augmented kernel estimating equations; Double robustness; Efficiency; Inverse probability weighted kernel estimating equations; Kernel smoothing (1)
- Biomarkers; Disease prognosis; Predictive accuracy; Risk prediction; Survival analysis (1)
- Frail older adults; Logistic regression; Physical activity; screening (1)
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Articles 1 - 10 of 10
Full-Text Articles in Medicine and Health Sciences
Minimum Description Length And Empirical Bayes Methods Of Identifying Snps Associated With Disease, Ye Yang, David R. Bickel
Minimum Description Length And Empirical Bayes Methods Of Identifying Snps Associated With Disease, Ye Yang, David R. Bickel
COBRA Preprint Series
The goal of determining which of hundreds of thousands of SNPs are associated with disease poses one of the most challenging multiple testing problems. Using the empirical Bayes approach, the local false discovery rate (LFDR) estimated using popular semiparametric models has enjoyed success in simultaneous inference. However, the estimated LFDR can be biased because the semiparametric approach tends to overestimate the proportion of the non-associated single nucleotide polymorphisms (SNPs). One of the negative consequences is that, like conventional p-values, such LFDR estimates cannot quantify the amount of information in the data that favors the null hypothesis of no disease-association.
We …
The Handling Of Missing Data In Molecular Epidemiologic Studies, Manisha Desai, Jessica Kubo, Denise Esserman, Mary Beth Terry
The Handling Of Missing Data In Molecular Epidemiologic Studies, Manisha Desai, Jessica Kubo, Denise Esserman, Mary Beth Terry
COBRA Preprint Series
Background: Molecular epidemiologic studies face a missing data problem as biospecimen data are often collected on only a proportion of subjects eligible for study.
Methods: We investigated all molecular epidemiologic studies published in CEBP in 2009 to characterize the prevalence of missing data and to elucidate how the issue was addressed. We considered multiple imputation (MI), a missing data technique that is readily available and easy to implement, as a possible solution.
Results: While the majority of studies had missing data, only 16% compared subjects with and without missing data. Furthermore, 95% of the studies with missing data performed a …
The Use Of Multiple Imputation In Molecular Epidemiologic Studies Assessing Interaction Effects, Manisha Desai, Denise Esserman, Marilie Gammon, Mary Beth Terry
The Use Of Multiple Imputation In Molecular Epidemiologic Studies Assessing Interaction Effects, Manisha Desai, Denise Esserman, Marilie Gammon, Mary Beth Terry
COBRA Preprint Series
Background: In molecular epidemiologic studies biospecimen data are collected on only a proportion of subjects eligible for study. This leads to a missing data problem. Missing data methods, however, are not typically incorporated into analyses. Instead, complete-case (CC) analyses are performed, which result in biased and inefficient estimates.
Methods: Through simulations, we characterized the bias that results from CC methods when interaction effects are estimated, as this is a major aim of many molecular epidemiologic studies. We also investigated whether standard multiple imputation (MI) could improve estimation over CC methods when the data are not missing at random (NMAR) and …
Landmark Prediction Of Survival, Layla Parast, Tianxi Cai
Landmark Prediction Of Survival, Layla Parast, Tianxi Cai
Harvard University Biostatistics Working Paper Series
No abstract provided.
Surrogate Screening Models For The Low Physical Activity Criterion Of Frailty, Sandrah P. Eckel, Karen Bandeen-Roche, Paulo H.M. Chaves, Linda P. Fried, Thomas A. Louis
Surrogate Screening Models For The Low Physical Activity Criterion Of Frailty, Sandrah P. Eckel, Karen Bandeen-Roche, Paulo H.M. Chaves, Linda P. Fried, Thomas A. Louis
Johns Hopkins University, Dept. of Biostatistics Working Papers
Background and Aims. Low physical activity, one of five criteria in a validated clinical phenotype of frailty, is assessed by a standardized questionnaire on up to 20 leisure time activities. Because of the time demanded to collect the interview data, it has been challenging to translate to studies other than the Cardiovascular Health Study (CHS), for which it was developed. Considering subsets of activities, we identified and evaluated streamlined surrogate assessment methods and compared them to one implemented in the Women’s Health and Aging Study (WHAS).
