From bdebe5cb5e84f3521afc716e0d339176dc1a32d5 Mon Sep 17 00:00:00 2001 From: catball007 Date: Tue, 6 Aug 2024 05:26:22 +0200 Subject: [PATCH 1/9] update --- SAS/Jonckheere-Terpstra Test.qmd | 65 ++++++++++++++++++++++++++++++++ 1 file changed, 65 insertions(+) create mode 100644 SAS/Jonckheere-Terpstra Test.qmd diff --git a/SAS/Jonckheere-Terpstra Test.qmd b/SAS/Jonckheere-Terpstra Test.qmd new file mode 100644 index 00000000..5cf87118 --- /dev/null +++ b/SAS/Jonckheere-Terpstra Test.qmd @@ -0,0 +1,65 @@ +------------------------------------------------------------------------ + +title: " " + +------------------------------------------------------------------------ + +*Italic words are note for you.\ +Your choice to use part or all to fit your topic.* + +## R package versions + +R 4.3.1\ +mmrm 0.2.2 + +*If there is only one available package this can be deleted. Otherwise please make a short list, paragraph or table. If there is a reason to use one package vs another please include it. Please make sure to include what version of the packages you are using* + +## Data used + +```{eval = FALSE} +data ExData; + input Var1 $ Var2 ...; + datalines; +[Data Lines]; +run; +``` + +```{r} +exData <- tibble::tribble( + ~Var1, ~Var2, + "val1", 1, + "val2", 2) +``` + +*a sentence or two about the data. Consider using a package like {skimr} to help illustrate the data.* + +## Example Code using + +*Describe more details about the method.\ +If needed, add link to other web page/paper/SAS help methodology page, and list them in reference section.\ +Print key results as needed.\ +Add explanations about code and results as needed.* + +## Example Code using + +*Add if there is more than one package* + +## Comparison + +*If you are comparing more than one package or between different languages, consider adding in a table to illustrate some of the differences. When comparing between languages it can be nice to have a table with links to the pages with deeper dive of each language for a given method method like this table from the ANCOVA page:* + +| Analysis | Supported in R | Supported in SAS | Results Match | Notes | +|---------------|---------------|---------------|---------------|---------------| +| ANCOVA using general linear model and lsmeans | [Yes](../R/ancova.html) | [Yes](../SAS/ancova.html) | [Yes](#comp) | `GLM()` function from **sasLM** with `EMEANS=TRUE` is the easiest to use and matches SAS | + +*But, even if you are just comparing the difference in same language it can be helpful to have a table with values of interest across different packages. Using dynamic content you can add the values to a table without manually putting in each value.\ +SAS result is very reliable in many cases.* + +## Conclusion + +*add a conclusion about which language(s) or package(s) are more recommended.\ +This section could have a feature like paper abstract, e.g. about the selection of package or SAS PROC.* + +## Reference + +*Cite all sources and references used in the analysis.* From 614d65dad13d04007973ec67de301a3eb101a5b8 Mon Sep 17 00:00:00 2001 From: catball007 Date: Tue, 6 Aug 2024 05:31:22 +0200 Subject: [PATCH 2/9] update --- SAS/Jonckheere-Terpstra Test.qmd | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/SAS/Jonckheere-Terpstra Test.qmd b/SAS/Jonckheere-Terpstra Test.qmd index 5cf87118..0152ea23 100644 --- a/SAS/Jonckheere-Terpstra Test.qmd +++ b/SAS/Jonckheere-Terpstra Test.qmd @@ -1,16 +1,15 @@ ------------------------------------------------------------------------ -title: " " +title: "SAS Jonckheere-Terpstra Test" ------------------------------------------------------------------------ *Italic words are note for you.