Cancer Registry Data Analysis and Visualisation

Description

Tools for basic and advance cancer statistics and graphics. Groups individual data, merges registry data and population data, calculates age-specific rate, age-standardized rate, cumulative risk, estimated annual percentage rate with standards error. Creates graphics across variable and time, such as age-specific trends, bar chart and period-cohort trends.

Author(s)

Mathieu Laversanne [aut, cre], Jerome Vignat [aut], Cancer Surveillance Unit [cph]

Maintainer: Mathieu Laversanne <laversannem@iarc.who.int>

References

https://publications.iarc.fr/_publications/media/download/3753/609d0d7711047dd76d7f3dbaa25d7f041fcd013e.pdf

See Also

csu_group_cases csu_merge_cases_pop csu_asr csu_cumrisk csu_eapc csu_ageSpecific csu_ageSpecific_top csu_bar_top csu_time_trend csu_trendCohortPeriod

Examples

data(ICD_group_GLOBOCAN)
data(data_individual_file)


#group individual data by 
#	5 year age group 
#	ICD grouping from dataframe ICD_group_GLOBOCAN
df_data_year <- csu_group_cases(data_individual_file,
  var_age="age",
  group_by=c("sex", "regcode", "reglabel"),
  df_ICD = ICD_group_GLOBOCAN,
  var_ICD  ="site",
  var_year = "doi")     

head(df_data_year)
# individual cases grouped by ICD and 5 years age group and year.
readline(prompt="Press [enter] to continue to merge cases and population)")	


data(data_population_file)	

df_data <- csu_merge_cases_pop(
	df_data_year, 
	data_population_file, 
	var_age = "age_group",
	var_cases = "cases",
	var_py = "pop",
	group_by = c("sex"))

head(df_data)
#Merge 5-years age grouped data with population by year (automatic) and sex
readline(prompt="Press [enter] to continue to merge cases and population)")	


data(csu_registry_data_2)
				  
# ASR with standard error with missing age.
df_asr <- csu_asr(csu_registry_data_2, 
                  "age", "cases", "py",
                  group_by = c("registry", "registry_label", "sex", "year", "ethnic" ),
                  var_age_group = c("registry_label"), 
                  var_st_err = "st_err")

df_asr[1:4,]	



# ASR with standard error with missing age.		  
readline(prompt="Press [enter] to continue to EAPC")


# EAPC with standard error		
	  
df_eapc <- csu_eapc(df_asr,
					"asr", "year",
					group_by=c("registry", "registry_label", "sex", "ethnic" ))
					
df_eapc[1:4,]	

# EAPC with standard error				  
readline(prompt="Press [enter] to continue to age specific graph")

data(csu_registry_data_1)

# plot age specific rate for 1 population.
df_colombia <- subset(csu_registry_data_1, registry_label=="Colombia, Cali")

csu_ageSpecific(df_colombia,
			plot_title = "Colombia, Liver, male")
				
# plot age specific rate for 1 population, and comparison with CI5XII data.
csu_ageSpecific(df_colombia,
				plot_title = "Colombia, Liver, male",
				CI5_comparison = "Liver")
			
# plot age specific rate for 4 population, legend at the bottom and comparison with CI5XII data.
csu_ageSpecific(csu_registry_data_1,
				group_by="registry_label",
				legend=csu_trend_legend(position="bottom", nrow = 2),
				plot_title = "Liver, male",
				CI5_comparison = 16)
	
	

ICD10 group detailed example

Description

ICD10 group example for the function csu_group_cases based on CI5XII ICD grouping

Usage

data("ICD_group_CI5")

Format

A data frame with 97 observations on the following 2 variables.

ICD

ICD10 code

LABEL

label for cancer group

Details

This dataset provide an example how to regroup ICD code using the function csu_group_cases For instance this group

Will become:

See: csu_group_cases

See Also

csu_group_cases ICD_group_GLOBOCAN

Examples

data(ICD_group_CI5)
data(data_individual_file)

#group individual data by 
#	5 year age group 
#	ICd grouping from dataframe ICD_group_CI5

df_data_icd <- csu_group_cases(data_individual_file,
	var_age="age",
	group_by=c("sex", "regcode", "reglabel"),
	df_ICD = ICD_group_CI5,
	var_ICD  ="site") 

ICD10 group example

Description

ICD10 group example for the function csu_group_cases based on GLOBOCAN

Usage

data("ICD_group_GLOBOCAN")

Format

A data frame with 97 observations on the following 2 variables.

ICD

ICD10 code

LABEL

label for cancer group

Details

This dataset provide an example how to regroup ICD code using the function csu_group_cases For instance this group

Will become:

See: csu_group_cases

See Also

csu_group_cases ICD_group_CI5

Examples

data(ICD_group_GLOBOCAN)
data(data_individual_file)

#group individual data by 
#	5 year age group 
#	ICd grouping from dataframe ICD_group_GLOBOCAN

df_data_icd <- csu_group_cases(data_individual_file,
	var_age="age",
	group_by=c("sex", "regcode", "reglabel"),
	df_ICD = ICD_group_GLOBOCAN,
	var_ICD  ="site") 

Cancer in five continent volume XII.