Methods. Using data on men and women ages 65 and older from the CHS, …
Improving Statistical Analysis Of Prospective Clinical Trials In Stem Cell Transplantation. An Inventory Of New Approaches In Survival Analysis, Aurelien Latouche
Improving Statistical Analysis Of Prospective Clinical Trials In Stem Cell Transplantation. An Inventory Of New Approaches In Survival Analysis, Aurelien Latouche
COBRA Preprint Series
The CLINT project is an European Union funded project, run as a specific support action, under the sixth framework programme. It is a 2 year project aimed at supporting the European Group for Blood and Marrow Transplantation (EBMT) to develop its infrastructure for the conduct of trans-European clinical trials in accordance with the EU Clinical Trials Directive, and to facilitate International prospective clinical trials in stem cell transplantation. The initial task is to create an inventory of the existing biostatistical literature on new approaches to survival analyses that are not currently widely utilised. The estimation of survival endpoints is introduced, …
Nonparametric Regression With Missing Outcomes Using Weighted Kernel Estimating Equations, Lu Wang, Andrea Rotnitzky, Xihong Lin
Nonparametric Regression With Missing Outcomes Using Weighted Kernel Estimating Equations, Lu Wang, Andrea Rotnitzky, Xihong Lin
Harvard University Biostatistics Working Paper Series
No abstract provided.
The Impact Of Coarsening The Explanatory Variable Of Interest In Making Causal Inferences: Implicit Assumptions Behind Dichotomizing Variables, Ori M. Stitelman, Alan E. Hubbard, Nicholas P. Jewell
The Impact Of Coarsening The Explanatory Variable Of Interest In Making Causal Inferences: Implicit Assumptions Behind Dichotomizing Variables, Ori M. Stitelman, Alan E. Hubbard, Nicholas P. Jewell
U.C. Berkeley Division of Biostatistics Working Paper Series
It is common in analyses designed to estimate the causal effect of a continuous exposure/treatment to dichotomize the variable of interest. By dichotomizing the variable and assessing the causal effect of the newly fabricated variable practitioners are implicitly making assumptions. However, in most analyses these assumptions are ignored. In this article we formally address what assumptions are made in dichotomizing variables to assess causal effects. We introduce two assumptions, either of which must be met, in order for the estimates of the causal effects to be unbiased estimates of the parameters of interest. We title those assumptions the Mechanism Equivalence …
Optimizing Vaccine Allocation At Different Points In Time During An Epidemic, Laura Matrajt, Ira M. Longini Jr.
Optimizing Vaccine Allocation At Different Points In Time During An Epidemic, Laura Matrajt, Ira M. Longini Jr.
UW Biostatistics Working Paper Series
For current pandemic influenza H1N1, vaccine production started in the early summer, and vaccination started in the fall. In most countries, by the time vaccination started, the second wave of H1N1 was already under way. With limited supplies of vaccine, it might be a good strategy to vaccinate the high-transmission groups earlier in the epidemic, but it might be a better use of resources to protect instead the high-risk groups later on. We develop a deterministic epidemic model with two age-groups (children and adults) and further subdivide each age group in low and high risk. We compare optimal vaccination strategies …
Using The Stages Of Change Model To Choose An Optimal Health Marketing Target, Paula Diehr, Peggy A. Hannon, Barbara Pizacani, Mark Forehand, Jeffrey Harris, Hendrika Meischke, Susan J. Curry, Diane P. Martin, Marcia R. Weaver
Using The Stages Of Change Model To Choose An Optimal Health Marketing Target, Paula Diehr, Peggy A. Hannon, Barbara Pizacani, Mark Forehand, Jeffrey Harris, Hendrika Meischke, Susan J. Curry, Diane P. Martin, Marcia R. Weaver
UW Biostatistics Working Paper Series
Background: In the transtheoretical model of behavior change, “stages of change” are defined as Precontemplation (not even thinking about changing), Contemplation, Preparation, Action, and Maintenance (maintaining the behavior change). Marketing principles suggest that efforts should be targeted at persons most likely to “buy the product.”
Objectives: To examine the effect of intervening at different stages in populations of smokers, with various numbers of people in each “stage of change.” One type of intervention would increase by 10% the probability of a person moving to the next higher stage of change, such as from Precontemplation to Contemplation. The second type would …