Updated example dataset: cohordata

DESCRIPTION

@@ -41,7 +41,6 @@ Imports:
     survival
 Suggests: 
     knitr,
-    qtl,
     rmarkdown,
     spelling,
     testthat (>= 3.0.0)

R/cohortdata.R

@@ -1,13 +1,35 @@
 #' Cohort data on vaccineff
 #'
-#' Data from
+#' Subset of data from an anonymised, real-world dataset produced as part of
+#' the early stage of the immunization program against COVID-19 in Bogota,
+#' Colombia between February 2021 and December 2021. Cohort dataset contains
+#' registers of homologous schemes for two different brands for adults aged 50
+#' years or older. This cohort received two doses of a vaccine aimed at
+#' reducing the risk of death. All the registers were anonymised and
+#' de-identified to preserve the privacy of data.
+#' The dataset includes disaggregated information on the first and second
+#' vaccine doses (vaccine_date1, vaccine_date2, vaccine1, and vaccine2) for each
+#' participant and relevant demographic details (sex and age).
+#' Additionally, the dataset includes the dates of two outcomes: death
+#' associated with COVID-19 (death_date) and death from other causes
+#' (death_other_causes).
 #'
 #' @docType data
 #'
 #' @usage cohortdata
 #'
-#' @format An object of class \code{"cross"}; see \code{\link[qtl]{read.cross}}.
-#'
+#' @format ## `cohortdata`
+#' \describe{
+#' \item{id}{Anonymous ID of the individual}
+#' \item{sex}{Sex F/M}
+#' \item{age}{Age (50-100)}
+#' \item{death_date}{Registered death by COVID-19}
+#' \item{death_other_causes}{Registered death by other causes}
+#' \item{vaccine_date_1}{Registered date of the first dose}
+#' \item{vaccine_date_2}{Registered date of the second dose}
+#' \item{vaccine_1}{Brand of the first dose}
+#' \item{vaccine_2}{Brand of the second dose}
+#' }
 #' @keywords datasets
 #'
 #' @examples

R/estimate_vaccineff.R

@@ -36,7 +36,7 @@
 #'   vaccinated_status = "v",
 #'   unvaccinated_status = "u",
 #'   immunization_delay = 15,
-#'   end_cohort = as.Date("2044-12-31"),
+#'   end_cohort = as.Date("2021-12-31"),
 #'   match = TRUE,
 #'   exact = c("age", "sex"),
 #'   nearest = NULL

R/make_vaccineff_data.R

@@ -46,7 +46,7 @@
 #'   vaccinated_status = "v",
 #'   unvaccinated_status = "u",
 #'   immunization_delay = 15,
-#'   end_cohort = as.Date("2044-12-31"),
+#'   end_cohort = as.Date("2021-12-31"),
 #'   match = TRUE,
 #'   exact = c("age", "sex"),
 #'   nearest = NULL

README.Rmd

@@ -71,7 +71,7 @@ vaccineff_data <- make_vaccineff_data(
   vaccinated_status = "v",
   unvaccinated_status = "u",
   immunization_delay = 15,
-  end_cohort = as.Date("2044-12-31"),
+  end_cohort = as.Date("2021-12-31"),
   match = TRUE,
   exact = "sex",
   nearest = c(age = 1)