Description

CI5 XII summary database.

Usage

data("csu_CI5XII_data")

Format

A data frame with 1113210 observations on the following 10 variables.

id_code

population code (integer)

id_label

population label (character)

country_code

UN country code (integer)

ethnic_code

ethnic code (integer)

cancer_code

cancer code (integer)

cancer_label

cancer label (character)

sex

sex (integer)

age

age variable from 1 to 19. 1 is 0-4 years, 2 is 5-9, etc..., 17 is 80-84, 18 is 85+, 19 represents missing age. (integer)

cases

Number of cases (incidence) (integer)

py

Population-year: Reference population of the registry (integer)

period

Period (character)

Details

All information are available here: https://ci5.iarc.fr/ci5-xii/

Source

https://ci5.iarc.fr/ci5-xii/

See Also

csu_asr csu_cumrisk csu_eapc csu_ageSpecific

Examples

data("csu_CI5XII_data")

#get the id code asssociate to id_label
#print(unique(csu_CI5XII_data[,c("id_code", "id_label" )]),nrows = 1000)

#get the cancer code asssociate to cancer_label
#print(unique(csu_CI5XII_data[,c("cancer_label", "cancer_code")]),nrows = 1000)

#remove all cancers:
df_data <- subset(csu_CI5XII_data ,cancer_code < 62)

#change group_by option to factor
df_data$sex <- factor(df_data$sex, levels=c(1,2), labels=c("Male", "Female"))


#select Thailand changmai
df_data_1 <- subset(df_data, id_code==476400199)



dt_result_1 <- csu_ageSpecific_top(df_data_1,
                    var_age="age", 
                    var_cases="cases", 
                    var_py="py",
                    var_top="cancer_label", 
                    group_by="sex", 
                    plot_title= "Thailand, Chiangmai",
                    plot_subtitle = "Top 5 cancer",
                    missing_age = 19)



					
#select USA
df_data_2 <- subset(df_data,id_code== 384008099 & ethnic_code == 99)

dt_result_2 <- csu_ageSpecific_top(df_data_2,
                                   var_age="age", 
                                   var_cases="cases", 
                                   var_py="py",
                                   var_top="cancer_label", 
                                   group_by="sex", 
                                   plot_title= "USA",
                                   plot_subtitle = "Top 5 cancer",
                                   missing_age = 19)


				  
			  

csu_ageSpecific

Description

csu_ageSpecific calculate and plot Age-Specific Rate.

Usage

csu_ageSpecific(df_data,
		var_age="age",
        var_cases="cases",
        var_py="py",
        group_by = NULL, 
        missing_age = NULL,
        db_rate = 100000,
		logscale=FALSE,
        plot_title=NULL,
        legend=csu_trend_legend(),
        color_trend = NULL,
        CI5_comparison=NULL,
        var_rate="rate") 

Arguments

Details

This function calculate and plot the age specific rate. The group_by option allow to compare different population or cancer. The CI5_comparison option allow to compare with the CI5XII and therefore test the quality of the data. If the population data stops before 85+ (75+ for instance), the population data must be 0 when the population data is unknown so, the program can detect automatically the last age group (70+,75+,80+ or 85+) for population.

Value

Return a plot and a data.frame.

Author(s)

Mathieu Laversanne

References

https://publications.iarc.fr/_publications/media/download/3753/609d0d7711047dd76d7f3dbaa25d7f041fcd013e.pdf

See Also

csu_group_cases csu_merge_cases_pop csu_asr csu_cumrisk csu_eapc csu_ageSpecific_top csu_bar_top csu_time_trend csu_trendCohortPeriod

Examples

data(csu_registry_data_1)
data(csu_registry_data_2)

# you can import your data from csv file using read.csv:
# mydata <-  read.csv("mydata.csv", sep=",")

# to select only 1 population.
test <- subset(csu_registry_data_1 , registry_label == "Colombia, Cali")


# plot age specific rate for 1 population.
csu_ageSpecific(test,
				plot_title = "Colombia, Liver, male")

# plot age specific rate for 1 population, and comparison with CI5XII data.
csu_ageSpecific(test,
				plot_title = "Colombia, Liver, male",
				CI5_comparison = "Liver")

# plot age specific rate for 4 population, 
# legend at the bottom and comparison with CI5XII data using cancer code.
csu_ageSpecific(
	csu_registry_data_1,
	group_by="registry_label",
	legend=csu_trend_legend(position="bottom", nrow = 1),
	plot_title = "Liver, male",
	CI5_comparison = 16
	)

	

# plot age specific rate for 4 population, legend at the right.
csu_ageSpecific(
	csu_registry_data_1,
	group_by="registry_label",
	legend=csu_trend_legend(
		position="right", right_space_margin = 6.5
	),
	plot_title = "Liver, male")	



# Plot embedded in a graphic device
pdf(paste0(tempdir(),"/test.pdf"),width = 11.692 , height =  8.267) 


csu_ageSpecific(
	csu_registry_data_1,
	group_by="registry_label",
	legend=csu_trend_legend(position="bottom", nrow = 2),
	plot_title = "Liver, male",
	CI5_comparison = 16)

plot.new()

csu_ageSpecific(
	csu_registry_data_1,
	group_by="registry_label",
	legend=csu_trend_legend(
		position="right", right_space_margin = 6.5
	),
	plot_title = "Liver, male")	



dev.off()





	

csu_ageSpecific_top

Description

csu_ageSpecific_top calculate and plot Age-Specific Rate for the top X cancer or top X population.

