O1-2.2 Sparse instrumental variables: an integrative approach to biomarker validation

An Introduction to Instrumental Variables

Instrumental variables (IVs) are used to control for confounding and measurement error in observational studies. They allow for the possibility of making causal inferences with observational data. Like propensity scores, IVs can adjust for both observed and unobserved confounding effects. Other methods of adjusting for confounding effects, which include stratification, matching and multiple reg...

An Integrative Genomics Approach to Biomarker Discovery in Breast Cancer

Genome-wide association studies (GWAS) have successfully identified genetic variants associated with risk for breast cancer. However, the molecular mechanisms through which the identified variants confer risk or influence phenotypic expression remains poorly understood. Here, we present a novel integrative genomics approach that combines GWAS information with gene expression data to assess the ...

An introduction to instrumental variables for epidemiologists.

Instrumental-variable (IV) methods were invented over 70 years ago, but remain uncommon in epidemiology. Over the past decade or so, non-parametric versions of IV methods have appeared that connect IV methods to causal and measurement-error models important in epidemiological applications. This paper provides an introduction to those developments, illustrated by an application of IV methods to ...

Internet Job Search and Employment Outcomes: An Instrumental Variables Approach

Police levels and crime rates: An instrumental variables approach

While police levels may affect crime, governments may react to crime by increasing police levels. The instrumental variables (IV) approach to this problem has proven difficult due to the problem of locating instruments for police levels. Using panel data from over 5000 cities (1990–2001), we instrumented police levels with two types of federal law enforcement grants, thus yielding over-identifi...