\ Your choice to use part or all to fit your topic.* -## R package versions +## SAS Procedure -R 4.3.1\ -mmrm 0.2.2 +Proc Freq ... *If there is only one available package this can be deleted. Otherwise please make a short list, paragraph or table. If there is a reason to use one package vs another please include it. Please make sure to include what version of the packages you are using* From 11cb3153b5aaedca0e5b45cec7e27b77da2a9773 Mon Sep 17 00:00:00 2001 From: catball007 Date: Tue, 6 Aug 2024 17:33:24 +0200 Subject: [PATCH 3/9] update --- SAS/Jonckheere-Terpstra Test.qmd | 92 +++++++++++++++++++------------- 1 file changed, 55 insertions(+), 37 deletions(-) diff --git a/SAS/Jonckheere-Terpstra Test.qmd b/SAS/Jonckheere-Terpstra Test.qmd index 0152ea23..00188664 100644 --- a/SAS/Jonckheere-Terpstra Test.qmd +++ b/SAS/Jonckheere-Terpstra Test.qmd @@ -4,55 +4,73 @@ title: "SAS Jonckheere-Terpstra Test" ------------------------------------------------------------------------ -*Italic words are note for you.\ -Your choice to use part or all to fit your topic.* - ## SAS Procedure -Proc Freq ... +To request Jonckheere-Terpstra test, specify the JT option in the Table statement like below: + +Preq; table Var1 \* Var2 / JT; Quit; + +The JT option in the TABLES statement provides the Jonckheere-Terpstra test, which is a nonparametric test for ordered differences among classes. -*If there is only one available package this can be deleted. Otherwise please make a short list, paragraph or table. If there is a reason to use one package vs another please include it. Please make sure to include what version of the packages you are using* +PROC FREQ also provides exact p-values for the Jonckheere-Terpstra test. You can request the exact test by specifying the JT option in the EXACT statement. ## Data used -```{eval = FALSE} -data ExData; - input Var1 $ Var2 ...; - datalines; -[Data Lines]; -run; +``` sas +data breaking; +input speed distance @@; +cards; +20 48 +25 33 +25 59 +25 48 +25 56 +30 60 +30 101 +30 67 +35 85 +35 107 +; +run; ``` -```{r} -exData <- tibble::tribble( - ~Var1, ~Var2, - "val1", 1, - "val2", 2) +``` sas +data contin; +input group subject response; +cards; +0 01 27 +0 02 28 +0 03 27 +0 04 31 +0 05 34 +0 06 32 +1 01 31 +1 02 35 +1 03 34 +1 04 32 +1 05 31 +1 06 33 +2 01 32 +2 02 33 +2 03 30 +2 04 34 +2 05 37 +2 06 36 +3 01 40 +3 02 39 +3 03 41 +3 04 38 +3 05 42 +3 06 43 +; +run; ``` -*a sentence or two about the data. Consider using a package like {skimr} to help illustrate the data.* - -## Example Code using - -*Describe more details about the method.\ -If needed, add link to other web page/paper/SAS help methodology page, and list them in reference section.\ -Print key results as needed.\ -Add explanations about code and results as needed.* - -## Example Code using - -*Add if there is more than one package* - -## Comparison - -*If you are comparing more than one package or between different languages, consider adding in a table to illustrate some of the differences. When comparing between languages it can be nice to have a table with links to the pages with deeper dive of each language for a given method method like this table from the ANCOVA page:* +## Example Code using -| Analysis | Supported in R | Supported in SAS | Results Match | Notes | -|---------------|---------------|---------------|---------------|---------------| -| ANCOVA using general linear model and lsmeans | [Yes](../R/ancova.html) | [Yes](../SAS/ancova.html) | [Yes](#comp) | `GLM()` function from **sasLM** with `EMEANS=TRUE` is the easiest to use and matches SAS | +proc freq; table speed\*distance / jt nopercent nocol norow; run; -*But, even if you are just comparing the difference in same language it can be helpful to have a table with values of interest across different packages. Using dynamic content you can add the values to a table without manually putting in each value.