Usage

csu_ageSpecific_top(df_data,
           var_age="age",
       	   var_cases="cases",
           var_py="py",
           var_top, 
           group_by=NULL,
           missing_age=NULL,
           db_rate = 100000,
           logscale = FALSE,
           nb_top = 5,
           plot_title=NULL,
           plot_subtitle=NULL,
           var_color=NULL)

Arguments

Details

This function keep only the top X cancer and plot their age specific rate. The group_by option allow to compare different population. If the population data stops before 85+ (75+ for instance), the population data must be 0 when the population data is unknown so, the program can detect automatically the last age group (70+,75+,80+ or 85+) for population.

Value

Return plots and a data.frame.

Author(s)

Mathieu Laversanne

References

https://publications.iarc.fr/_publications/media/download/3753/609d0d7711047dd76d7f3dbaa25d7f041fcd013e.pdf

See Also

csu_group_cases csu_merge_cases_pop csu_asr csu_cumrisk csu_eapc csu_ageSpecific csu_bar_top csu_time_trend csu_trendCohortPeriod

Examples

library(Rcan)
data("csu_CI5XII_data")

#get the id_code asssociate to id_label
#print(unique(csu_CI5XII_data[,c("id_label", "id_code")]),nrows = 1000)

#get the cancer code asssociate to cancer_label
#print(unique(csu_CI5XII_data[,c("cancer_label", "cancer_code")]),nrows = 1000)

#remove all cancers:
df_data <- subset(csu_CI5XII_data ,cancer_code < 62)
df_data$sex <- factor(df_data$sex, levels=c(1,2), labels=c("Male", "Female"))

#select Thailand changmai
df_data_1 <- subset(df_data, id_code==476400199)


# plot for Thailand Changmai
dt_result_1 <- 
	csu_ageSpecific_top(df_data_1,
		var_age="age", 
		var_cases="cases", 
		var_py="py",
		var_top="cancer_label", 
		group_by="sex", 
		plot_title= "Thailand, Chiangmai",
		plot_subtitle = "Top 5 cancer",
		missing_age = 19)



	#select USAm NPCR
	df_data_2 <- subset(df_data,id_code== 384008099)


	# plot for USA NPCR
	dt_result_2 <- 
		csu_ageSpecific_top(
			df_data_2,
			var_age="age", 
			var_cases="cases", 
			var_py="py",
			var_top="cancer_label", 
			group_by="sex", 
			plot_title= "USA",
			plot_subtitle = "Top 5 cancer",
			missing_age = 19
			)




	

csu_asr

Description

csu_asr calculate Age-Standardized Rate (ASR) and Truncated Age-Standardized Rate (TASR) across different population (Registry, year, sex...)

Usage

csu_asr(df_data,
		var_age = "age",
		var_cases = "cases",
		var_py ="py",
		group_by=NULL,
		var_age_group=NULL,
		missing_age = NULL,
		db_rate = 100000,
		first_age = 1,
		last_age = 18,
		pop_base = "SEGI",
		crude_rate = FALSE,
		var_st_err = NULL,
		correction_info = FALSE,
		var_asr = "asr",
		age_dropped = FALSE)

Arguments

Details

This function take automatically account the number of age group (last age group can be 70+,75+,80+,85+):
If the population data stops before 85+ (75+ for instance), the population data must be 0 when the population data is unknown so, the program can detect automatically the last age group (70+,75+,80+ or 85+) for population.

Value

Give the list of population with less than 18 age group (last age group: 70+, 75+, 80+).
Return a data.frame.

Author(s)

Mathieu Laversanne

References

https://publications.iarc.fr/_publications/media/download/3753/609d0d7711047dd76d7f3dbaa25d7f041fcd013e.pdf

See Also

csu_group_cases csu_merge_cases_pop csu_cumrisk csu_eapc csu_ageSpecific csu_ageSpecific_top csu_bar_top csu_time_trend csu_trendCohortPeriod

Examples

data(csu_registry_data_1)
data(csu_registry_data_2)

# you can import your data from csv file using read.csv:
# mydata <-  read.csv("mydata.csv", sep=",")

# Age standardized rate (ASR) with no missing age cases.
result <- csu_asr(csu_registry_data_1, 
                  "age", "cases", "py",
                  group_by = c("registry", "registry_label" ),
                  var_age_group = c("registry_label"))
				  
# you can export your result as csv file using write.csv:
# write.csv(result, file="result.csv")
				  
# ASR,  with the percentage of correction due to missing age cases. 
result <- csu_asr(csu_registry_data_1, 
                  "age", "cases", "py",
                  group_by = c("registry", "registry_label" ),
                  var_age_group = c("registry_label"),
				  missing_age = 19,				  
				  correction_info = TRUE)	
				  
