This page is always a good reference to see what support we have and how long it's valid for: https://support.sas.com/en/technical-support/support-levels.html   As mentioned, latest version of Base SAS, M8, is currently supported fully to February 2028. After that it will likely move to Limited support, not getting new hotfixes, updates, etc. But will still get limited technical support until completely retired. ... View more

Thank you very much for your code, especially the SHEET statement available in the IMPORT procedure of SAS version TS1M5 but was regrettably not mentioned at all in its SAS documentation (, which directly caused a huge waste of time of my entire weekend on searching for an answer online and adapting R codes where the Excel spreadsheets intended to import into SAS came from clumsily)!! Anyway, your code solved a big problem troubling me in the past few days. Thank you! ... View more

I am also glad that my questions somewhat benefit you and provide you with excitement by prompting your encounter with this nice paper. It is also a wonderful experience for me. ... View more

Thank you for your reply! @FreelanceReinh wrote: Hello @Season,   It appears to me that the discrepancies vanish (as they should) if you tighten the convergence criteria, e.g., add the option GCONV=1E-10 to the MODEL statements (default is 1E-8). So it's just the iterative algorithms used by PROC PHREG approaching the same limit in different ways and thus reaching convergence at slightly different values of the parameter estimates. Yes, you are right. Imposing a more stringent restriction on model convergence criteria like the one you mentioned yields concordant results. ... View more

There is a paper on the concordance statistic for competing risks models: Concordance for prognostic models with competing risks | Biostatistics | Oxford Academic. The authors themselves provided R code in the supplemental material of the paper: Supplementary material to “Concordance for prognostic models with competing risks”. Another person has also developed an R package for this calculation based upon the paper mentioned above: R: Concordance index in the Presence of Competing Risks. An issue to note is that independence of observations was assumed in the paper. Therefore, this method is not applicable in the analysis of clustered data with competing risks. ... View more

I ran into your problem as well. Here is what I have found after conducting extensive search on the Internet for a solution: Re: How to conduct competing-risk based on proc surveyphreg using sas ... - SAS Support Communities ... View more

Update on my reply: hours after posting this thread, I found not only that I could no longer  change its contents but also solutions to this problem. An insidious tactic of finding the solution lies in correctly specifying the keyword in the search engine. I used "competing risks" and virtually retrieved no result on my problem. However, when substituting the words for "cause-specific hazard", a subdomain of competing risks analysis, things worked. To summarize my finding, there is a research paper (Population-based absolute risk estimation with survey data | Lifetime Data Analysis) that discusses this issue. From the first sentence of its abstract (i.e., "Absolute risk is the probability that a cause-specific event occurs in a given time interval in the presence of competing events."), I came to know "absolute risk", a term whose meaning puzzled me before, could also serve as a keyword in search engines in this setting. I then found a book entitled Absolute Risk: Methods and Applications in Clinical Management and Public Health (Chapman & Hall/CRC Monographs on Statistics and Applied Probability Book 154), Pfeiffer, Ruth M., Gail, Mitchell H., eBook - Amazon.com, whose cover contained a picture depicting competing risks. It was exciting to find in the book that the intersection of complex survey data analysis and competing risks modeling was introduced as a few sections in this book, including an independent section on variance estimation. You- and of course, I- can refer to it for more details. ... View more

Never mind. I was once in your shoes when I was trying to find answers to my problems and was offered helping hands by others in this Community. I have just won the badge of two years of membership in this Community a couple of minutes ago, reminding me of the time and feelings when I first raised my question in this Community. If you are interested, you can look at my first post in this Community that was about the selection of an optimal tuning parameter in generalized additive models. I spent months on searching for a solution to this problem but ended up in failure on reaching a definite conclusion. I posted my questions here and was informed that there was not a universal solution to the problem I encountered, dismissing the need of any further attempt to conduct more extensive search for it on the Internet. I therefore felt obliged to help others in need when I am the one that can be of some help. As for your very specific question of whether "residual bias" is an acknowledged and widely used term or not, I am sorry to say that I do not have the expertise to make this judgement because my past research experience did not necessitate the use of this method. I have therefore known only the basics of this method, including its general and superficial rationale and some of its limitations, including the one I mentioned in my earliest reply to this question. Unlike other statistical methods like logistic regression and multiple imputation, I have not yet read a single monograph on this topic and cannot be sure whether it is a term or not. I therefore do not have the expertise to make this judgement. I think you can wait in the Community to see if anybody who knows more on this method than I do answer your question. But it is also likely that you may never get a reply. I suggest that you read some monographs or reviews on this method to see if experts on this topic use the term "residual bias" while waiting for a reply on this Community. I found a few books, you may also look for them by yourself: Propensity Score Analysis: Statistical Methods and Applications (Advanced Quantitative Techniques in the Social Sciences Book 11) 2, Guo, Shenyang, Fraser, Mark W. W. - Amazon.com Amazon.com: Practical Propensity Score Methods Using R: 9781452288888: Leite, Walter L.: Books Amazon.com: Propensity Score Analysis: Statistics Methods and Applications (Chinese Edition): 9787562466222: ( mei ) guo shen yang: Books ... View more