\ -SAS result is very reliable in many cases.* +proc freq data=contin; tables group\*response/jt; title "Jonckheere-Terpstra Trend Test for Continuous Data"; run; ## Conclusion From 23ffdf580915ef0ef46486732877d28dc96346ea Mon Sep 17 00:00:00 2001 From: catball007 Date: Wed, 7 Aug 2024 04:07:48 +0200 Subject: [PATCH 4/9] update --- SAS/Jonckheere-Terpstra Test.qmd | 21 +++++++++++++++------ 1 file changed, 15 insertions(+), 6 deletions(-) diff --git a/SAS/Jonckheere-Terpstra Test.qmd b/SAS/Jonckheere-Terpstra Test.qmd index 00188664..7b252f18 100644 --- a/SAS/Jonckheere-Terpstra Test.qmd +++ b/SAS/Jonckheere-Terpstra Test.qmd @@ -3,6 +3,11 @@ title: "SAS Jonckheere-Terpstra Test" ------------------------------------------------------------------------ +## Background + +The Jonckheere-Terpstra (JT) test is a nonparametric method designed to detect ordered differences across categories. It offers an advantageous alternative to more general tests for class differences, such as the Kruskal-Wallis test, particularly when the analysis is conducted using the WILCOXON option within the NPAR1WAY procedure.[1] + +The JT test is particularly well-suited for dose-response or trend analysis with ordered categorical data, where the objective is to ascertain whether an increment in dosage leads to a corresponding escalation or reduction in the response variable.[2] Unlike other statistical evaluations that might focus on identifying isolated differences between groups, this test is specifically tailored to uncover an overarching trend within the data. ## SAS Procedure @@ -10,9 +15,9 @@ To request Jonckheere-Terpstra test, specify the JT option in the Table statemen Preq; table Var1 \* Var2 / JT; Quit; -The JT option in the TABLES statement provides the Jonckheere-Terpstra test, which is a nonparametric test for ordered differences among classes. +The JT option in the TABLES statement provides the Jonckheere-Terpstra test. -PROC FREQ also provides exact p-values for the Jonckheere-Terpstra test. You can request the exact test by specifying the JT option in the EXACT statement. +PROC FREQ also provides exact p-values for the Jonckheere-Terpstra test. You can request the exact test by specifying the JT option in the EXACT statement.[3] ## Data used @@ -34,7 +39,7 @@ cards; run; ``` -``` sas +``` sas data contin; input group subject response; cards; @@ -74,9 +79,13 @@ proc freq data=contin; tables group\*response/jt; title "Jonckheere-Terpstra Tre ## Conclusion -*add a conclusion about which language(s) or package(s) are more recommended.\ -This section could have a feature like paper abstract, e.g. about the selection of package or SAS PROC.* +The JT test is particularly useful in scenarios such as dose-response studies in pharmacology, where the interest lies in whether increasing doses of a drug lead to a monotonic increase in the response. ## Reference +[1] SAS Institute Inc. (n.d.). SAS Help Center. Retrieved August 7, 2024, from https://documentation.sas.com/doc/en/pgmsascdc/9.4_3.5/statug/statug_freq_details77.htm + +[2] Pennsylvania State University. (n.d.). 11.4 - Safety and Efficacy (Phase II) Studies: Trend Analysis. In STAT 509: Advanced Statistics for the Health Sciences. Retrieved August 7, 2024, from https://online.stat.psu.edu/stat509/lesson/11/11.4 + +[3] SAS Institute Inc. (n.d.). FREQ Procedure: Syntax. In SAS/STAT 14.2 User's Guide. Retrieved August 7, 2024, from https://documentation.sas.com/doc/en/pgmsascdc/9.4_3.5/statug/statug_freq_syntax08.htm -*Cite all sources and references used in the analysis.