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When stratifying a cohort, it is generally desirable to calculate SMRs for different levels of a strata (such as a time-dependent exposure).
LTASR provides options to stratify a cohort by a fixed strata defined within the person file, or by a time-dependent exposure variable with information found in a separate history file.
For example, below will strata the example person and history file,
included in LTASR, by a cumulative exposure variable
exposure_level:
#Define exposure cutpoints
exp <- exp_strata(var = 'exposure_level',
cutpt = c(-Inf, 0, 10000, 20000, Inf),
lag = 10)
#Read in and format person file
person <- person_example %>%
mutate(dob = as.Date(dob, format='%m/%d/%Y'),
pybegin = as.Date(pybegin, format='%m/%d/%Y'),
dlo = as.Date(dlo, format='%m/%d/%Y'))
#Read in and format history file
history <- history_example %>%
mutate(begin_dt = as.Date(begin_dt, format='%m/%d/%Y'),
end_dt = as.Date(end_dt, format='%m/%d/%Y'))
#Stratify cohort
py_table <- get_table_history(persondf = person,
rateobj = us_119ucod_19602021,
historydf = history,
exps = list(exp))This creates the following table (top 6 rows):
| ageCat | CPCat | gender | race | exposure_levelCat | pdays | _o55 | _o52 |
|---|---|---|---|---|---|---|---|
| [15,20) | [1970,1975) | F | W | (-Inf,0] | 746 | 1 | 0 |
| [25,30) | [1970,1975) | M | N | (-Inf,0] | 55 | 0 | 0 |
| [25,30) | [1970,1975) | M | W | (-Inf,0] | 1472 | 0 | 0 |
| [25,30) | [1975,1980) | M | W | (-Inf,0] | 323 | 0 | 0 |
| [30,35) | [1970,1975) | M | N | (-Inf,0] | 1023 | 0 | 0 |
| [30,35) | [1975,1980) | M | N | (-Inf,0] | 803 | 0 | 0 |
smr_minor and smr_major will calculate SMRs
for the entire cohort that is read in.
To calculate SMRs separately for each strata of
exposure_levelCat, one option would be to create separate
person-year tables for each level:
#Subset py_table to the highest exposed group
py_table_high <- py_table %>%
filter(exposure_levelCat == '(2e+04, Inf]')
smr_minor_table_high <- smr_minor(py_table_high, us_119ucod_19602021)
#> Warning in stats::optim(.x, function(x) abs(1 - stats::ppois((.x - 1), x) - : one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(1 - stats::ppois((.x - 1), x) - : one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::ppois((.x - 1), x): NaNs produced
#> Warning in stats::ppois((.x - 1), x): NaNs produced
#> Warning in stats::ppois((.x - 1), x): NaNs produced
#> Warning in stats::ppois((.x - 1), x): NaNs produced
#> Warning in stats::ppois((.x - 1), x): NaNs produced
#> Warning in stats::ppois((.x - 1), x): NaNs produced
#> Warning in stats::ppois((.x - 1), x): NaNs produced
#> Warning in stats::ppois((.x - 1), x): NaNs produced
#> Warning in stats::ppois((.x - 1), x): NaNs produced
#> Warning in stats::optim(.x, function(x) abs(1 - stats::ppois((.x - 1), x) - : one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::ppois((.x - 1), x): NaNs produced
#> Warning in stats::ppois((.x - 1), x): NaNs produced
#> Warning in stats::ppois((.x - 1), x): NaNs produced
#> Warning in stats::ppois((.x - 1), x): NaNs produced
#> Warning in stats::ppois((.x - 1), x): NaNs produced
#> Warning in stats::optim(.x, function(x) abs(1 - stats::ppois((.x - 1), x) - : one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::ppois((.x - 1), x): NaNs produced
#> Warning in stats::ppois((.x - 1), x): NaNs produced
#> Warning in stats::optim(.x, function(x) abs(1 - stats::ppois((.x - 1), x) - : one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::ppois((.x - 1), x): NaNs produced
#> Warning in stats::ppois((.x - 1), x): NaNs produced
#> Warning in stats::ppois((.x - 1), x): NaNs produced
#> Warning in stats::optim(.x, function(x) abs(1 - stats::ppois((.x - 1), x) - : one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::ppois((.x - 1), x): NaNs produced
#> Warning in stats::ppois((.x - 1), x): NaNs produced
#> Warning in stats::optim(.x, function(x) abs(1 - stats::ppois((.x - 1), x) - : one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(1 - stats::ppois((.x - 1), x) - : one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::ppois((.x - 1), x): NaNs produced
#> Warning in stats::optim(.x, function(x) abs(1 - stats::ppois((.x - 1), x) - : one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::ppois((.x - 1), x): NaNs produced
#> Warning in stats::optim(.x, function(x) abs(1 - stats::ppois((.x - 1), x) - : one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::ppois((.x - 1), x): NaNs produced
