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This reply is primarily comments aimed at creating a reliable workflow for such an analysis. The suggested procedure is this: Prepare the data by creating a complete dataset with just the needed variables and no missing values. This reduces the chances of errors when the software (perhaps silently) removes incomplete cases. Fit the model. Encapsulate the ...


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This goes against what Harrell seems to mean when he writes about bootstrap validation. Harrell's argument basically goes like this. Splitting the data wastes data that could have been used for training, so train on the entire dataset. However, then we risk overfitting. We always risk overfitting, but when we have holdout data, we can catch that we have ...


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My short answer would be: Yes, if samples are very small, this can definitely be a problem since the sample may not contain enough information to get a good estimate of the desired population parameter. This problem affects all statistical methods, not just the bootstrap. The good news, however, is that ‘small’ may be smaller than most people (with knowledge ...


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You will often be disappointed in the confidence interval coverage probability in at least one of the two tails when using either the bootstrap or the delta method. This is one area where traditional statistical education, which focuses on frequentist methods, has done us a disservice. Contrast this with Bayesian modeling and using MCMC to generate ...


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People really do find the delta method more useful in some cases. It is really impossible to tell which is more appropriate without being given a specific context since the context would largely determine which method is "best" for the given situation. I think that you've considered this to at least some extent, because you've listed some downsides ...


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The coxed package is designed "to calculate duration-based quantities from Cox model results" rather than to show hazard ratios, etc. See the vignette. So what you found is probably a feature, not a bug. You should be able to do what you want directly with the boot() and boot.ci() functions in the standard R boot package, following standard ...


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By itself, bootstrapping does not address the multiple comparisons issue. However, by appropriately bootstrapping certain maximal statistics, you can indeed control the usual multiple comparisons error rates, at least approximately. See the book Resampling-Based Multiple Testing: Examples and Methods for p-Value Adjustment, in particular, the chapter "...


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Method 1 determines the sampling distribution of the test statistic under the observed data distributions. I'm not sure this is particularly useful. I suppose it can give you a 95% confidence interval for the test statistic. I would use Method 1 to construct a 95% confidence interval for the difference in means rather than perform a hypothesis test. Often ...


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Either of those approaches could be appropriate, depending on the initial sampling scheme. Standard bootstrapping from a data sample should represent the same type of sampling as was done to obtain the original sample from the full population. So, if you had two separate populations $X$ and $Y$ and deliberately chose respectively $n_X$ and $n_Y$ separately ...


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Did I correctly identify that a poly(Age,3) model was most appropriate? I don't think so. There may be some issues of multiple testing here because you've done several likelihood ratio tests. A better approach would be to have a linear model vs a spline model. Here is how to do this. I'll fit a model using natural splines with knots placed at the ...


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