* +[4] Park, C., Hsiung, J.-T., Soohoo, M., & Streja, E. (2019). Choosing Wisely: Using the Appropriate Statistical Test for Trend in SAS From 5a38b2c37683017ad0936c947fe99149d9840378 Mon Sep 17 00:00:00 2001 From: catball007 Date: Wed, 7 Aug 2024 04:17:19 +0200 Subject: [PATCH 5/9] update --- SAS/Jonckheere-Terpstra Test.qmd | 26 ++++++++++++++------------ 1 file changed, 14 insertions(+), 12 deletions(-) diff --git a/SAS/Jonckheere-Terpstra Test.qmd b/SAS/Jonckheere-Terpstra Test.qmd index 7b252f18..4d976ad4 100644 --- a/SAS/Jonckheere-Terpstra Test.qmd +++ b/SAS/Jonckheere-Terpstra Test.qmd @@ -3,21 +3,22 @@ title: "SAS Jonckheere-Terpstra Test" ------------------------------------------------------------------------ + ## Background -The Jonckheere-Terpstra (JT) test is a nonparametric method designed to detect ordered differences across categories. It offers an advantageous alternative to more general tests for class differences, such as the Kruskal-Wallis test, particularly when the analysis is conducted using the WILCOXON option within the NPAR1WAY procedure.[1] +The Jonckheere-Terpstra (JT) test is a nonparametric method designed to detect ordered differences across categories. It offers an advantageous alternative to more general tests for class differences, such as the Kruskal-Wallis test, particularly when the analysis is conducted using the WILCOXON option within the NPAR1WAY procedure. $^{[1]}$ -The JT test is particularly well-suited for dose-response or trend analysis with ordered categorical data, where the objective is to ascertain whether an increment in dosage leads to a corresponding escalation or reduction in the response variable.[2] Unlike other statistical evaluations that might focus on identifying isolated differences between groups, this test is specifically tailored to uncover an overarching trend within the data. +The JT test is particularly well-suited for dose-response or trend analysis with ordered categorical data, where the objective is to ascertain whether an increment in dosage leads to a corresponding escalation or reduction in the response variable.$^{[2]}$ Unlike other statistical evaluations that might focus on identifying isolated differences between groups, this test is specifically tailored to uncover an overarching trend within the data. ## SAS Procedure -To request Jonckheere-Terpstra test, specify the JT option in the Table statement like below: +To request Jonckheere-Terpstra test, specify the **JT** option in the Table statement like below: -Preq; table Var1 \* Var2 / JT; Quit; +Preq; table Var1 \* Var2 / **JT** ; Quit; The JT option in the TABLES statement provides the Jonckheere-Terpstra test. -PROC FREQ also provides exact p-values for the Jonckheere-Terpstra test. You can request the exact test by specifying the JT option in the EXACT statement.[3] +PROC FREQ also provides exact p-values for the Jonckheere-Terpstra test. You can request the exact test by specifying the **JT** option in the EXACT statement.