Blog Archives

Survival Analysis – Fitting Weibull Models for Improving Device Reliability in R

January 26, 2020
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Survival Analysis – Fitting Weibull Models for Improving Device Reliability in R

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...

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Creating and Using a Simple, Bayesian Linear Model (in brms and R)

November 30, 2019
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Creating and Using a Simple, Bayesian Linear Model (in brms and R)

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...

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Confounders and Colliders – Modeling Spurious Correlations in R

October 28, 2019
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Confounders and Colliders – Modeling Spurious Correlations in R

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...

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Heart Disease Prediction From Patient Data in R

September 28, 2019
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Heart Disease Prediction From Patient Data in R

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...

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Modeling Particulate Counts as a Poisson Process in R

September 17, 2019
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Modeling Particulate Counts as a Poisson Process in R

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...

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Stopping Rules for Significance Testing in R

September 5, 2019
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Stopping Rules for Significance Testing in R

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...

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Assessing Design Verification Risk with Bayesian Estimation in R

August 22, 2019
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Assessing Design Verification Risk with Bayesian Estimation in R

Suppose our team is preparing to freeze a new implant design. In order to move into the next phase of the PDP, it is common to perform a suite of formal “Design Freeze” testing. If the results of the Design Freeze testing are acceptable, the project can advance from Design Freeze (DF) into Design Verification (DV). DV is an...

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Permutation Test for NHST of 2 Samples in R

August 9, 2019
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Permutation Test for NHST of 2 Samples in R

As engineers, it is not uncommon to be asked to determine whether or not two different configurations of a product perform the same. Perhaps we are asked to compare the durability of a next-generation prototype to the current generation. Sometimes we are testing the flexibility of our device versus a competitor for marketing purposes. Maybe we identify a new...

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About

August 5, 2019
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About

Hi! I’m Riley and I have been designing and developing Class III medical devices for over 13 years. My comfort zone is stents and stent grafts but my current project is an ablation catheter for treating chronic hypertension. Recently I’ve become interested in coding and predictive modeling and have been teaching myself the basics of the R programming...

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