# ASR and standard error with missing age.
result <- csu_asr(csu_registry_data_2, 
                  "age", "cases", "py",
                  group_by = c("registry", "registry_label", "sex", "year", "ethnic" ),
                  var_age_group = c("registry_label"), 
                  var_st_err = "st_err",
				  missing_age = 99)
				  
# Truncated ASR, 25-69 years.
result <- csu_asr(csu_registry_data_2, 
                  "age", "cases", "py",
                  group_by = c("registry", "registry_label", "sex", "year", "ethnic" ),
                  var_age_group = c("registry_label"), 
                  var_st_err = "st_err",
				  first_age = 6, 
				  last_age = 14,
				  missing_age = 99)	

# Truncated ASR, 0-15 with denominator population = 1000000. 
result <- csu_asr(csu_registry_data_2, 
                  "age", "cases", "py",
                  group_by = c("registry", "registry_label", "sex", "year", "ethnic" ),
                  var_age_group = c("registry_label"), 
                  var_st_err = "st_err",
				  first_age = 1, 
				  last_age = 3, 
				  missing_age = 99,	
				  db_rate = 1000000)		
				  
# ASR with EURO population as reference (instead of SEGI)
result <- csu_asr(csu_registry_data_1, 
                  "age", "cases", "py",
                  group_by = c("registry", "registry_label" ),
                  var_age_group = c("registry_label"),
				  missing_age = 19,
                  pop_base = "EURO")
				    		  

csu_bar_top

Description

csu_bar_top plots top X single-sided or double-sided bar chart.

Usage

csu_bar_top(df_data,
	var_value, 
	var_bar,
	group_by=NULL,
	nb_top = 10,
	plot_title=NULL,
	plot_subtitle=NULL,
	xtitle= NULL,
	label_by=NULL,
	color=NULL,
	digits = 1)

Arguments

Details

This function plots a top X (default is top 10) bar chart, single-sided or double sided.

Value

Return plots and a data.frame.

Author(s)

Mathieu Laversanne

See Also

csu_group_cases csu_merge_cases_pop csu_asr csu_cumrisk csu_eapc csu_ageSpecific csu_ageSpecific_top csu_time_trend csu_trendCohortPeriod

Examples

data(data_individual_file)
data(data_population_file)
data(ICD_group_GLOBOCAN)

#Group individual data by:
#5 year age group
#ICD grouping from dataframe ICD_group_GLOBOCAN
#year extract from date of incidence

df_data_year <- csu_group_cases(data_individual_file,
  var_age="age",
  group_by=c("sex", "regcode", "reglabel"),
  df_ICD = ICD_group_GLOBOCAN,
  var_ICD  ="site",
  var_year = "doi")     

#Merge 5-years age grouped data with population by year (automatic) and sex

df_data <- csu_merge_cases_pop(
  df_data_year, 
  data_population_file, 
  var_age = "age_group",
  var_cases = "cases",
  var_py = "pop",
  group_by = c("sex"))


#prepare for calculate ASR
df_data$age_group_label <- NULL # to avoid warning
df_data <- subset(df_data , year == 2012) # to keep only 2012 data
df_data$year <- NULL # to avoid warning

# calculate asr
df_asr <- csu_asr(df_data,
  "age_group", 
  "cases",
  "pop",
  group_by=c("sex", "ICD_group", "LABEL", "reglabel", "regcode"),
  missing_age =19)

#remove Other cancer
df_asr <- subset(df_asr , LABEL != "Other") 
df_asr <- subset(df_asr , LABEL != "Other skin")


#keep male
df_asr_M <- subset(df_asr , sex==1)

#Single sided bar plot 
data1 <- csu_bar_top(
   df_asr_M,
   var_value="cases",
   var_bar="LABEL",
   nb_top = 10,
   plot_title = "Top 10 cancer sites",
   xtitle= "Number of cases",
   color= c("#2c7bb6"),
   digits=0) 

#Double sided bar plot example 1
data2 <- csu_bar_top(
   df_asr,
   var_value="cases",
   var_bar="LABEL",
   group_by="sex",
   nb_top = 15,
   plot_title = "Top 15 cancer sites",
   xtitle= "Number of cases",
   label_by=c("Male", "Female"),
   color = c("#2c7bb6","#b62ca1"),
   digits=0) 

#Double sided bar plot example 2
data3 <- csu_bar_top(
   df_asr,
   var_value="asr",
   var_bar="LABEL",
   group_by="sex",
   nb_top = 10,
   plot_title = "Top 10 cancer sites",
   xtitle= "Age-standardized rate per 100,000",
   label_by=c("Male", "Female"),
   color = c("#2c7bb6","#b62ca1"),
   digits=1) 

cancer registry data

Description

CI5 XII data, all population grouped.

Usage

data("csu_ci5_mean")

Format

A data frame with 1026 observations on the following 5 variables.

ci5_cancer_code

cancer code

ci5_cancer_label

cancer label

CSU_age_factor

age variable from 1 to 18. 1 is 0-4 years, 2 is 5-9, etc..., 17 is 80-84, 18 is 85+.

