Blog Archives

A case where prospective matching may limit bias in a randomized trial

March 11, 2019
By
A case where prospective matching may limit bias in a randomized trial

Analysis is important, but study design is paramount. I am involved with the Diabetes Research, Education, and Action for Minorities (DREAM) Initiative, which is, among other things, estimating the effect of a group-based therapy program on weight loss for patients who have been identified as pre-diabetic (which means they have elevated HbA1c levels). The original plan was to randomize...

Read more »

A example in causal inference designed to frustrate: an estimate pretty much guaranteed to be biased

February 25, 2019
By
A example in causal inference designed to frustrate: an estimate pretty much guaranteed to be biased

I am putting together a brief lecture introducing causal inference for graduate students studying biostatistics. As part of this lecture, I thought it would be helpful to spend a little time describing directed acyclic graphs (DAGs), since they are an extremely helpful tool for communicating assumptions about the causal relationships underlying a researcher’s data. The strength of DAGs is that...

Read more »

Using the uniform sum distribution to introduce probability

February 4, 2019
By
Using the uniform sum distribution to introduce probability

I’ve never taught an intro probability/statistics course. If I ever did, I would certainly want to bring the underlying wonder of the subject to life. I’ve always found it almost magical the way mathematical formulation can be mirrored by computer simulation, the way proof can be guided by observed data generation processes, and the way DGPs can confirm analytic...

Read more »

Correlated longitudinal data with varying time intervals

January 21, 2019
By
Correlated longitudinal data with varying time intervals

I was recently contacted to see if simstudy can create a data set of correlated outcomes that are measured over time, but at different intervals for each individual. The quick answer is there is no specific function to do this. However, if you are willing to assume an “exchangeable” correlation structure, where measurements far apart in time are just...

Read more »

Considering sensitivity to unmeasured confounding: part 2

January 9, 2019
By
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...

Read more »

Considering sensitivity to unmeasured confounding: part 1

January 1, 2019
By
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...

Read more »

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

December 9, 2018
By
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...

Read more »

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

November 27, 2018
By
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...

Read more »

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

November 19, 2018
By
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...

Read more »

Causal mediation estimation measures the unobservable

November 5, 2018
By
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...

Read more »

Search R-bloggers


Sponsors

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)