Blog Archives

A little function to help generate ICCs in simple clustered data

May 23, 2018
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A little function to help generate ICCs in simple clustered data

In health services research, experiments are often conducted at the provider or site level rather than the patient level. However, we might still be interested in the outcome at the patient level. For example, we could be interested in understanding the effect of a training program for physicians on their patients. It would be very difficult to randomize patients...

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Is non-inferiority on par with superiority?

May 13, 2018
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Is non-inferiority on par with superiority?

It is grant season around here (actually, it is pretty much always grant season), which means another series of problems to tackle. Even with the most straightforward study designs, there is almost always some interesting twist, or an approach that presents a subtle issue or two. In this case, the investigator wants compare two interventions, but doesn’t feel the...

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How efficient are multifactorial experiments?

May 1, 2018
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How efficient are multifactorial experiments?

I recently described why we might want to conduct a multi-factorial experiment, and I alluded to the fact that this approach can be quite efficient. It is efficient in the sense that it is possible to test simultaneously the impact of multiple interventions using an overall sample size that would be required to test a single intervention in a...

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Testing multiple interventions in a single experiment

April 18, 2018
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Testing multiple interventions in a single experiment

A reader recently inquired about functions in simstudy that could generate data for a balanced multi-factorial design. I had to report that nothing really exists. A few weeks later, a colleague of mine asked if I could help estimate the appropriate sample size for a study that plans to use a multi-factorial design to choose among a set of...

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Exploring the underlying theory of the chi-square test through simulation – part 2

March 24, 2018
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Exploring the underlying theory of the chi-square test through simulation – part 2

In the last post, I tried to provide a little insight into the chi-square test. In particular, I used simulation to demonstrate the relationship between the Poisson distribution of counts and the chi-squared distribution. The key point in that post was the role conditioning plays in that relationship by reducing variance. To motivate some of the key issues, I talked...

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Exploring the underlying theory of the chi-square test through simulation – part 1

March 17, 2018
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Exploring the underlying theory of the chi-square test through simulation – part 1

Kids today are so sophisticated (at least they are in New York City, where I live). While I didn’t hear about the chi-square test of independence until my first stint in graduate school, they’re already talking about it in high school. When my kids came home and started talking about it, I did what I usually do when they...

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Another reason to be careful about what you control for

March 6, 2018
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Another reason to be careful about what you control for

Modeling data without any underlying causal theory can sometimes lead you down the wrong path, particularly if you are interested in understanding the way things work rather than making predictions. A while back, I described what can go wrong when you control for a mediator when you are interested in an exposure and an outcome. Here, I describe the...

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“I have to randomize by cluster. Is it OK if I only have 6 sites?”

February 20, 2018
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“I have to randomize by cluster. Is it OK if I only have 6 sites?”

The answer is probably no, because there is a not-so-low chance (perhaps considerably higher than 5%) you will draw the wrong conclusions from the study. I have heard variations on this question not so infrequently, so I thought it would be useful (of course) to do a few quick simulations to see what happens when we try to conduct...

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Have you ever asked yourself, “how should I approach the classic pre-post analysis?”

January 27, 2018
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Have you ever asked yourself, “how should I approach the classic pre-post analysis?”

Well, maybe not, but this comes up all the time. An investigator wants to assess the effect of an intervention on a outcome. Study participants are randomized either to receive the intervention (could be a new drug, new protocol, behavioral intervention, whatever) or treatment as usual. For each participant, the outcome measure is recorded at baseline - this is...

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Importance sampling adds an interesting twist to Monte Carlo simulation

January 17, 2018
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Importance sampling adds an interesting twist to Monte Carlo simulation

I’m contemplating the idea of teaching a course on simulation next fall, so I have been exploring various topics that I might include. (If anyone has great ideas either because you have taught such a course or taken one, definitely drop me a note.) Monte Carlo (MC) simulation is an obvious one. I like the idea of talking about...

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