CSU_C

Number of cases (incidence)

CSU_P

Population-year: Reference population

Details

This dataset have been created in order to compare age specific rate with the CI5XII mean directly on the age specific rate graph. Here is the table of cancer_label and cancer_code available:

See: csu_ageSpecific

Source

https://ci5.iarc.fr/ci5-xii/

See Also

csu_ageSpecific

Examples

data(csu_registry_data_1)
test <- subset(csu_registry_data_1 , registry_label == "Colombia, Cali")
csu_ageSpecific(test,
                plot_title = "Colombia, Liver, male",
                CI5_comparison = "Liver")
 
#See more examples here:
help(csu_ageSpecific)

csu_cumrisk

Description

csu_cumrisk Calculate cumulative risk across different population (Registry, year, sex...)

Usage

csu_cumrisk(df_data,
		var_age = "age",
		var_cases = "cases",
		var_py ="py",
		group_by=NULL,
		missing_age = NULL,
		last_age = 15,
		var_st_err=NULL,
		correction_info=FALSE,
		var_cumrisk="cumrisk") 

Arguments

Details

Calculate cumulative risk across different population
the age group include in cumulative risk will always exclude the last age group since we do not know the size of the last age group (ie: 85+, 80+ etc..)

Value

Return a data.frame.

Author(s)

Mathieu Laversanne

References

https://publications.iarc.fr/_publications/media/download/3753/609d0d7711047dd76d7f3dbaa25d7f041fcd013e.pdf

See Also

csu_group_cases csu_merge_cases_pop csu_asr csu_cumrisk csu_eapc csu_ageSpecific csu_ageSpecific_top csu_bar_top csu_time_trend csu_trendCohortPeriod

Examples

data(csu_registry_data_1)
data(csu_registry_data_2)

# you can import your data from csv file using read.csv:
# mydata <-  read.csv("mydata.csv", sep=",")

# Cumulative risk, 0-74 years, with no missing age cases.
result <- csu_cumrisk(csu_registry_data_1, 
                  "age", "cases", "py",
                  group_by = c("registry", "registry_label" ))
				  
# you can export your result as csv file using write.csv:
# write.csv(result, file="result.csv")
				  
# Cumulative risk, 0-74 years, with the percentage of correction due to missing age cases. 
result <- csu_cumrisk(csu_registry_data_1, 
                  "age", "cases", "py",
                  group_by = c("registry", "registry_label" ),
				  missing_age = 19,				  
				  correction_info = TRUE)	
				  
# Cumulative risk 0-74 years and standard error with missing age.
result <- csu_cumrisk(csu_registry_data_2, 
                  "age", "cases", "py",
                  group_by = c("registry", "registry_label", "sex", "year", "ethnic" ),
                  var_st_err = "st_err",
				  missing_age = 99)
				  
# Cumulative risk, 0-69 years.
result <- csu_cumrisk(csu_registry_data_2, 
                  "age", "cases", "py",
                  group_by = c("registry", "registry_label", "sex", "year", "ethnic" ),
                  var_st_err = "st_err",
				  last_age = 14,
				  missing_age = 99)	
		    		  

csu_eapc

Description

csu_eapc calculate the Estimated Annual Percentage Change (EAPC) of rates during a time period with the Confidence Interval (CI) across different population (Registry, year, sex...)

Usage

csu_eapc(df_data,
	var_rate="asr",
	var_year="year",
	group_by=NULL,
	var_eapc = "eapc",
	CI_level = 0.95)

Arguments

Details

This function use Generalized Linear Model (GLM):
glm(log(rate) ~ year, family=gaussian(link = "identity")).
0 value are ignored. More details in reference below.

Value

Return a dataframe.

Author(s)

Mathieu Laversanne

References

https://regstattools.iconcologia.net/stats/sart/eapc/eapc_method.pdf

See Also

csu_group_cases csu_merge_cases_pop csu_asr csu_cumrisk csu_ageSpecific csu_ageSpecific_top csu_bar_top csu_time_trend csu_trendCohortPeriod

Examples

data(csu_registry_data_2)

# you import your data from csv file using read.csv:
# mydata <-  read.csv("mydata.csv", sep=",")

# Estimated Annual Percentage Change (EAPC) base on ASR.


df_asr <- 
	csu_asr(csu_registry_data_2, 
	  "age", "cases", "py",
	  group_by = c("registry", "registry_label", "sex", "year", "ethnic" ),
	  var_age_group = c("registry_label"), 
	  missing_age = 99
	  )
				  
result <- 
	csu_eapc(df_asr,
		"asr", "year",
		group_by=c("registry", "registry_label", "sex", "ethnic" )
		)

					
# you can export your result as csv file using write.csv:
# write.csv(result, file="result.csv")
				  		  

csu_group_cases

Description

csu_group_cases groups individual data into 5 years age-group data and other user defined variable (sex, registry, etc...).
Optionally: Group cancer based on a standard ICD10 coding; Extract year from custom year format.

