I decided to do something a little bit different with this post and show how R can be used in tandem with a traditional engineering CAD program. Together they comprise a streamlined and repeatable workflow that I’ve tried to leverage on my job when it makes sense to do so. Solidworks is a 3d CAD program that is used commonly in industry. One powerful feature that I don’t see used that much is the Design Study module.
Measurements taken from patient anatomy are often correlated. For example, larger blood vessels might tend to have less curvature. Additionally, data are rarely Gaussian, favoring skewed shapes with some very large values and a lower bound of zero. These properties can make simulation and inference hard. In this post I will walk through a workflow for an engineering problem that might be presented in my industry.
I've heard it said that common statistical tests are just linear models.1 It turns out that Gage R&R, a commonly used measurement system analysis (MSA), is no different. In this post I'll attempt to provide some background on Gage R&R, describe the underlying model, and then walk through a method for simulation that can be useful for things like power analysis or visualization of uncertainty. What is Gage R&R?
Whether you are building bridges, baseball bats, or medical devices, one of the most basic rules of engineering is that the thing you build must be strong enough to survive its service environment. Although a simple concept in principle, variation in use conditions, material properties, and geometric tolerances all introduce uncertainty that can doom a product. Stress-Strength analysis attempts to formalize a more rigorous approach to evaluating overlap between the stress and strength distributions.
It’s time to get our hands dirty with some survival analysis! In this post, I’ll explore reliability modeling techniques that are applicable to Class III medical device testing. My goal is to expand on what I’ve been learning about GLM’s and get comfortable fitting data to Weibull distributions. I don’t have a ton of experience with Weibull analysis so I’ll be taking this opportunity to ask questions, probe assumptions, run simulations, explore different libraries, and develop some intuition about what to expect.
This post is my good-faith effort to create a simple linear model using the Bayesian framework and workflow described by Richard McElreath in his Statistical Rethinking book.1 As always - please view this post through the lens of the eager student and not the learned master. I did my best to check my work, but it’s entirely possible that something was missed. Please let me know - I won’t take it personally.
Like many engineers, my first models were based on Designed Experiments in the tradition of Cox and Montgomery. I hadn’t seen anything like a causal diagram until I picked the The Book of Why which explores all sorts of experimental relationships and structures I never imagined.1 Colliders, confounders, causal diagrams, M-bias - these concepts are all relatively new to me and I want to understand them better. In this post I will attempt to create some simple structural causal models (SCMs) for myself using the Dagitty and GGDag packages and then show the potential effects of confounders and colliders on a simulated experiment adapted from here.
In this post I’ll be attempting to leverage the parsnip package in R to run through some straightforward predictive analytics/machine learning. Parsnip provides a flexible and consistent interface to apply common regression and classification algorithms in R. I’ll be working with the Cleveland Clinic Heart Disease dataset which contains 13 variables related to patient diagnostics and one outcome variable indicating the presence or absence of heart disease.1 The data was accessed from the UCI Machine Learning Repository in September 2019.
I’ve never really worked much with Poisson data and wanted to get my hands dirty. I thought that for this project I might combine a Poisson data set with the simple Bayesian methods that I’ve explored before since it turns out the Poisson rate parameter lambda also has a nice conjugate prior (more on that later). Poisson distributed data are counts per unit time or space - they are events that arrive at random intervals but that have a characteristic rate parameter which also equals the variance.
When doing comparative testing it can be tempting to stop when we see the result that we hoped for. In the case of null hypothesis significance testing (NHST), the desired outcome is often a p-value of < .05. In the medical device industry, bench top testing can cost a lot of money. Why not just recalculate the p-value after every test and stop when the p-value reaches .05? The reason is that the confidence statement attached to your testing is only valid for a specific stopping rule.