The Royal Wolverhampton NHS Trust (RWT) will work together with British healthtech company Medopad as its official remote patient management solution to improve patient outcomes, adherence and operational gains across primary, secondary and community care. Medopad’s remote patient management platform for RWT will launch this Spring with projects in cardiology, hypertension, diabetes and many other areas. This will be the first deployment of Medopad in a primary care setting and will be part of a population health initiative to help patients who are diagnosed with hypertension in better managing their condition.
The NLEstimate macro allows you to estimate one or more linear or nonlinear combinations of parameters from any model for which you can save the model parameters and their variance-covariance matrix. Most modeling procedures which offer ESTIMATE, CONTRAST, or LSMEANS statements only provide for estimating or testing linear combinations of model parameters. However, common estimation problems often involve nonlinear combinations, particularly in generalized models with nonidentity link functions such as logistic and Poisson models.
This sample combines macro programming with PROC FREQ and DATA Step logic to count the number of missing and non-missing values for every variable in a data set. The results are stored in a data set.
This sample illustrates one method of counting the number of missing and non-missing values for each variable in a data set. Two methods for structuring the resulting data set are shown.
The %VARTEST macro provides a one-tailed test of the null hypothesis that the variance equals a non-zero constant for normally distributed data. It also provides point- and confidence interval estimates.
NOTE: The CIBASIC option in PROC UNIVARIATE provides one- and two-sided confidence intervals for the standard deviation and variance. PROC TTEST provides a confidence interval for the standard deviation using either of two methods.
PURPOSE:
The %VARTEST macro tests the null hypothesis that the variance (or standard deviation) of a set of independent and identically normally distributed values is equal to a specified constant against an alternative that the variance (or standard deviation) exceeds the constant. The macro also provides point- and confidence interval estimates for the variance and standard deviation.