#> Warning in stats::optim(.x, function(x) abs(1 - stats::ppois((.x - 1), x) - : one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(1 - stats::ppois((.x - 1), x) - : one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(1 - stats::ppois((.x - 1), x) - : one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(1 - stats::ppois((.x - 1), x) - : one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::ppois((.x - 1), x): NaNs produced
#> Warning in stats::optim(.x, function(x) abs(1 - stats::ppois((.x - 1), x) - : one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(1 - stats::ppois((.x - 1), x) - : one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(1 - stats::ppois((.x - 1), x) - : one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(1 - stats::ppois((.x - 1), x) - : one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(1 - stats::ppois((.x - 1), x) - : one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(1 - stats::ppois((.x - 1), x) - : one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(1 - stats::ppois((.x - 1), x) - : one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(stats::ppois(.x, x) - 0.05/2)): one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(stats::ppois(.x, x) - 0.05/2)): one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(stats::ppois(.x, x) - 0.05/2)): one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(stats::ppois(.x, x) - 0.05/2)): one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(stats::ppois(.x, x) - 0.05/2)): one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(stats::ppois(.x, x) - 0.05/2)): one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(stats::ppois(.x, x) - 0.05/2)): one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(stats::ppois(.x, x) - 0.05/2)): one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(stats::ppois(.x, x) - 0.05/2)): one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(stats::ppois(.x, x) - 0.05/2)): one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(stats::ppois(.x, x) - 0.05/2)): one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(stats::ppois(.x, x) - 0.05/2)): one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(stats::ppois(.x, x) - 0.05/2)): one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(stats::ppois(.x, x) - 0.05/2)): one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(stats::ppois(.x, x) - 0.05/2)): one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(stats::ppois(.x, x) - 0.05/2)): one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(stats::ppois(.x, x) - 0.05/2)): one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(stats::ppois(.x, x) - 0.05/2)): one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(stats::ppois(.x, x) - 0.05/2)): one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(stats::ppois(.x, x) - 0.05/2)): one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(stats::ppois(.x, x) - 0.05/2)): one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
smr_major_table_high <- smr_major(smr_minor_table_high, us_119ucod_19602021)
#> Warning in stats::optim(.x, function(x) abs(1 - stats::ppois((.x - 1), x) - : one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(1 - stats::ppois((.x - 1), x) - : one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::ppois((.x - 1), x): NaNs produced
#> Warning in stats::ppois((.x - 1), x): NaNs produced
#> Warning in stats::ppois((.x - 1), x): NaNs produced
#> Warning in stats::ppois((.x - 1), x): NaNs produced
#> Warning in stats::ppois((.x - 1), x): NaNs produced
#> Warning in stats::ppois((.x - 1), x): NaNs produced
#> Warning in stats::ppois((.x - 1), x): NaNs produced
#> Warning in stats::ppois((.x - 1), x): NaNs produced
#> Warning in stats::ppois((.x - 1), x): NaNs produced
#> Warning in stats::optim(.x, function(x) abs(1 - stats::ppois((.x - 1), x) - : one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::ppois((.x - 1), x): NaNs produced
#> Warning in stats::ppois((.x - 1), x): NaNs produced
#> Warning in stats::ppois((.x - 1), x): NaNs produced
#> Warning in stats::ppois((.x - 1), x): NaNs produced
#> Warning in stats::ppois((.x - 1), x): NaNs produced
#> Warning in stats::optim(.x, function(x) abs(1 - stats::ppois((.x - 1), x) - : one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::ppois((.x - 1), x): NaNs produced
#> Warning in stats::ppois((.x - 1), x): NaNs produced
#> Warning in stats::optim(.x, function(x) abs(1 - stats::ppois((.x - 1), x) - : one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::ppois((.x - 1), x): NaNs produced
#> Warning in stats::ppois((.x - 1), x): NaNs produced
#> Warning in stats::ppois((.x - 1), x): NaNs produced
#> Warning in stats::optim(.x, function(x) abs(1 - stats::ppois((.x - 1), x) - : one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::ppois((.x - 1), x): NaNs produced
#> Warning in stats::ppois((.x - 1), x): NaNs produced
