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epidemiology

Epidemiology

Immortal time bias: A source of bias in estimating life expectancy and other measures

One of the most important aspects of good research is proper study design; no type or amount of statistical analysis can make up for a poorly designed study. In longitudinal research one of the most important aspects of study design is making sure that the temporal sequence of events is understood and recorded correctly. Errors in this arena can lead to misclassification of exposure time and differential follow-up between comparison groups. It is therefore crucial to understand time at risk in studies and correctly and fairly apply rules of follow-up to study subjects.

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Losing sleep over denominators, Part III: Explaining the error and the bias in Plioplys 1998

As we saw in Part II of this three part series, there is something wrong with Figure 4 in Plioplys et al. 1998.1 As a matter of fact, there is something wrong with all of the figures in the study, though it is a bit more difficult in some cases to confirm this. If the reader should like to work out the details for another, Figure 2 is a fairly easy place to start. In Figure 4, as we have seen, data that should apparently run out by age 11 years continues all the way to age 34 years. Did the authors create these curves out of whole cloth?

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Losing sleep over denominators, Part II: The survival curves in Plioplys et al. 1998 cannot be right

We now turn our attention to 1998 study by Plioplys et al.,1 and in particular to one of the many figures in the study: Figure 4. We choose to focus on this figure because the number of individuals involved in the analyses is small enough to make it very easy to see that something is wrong.

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