SAS® Commands PIPE and CALL EXECUTE Dynamically Advancing from Strangers to Your Newest BFF (Best Friends Forever) The Joinless Join Expand the Power of SAS® Enterprise Guide® in a New Way Use of Social Media Data to Predict Retail Sales Performance Secret Sauce for reporting health care data using SAS and SQL Server Business Intelligence %LET me help you improve your reporting with the help of SAS macros Investigating the Irregular: Using Perl Regular Expressions Reporting The Facts: The ODSmemo macro suite for making RTF memos within SAS®
Managing the Organization of SAS® Format and Macro Code Libraries in Multiple Complex EnvironmentsĪnalyzing Collection Effectiveness using Incremental Response Modeling Preparing Interaction Variables for Logistic Regression Understanding Double Ampersand SAS® Macro Variables Understanding Change through Different Methodological LensĪn Object Oriented Framework for Simulations in base SAS® Tell Me What You Want: Conjoint Analysis Made Simple Using SASĪ SAS® Macro Using Parallel Genetic Algorithm to Automate Variable Selection Selection and Transformation of Continuous Predictors for Logistic Regression
Kaplan-Meier Survival Plotting Macro %NEWSURV Using the Delta Method with Proc Mixed to generate means and confidence intervals from a Linear Mixed Model on the original scale, when the analysis is done on the log scaleĬreating Code writing algorithms for producing n-lagged variablesĬreating an Easy to Use, Dynamic, Flexible Summary Table Macro with P-Values in SAS for Research Studies Should more of your PROC REGs be QUANTREGs and ROBUSTREGs? Click here for descriptions of each section.Ĭomparing regression, propensity matching and coarsened exact matching in healthcare observational studies using SAS: An example from the Medical Expenditure Panel Survey (MEPS) SectionsĬlick on a section title to view abstracts for that section, or scroll down to view them all. Note: Content and schedule are subject to change. You can also view all papers in our convenient Interactive Schedule Grid Papers are organized into 11 academic sections and cover a variety of topics and experience levels. MWSUG 2014 will feature over 130 paper presentations, posters, and hands-on workshops. The interested reader should look at Furukawa and Leucht (2011) where a convincing argument is given to why this complicates the interpretation of NNT.Paper presentations are the heart of a SAS users group meeting. It is possible to convert Cohen’s d into a version of NNT that is invariant to the event rate of the control group. bellow some cut-off on a standardized questionnaire. The definition of an “event” or a “response” is arbitrary and could be defined as the proportion of patients who are in remission, e.g. You can change this be pressing the settings symbol to the right of the slider. CER is set to 20 % in the visualization above. Where Φ is the cumulative distribution function of the standard normal distribution and Ψ its inverse, CER is the control group’s event rate and δ the population Cohen’s d. Furukawa and Leucht (2011) gives the following formula for converting Cohen’s d into NNT NNT is the number of patients we would need to treat with the intervention to achieve one more favorable outcome compared to the control group. Where Φ is the cumulative distribution function of the standard normal distribution, and δ the population Cohen’s d. Cohen’s d can be converted CL using the following formula (Ruscio, 2008) The effect size gives the probability that a person picked at random from the treatment group will have a higher score than a person picked at random from the control group.
It is meant to be more intuitive for persons without any training in statistics. This is effect size with many names: common language effect size (CL), Area under the receiver operating characteristics (AUC) or just A for its non-parametric version (Ruscio & Mullen, 2012). Cohen’s d can be converted to OVL using the following formula (Reiser and Faraggi, 1999) Generally called the overlapping coefficient (OVL). Cohen’s d can be converted to Cohen’s U 3 using the following formula Cohen’s U 3Ĭohen (1977) defined U 3 as a measure of non-overlap, where “we take the percentage of the A population which the upper half of the cases of the Β population exceeds”. And μ i is the mean of the respective population. Where it is assumed that σ 1 = σ 2 = σ, i.e., homogeneous population variances. Where δ is the population parameter of Cohen’s d. Cohen’s d is simply the standardized mean difference,