README.md

@@ -50,17 +50,17 @@ out the models.
 data("cohortdata")
 head(cohortdata, 5)
 #>         id sex age death_date death_other_causes vaccine_date_1 vaccine_date_2
-#> 1 afade1b2   F  37       <NA>               <NA>           <NA>           <NA>
-#> 2 556c8c76   M  19       <NA>               <NA>           <NA>           <NA>
-#> 3 04edf85a   M  50       <NA>               <NA>           <NA>           <NA>
-#> 4 7e51a18e   F   8       <NA>               <NA>           <NA>           <NA>
-#> 5 c5a83f56   M  66       <NA>               <NA>           <NA>           <NA>
+#> 1 04edf85a   M  50       <NA>               <NA>           <NA>           <NA>
+#> 2 c5a83f56   M  66       <NA>               <NA>           <NA>           <NA>
+#> 3 82991731   M  81       <NA>               <NA>           <NA>           <NA>
+#> 4 afbab268   M  74       <NA>               <NA>     2021-03-30     2021-05-16
+#> 5 3faf2474   M  54       <NA>               <NA>     2021-06-01     2021-06-22
 #>   vaccine_1 vaccine_2
 #> 1      <NA>      <NA>
 #> 2      <NA>      <NA>
 #> 3      <NA>      <NA>
-#> 4      <NA>      <NA>
-#> 5      <NA>      <NA>
+#> 4    BRAND2    BRAND2
+#> 5    BRAND1    BRAND1
 ```
 
 The function `make_vaccineff_data` allows defining different aspects of
@@ -84,7 +84,7 @@ vaccineff_data <- make_vaccineff_data(
   vaccinated_status = "v",
   unvaccinated_status = "u",
   immunization_delay = 15,
-  end_cohort = as.Date("2044-12-31"),
+  end_cohort = as.Date("2021-12-31"),
   match = TRUE,
   exact = "sex",
   nearest = c(age = 1)
@@ -97,12 +97,10 @@ ve <- estimate_vaccineff(vaccineff_data, at = 180)
 summary(ve)
 #> Vaccine Effectiveness at 180 days computed as VE = 1 - HR:
 #>      VE lower.95 upper.95
-#>  0.8457   0.7429   0.9074
+#>  0.7254   0.5437   0.8347
 #> 
 #> Schoenfeld test for Proportional Hazards assumption:
-#> p-value = 0.0046
-#> Warning in print.summary_vaccineff(x): 
-#> p-value < 0.05. Please check loglog plot for Proportional Hazards assumption
+#> p-value = 0.1507
 
 # Generate loglog plot to check proportional hazards
 plot(ve, type = "loglog")

inst/WORDLIST

@@ -1,68 +1,56 @@
+Bermúdez
 CMD
 COVID
 Codecov
-Epidemiologic
 Epiverse
 Gómez
-Individuals’
+Halloran
+IDRC
 Javeriana
+Kaplan
+Longini
 Mariscal
 Millán
 ORCID
 PCV
 PPV
 Pavlich
 Pontificia
+SMD
 Schoenfeld
+Springer
+Struchiner
 TND
-Torvaldsen
 Universidad
+Unvaccinated
 VE
 WIP
 Zulma
+anonymised
 codecov
 confounders
+cumincidence
+de
 disaggregated
-doi
+eq
 frac
+funder
+ggplot
 gh
 github
 https
-informit
-inmunization
+img
+invokeRestart
 io
 lifecycle
-md
+muffleWarning
 packagename
-recomended
 repo
+rmarkdown
+surv
 svg
+unadjusted
 unvaccinated
-vacc
 www
 yaml
-Kaplan
-ggplot2
-ggplot
-steelblue
-darkred
-coh
-MatchIt
-loglog
-cumincidence
-SMD
-surv
-Bermúdez
-IDRC
 Érika
-funder
-invokeRestart
-muffleWarning
-unadjusted
-eq
-img
-rmarkdown
-Halloran
-Longini
-Springer
-Struchiner

tests/testthat/_snaps/coh_immunization.md

@@ -3,19 +3,19 @@
     Code
       head(immunization_dose, 20)
     Output
-       [1] NA               NA               NA               NA              
-       [5] NA               "vaccine_date_2" NA               NA              
-       [9] "vaccine_date_2" "vaccine_date_2" NA               "vaccine_date_2"
-      [13] NA               NA               NA               NA              
-      [17] NA               "vaccine_date_2" "vaccine_date_2" "vaccine_date_2"
+       [1] NA               NA               NA               "vaccine_date_2"
+       [5] "vaccine_date_2" "vaccine_date_2" NA               "vaccine_date_2"
