%INCLUDE /sasdata/path_to_file/file.sas statement returns with ERROR when ran in scheduler. It works fine when run in Studio but throws error when scheduled. WARNING: Physical file does not exist, ERROR: Cannot open %INCLUDE file sasdata/path_to_file/file.sas This is on SASVIYA 4 2026.06 ... View more

Small-sample correction for sandwich variance in PROC GEE/PROC GENMOD with few clusters I am fitting a marginal GEE with a small number of clusters (16) and would like to apply a finite-sample correction to the empirical/sandwich covariance estimator. I have reviewed the PROC GEE and PROC GENMOD documentation and see options for the usual model-based and empirical/sandwich covariance estimates, but I have not found an option for a small-number-of-clusters correction such as Kauermann-Carroll, Mancl-DeRouen, Fay-Graubard, or a similar bias-corrected sandwich estimator. For example, the model is of the general form: proc gee data=have; class facility period; model y = treatment period / dist=binomial link=log; repeated subject=facility / type=exch; run; where facility is the cluster and there are 16 facilities. Does PROC GEE or PROC GENMOD have a built-in option for a small-sample/finite-cluster correction to the empirical sandwich covariance estimator? If not, is there another SAS procedure or recommended SAS implementation for obtaining a small-cluster-corrected GEE variance while retaining the same marginal GEE model? I am specifically interested in correcting inference for the small number of clusters rather than changing to a mixed-effects model. Thank you. ... View more

Life sciences organizations are facing growing pressure to make faster, more informed decisions while managing increasingly complex data. Clinical trials, real-world evidence, operational metrics, and patient data all contribute valuable insights, but turning that information into action remains a challenge. ... View more

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Team Name PesoShield Track Financial Services Use Case Optimize FX hedging for Mexican agri-food exporters by jointly modeling exchange-rate, commodity-price, and uncertain export-volume risk. Technology SAS Viya – VARMAX, multivariate GARCH, copulas, Monte Carlo simulation, optimization and backtesting Region LATAM- México Team lead Mariano Romero Ochia Team members @mormin @Luissss  Social media handles linkedin.com/in/mariano-romero-ochoa  linkedin.com/in/luis-carlos-carbajal-gonzález Is your team interested in participating in an interview? Yes Optional: Expand on your technology expertise Our team combines actuarial science, financial risk management, statistical modeling and software development. For PesoShield, we plan to use SAS Viya for time-series modeling, volatility estimation, dependency modeling, Monte Carlo simulation and hedge optimization. The analytical pipeline will integrate Banxico, CME, SOFR/Treasury, USDA and exporter-level data to estimate Cash-Flow-at-Risk and compare optimized hedging strategies against conventional full-forward hedging.   ... View more