Competing-risk models are survival models in which there is more than one type of event of interest. In the case where events can be treated as occurring at discrete, regularly-spaced times, it is relatively simple to estimate models for the hazard rates and to account for left-truncation and...

Marginal structural models (MSM) can be used to estimate the effect of a time dependent exposure in presence of time dependent confounding. Previously Fewell et al. (2004) described how to estimate this model in Stata based on a weighted pooled logistic model approximation. However, based on the...

There are occasions when a task is difficult in Stata, but fairly easy in a more general programming language. Python is a popular language for a range of uses. It is easy to use, has many high-quality packages, and programs can be written relatively quickly. Is there any advantage in combining...

Many methods have been developed to predict mortality following trauma. Two classification systems are used to provide a taxonomy for diseases, including injuries. The ICD-9 is the classification system for administrative data in the U.S.A. AIS was developed for characterization of injuries...

The use of Statistics Canada census and survey data in research data centres (RDCs) includes very specific and sometimes complex confidentiality requirements. Ensuring that statistical output meets these requirements adds an additional step to analysis that can be difficult and time-consuming....

IV estimators of parameters in single equation structural models, like 2SLS and the LIML, are the most commonly used econometric estimators. Hausman-type tests are commonly used to choose between OLS and IV estimators. However, recent research has revealed troublesome size properties of Wald...

Competing Risks Models have a various field of application in medical and public health studies. A challenging clue for applying cause-specific survival models yield on the problem of missing and misclassification in cause of death. The masked cause of death is related to incomplete or only...

Missing data occurs frequently in agricultural household surveys, which can lead to biased and inefficient regression estimates. Multiple-imputation can be used to overcome the missing-data problem. Previous studies applied multiple-imputation to datasets, where only some of the variables have...

How to plot (and summarize) univariate distributions is a staple of introductory data analysis. Graphical (and numerical) assessment of marginal and conditional distributions remains important for much statistical modeling. Research problems can easily evoke needs for many comparisons, across...

Stratification is a sampling design that can improve efficiency. It works by first partitioning the population into homogeneous subgroups and then performing simple random sampling within each group. For a continuous variable, stratification involves determining strata boundaries. Holding the...