+       [9] NA               NA               NA               "vaccine_date_2"
+      [13] "vaccine_date_2" "vaccine_date_2" "vaccine_date_2" "vaccine_date_2"
+      [17] "vaccine_date_2" "vaccine_date_2" "vaccine_date_2" "vaccine_date_2"
 
 # `immunization_vaccine`: Basic expectations
 
     Code
       head(which_vaccine, 30)
     Output
-       [1] NA       NA       NA       NA       NA       "BRAND1" NA       NA      
-       [9] "BRAND1" "BRAND1" NA       "BRAND2" NA       NA       NA       NA      
-      [17] NA       "BRAND2" "BRAND1" "BRAND2" NA       "BRAND1" NA       NA      
-      [25] NA       NA       "BRAND2" NA       NA       NA      
+       [1] NA       NA       NA       "BRAND2" "BRAND1" "BRAND2" NA       "BRAND2"
+       [9] NA       NA       NA       "BRAND1" "BRAND1" "BRAND2" "BRAND2" "BRAND2"
+      [17] "BRAND1" "BRAND1" "BRAND1" "BRAND1" "BRAND2" "BRAND2" NA       NA      
+      [25] "BRAND2" "BRAND1" "BRAND2" NA       NA       "BRAND2"
 

tests/testthat/_snaps/coh_match.md

@@ -33,17 +33,17 @@
             {
               "type": "double",
               "attributes": {},
-              "value": [30.18416801, 0.5088853, 0.4911147]
+              "value": [63.23626374, 0.52197802, 0.47802198]
             },
             {
               "type": "double",
               "attributes": {},
-              "value": [47.51706037, 0.54855643, 0.45144357]
+              "value": [63.66823899, 0.59748428, 0.40251572]
             },
             {
               "type": "double",
               "attributes": {},
-              "value": [0.87245622, 0.0795363, -0.0795363]
+              "value": [0.03984279, 0.15254353, -0.15254353]
             }
           ]
         }
@@ -85,17 +85,17 @@
             {
               "type": "double",
               "attributes": {},
-              "value": [42.55192878, 0.54599407, 0.45400593]
+              "value": [63.2005571, 0.52089136, 0.47910864]
             },
             {
               "type": "double",
               "attributes": {},
-              "value": [44.90801187, 0.54599407, 0.45400593]
+              "value": [63.54038997, 0.52089136, 0.47910864]
             },
             {
               "type": "double",
               "attributes": {},
-              "value": [0.12717971, 0, 0]
+              "value": [0.03161695, 0, 0]
             }
           ]
         }
@@ -137,12 +137,12 @@
             {
               "type": "integer",
               "attributes": {},
-              "value": [619, 337, 282]
+              "value": [364, 359, 5]
             },
             {
               "type": "integer",
               "attributes": {},
-              "value": [381, 337, 44]
+              "value": [636, 359, 277]
             }
           ]
         }

tests/testthat/_snaps/coh_match_utils.md

@@ -4,7 +4,7 @@
       summary
     Output
                   u   v
-      All       619 381
-      Matched   337 337
-      Unmatched 282  44
+      All       364 636
+      Matched   359 359
+      Unmatched   5 277
 

tests/testthat/_snaps/coh_time_to_event.md

@@ -3,6 +3,6 @@
     Code
       head(exposition_time, 20)
     Output
-       [1] 280 280 162 171 169 280 280  37 280 280 280 280 280 280 280 280 171 213 280
-      [20]  98
+       [1] 152 161 167 224 221 242 281 183 281 250 165 281 194 281 123 225 281 281 281
+      [20] 281
 

tests/testthat/test-coh_eff_plot.R

@@ -17,7 +17,7 @@ vaccineff_data <- make_vaccineff_data(
   vaccinated_status = "v",
   unvaccinated_status = "u",
   immunization_delay = 15,
-  end_cohort = as.Date("2044-12-31"),
+  end_cohort = as.Date("2021-12-31"),
   match = FALSE
 )