Different combinations of error-ridden or error-free observations characterize latent classes. Latent class probabilities depend on the probabilities of the different types of error. I introduce a set of Stata commands to fit a general class of finite mixture models to fit to linked survey–administrative data. I also provide postestimation commands for assessment of reliability, marginal effects, data simulation, and prediction of hybrid earnings variables that combine information from both data sources.

Whether TAP engenders indirect (herd) effects on concurrent ICU patients is of great interest.

Objective: To estimate the indirect effects of TAP among concurrent control group patients using data from six broad categories of VAP prevention arranged to simulate a cluster randomized trial (CRT).

Design, setting and participants: Control and intervention groups from 210 published RCCTs from 14 Cochrane reviews of VAP prevention interventions among ICU patients excluding RCCTs with fewer than 50% of patients receiving mechanical ventilation (MV).

Exposures: Indirect exposure of control groups within TAP RCCTs versus within RCCTs of other VAP prevention interventions in five broad categories.

Main outcomes and measures: The proportion of patients with VAP or ICU mortality (primary outcomes), aggregated separately for control and for intervention groups, as abstracted towards deriving study level intervention effect size estimates within each Cochrane review.

Results: Among 14 Cochrane reviews, the strongest summary effect sizes were apparent for VAP and mortality prevention among TAP RCCTs. Surprisingly, the ICU mortality among TAP intervention groups (23.3 [95% CI, 19.5–27.5]) versus an intervention group benchmark derived from the five other categories of intervention (21.1 [95% CI, 19.0–23.3]), respectively, were similar. By contrast, the ICU mortality among TAP control groups was higher versus a control group benchmark derived from the five broad categories of intervention, being 27.9 (95% CI, 23.5–32.5) versus 21.3 (95% CI, 19.2–23.2), respectively. This higher control group mortality remained evident in sensitivity analyses incorporating groups with late mortality, adjusting for group mean age and year of study publication.

Conclusions and Relevance: The control group event rates that underlie the strong summary intervention effect sizes within TAP RCCT’s among patients requiring MV are unusually high. This implicates strong indirect effects from TAP prophylaxis among MV patients and raises concern that the indirect effects to concurrent control group patients, whilst harmful, underlie the appearance of overall benefit.

Examples include the comparison of Stata’s ktau command, which requires a calculation time of O(n2) to obtain Kendall’s tau between two variables with sample size n, to my own community-contributed Stata command py_ktau, which implements Python’s algorithm to compute Kendall’s tau with a calculation time of O(nlog(n)). I will also discuss how to use Stata’s profiler and timer commands and provide examples for how to set a seed in Python when running Python code in Stata. Time permitting, I will talk about applications to the search for random permutations.

While there is general understanding with regards to the analysis and interpretation of changes in continuous variables, difficulties remain when understanding and interpreting dummies that describe qualitative characteristics. On one hand, the implicit interrelationship among binary variables is usually ignored, and on the other hand, standard RIF regressions only capture effects at the margins, not distributional treatment effects. This presentation suggests the use of restricted least-squares regression analysis (Haisken-DeNew and Schmidt, 1997), combined with the use of centered continuous variables and rescaling, to isolate the constant cleanly as the distributional statistic of interest and better interpret the results of RIF regressions in the presence of dummy variables. A Stata ado-file implements this methodology.

The PLOT family of commands is particularly useful for visual analyses of admin data, enabling users to produce a variety of highly customizable plots in a fraction of time required by Stata's native graphing commands. Benchmarking of the graphs show that PLOT commands can prove more than 100 times faster than the native commands, with the efficiency gains growing with sample size.

However, it is often left for the receiving user to update any specific parameters to make the code work for their needs. In this presentation, I show how we created a Stata program that incorporated Python and then built a Stata dialog box around it to allow the end user to be able to quickly and easily update the parameters. This development was used to extract tweets from Twitter and allowed the end user to enter their relevant twitter API keys, the search period, the search term and other relevant data to the search. The code used for this is available on GitHub at https://github.com/SDASANZ/Stata-Python-Twitter-API.