Usage

csu_group_cases(df_data, 
	var_age ,
	group_by=NULL,
	var_cases = NULL,
	df_ICD = NULL,
	var_ICD=NULL,
	var_year = NULL,
	all_cancer=FALSE) 
	

Arguments

Details

For most analysis, individual cases database need to be grouped by category.
This function groups data by 5 years age-group and other user defined variable.
Next step will be to add 5 years population data. (see csu_merge_cases_pop).

Value

Return a dataframe.

Author(s)

Mathieu Laversanne

See Also

csu_merge_cases_pop csu_asr csu_cumrisk csu_eapc csu_ageSpecific csu_ageSpecific_top csu_bar_top csu_time_trend csu_trendCohortPeriod

Examples

# you can import your data from csv file using read.csv:
# mydata <-  read.csv("mydata.csv", sep=",")



	
data(ICD_group_GLOBOCAN)
data(data_individual_file)

#group individual data by 
# 5 year age group 
df_data_age <- csu_group_cases(data_individual_file,
  var_age="age",
  group_by=c("sex", "regcode", "reglabel", "site")) 



	#group individual data by 
	# 5 year age group 
	# ICD grouping from dataframe ICD_group_GLOBOCAN

	df_data_icd <- csu_group_cases(data_individual_file,
	  var_age="age",
	  group_by=c("sex", "regcode", "reglabel"),
	  df_ICD = ICD_group_GLOBOCAN,
	  var_ICD  ="site") 

	#group individual data by 
	# 5 year age group 
	# ICD grouping from dataframe ICD_group_GLOBOCAN
	# year (extract from date of incidence)

	df_data_year <- csu_group_cases(data_individual_file,
	  var_age="age",
	  group_by=c("sex", "regcode", "reglabel"),
	  df_ICD = ICD_group_GLOBOCAN,
	  var_ICD  ="site",
	  var_year = "doi")       
	


# you can export your result as csv file using write.csv:
# write.csv(result, file="result.csv")
				  		  

csu_merge_cases_pop

Description

csu_merge_cases_pop merges registry data and population data, group by year and other user defined variable (sex, registry, etc...).

Usage

csu_merge_cases_pop(df_cases, 
	df_pop,
	var_age,
	var_cases="cases",
	var_py=NULL,
	group_by=NULL) 
	

Arguments

Details

This function merges registry data and population for further analysis.
Both datasets must be group by 5-years age group.
If present, the year information in format "yyyy" will be detected automatically.
2 formats are accepted for population data:.
Long format: (year and population are 2 variables)

Wide format: (One column per year and no population variable, "yyyy" year format must be included in columns name)

Value

Return a dataframe.

Author(s)

Mathieu Laversanne

See Also

csu_group_cases csu_asr csu_cumrisk csu_eapc csu_ageSpecific csu_ageSpecific_top csu_bar_top csu_time_trend csu_trendCohortPeriod

Examples

# you can import your data from csv file using read.csv:
# mydata <-  read.csv("mydata.csv", sep=",")

data(ICD_group_GLOBOCAN)
data(data_individual_file)
data(data_population_file)

#group individual data by 
# 5 year age group 
# ICD grouping from dataframe ICD_group_GLOBOCAN
# year (extract from date of incidence)

df_data_year <- csu_group_cases(data_individual_file,
  var_age="age",
  group_by=c("sex", "regcode", "reglabel"),
  df_ICD = ICD_group_GLOBOCAN,
  var_ICD  ="site",
  var_year = "doi")     

#Merge 5-years age grouped data with population by year (automatic) and sex

df_data <- csu_merge_cases_pop(
	df_data_year, 
	data_population_file, 
	var_age = "age_group",
	var_cases = "cases",
	var_py = "pop",
	group_by = c("sex"))


# you can export your result as csv file using write.csv:
# write.csv(result, file="result.csv")
				  		  

cancer registry data

Description

Cancer registry data for liver cancer, males, 2007, 4 registries.

Usage

data("csu_registry_data_1")

Format

A data frame with 76 observations on the following 5 variables.

age

age variable from 1 to 19. 1 is 0-4 years, 2 is 5-9, etc..., 17 is 80-84, 18 is 85+, 19 represents missing age.

cases

Number of cases (incidence)

py

Population-year: Reference population of the registry

registry_label

Name of the registry

registry

Registry code

Details

this 4 registries have been selected for this example as they different number of age group (75+, 80+, 85+) and some have missing age cases.

Source

https://ci5.iarc.fr/ci5-xii/

See Also

csu_asr csu_cumrisk csu_eapc csu_ageSpecific csu_ageSpecific_top csu_bar_top

Examples

data(csu_registry_data_1)

# Age standardized rate (ASR) with no missing age cases.
result <- csu_asr(csu_registry_data_1, 
                  "age", "cases", "py",
                  group_by = c("registry", "registry_label" ),
                  var_age_group = c("registry_label"))
				  
#See more examples here:
help(csu_asr)

				  
				  

cancer registry data

Description

Cancer registry data for liver cancer

Usage

data("csu_registry_data_2")

Format

A data frame with 125856 observations on the following 8 variables.

sex

sex variable: 1 male, 2 female

year

year variable, from 1953 to 2007

age

age variable from 0 to 85 with missing age.
0 is 0-4 years, 5 is 5-9, ..., 80 is 80-84, 85 is 85+, 99 represents missing age.

cases

Number of cases (incidence)

py

Population-year: Reference population of the registry

registry_label

Name of the registry (118 populations)

registry

Registry code (102 registries)

ethnic

ethnic code: white (10), black (30), ..., all(99).

