27

The reviewer should have told you why the Spearman $\rho$ is not appropriate. Here is one version of that: Let the data be $(Z_i, I_i)$ where $Z$ is the measured variable and $I$ is the gender indicator, say it is 0 (man), 1 (woman). Then Spearman's $\rho$ is calculated based on the ranks of $Z, I$ respectively. Since there are only two possible values for ...


27

This is referred to as current status data. You get one cross sectional view of the data, and regarding the response, all you know is that at the observed age of each subject, the event (in your case: transitioning from A to B) has happened or not. This is a special case of interval censoring. To formally define it, let $T_i$ be the (unobserved) true event ...


23

I've been estimating continuous positive outcome Poisson regressions with the Huber/White/Sandwich linearized estimator of variance fairly frequently. However, that's not a particularly good reason to do anything, so here are some actual references. From the theory side, $y$ does not need to be an integer for for the estimator based on the Poisson ...


17

Standard practice to test for enrichment of gene lists is to do a hypergeometric test or, equivalently, a one-sided Fisher's exact test. You have the following $2\times2$ contingency table: $$ \array{& \text{DNA Repair} & \text{Other} \\\text{Sensitive} & 38 & 232 & 270\\\text{Not Sensitive} & 74 & 3324 & 3398 \\ & 112 &...


14

I'd use a permutation test instead of either the Normal approximation or the chi-square. The permutation test is exact and most powerful, conditional upon the data. In this case, we can't calculate all the permutations of the groups, but we can generate a lot of random permutations of the data and get a pretty precise value: group <- c(rep("A",90),rep("...


13

This review, by Eknoyan (2007) has far more than your probably wanted to know about Quetelet and his invention of the body mass index. The short version is that BMI looks approximately normally distributed, while weight alone, or weight/height doesn't, and Quetelet was interested in describing a "normal" man via normal distributions. There are some first-...


12

Peter Flom's answer makes a good point, but strictly speaking, the situation is more complicated than that. Treatment 2 is administered conditionally on the failure of treatment 1. Suppose the probability of success with treatment 1 is $p_1$, (respectively $p_2$ for treatment 2.) Each patient who receives treatment 2 and is cured thereby contributes $(1-p_1)...


12

Almost. For a vector $U,$ the Yeo-Johnson with $\lambda=0$ is equivalent to the natural logarithm of $( U + 1 ).$


11

Here are a few examples which worked well for me when I was teaching statistics. I like to begin the class with the martingale, because somehow everybody finds a winning strategy at roulette interesting, and it is fairly easy to grasp. Then later you can have people try it out for themselves, if you are doing computer labs and can find an online roulette ...


11

@jbowman has given you a good option. I thought I might provide some information regarding your explicit questions about the appropriateness of the $z$-test vs. the $\chi^2$ test. $\boldsymbol z$-test: There are two concerns about the appropriateness of using the $z$-test, both regarding whether the assumed sampling distribution is correct. First, ...


11

I am going to try and answer your first question A random walk is a series of measurements in which the value at any given point in the series is the value of the previous point in the series plus some random quantity. For example, suppose you flip a fair coin in a series of tosses, and every time the coin comes up heads you add 1 to the previous value of ...


10

From Adolphe Quetelet's "A Treatise on Man and the Development of his Faculties": If man increased equally in all dimensions, his weight at different ages would be as the cube of his height. Now, this is not what we really observe. The increase of weight is slower, except during the first year after birth; then the proportion we have just pointed ...


10

Basic properties of expectation and variance give us: $$E[aX+bY] = aE[X]+bE[Y]$$ $$\text{Var}[aX+bY] = a^2\text{Var}[X]+b^2\text{Var}[Y]+2ab\text{Cov}[X,Y]$$ a) With $a=1,\,b=-1$ and assuming independence, we have $$E[X-Y] = E[X]-E[Y]$$ $$\text{Var}[X-Y] = \text{Var}[X]+\text{Var}[Y]$$ Taking square roots yields the result for the standard deviation. ...


9

In mid 2000s, a group of medical statisticians put their heads together and issued STROBE statement (http://www.strobe-statement.org): STrengthening the Reporting of OBservational studies in Epidemiology. It was published in the same form in Lancet, PLoS Medicine, Journal of Clinical Epidemiology, and several others, which to me seems like the most amazing ...


