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Last updated: 24 September 2007

2007 Nordic and Baltic Stata Users Group meeting

7 September 2007

Karolinska Framsidan

Karolinska Institutet
Department of Medical Epidemiology and Biostatistics
Nobels väg 12A
SE-171 77 Stockholm


Quantiles, L-moments and modes: bringing order to descriptive statistics

Nicholas J. Cox
Durham University
Describing batches of data in terms of their order statistics or quantiles has long roots but remains underrated in graphically based exploration, data reduction, and data reporting. In 1990, Hosking proposed L-moments based on quantiles as a unifying framework for summarizing distribution properties, but despite several advantages they still appear to be little known outside their main application areas of hydrology and climatology. Similarly, the mode can be traced to the prehistory of statistics, but it is often neglected or disparaged despite its value as a simple descriptor and even as a robust estimator of location. This presentation reviews and exemplifies these approaches with detailed reference to Stata implementations. Several graphical displays are discussed, some novel. Specific attention is given to the use of Mata for programming core calculations directly and rapidly.

Additional information

Recent developments in multilevel modeling

Bobby Gutierrez
Mixed-effects models contain both fixed and random effects. The fixed effects are analogous to standard regression coefficients and are estimated directly. The random effects are not directly estimated but instead are summarized according to their estimated variances and covariances, known as variance components. Random effects take the form of either random intercepts or random coefficients, and the grouping structure of the data may consist of multiple levels of nested groups. In Stata, one can fit mixed models with continuous (Gaussian) responses by using xtmixed and, in Stata 10, fit mixed models with binary and count responses by using and xtmepoisson, respectively. All three commands have a common multiequation syntax and output, and postestimation tasks such as the prediction of random effects and likelihood-ratio comparisons of nested models also take a common form. This presentation will cover many models that one can fit using these three commands. Among these are simple random intercept models, random-coefficient models, growth curve models, and crossed-effects models.

Additional information
gutierrez_sweden07.pdf (presentation slides)

Report to users

Alan Riley
No abstract is available at this time.

Additional information
alan_riley.pdf (presentation slides)

An informal tutorial on the ice command for chained equations imputation in Stata

Patrick Royston
MRC Clinical Trials Unit, London
No abstract is available at this time.

Additional information
Royston_ice_tutorial_2007.pdf (presentation slides)

Probabilistic bias analysis of epidemiological results

Nicola Orsini
Karolinska Institutet)
Classification errors, selection bias, and uncontrolled confounding are likely to be present in most epidemiological studies, but the uncertainty introduced by this type of biases is seldom quantified. The authors present a simple yet easy-to-use method to adjust the relative risk of a disease for misclassification of a binary exposure, selection bias, and unmeasured confounding variable. The accompanying Stata tool implements both ordinary and probabilistic sensitivity analysis. It allows the user to specify a variety of probability densities for the bias parameters, and use these densities to obtain simulation limits for the bias adjusted exposure-disease relative risk. The authors illustrate the method by applying it to a published positive association between occupational resin exposure and lung-cancer deaths in a case-control study. By employing plausible probability distributions for the bias parameters, investigators can report results that incorporate their uncertainties regarding unmeasured or uncontrolled confounding, and thus avoid overstating their certainty about the effect under study. These results can usefully supplement standard data descriptions and conventional results.

Additional information
orsinietal_slide_7sep07.pdf (presentation slides)

Fractional polynomials and model averaging

Paul C. Lambert
University of Leicester
No abstract is available at this time.

Additional information
lambert_fpma.pdf (presentation slides)

Linking process to outcome: The seqlogit package

Maarten L. Buis
Vrije Universiteit Amsterdam
No abstract is available at this time.

Additional information
buis_seqlogit.pdf (presentation slides)

Scientific organizers

Peter Hedström, Metrika Consulting, Oxford University and Oxford University

Nicola Orsini, Karolinska Institutet

Paul Dickman, Karolinska Institutet

Logistics organizers

Metrika Consulting, the official distributor of Stata in the Nordic and Baltic regions, and the Karolinska Institutet.