## Survival distributions in R

My former colleague Devin Incerti has a nice summary of how to implement survival function estimation in R. Not only does he mathematically describe the probability density function (PDF), cumulative density function (CDF), and hazard rates for 8 commonly used parametric survival curves [see table below], he also describes how to implement these using the…

## What is confounding and how do you deal with it?

If you work in research, you may have heard that you need to worry about confounding. But what is confounding? And how can you address the problems it causes. What is confounding? An example Let us say that you are interested in the effect of an Ivy League education on a child’s long-term income. You…

## What is Cronbach’s alpha and how do I calculate it?

Let’s say you have a test and it is composed of a bunch of different questions.  Or you have a set of statistics–say statistics measuring how good a basketball player is–and you want to know how closely these statistics reliably measure the same thing.  What you are looking for is a way to measure test…

## 2SLS vs. 2SRI: Which to use with bivariate outcomes?

IV methods are usually implemented using a two‐stage approach where the first stage estimates an expectation of the endogenous variable conditional on measured confounders and one or more IV. The second stage model then predicts outcomes as a function of the estimated treatment values from the first stage, measured confounders, and potentially other control variables.…

## Which inflation index should I use?

Many studies use data on health care costs from multiple time periods.  To make costs comparable over time, researchers often use an inflation index to translate previous years costs to current dollars.  The first question is, what inflation indices are available to make this adjustment.  A paper by Dunn et al. (2018) reviews the potential…

## Addressing Type S vs. Type M errors with Bayesian Hierarchical Modeling

In statistics, most statistical tests aim to trade off type I and type II errors.  Type I error is the incorrect rejection of a true null hypothesis, in other words a false positive.  Type II errors are the incorrect retaining a false null hypothesis; in other words, a false negative.  Oftentimes, the null hypothesis is posed…

## Bayes vs. Fisher

An interesting People of Science video compares the approaches of two titans of statistics, Ronald A. Fisher and Thomas Bayes.

## Cancer survival around the world

An interesting study measuring trends in cancer survival between 2000 and 2014 found, unsurprisingly, that patients in more developed countries had better survival. For women diagnosed with breast cancer between 2010 and 2014, 5-year survival rates reached 89.5% in Australia and 90.2% in the United States, but generally varied worldwide and remained low in some…

## Which cancer treatment is best?

This seems like a straightforward question, but clearly depends on what you mean by “best”.  Some drugs will be more efficacious and have more adverse events; other drugs may be less efficacious but have fewer adverse events.  What if a one drug shows an 80% improvement in progression free survival (PFS), but a 50% improvement in overall…

## Cancer deaths are rising…is that a good thing?

A JAMA Oncology paper estimating the global burden of cancer is getting a lot of attention in the press.  The study’s key findings are: In 2015, there were 17.5 million cancer cases worldwide and 8.7 million deaths. Between 2005 and 2015, cancer cases increased by 33% A 33% increase in cancer cases!!! There must be an…