Source

https://ci5.iarc.fr/ci5-xii/

See Also

csu_asr csu_cumrisk csu_eapc csu_ageSpecific csu_ageSpecific_top csu_bar_top csu_time_trend csu_trendCohortPeriod

Examples

data(csu_registry_data_2)
				  
# ASR and standard error with missing age.
result <- csu_asr(csu_registry_data_2, 
                  "age", "cases", "py",
                  group_by = c("registry", "registry_label", "sex", "year", "ethnic" ),
                  var_age_group = c("registry_label"), 
				  missing_age = 99,
                  var_st_err = "st_err")
				  
#See more examples here:
help(csu_asr)	
				  

csu_time_trend

Description

csu_time_trend plot stats over year.

Usage

csu_time_trend(df_data,
		var_trend = "asr",
		var_year = "year",
		group_by = NULL,
		logscale = FALSE,
		smoothing = NULL,
		legend = csu_trend_legend(),
		color_trend = NULL,
		ytitle = "Age standardized rate per 100,000",
		plot_title = "csu_title") 

Arguments

Details

This function is design the plot a statistics over time. It has been design for the ASR by year, but can be used for other statistics over time period. The group_by option allow to compare different population or cancer.

Value

Return a plot.

Author(s)

Mathieu Laversanne

See Also

csu_group_cases csu_merge_cases_pop csu_asr csu_cumrisk csu_eapc csu_ageSpecific csu_ageSpecific_top csu_bar_top csu_trendCohortPeriod

Examples

	data(csu_registry_data_2)

	# you can import your data from csv file using read.csv:
	# mydata <-  read.csv("mydata.csv", sep=",")

	# to select only 1 population 

	test <- subset(csu_registry_data_2 , registry_label == "Colombia, Cali")

	# to change sex variable to factor with label
	test$sex <- factor(test$sex, levels=c(1,2), labels=c("Male", "Female"))

	# to calculate the asr
	df_asr <- csu_asr(
		test,missing_age = 99,
		group_by  = c("registry", "registry_label", "year", "sex", "ethnic"),
		var_age_group =  c("registry", "registry_label")
		)


	# plot ASR ove year, by sex.
	csu_time_trend(df_asr, group_by="sex",
			  plot_title = "Colombia, Liver")

	# plot ASR over year, by sex, with small smoothing.
	csu_time_trend(df_asr, group_by="sex",
			  plot_title = "Colombia, Liver",
			  smoothing = 0.3)


	# plot ASR over year, by sex, with high smoothing.
	csu_time_trend(df_asr, group_by="sex",
			  plot_title = "Colombia, Liver",
			  smoothing = 0.5)

	# Plot embedded in a graphic device
	pdf(paste0(tempdir(),"/test.pdf"),width = 11.692 , height =  8.267) 
	csu_time_trend(df_asr, group_by="sex",
			  plot_title = "Colombia, Liver",
			  smoothing = 0.3)

	csu_time_trend(df_asr, group_by="sex",
			  plot_title = "Colombia, Liver",
			  smoothing = 0.5)

	dev.off()
			

csu_trendCohortPeriod

Description

csu_trendCohortPeriod plot cohort period age specific graph.

Usage

csu_trendCohortPeriod(
  df_data,
  var_age = "age",
  var_cases="cases",
  var_py="py",
  var_year = "year",
  type = "Cohort",
  missing_age = NULL,
  logscale = TRUE,
  db_rate = 100000,
  first_age = 6,
  last_age = 16,
  year_group = 5,
  age_dropped=FALSE,
  plot_title = "csu_title",
  format_export = NULL,
  graph_dev =FALSE) 

Arguments

Details

This function is design the plot a the age-specific cohort and period plot. The type option allow to choose between the 3 different graphics: "Cohort", "Period", or "Both". Please note than the cohort plot and the period plot can be superimposed if the first_age is too low.

Value

Return a plot.