$^{[3]}$ ## Data used @@ -39,7 +40,7 @@ cards; run; ``` -``` sas +``` sas data contin; input group subject response; cards; @@ -73,19 +74,20 @@ run; ## Example Code using -proc freq; table speed\*distance / jt nopercent nocol norow; run; +proc freq; table speed\*distance / **JT** nopercent nocol norow; run; -proc freq data=contin; tables group\*response/jt; title "Jonckheere-Terpstra Trend Test for Continuous Data"; run; +proc freq data=contin; tables group\*response/**JT**; title "Jonckheere-Terpstra Trend Test for Continuous Data"; run; ## Conclusion The JT test is particularly useful in scenarios such as dose-response studies in pharmacology, where the interest lies in whether increasing doses of a drug lead to a monotonic increase in the response. ## Reference -[1] SAS Institute Inc. (n.d.). SAS Help Center. Retrieved August 7, 2024, from https://documentation.sas.com/doc/en/pgmsascdc/9.4_3.5/statug/statug_freq_details77.htm -[2] Pennsylvania State University. (n.d.). 11.4 - Safety and Efficacy (Phase II) Studies: Trend Analysis. In STAT 509: Advanced Statistics for the Health Sciences. Retrieved August 7, 2024, from https://online.stat.psu.edu/stat509/lesson/11/11.4 +\[1\] SAS Institute Inc. (n.d.). SAS Help Center. Retrieved August 7, 2024, from https://documentation.sas.com/doc/en/pgmsascdc/9.4_3.5/statug/statug_freq_details77.htm + +\[2\] Pennsylvania State University. (n.d.). 11.4 - Safety and Efficacy (Phase II) Studies: Trend Analysis. In STAT 509: Advanced Statistics for the Health Sciences. Retrieved August 7, 2024, from https://online.stat.psu.edu/stat509/lesson/11/11.4 -[3] SAS Institute Inc. (n.d.). FREQ Procedure: Syntax. In SAS/STAT 14.2 User's Guide. Retrieved August 7, 2024, from https://documentation.sas.com/doc/en/pgmsascdc/9.4_3.5/statug/statug_freq_syntax08.htm +\[3\] SAS Institute Inc. (n.d.). FREQ Procedure: Syntax. In SAS/STAT 14.2 User's Guide. Retrieved August 7, 2024, from https://documentation.sas.com/doc/en/pgmsascdc/9.4_3.5/statug/statug_freq_syntax08.htm -[4] Park, C., Hsiung, J.-T., Soohoo, M., & Streja, E. (2019). Choosing Wisely: Using the Appropriate Statistical Test for Trend in SAS +\[4\] Park, C., Hsiung, J.-T., Soohoo, M., & Streja, E. (2019). Choosing Wisely: Using the Appropriate Statistical Test for Trend in SAS From 701c0a0d558ab0b26e57f993ce67e863a96241b7 Mon Sep 17 00:00:00 2001 From: catball007 Date: Wed, 7 Aug 2024 04:46:50 +0200 Subject: [PATCH 6/9] update data based on [5] --- SAS/Jonckheere-Terpstra Test.qmd | 43 +++++++++++++++++++++----------- 1 file changed, 28 insertions(+), 15 deletions(-) diff --git a/SAS/Jonckheere-Terpstra Test.qmd b/SAS/Jonckheere-Terpstra Test.qmd index 4d976ad4..e785be33 100644 --- a/SAS/Jonckheere-Terpstra Test.qmd +++ b/SAS/Jonckheere-Terpstra Test.qmd @@ -8,7 +8,7 @@ title: "SAS Jonckheere-Terpstra Test" The Jonckheere-Terpstra (JT) test is a nonparametric method designed to detect ordered differences across categories. It offers an advantageous alternative to more general tests for class differences, such as the Kruskal-Wallis test, particularly when the analysis is conducted using the WILCOXON option within the NPAR1WAY procedure. $^{[1]}$ -The JT test is particularly well-suited for dose-response or trend analysis with ordered categorical data, where the objective is to ascertain whether an increment in dosage leads to a corresponding escalation or reduction in the response variable.$^{[2]}$ Unlike other statistical evaluations that might focus on identifying isolated differences between groups, this test is specifically tailored to uncover an overarching trend within the data. +The JT test is particularly well-suited for dose-response or trend analysis with ordered categorical data, where the objective is to ascertain whether an increment in dosage leads to a corresponding escalation or reduction in the response variable.