Thank you! ... View more

Here is a webpage on power analysis of regression models with interactions that I have found: Power Analyses for Interaction Effects in Cross-Sectional Regressions • InteractionPoweR. By the way, the analysis of power is a rather tricky aspect of regression. Research on this field has generally be confined to very simple cases. For instance, despite the popularity of comparing three and more survival curves in survival analysis, the version of SAS I am using (9.4 M5) only supports calculation of power of the test of comparing of two survival curves. So it would be better if you first searched for the method suitable for your problem on the Internet and then posted them here for questions of the implementation of this method in SAS. Also, be prepared that maybe there isn't a solution to your problem yet. ... View more

I agree with what @PaigeMiller has said. The residuals of your model is clearly non-normal. Both ANOVA and linear regression are not applicable. My suggestions on your problem is that nonparametric tests and quantile regression can be employed for inter-group comparisons of quantiles instead of means, a statistic that ANOVA aims to compare among groups. For instance, Median and quantile tests under complex survey design using SAS and R - ScienceDirect contains a SAS program for testing for the equality of medians and other quantiles among groups. Quantile Regression Analysis of Survey Data Under Informative Sampling | Journal of Survey Statistics and Methodology | Oxford Academic and other research papers discuss quantile regression of complex survey data. There is a note on the ANOVA option in the SURVEYREG procedure. As page 104 of Complex Survey Data Analysis with SAS | Taylor H. Lewis | Taylor & Francis says, this option should be deprecated in analysis in that the F statistic in the ANOVA table reported by PROC SURVEYREG does not carry the same interpretation as it has for analysis of simple random sampling data and should be ignored. In other words, the F statistic reported in the ANOVA table does not represent the test statistic for the null hypothesis that all parameters are jointly equal to zero except for the intercept. Instead, it adviced by the author that analysts should refer to the line of "Model" in the table entitled "Tests of Model Effects" in the output produced by the SURVEYREG procedure for the correct statistic. ... View more

Please be aware that the assumption of normality of the residuals is also required for linear regression of complex survey data. See page 2 of Linear regression diagnostics for survey data for more details. So in theory, a nonparametric test is required. ... View more

I have no idea on the solution to this specific question, but would like to recommend the only monograph I have found on building overdispersion models with SAS: Amazon.com: Overdispersion Models in SAS: 9781607648819: Morel PhD, Jorge G., Neerchal PhD, Nagaraj: Books. I have read part of it and am not sure whether it contains the answer to your specific question. But I am sure it is a good monograph for those who wish to have a systematic understanding on this field. ... View more

Although not solved yet, I have made some progress on the question I raised in the original post and would like to share my findings. (1) Theoretical issues: the non-zero part of my dependent variable follows a lognormal distribution. Theoretical discussions revolving the maximum likelihood estimation of these models can be found in Amazon.com: Generalized Linear Models for Categorical and Continuous Limited Dependent Variables (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences): 9781032477466: Smithson, Michael, Merkle, Edgar C.: Books. Moreover, heteroscedasticity of limited dependent variable models is an problem I encountered and named by the authors of this book as an issue extensively discussed therein. Truncated and Censored Samples: Theory and Applications (Statistics: A Series of Textbooks and Monographs) by A. Clifford Cohen (1991-05-23): A. Clifford Cohen: Amazon.com: Books also contains a brief summary of maximum likelihood estimators of limited dependent variable models whose dependent variable follows a lognormal distribution. (2) Software implementation: Amazon.com: Maximum Likelihood Estimation and Inference: With Examples in R, SAS and ADMB: 9780470094822: Millar, Russell B.: Books is a monograph on maximum likelihood estimation (MLE) with special emphasis on software implementation, including issues of conducting MLE with SAS. ... View more