Blog Archives

Estimating the mean and standard deviation from the median and the range

December 3, 2015
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Estimating the mean and standard deviation from the median and the range

While preparing the data for a meta-analysis, I run into the problem that a few of my sources did not report the outcome of interest as means and standard deviations, but rather as medians and range of values. After looking around, I found this interesting paper which derived (and validated through simple simulations), simple formulas that

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The little mixed model that could, but shouldn’t be used to score surgical performance

July 30, 2015
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The little mixed model that could, but shouldn’t be used to score surgical performance

The Surgeon Scorecard Two weeks ago, the world of medical journalism was rocked by the public release of ProPublica’s Surgeon Scorecard. In this project ProPublica “calculated death and complication rates for surgeons performing one of eight elective procedures in Medicare, carefully adjusting for differences in patient health, age and hospital quality.”  By making the dataset

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Empirical bias analysis of random effects predictions in linear and logistic mixed model regression

July 30, 2015
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Empirical bias analysis of random effects predictions in linear and logistic mixed model regression

In the first technical post in this series, I conducted a numerical investigation of the biasedness of random effect predictions in generalized linear mixed models (GLMM), such as the ones used in the Surgeon Scorecard, I decided to undertake two explorations: firstly, the behavior of these estimates as more and more data are gathered for each

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Survival Analysis With Generalized Additive Models: Part V (stratified baseline hazards)

May 9, 2015
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Survival Analysis With Generalized Additive Models: Part V (stratified baseline hazards)

In the fifth part of this series we will examine the capabilities of Poisson GAMs to stratify the baseline hazard for survival analysis. In a stratified Cox model, the baseline hazard is not the same for all individuals in the study. Rather, it is assumed that the baseline hazard may differ between members of groups, even though it will

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Survival Analysis With Generalized Additive Models : Part IV (the survival function)

May 2, 2015
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Survival Analysis With Generalized Additive Models : Part IV (the survival function)

The ability of PGAMs to estimate the log-baseline hazard rate, endows them with the capability to be used as smooth alternatives to the Kaplan Meier curve. If we assume for the shake of simplicity that there are no proportional co-variates in the PGAM regression, then the quantity modeled  corresponds to the log-hazard of the  survival

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Survival Analysis With Generalized Additive Models : Part III (the baseline hazard)

May 2, 2015
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Survival Analysis With Generalized Additive Models : Part III (the baseline hazard)

In the third part of the series on survival analysis with GAMs we will review the use of the baseline hazard estimates provided by this regression model. In contrast to the Cox mode, the log-baseline hazard is estimated along with other quantities (e.g. the log hazard ratios) by the Poisson GAM (PGAM) as: In the

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Survival Analysis With Generalized Models: Part II (time discretization, hazard rate integration and calculation of hazard ratios)

May 2, 2015
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Survival Analysis With Generalized Models: Part II (time discretization, hazard rate integration and calculation of hazard ratios)

In the second part of the series we will consider the time discretization that makes the Poisson GAM approach to survival analysis possible. Consider a set of s individual observations at times , with censoring indicators assuming the value of 0 if the corresponding observation was censored and 1 otherwise. Under the assumption of non-informative

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Survival Analysis With Generalized Additive Models : Part I (background and rationale)

May 1, 2015
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Survival Analysis With Generalized Additive Models : Part I (background and rationale)

After a really long break, I’d will resume my blogging activity. It is actually a full circle for me, since one of the first posts that kick started this blog, matured enough to be published in a peer-reviewed journal last week. In the next few posts I will use the R code included to demonstrate the

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The little non-informative prior that could (be informative)

November 26, 2013
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The little non-informative prior that could (be informative)

Christian Robert reviewed on line a paper that was critical of non-informative priors. Among the points that were discussed by him and other contributors (e.g. Keith O’Rourke), was the issue of induced priors, i.e. priors which arise from a transformation of original parameters, or of observables. I found this exchange interesting because I did something

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Bayesian Linear Regression Analysis (with non-informative priors but without Monte Carlo) In R

November 24, 2013
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Bayesian Linear Regression Analysis (with non-informative priors but without Monte Carlo) In R

Continuing the previous post concerning linear regression analysis with non-informative priors in R, I will show how to derive numerical summaries for the regression parameters without Monte Carlo integration. The theoretical background for this post is contained in Chapter 14 of Bayesian Data Analysis which should be consulted for more information. The Residual Standard Deviation The

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