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Considering sensitivity to unmeasured confounding: part 2

January 9, 2019
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Considering sensitivity to unmeasured confounding: part 2

In part 1 of this 2-part series, I introduced the notion of sensitivity to unmeasured confounding in the context of an observational data analysis. I argued that an estimate of an association between an observed exposure \(D\) and outcome \(Y\) is sensitive to unmeasured confounding if we can conceive of a reasonable alternative data generating process (DGP) that includes...

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Considering sensitivity to unmeasured confounding: part 1

January 1, 2019
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Considering sensitivity to unmeasured confounding: part 1

Principled causal inference methods can be used to compare the effects of different exposures or treatments we have observed in non-experimental settings. These methods, which include matching (with or without propensity scores), inverse probability weighting, and various g-methods, help us create comparable groups to simulate a randomized experiment. All of these approaches rely on a key assumption of no...

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Parallel processing to add a little zip to power simulations (and other replication studies)

December 9, 2018
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Parallel processing to add a little zip to power simulations (and other replication studies)

It’s always nice to be able to speed things up a bit. My first blog post ever described an approach using Rcpp to make huge improvements in a particularly intensive computational process. Here, I want to show how simple it is to speed things up by using the R package parallel and its function mclapply. I’ve been using this...

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Horses for courses, or to each model its own (causal effect)

November 27, 2018
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Horses for courses, or to each model its own (causal effect)

In my previous post, I described a (relatively) simple way to simulate observational data in order to compare different methods to estimate the causal effect of some exposure or treatment on an outcome. The underlying data generating process (DGP) included a possibly unmeasured confounder and an instrumental variable. (If you haven’t already, you should probably take a quick look.) A...

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Generating data to explore the myriad causal effects that can be estimated in observational data analysis

November 19, 2018
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Generating data to explore the myriad causal effects that can be estimated in observational data analysis

I’ve been inspired by two recent talks describing the challenges of using instrumental variable (IV) methods. IV methods are used to estimate the causal effects of an exposure or intervention when there is unmeasured confounding. This estimated causal effect is very specific: the complier average causal effect (CACE). But, the CACE is just one of several possible causal estimands...

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Causal mediation estimation measures the unobservable

November 5, 2018
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Causal mediation estimation measures the unobservable

I put together a series of demos for a group of epidemiology students who are studying causal mediation analysis. Since mediation analysis is not always so clear or intuitive, I thought, of course, that going through some examples of simulating data for this process could clarify things a bit. Quite often we are interested in understanding the relationship between an...

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Cross-over study design with a major constraint

October 22, 2018
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Cross-over study design with a major constraint

Every new study presents its own challenges. (I would have to say that one of the great things about being a biostatistician is the immense variety of research questions that I get to wrestle with.) Recently, I was approached by a group of researchers who wanted to evaluate an intervention. Actually, they had two, but the second one was...

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In regression, we assume noise is independent of all measured predictors. What happens if it isn’t?

October 8, 2018
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In regression, we assume noise is independent of all measured predictors. What happens if it isn’t?

A number of key assumptions underlie the linear regression model - among them linearity and normally distributed noise (error) terms with constant variance In this post, I consider an additional assumption: the unobserved noise is uncorrelated with any covariates or predictors in the model. In this simple model: \ \(Y_i\) has both a structural and stochastic...

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simstudy update: improved correlated binary outcomes

September 24, 2018
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simstudy update: improved correlated binary outcomes

An updated version of the simstudy package (0.1.10) is now available on CRAN. The impetus for this release was a series of requests about generating correlated binary outcomes. In the last post, I described a beta-binomial data generating process that uses the recently added beta distribution. In addition to that update, I’ve added functionality to genCorGen and addCorGen, functions...

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Binary, beta, beta-binomial

September 10, 2018
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Binary, beta, beta-binomial

I’ve been working on updates for the simstudy package. In the past few weeks, a couple of folks independently reached out to me about generating correlated binary data. One user was not impressed by the copula algorithm that is already implemented. I’ve added an option to use an algorithm developed by Emrich and Piedmonte in 1991, and will be...

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