Author(s)

Mathieu Laversanne

See Also

csu_group_cases csu_merge_cases_pop csu_asr csu_cumrisk csu_eapc csu_ageSpecific csu_ageSpecific_top csu_bar_top csu_time_trend

Examples

	data(csu_registry_data_2)

	# you can import your data from csv file using read.csv:
	# mydata <-  read.csv("mydata.csv", sep=",")
	
	# to select only 1 population 
	test <- subset(csu_registry_data_2,registry == 84020 & sex == 1)


	
	# plot cohort graph from 25-29 years until 75-79 years.
	csu_trendCohortPeriod(df_data=test,
                        missing_age =99,
                        plot_title = "USA, Liver, males")
						
	# plot Period graph from 0-5 until 85+.
	csu_trendCohortPeriod(df_data=test,
                        missing_age =99,
                        plot_title = "USA, Liver, males",
						type="Period",
						first_age=1,
						last_age=18)
						
	# plot Cohort-Period graph from 30-34 years until 70-74 years.
	csu_trendCohortPeriod(df_data=test,
                        missing_age =99,
                        plot_title = "USA, Liver, males",
						type="Both",
						first_age=7,
						last_age=15)

	# plot Cohort-Period graph from 30-34 years until 70-74 years with Y axis normal scale.
	csu_trendCohortPeriod(df_data=test,
                        missing_age =99,
                        plot_title = "USA, Liver, males",
						type="Both",
						first_age=7,
						last_age=15,
						logscale=FALSE)

					
	# plot Cohort graph from 25-29 years until 75-79 years, with data grouped in 2 years period.
	csu_trendCohortPeriod(df_data=test,
						  missing_age =99,
						  plot_title = "USA, Liver, males",
						  type="Cohort",
						  year_group = 2)
						  
	# Plot embedded in a graphic device
	pdf(paste0(tempdir(),"/example_test.pdf"))
	csu_trendCohortPeriod(df_data=test,
                        missing_age =99,
                        plot_title = "USA, Liver, males",
						type="Both",
						first_age=7,
						last_age=15,
						graph_dev=TRUE)
			  
	csu_trendCohortPeriod(df_data=test,
                        missing_age =99,
                        plot_title = "USA, Liver, males",
						type="Both",
						first_age=7,
						last_age=15,
						logscale=FALSE)
				
	dev.off()
		

csu_trend_legend

Description

csu_trend_legend legend option use in Rcan package for trends.

Usage

csu_trend_legend(title=NULL, position="bottom",nrow=1, right_space_margin=1)

Arguments

Details

This function return a list for the trend graphics of the package Rcan

Value

Return a structured list.

Author(s)

Mathieu Laversanne

See Also

csu_ageSpecific

Examples

data(csu_registry_data_1)

csu_ageSpecific(csu_registry_data_1,
                group_by="registry_label",
                legend=csu_trend_legend(title="registry", position="bottom", nrow = 2),
                plot_title = "Legend: bottom")
				
csu_ageSpecific(csu_registry_data_1,
                group_by="registry_label",
                legend=csu_trend_legend(position="right", right_space_margin = 2),
                plot_title = "Legend: right, cut")

csu_ageSpecific(csu_registry_data_1,
                group_by="registry_label",
                legend=csu_trend_legend(position="right", right_space_margin = 6.5),
                plot_title = "Legend: right")
				
#See more examples here:
help(csu_ageSpecific)
	  		  

Data individual example

Description

Data individual example for the function csu_group_cases
1 line = 1 cases.

Usage

data("data_individual_file")

Format

A data frame with 19284 observations on the following 10 variables.

regcode

registry code

reglabel

registry label

sex

sex

age

age

doi

date of birth (yyyymmdd)

site

ICD10 code

histo

histology

beh

behavior code

grade

grade

basis

basis

Details

This dataset provide an example how to group individual cases to 5 years data, grouped by ICD code and year using the function csu_group_cases and the database ICD_group_GLOBOCAN

See Also

csu_group_cases ICD_group_GLOBOCAN ICD_group_CI5

Examples

data(ICD_group_GLOBOCAN)
data(data_individual_file)

#group individual data by 
#	5 year age group 

df_data_age <- csu_group_cases(data_individual_file,
	var_age="age",
	group_by=c("sex", "regcode", "reglabel", "site"))


#group individual data by 
#	5 year age group 
#	ICD grouping from dataframe ICD_group_GLOBOCAN

df_data_icd <- csu_group_cases(data_individual_file,
	var_age="age",
	group_by=c("sex", "regcode", "reglabel"),
	df_ICD = ICD_group_GLOBOCAN,
	var_ICD  ="site") 

Population data example

Description

Population data example for the function csu_merge_cases_pop.

Usage

data("data_population_file")

Format

A data frame with 160 observations on the following 4 variables.

year

year

sex

sex

age_group

5 year age group

pop

Count of population year

Details

This dataset provide an example to merge 5 years age grouped data with population data using the function csu_merge_cases_pop

See Also

csu_merge_cases_pop

Examples

data(ICD_group_GLOBOCAN)
data(data_individual_file)
data(data_population_file)

#group individual data by 
# 5 year age group 
# ICD grouping from dataframe ICD_group_GLOBOCAN
# year (extract from date of incidence)

df_data_year <- csu_group_cases(data_individual_file,
  var_age="age",
  group_by=c("sex", "regcode", "reglabel"),
  df_ICD = ICD_group_GLOBOCAN,
  var_ICD  ="site",
  var_year = "doi")     

#Merge 5-years age grouped data with population by year (automatic) and sex

df_pop <- csu_merge_cases_pop(df_data_year, data_population_file, var_age = "age_group", 
                           var_cases = "cases", var_py = "pop", group_by = c("sex"))