Cgwpgsd #44
Cgwpgsd #44
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Hi @guangguangzai , please find my comments below, and we can catch up tomorrow.
vignettes/wpgsd_corr_example.Rmd
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| ## Examples | ||
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| In a 2-arm controlled clinical trial example with one primary endpoint, there are 3 patient populations defined by the status of two biomarkers A and B: |
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Please cite where this example is from.
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Cite paper, example 1
vignettes/wpgsd_corr_example.Rmd
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| tab_header(title = "Number of events at each population") | ||
| ``` | ||
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| ### Example 1 - Same Analyses Different Population |
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Shall we call it "Correlation of different populations within the same analysis"?
vignettes/wpgsd_corr_example.Rmd
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| round(Corr1, 2) | ||
| ``` | ||
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| ### Example 2 - Same Population Different Analyses |
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Shall we call it "Correlation of different analyses within the same population"?
vignettes/wpgsd_corr_example.Rmd
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| tab_header(title = "Number of events at each analyses in example 2") | ||
| ``` | ||
| The the corrleation could be simply calculated as | ||
| $$Corr(Z_{11},Z_{12})=\frac{100}{\sqrt{100*200}}=0.71$$ |
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Please explain the 100 at the numerator.
vignettes/wpgsd_corr_example.Rmd
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| Corr1 <- 100 / sqrt(100 * 200) | ||
| round(Corr1, 2) | ||
| ``` | ||
| ### Example 3 - Cross Population Cross Analyses |
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Shall we call it "Correlation of different analyses and different population"?
vignettes/wpgsd_corr_example.Rmd
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| ``` | ||
| Now we know how to calculate the correlation values under different situations, and the generate_corr function was built based on this logic. We can directly calculate the results for each cross situation via the function. | ||
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| First, we need a event table including the information of the cohort. |
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The word "cohort" is confusing...
vignettes/wpgsd_corr_example.Rmd
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| gt() %>% | ||
| tab_header(title = "Number of events at each population & analyses") | ||
| ``` | ||
| "H1" indicates that the experimental treatment is superior to the control in population 1/experimental arm 1. "H2" indicates that the experimental treatment is superior to the control in population 2/experimental arm 2. "Analysis" refers to different stages of analysis, such as 1 for interim analysis and 2 for final analysis. "Event" represents the number of events in this condition. |
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This paragraph looks not correct to me... H1 is 1 hypothesis, H2 is the other hypothesis. Event is the common events overlap by H1 and H2.
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H1 could be the anyone from the hypotheses, listed in the multiplicity/to be tested, depending on the one interested.
vignettes/wpgsd_corr_example.Rmd
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| ``` | ||
| "H1" indicates that the experimental treatment is superior to the control in population 1/experimental arm 1. "H2" indicates that the experimental treatment is superior to the control in population 2/experimental arm 2. "Analysis" refers to different stages of analysis, such as 1 for interim analysis and 2 for final analysis. "Event" represents the number of events in this condition. | ||
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| For example: H1=1, H2=1, Analysis=1, Event=100 indicates that in the first population, there are 100 cases where the experimental treatment is superior to the control in the interim analysis. |
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Echo with my previous comment. We ought to say what is H1=1 means, and then H2 = 1 means first. Then explain what Event is under H1=1 and H2=1.
vignettes/wpgsd_corr_example.Rmd
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| For example: H1=1, H2=1, Analysis=1, Event=100 indicates that in the first population, there are 100 cases where the experimental treatment is superior to the control in the interim analysis. | ||
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| Another example: H1=1, H2=2, Analysis=2, Event=160 indicates that the number of overlapping cases where the experimental treatment is superior to the control in population 1 and 2 in the final analysis is 160. |
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Echo with my previous comment.
vignettes/wpgsd_corr_example.Rmd
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| *To be noticed, the column names in this function are fixed to be 'H1, H2, Analysis, Event'. | ||
| After we have the event table, we can use generate_corr function to calculate correlation. | ||
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| ```{r} |
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I guess we will no longer need the things after line 150, right?
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Hi @guangguangzai! Thanks for the revision. The vignette looks great and I only have few editorial comments below.
vignettes/wpgsd_corr_example.Rmd
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| @@ -47,7 +51,7 @@ event_tb %>% | |||
| tab_header(title = "Number of events at each population") | |||
| ``` | |||
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| ### Example 1 - Same Analyses Different Population | |||
| ### Example 1 - Correlation of different populations within the same analysis | |||
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Shall we delete "Example 1"?
vignettes/wpgsd_corr_example.Rmd
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| @@ -18,7 +19,7 @@ $$Corr(Z_{ik},Z_{i'k'})=\frac{n_{i \wedge i',k \wedge k'}}{\sqrt{n_{ik}*n_{i'k'} | |||
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| ## Examples | |||
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| In a 2-arm controlled clinical trial example with one primary endpoint, there are 3 patient populations defined by the status of two biomarkers A and B: | |||
| In a 2-arm controlled clinical trial example with one primary endpoint (@anderson_unified_2022), there are 3 patient populations defined by the status of two biomarkers A and B : | |||
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Please be more specific where the example is from, say, which section, which example. Some explanations like, "this example is borrowed from xxx paper" is helpful.
vignettes/wpgsd_corr_example.Rmd
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| ``` | ||
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| ### Example 2 - Same Population Different Analyses | ||
| Let's consider another simple situation, we want to compare single population, for example population 1, but in different analyses, interim and final analyses. Then $i=1$, and to compare IA and FA, the $k$ will be $k=1$ and $k=2$. | ||
| ### Example 2 - Correlation of different analyses within the same population |
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Shall we delete "Example 2"?
vignettes/wpgsd_corr_example.Rmd
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| ``` | ||
| ### Example 3 - Cross Population Cross Analyses | ||
| ### Example 3 - Correlation of different analyses and different population |
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Shall we delete "Example 3"?
vignettes/wpgsd_corr_example.Rmd
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| @@ -66,197 +70,90 @@ event_tbl %>% | |||
| The the corrleation could be simply calculated as | |||
| $$Corr(Z_{11},Z_{21})=\frac{80}{\sqrt{100*110}}=0.76$$ | |||
| ```{r} | |||
| Corr1 <- 80 / sqrt(100 * 110) | |||
| round(Corr1, 2) | |||
| Corr1=80/sqrt(100*110) | |||
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Please use <- instead of =, for consistency with the entire package. Please check the entire Rmd file for the usage of =.
vignettes/wpgsd_corr_example.Rmd
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| Now we know how to calculate the correlation values under different situations, and the generate_corr function was built based on this logic. We can directly calculate the results for each cross situation via the function. | ||
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| First, we need a event table including the information of the cohort. | ||
| Now we know how to calculate the correlation values under different situations, and the generate_corr() function was built based on this logic. We can directly calculate the results for each cross situation via the function. |
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When you cite a R function name please use single quotation, i.e., use "generate_corr" install of "generate_corr". Please check the entire of the Rmd file.
The file is for wpgsd correlation calculation