$^{[2]}$$^{[5]}$ Unlike other statistical evaluations that might focus on identifying isolated differences between groups, this test is specifically tailored to uncover an overarching trend within the data. ## SAS Procedure @@ -23,21 +23,32 @@ PROC FREQ also provides exact p-values for the Jonckheere-Terpstra test. You can ## Data used ``` sas -data breaking; -input speed distance @@; -cards; -20 48 -25 33 -25 59 -25 48 -25 56 -30 60 -30 101 -30 67 -35 85 -35 107 +data ICU_Stay; + input ICU $ Stay; + label Stay = 'Length of Stay in Days'; + datalines; +Cardiothoracic 7 +Medical 4 +Cardiothoracic 1 +Medical 7 +Cardiothoracic 2 +Medical 16 +Cardiothoracic 6 +Medical 11 +Cardiothoracic 11 +Medical 21 +Cardiothoracic 8 +Neurosurgical 20 +Neurosurgical 25 +Neurosurgical 13 +Neurosurgical 9 +Neurosurgical 14 +Neurosurgical 11 ; run; +proc sort data=ICU_Stay; + by ICU Stay; +run; ``` ``` sas @@ -74,7 +85,7 @@ run; ## Example Code using -proc freq; table speed\*distance / **JT** nopercent nocol norow; run; +proc freq data=ICU_Stay; table ICU\*Stay / JT nopercent nocol norow; run; proc freq data=contin; tables group\*response/**JT**; title "Jonckheere-Terpstra Trend Test for Continuous Data"; run; @@ -91,3 +102,5 @@ The JT test is particularly useful in scenarios such as dose-response studies in \[3\] SAS Institute Inc. (n.d.). FREQ Procedure: Syntax. In SAS/STAT 14.2 User's Guide. Retrieved August 7, 2024, from https://documentation.sas.com/doc/en/pgmsascdc/9.4_3.5/statug/statug_freq_syntax08.htm \[4\] Park, C., Hsiung, J.-T., Soohoo, M., & Streja, E. (2019). Choosing Wisely: Using the Appropriate Statistical Test for Trend in SAS + +\[5\] Bewick V, Cheek L, Ball J. Statistics review 10: Further nonparametric methods. Crit Care. 2004;8(4):R131-R139. doi:10.1186/cc468904. PMCID: PMC468904.[PubMed Central](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC468904/). From c836154742e663e4479f5014d159c929e8f67afe Mon Sep 17 00:00:00 2001 From: catball007 Date: Wed, 7 Aug 2024 04:48:15 +0200 Subject: [PATCH 7/9] update --- SAS/Jonckheere-Terpstra Test.qmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/SAS/Jonckheere-Terpstra Test.qmd b/SAS/Jonckheere-Terpstra Test.qmd index e785be33..95ecb8fa 100644 --- a/SAS/Jonckheere-Terpstra Test.qmd +++ b/SAS/Jonckheere-Terpstra Test.qmd @@ -85,7 +85,7 @@ run; ## Example Code using -proc freq data=ICU_Stay; table ICU\*Stay / JT nopercent nocol norow; run; +proc freq data=ICU_Stay; table ICU\*Stay / **JT** nopercent nocol norow; run; proc freq data=contin; tables group\*response/**JT**; title "Jonckheere-Terpstra Trend Test for Continuous Data"; run; From fb5d3568137943bcbcb00da709a7a72a5a31b8d9 Mon Sep 17 00:00:00 2001 From: catball007 Date: Mon, 12 Aug 2024 09:24:16 +0200 Subject: [PATCH 8/9] update according to review comments from https://github.com/PSIAIMS/CAMIS/pull/265 --- ...stra Test.qmd => jonchkheere_terpstra.qmd} | 101 +++++++++++------- images/jonchkheere_terpstra/result1.png | Bin 0 -> 34155 bytes images/jonchkheere_terpstra/result2.png | Bin 0 -> 45172 bytes 3 files changed, 65 insertions(+), 36 deletions(-) rename SAS/{Jonckheere-Terpstra Test.qmd => jonchkheere_terpstra.qmd} (50%) create mode 100644 images/jonchkheere_terpstra/result1.png create mode 100644 images/jonchkheere_terpstra/result2.png diff --git a/SAS/Jonckheere-Terpstra Test.qmd b/SAS/jonchkheere_terpstra.qmd similarity index 50% rename from SAS/Jonckheere-Terpstra Test.qmd rename to SAS/jonchkheere_terpstra.qmd index 95ecb8fa..6af587b0 100644 --- a/SAS/Jonckheere-Terpstra Test.qmd +++ b/SAS/jonchkheere_terpstra.qmd @@ -1,8 +1,6 @@ ------------------------------------------------------------------------- - +--- title: "SAS Jonckheere-Terpstra Test" - ------------------------------------------------------------------------- +--- ## Background @@ -14,15 +12,19 @@ The JT test is particularly well-suited for dose-response or trend analysis with To request Jonckheere-Terpstra test, specify the **JT** option in the Table statement like below: -Preq; table Var1 \* Var2 / **JT** ; Quit; +```{r, eval=FALSE} +Proc freq; table Var1 \* Var2 / JT ; Quit; +``` The JT option in the TABLES statement provides the Jonckheere-Terpstra test. PROC FREQ also provides exact p-values for the Jonckheere-Terpstra test. You can request the exact test by specifying the **JT** option in the EXACT statement.