There are a number of simplistic methods available for tackling the problem of missing data. Unfortunately there is a very high likelihood that each of these simplistic methods introduces bias into our model results.
Multiple imputation is considered to be the superior method of working with missing data. It eliminates the bias introduced by the simplistic methods in many missing data situations.
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One of the key concepts in Survival Analysis is the Hazard Function.
But like a lot of concepts in Survival Analysis, the concept of “hazard” is similar, but not exactly the same as, its meaning in everyday English. Since it’s so important, though, let’s take a look. (more…)
Meta-analysis is the quantitative pooling of data from multiple studies. Meta-analysis done well has many strengths, including statistical power, precision in effect size estimates, and providing a summary of individual studies.
Most analysts would respond, “a mixed model,” but have you ever heard of latent growth curves? How about latent trajectories, latent curves, growth curves, or time paths, which are other names for the same approach?