8

Canonical correspondence analysis is a technique developed, I believe, by the community ecology people. A founding paper is Canonical correspondence analysis: a new eigenvector technique for multivariate direct gradient analysis by Cajo J.F. Ter Braak (1986). The method involves a canonical correlation analysis and a direct gradient analysis. The idea is to ...


8

BMI is primarily used nowadays because of its ability to approximate abdominal visceral fat volume, useful in studying cardiovascular risk. For a case study analyzing the adequacy of BMI in screening for diabetes see Chapter 15 of http://biostat.mc.vanderbilt.edu/CourseBios330 under Handouts. Several assessments are there. You will see that a better power ...


8

I'm having the same issue now. I didn't see anyone reference this just yet, but I'm researching the Point-Biserial Correlation which is built off the Pearson correlation coefficient. It is mean for a continuous variable and a dichotomous variable. Quick read: https://statistics.laerd.com/spss-tutorials/point-biserial-correlation-using-spss-statistics.php ...


7

Given your assumption that 1 in 160 millions being P(matching DNA evidence|random person), the number 16/19 is roughly the chance that none of the other 30 million males in the UK would also match the DNA evidence: binomial chance of 0 hits, given 30 millions trials with p = 1/160 millions. I get about 0.83 for this probability and 16/19 is roughly 0.84. ...


7

I think the best example of this may likely be the controversy around hormone replacement therapy and cardiovascular risk - large cohort epidemiological studies seem to suggest a protective effect and health policy and physician recommendations were made on this information. Follow-up RCTs then seem to show that there's actually an increased risk of ...


7

I'd strongly suggest you get Frank Harrell's Regression Modeling Strategies. Possibly not as your only book - you'll might want something more elementary as well (though since you've already read some books, this may be covered) - but this book is full of practical and important information, and if you use R (which is also an important tool, I think), then ...


7

I'm not aware of a single-reference that I think gives particularly good coverage of the gamut of typical methods; a reference that's great on MLE may not be so great on Bayesian methods or might omit any discussion of quantile matching or minimizing a goodness of fit statistic, and some of the best reference on Bayesian methods tend to focus only on that. [...


7

This is an excellent question because the two are being terribly misused in personalized medicine and biomarker studies. The correct definition of the two, at least when it comes to data, is the same. Here is how the terms are being misused in personalized/precision medicine: prognostic is taken to mean predictive and predictive is taken to mean ...


6

As someone who took courses from the Statistics department of a university which did not offer a Biostatistics major and worked in clinical trials with biostatisticians and read many papers written by biostatisticians, I can offer a particular perspective. I see biostatistics as a field that applies a subset of standard statistical techniques to clinical ...


6

Try the pheatmap package. The link includes example code and images for clustering and displaying genetic data. It can apply a variety of clustering methods to your data before displaying them. You should be able to get started with just library(pheatmap) pheatmap(mat) and then you'll have lots of options for adjusting the display and clustering ...


6

Poisson distribution is for count data only, trying to feed it with continuous data is nasty and I believe should not be done. One of the reasons is that you don't know how to scale your continuous variable. And the Poisson depends very much on the scale! I tried to explain it with a simple example here. So For this reason alone I'd not use Poisson for ...


6

You could use some sort of permutation test, but with only 4 people there are only $2^4 = 16$ possible combinations. Yours is the most extreme one, so its p-value will be $\frac{1}{16} = 0.0625$ (This is a case where one can do the permutation test in one's head! That doesn't happen too often.)


6

This reminds me of situations where a reference interval is often used. You basically just take the 5th and 95th percentile of your data (for a 90% reference interval), and look to see if your patient of interest is inside that interval.


6

Not sure exactly what you want. Not too specialized, I think, and with some biological content, but also statistical. I don't know spatial analysis, but have collaborated with statistical geneticists, which gives me a distorted view of the field. The two-volume Handbook of Statistical Genetics. You might want to consider just constructing your own ...


6

It is truly a mystery to me why rtPCR data are so often analyzed on the multiplicative scale. My experience is that those data and their noise variance are much more naturally represented on the $C_t$ scale. That includes analyses performed on preserved samples. Meaning, that I think it is really best to analyze the data on the $C_t$ scale, not after ...


6

You can use the Delta method to obtain an approximate distribution of your relative risk, as shown by that link. Then you can define a pivot and use this to obtain a CI. I understand that there might be some confusion regarding the use of the Delta method, so here are a few simple steps that show how to construct an approximate CI for the relative risk. ...


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