MedStat Stutter

Phantasies of a physicist fallen among physicians

Tabelle für den Vorzeichentest

Politiker oder Banker werden manchmal gefragt, wieviel ein Brötchen koste. Wenn dann Beträge wie "10 Cent" genannt werden, soll das anzeigen, wie weit der oder die Gefragte von der Lebenwirklichkeit entfernt ist.

So erging es mir neulich in einem Seminar. Vorgestel...

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A colleague asked me how to present the results of a study in gastric emptying. As you have seen multiple time here, the emptying time t50 is the target variable. But read on even if you work with blood pressure (also often misused as example), rehabilitation, or pharmacology. The methods are the sa...

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Summary

Using vectors for indexed access to ragged arrays in Stan

volume ~ normal(
      v0[record] .* (1+ kappa[record] .* (minute ./ tempt[record])) .*
      exp(-minute ./ tempt[record]), sigma);

Fitting gastric emptying time series

Gastric volume times serie measured by MRI techniques can have an initial volume overshoot due to secretion. To estimate half-times and the degree of overshoot, the author of this blog has introduced the linexp function [@Fruehauf2011] with three parameters to complement the power exponential function used in earlier research [@Elashoff1982, @Elashoff1983b] with scintigraphic data.

linexp:

v = v0* (1+t*kappa/tempt)*exp(-t/t_{empt})

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Summary

Time series from 13C breath test recordings are used as a surrogate indicator for gastric emptying. Gastric emptying half time t50 is computed by fitting the series with an exponential beta or a Gamma function. Often, records are too short to obtain stable estimates of the trailing part. One can easily get unrealistic emptying times when single curves are fitted (Ghoos et al. (1993), Maes et al. (1998)).

Population methods can come to the rescue. When these fail, Bayesian methods provide a robust approach to non-linear curve fitting.

Zipped source files and data

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For a clinical study we needed a randomization table with the option to use unequal cell counts, e.g n = 8 for treatment 1 and 2 and n=12 for treatment 3 and 4. Using the R package by Kornelius Rohmeyer, I have written a little web app with Shiny.

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