$^{[3]}$ -## Data used +## Data used 1 -``` sas +This dataset has been generated using example data which aligned with the specifications outlined in the section on the Jonckheere–Terpstra test from reference \[5\]. It represents the duration of hospital stays for a randomly selected group of patients across three distinct ICU departments: cardiothoracic, medical, and neurosurgical. + +```{r, eval=FALSE} data ICU_Stay; input ICU $ Stay; label Stay = 'Length of Stay in Days'; @@ -51,43 +53,70 @@ proc sort data=ICU_Stay; run; ``` -``` sas + +## Example Code using 1 + +The code performs a frequency analysis on the 'ICU_Stay' dataset, examining the relationship between 'ICU' and 'Stay' variables. It applies the Jonckheere-Terpstra test using JT option to identify trends in the ordered categorical 'Stay' variable. The output is streamlined by omitting percentages and totals for columns and rows with the 'nopercent nocol norow' options, emphasizing the Jonckheere-Terpstra test outcomes. + +```{r, eval=FALSE} +proc freq data=ICU_Stay; table ICU\*Stay / JT nopercent nocol norow; run; + +``` + +## Test Result 1 + +![Test Result 1](../../CAMIS/images/jonchkheere_terpstra/result1.png "Test Result 1") + +Comparing this with a standard Normal distribution gives a P value of 0.005, indicating that the increase in length of stay with ICU is significant, in the order cardiothoracic, medical and neurosurgical. + +## Data used 2 + +This dataset incorporates illustrative data extracted from reference \[3\]. It encapsulates the responses of subjects randomly assigned to one of four treatment arms: placebo, low dosage(20mg), medium dosage(60mg), and high dosage(180mg). The variable of interest is a continuous measure. The variable 'groupn' is used to provide an order of 'group'. + +```{r, eval=FALSE} data contin; -input group subject response; +input groupn group $ subject response; cards; -0 01 27 -0 02 28 -0 03 27 -0 04 31 -0 05 34 -0 06 32 -1 01 31 -1 02 35 -1 03 34 -1 04 32 -1 05 31 -1 06 33 -2 01 32 -2 02 33 -2 03 30 -2 04 34 -2 05 37 -2 06 36 -3 01 40 -3 02 39 -3 03 41 -3 04 38 -3 05 42 -3 06 43 +0 Placebo 01 27 +0 Placebo 02 28 +0 Placebo 03 27 +0 Placebo 04 31 +0 Placebo 05 34 +0 Placebo 06 32 +1 20mg 01 31 +1 20mg 02 35 +1 20mg 03 34 +1 20mg 04 32 +1 20mg 05 31 +1 20mg 06 33 +2 60mg 01 32 +2 60mg 02 33 +2 60mg 03 30 +2 60mg 04 34 +2 60mg 05 37 +2 60mg 06 36 +3 180mg 01 40 +3 180mg 02 39 +3 180mg 03 41 +3 180mg 04 38 +3 180mg 05 42 +3 180mg 06 43 ; run; ``` -## Example Code using +## Example Code using 2 + +The code is performing a Jonckheere-Terpstra trend test on a continuous 'response' variable, categorized by a 'group' variable, using the 'proc freq' procedure. The analysis is applied to the dataset named 'contin'. The result is presented with a title "Jonckheere-Terpstra Trend Test for Continuous Data", indicating the specific nature of the test being conducted. The 'JT' option is used to specify the Jonckheere-Terpstra test. + +```{r, eval=FALSE} +proc freq data=contin; tables group\*response/JT; title "Jonckheere-Terpstra Trend Test for Continuous Data"; run; +``` +## Test Result 2 -proc freq data=ICU_Stay; table ICU\*Stay / **JT** nopercent nocol norow; run; +![Test Result 2](../../CAMIS/images/jonchkheere_terpstra/result2.png "Test Result 2") -proc freq data=contin; tables group\*response/**JT**; title "Jonckheere-Terpstra Trend Test for Continuous Data"; run; +There is a significant trend across different groups in the response gives a P value of <.0001. ## Conclusion diff --git a/images/jonchkheere_terpstra/result1.png b/images/jonchkheere_terpstra/result1.png new file mode 100644 index 0000000000000000000000000000000000000000..7812e4db7a1f51c71ddea855ab45e32c402ff228 GIT binary patch literal 34155 zcmd?QWmKHOwyujiGz51I5;VBG1%g9xcW;8byGw$*I|PEe2X}YZ#u~Rn*4}%awf4Pt 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zH6R6R?@dAUs@dTxC#U>m?F@|V-7o0Ac)X7^2Vn(3alYTatU>}*R9bFq_I*I!>?^S)fy&*!H&dMPS>+Umk!FfH8fOHNK$R%6l`K*V))=d+c9zzgo*waz1|0Yiu^4 zve1Bt@5I(Q{g~@>qf31UX8l?5;F%)8Ojjdc^CyxbD=xa3%8h^V#U^x0$=--;82Lgu zS6IVbIbngEi*Q{@u!bhW1vYwM;(sT5K)==u3d7 z4RBn}SgLFar~J{e1L|g4<7S80yp(X6{Ne$kIhsG3_pO`p%!dkOAwI18C)UM7j*Rf2 z_)vk%@?HxCF|`-d=Mr%BAjLu)b6b$crW?rTLP=Uts3o}ust(x>sWxFW_BR)J#}OWd zS*S_9BsYBLSSM2W9a4c$-U*u`Xr5kn+if5)cfE=e`(Ml`JAn%bw!@L7bDLlA_HxE@ z4p24=(;w`A&}eTSYgsTtebGeW$1-z`fcT|CCa}5Mz<^Hx6li8;!QxU#!m9dRTuS3|SOq z{WQ`x=n{dSO>aQfq$_QHOwutwLqh5(tj&IPhOj-_V`a}jnRudvC&}-%@62UQpjgSg zmC#FPe`sWTa!2z<8na=ran_G{?hi4vP(-_^vKkw(-uv&2{{}~|{`})Z|2GOO{m|AJ ztu@B|R~_q8HovpP|3piSS@Dw&{}V5z{=+JdM^XOMh_^BRVgHYq6V3z4JJul#c8~+k QlgA_{rTn=@;%o5#0FFUQ*8l(j literal 0 HcmV?d00001 From 288d2a20ff3da9ae1c96f17a89d16f891399c321 Mon Sep 17 00:00:00 2001 From: catball007 Date: Mon, 12 Aug 2024 09:38:21 +0200 Subject: [PATCH 9/9] update code block --- SAS/jonchkheere_terpstra.qmd | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/SAS/jonchkheere_terpstra.qmd b/SAS/jonchkheere_terpstra.qmd index 6af587b0..f11b7c58 100644 --- a/SAS/jonchkheere_terpstra.qmd +++ b/SAS/jonchkheere_terpstra.qmd @@ -13,7 +13,7 @@ The JT test is particularly well-suited for dose-response or trend analysis with To request Jonckheere-Terpstra test, specify the **JT** option in the Table statement like below: ```{r, eval=FALSE} -Proc freq; table Var1 \* Var2 / JT ; Quit; +Proc freq; table Var1 * Var2 / JT ; Quit; ``` The JT option in the TABLES statement provides the Jonckheere-Terpstra test. @@ -59,7 +59,7 @@ run; The code performs a frequency analysis on the 'ICU_Stay' dataset, examining the relationship between 'ICU' and 'Stay' variables. It applies the Jonckheere-Terpstra test using JT option to identify trends in the ordered categorical 'Stay' variable. The output is streamlined by omitting percentages and totals for columns and rows with the 'nopercent nocol norow' options, emphasizing the Jonckheere-Terpstra test outcomes. ```{r, eval=FALSE} -proc freq data=ICU_Stay; table ICU\*Stay / JT nopercent nocol norow; run; +proc freq data=ICU_Stay; table ICU * Stay / JT nopercent nocol norow; run; ``` @@ -110,7 +110,7 @@ run; The code is performing a Jonckheere-Terpstra trend test on a continuous 'response' variable, categorized by a 'group' variable, using the 'proc freq' procedure. The analysis is applied to the dataset named 'contin'. The result is presented with a title "Jonckheere-Terpstra Trend Test for Continuous Data", indicating the specific nature of the test being conducted. The 'JT' option is used to specify the Jonckheere-Terpstra test. ```{r, eval=FALSE} -proc freq data=contin; tables group\*response/JT; title "Jonckheere-Terpstra Trend Test for Continuous Data"; run; +proc freq data=contin; tables group * response/JT; title "Jonckheere-Terpstra Trend Test for Continuous Data"; run; ``` ## Test Result 2