Logistic regression Degrees of Freedom Parameter Estimate Standard Error Odds Ratio Wald 95% CI p-value
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Planned Arm Code 2 0.0408
Reference ARM A, n = 134
ARM B, n = 134 1 -2.094 1.080 0.12 (0.01, 1.02) 0.0524
ARM C, n = 132 1 -0.074 1.423 0.93 (0.06, 15.09) 0.9584
Sex
Reference M, n = 169
F, n = 231 1 0.331 0.695 1.39 (0.36, 5.44) 0.6339
Age
Age 1 0.070 0.054 1.07 (0.97, 1.19) 0.1945
Experimental use!
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Logistic regression with interaction Degrees of Freedom Parameter Estimate Standard Error Odds Ratio Wald 95% CI p-value
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Age
Age 1 0.067 0.054 1.07 (0.96, 1.19) 0.2084
Planned Arm Code 2 0.4882
Reference ARM A, n = 134
ARM B, n = 134 1 -17.850 2362.767 0.9940
Sex
F 0.23 (0.02, 2.11)
M 0.00 (0.00, >999.99)
ARM C, n = 132 1 -16.442 2362.767 0.9944
Sex
F >999.99 (0.00, >999.99)
M 0.00 (0.00, >999.99)
Sex
Reference M, n = 169
F, n = 231 1 -16.044 2362.767 0.9946
Planned Arm Code
ARM A 0.00 (0.00, >999.99)
ARM B 1.39 (0.29, 6.59)
ARM C >999.99 (0.00, >999.99)
Interaction of Planned Arm Code * Sex 2 0.9999
Reference ARM A or M, n = 248
ARM B * F, n = 82 1 16.373 2362.767 0.9945
ARM C * F, n = 70 1 32.492 3156.732 0.9918
Experimental use!
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model <-fit_logistic( adrs,variables =list(response ="Response",arm ="ARMCD",covariates =c("SEX", "AGE", "RACE") ))conf_level <-0.95df <- broom::tidy(model, conf_level = conf_level)# empty string flagdf <-df_explicit_na(df, na_level ="_NA_")result <-basic_table() %>%summarize_logistic(conf_level = conf_level,drop_and_remove_str ="_NA_" ) %>%append_topleft("y ~ ARM + SEX + AGE + RACE") %>%build_table(df = df)result
y ~ ARM + SEX + AGE + RACE Degrees of Freedom Parameter Estimate Standard Error Odds Ratio Wald 95% CI p-value
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Planned Arm Code 2 0.0346
Reference ARM A, n = 134
ARM B, n = 134 1 -2.162 1.084 0.12 (0.01, 0.96) 0.0461
ARM C, n = 132 1 -0.090 1.426 0.91 (0.06, 14.97) 0.9499
Sex
Reference M, n = 169
F, n = 231 1 0.364 0.701 1.44 (0.36, 5.69) 0.6032
Age
Age 1 0.071 0.053 1.07 (0.97, 1.19) 0.1866
Race 5 0.9685
Reference AMERICAN INDIAN OR ALASKA NATIVE, n = 25
ASIAN, n = 208 1 -16.246 2017.122 0.00 (0.00, >999.99) 0.9936
BLACK OR AFRICAN AMERICAN, n = 91 1 -15.205 2017.122 0.00 (0.00, >999.99) 0.9940
WHITE, n = 74 1 -15.955 2017.122 0.00 (0.00, >999.99) 0.9937
MULTIPLE, n = 1 1 -0.363 10941.553 0.70 (0.00, >999.99) 1.0000
NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER, n = 1 1 1.036 10941.553 2.82 (0.00, >999.99) 0.9999
Experimental use!
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model <-fit_logistic( adrs,variables =list(response ="Response",arm ="ARMCD",covariates =c("SEX", "AGE"),interaction ="AGE" ),response_definition ="1 - response")conf_level <-0.9df <- broom::tidy(model, conf_level = conf_level, at =c(30, 50))# empty string flagdf <-df_explicit_na(df, na_level ="_NA_")result <-basic_table() %>%summarize_logistic(conf_level = conf_level,drop_and_remove_str ="_NA_" ) %>%append_topleft("Estimations at age 30 and 50") %>%build_table(df = df)result
Estimations at age 30 and 50 Degrees of Freedom Parameter Estimate Standard Error Odds Ratio Wald 90% CI p-value
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Sex
Reference M, n = 169
F, n = 231 1 -0.381 0.710 0.68 (0.21, 2.20) 0.5915
Planned Arm Code 2 0.2768
Reference ARM A, n = 134
ARM B, n = 134 1 -20.020 13.714 0.1443
Age
30 234.91 (0.30, >999.99)
50 >999.99 (0.04, >999.99)
ARM C, n = 132 1 -15.622 14.810 0.2915
Age
30 31.95 (0.03, >999.99)
50 >999.99 (<0.01, >999.99)
Age
Age 1 -0.877 0.581 0.1309
Planned Arm Code
ARM A 0.42 (0.16, 1.08)
ARM B 0.97 (0.89, 1.06)
ARM C 0.79 (0.55, 1.11)
Interaction of Planned Arm Code * Age 2 0.2213
Reference ARM A, n = 134
ARM B, n = 134 1 0.849 0.583 0.1449
ARM C, n = 132 1 0.636 0.618 0.3034
Experimental use!
WebR is a tool allowing you to run R code in the web browser. Modify the code below and click run to see the results. Alternatively, copy the code and click here to open WebR in a new tab.
Code
library(dplyr)library(tern)adsl <- random.cdisc.data::cadsladrs <- random.cdisc.data::cadrs# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.adsl <-df_explicit_na(adsl)adrs <-df_explicit_na(adrs)adsl <- adsl %>% dplyr::filter(SEX %in%c("F", "M"))adrs <- adrs %>% dplyr::filter(PARAMCD =="BESRSPI") %>% dplyr::mutate(Response =case_when(AVALC %in%c("PR", "CR") ~1, TRUE~0),SEX =factor(SEX, c("M", "F")),RACE =factor( RACE,levels =c("AMERICAN INDIAN OR ALASKA NATIVE", "ASIAN", "BLACK OR AFRICAN AMERICAN","WHITE", "MULTIPLE", "NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER" ) ) ) %>%var_relabel(Response ="Response", SEX ="Sex", RACE ="Race")
shinylive allow you to modify to run shiny application entirely in the web browser. Modify the code below and click re-run the app to see the results. The performance is slighly worse and some of the features (e.g. downloading) might not work at all.