#> Warning in stats::optim(.x, function(x) abs(1 - stats::ppois((.x - 1), x) - : one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(1 - stats::ppois((.x - 1), x) - : one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::ppois((.x - 1), x): NaNs produced
#> Warning in stats::optim(.x, function(x) abs(1 - stats::ppois((.x - 1), x) - : one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::ppois((.x - 1), x): NaNs produced
#> Warning in stats::optim(.x, function(x) abs(1 - stats::ppois((.x - 1), x) - : one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::ppois((.x - 1), x): NaNs produced
#> Warning in stats::optim(.x, function(x) abs(1 - stats::ppois((.x - 1), x) - : one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(1 - stats::ppois((.x - 1), x) - : one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(1 - stats::ppois((.x - 1), x) - : one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(1 - stats::ppois((.x - 1), x) - : one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::ppois((.x - 1), x): NaNs produced
#> Warning in stats::optim(.x, function(x) abs(1 - stats::ppois((.x - 1), x) - : one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(1 - stats::ppois((.x - 1), x) - : one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(1 - stats::ppois((.x - 1), x) - : one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(1 - stats::ppois((.x - 1), x) - : one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(1 - stats::ppois((.x - 1), x) - : one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(1 - stats::ppois((.x - 1), x) - : one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(1 - stats::ppois((.x - 1), x) - : one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(stats::ppois(.x, x) - 0.05/2)): one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(stats::ppois(.x, x) - 0.05/2)): one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(stats::ppois(.x, x) - 0.05/2)): one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(stats::ppois(.x, x) - 0.05/2)): one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(stats::ppois(.x, x) - 0.05/2)): one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(stats::ppois(.x, x) - 0.05/2)): one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(stats::ppois(.x, x) - 0.05/2)): one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(stats::ppois(.x, x) - 0.05/2)): one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(stats::ppois(.x, x) - 0.05/2)): one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(stats::ppois(.x, x) - 0.05/2)): one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(stats::ppois(.x, x) - 0.05/2)): one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(stats::ppois(.x, x) - 0.05/2)): one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(stats::ppois(.x, x) - 0.05/2)): one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(stats::ppois(.x, x) - 0.05/2)): one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(stats::ppois(.x, x) - 0.05/2)): one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(stats::ppois(.x, x) - 0.05/2)): one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(stats::ppois(.x, x) - 0.05/2)): one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(stats::ppois(.x, x) - 0.05/2)): one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(stats::ppois(.x, x) - 0.05/2)): one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(stats::ppois(.x, x) - 0.05/2)): one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly
#> Warning in stats::optim(.x, function(x) abs(stats::ppois(.x, x) - 0.05/2)): one-dimensional optimization by Nelder-Mead is unreliable:
#> use "Brent" or optimize() directly| minor | Description | observed | expected | smr | lower | upper |
|---|---|---|---|---|---|---|
| 52 | Other diseases of the nervous system and sense org | 0 | 0.01 | 0 | 0 | 368.89 |
| 55 | Ischemic heart disease | 0 | 0.06 | 0 | 0 | 61.48 |
| major | Description | observed | expected | smr | lower | upper |
|---|---|---|---|---|---|---|
| 16 | Diseases of the heart (Major) | 0 | 0.06 | 0 | 0 | 61.48 |
These results can be saved through repeated calls to
write_csv(). This can be tedious for strata with many
levels.
Alternatively, the below code will loop through each level of the a
variable (defined by var) and outputs results into an excel
file (using the writexl library) with a separate tab for
each strata level:
#Define the name of the person year table (py_table)
#and the variable to calcualte SMRs accross
pyt <- py_table
var <- 'exposure_levelCat'
#Loop through levels of the above variable
lvls <- unique(pyt[var][[1]])
smr_minors <-
map(lvls,
~ {
pyt %>%
filter(!!sym(var) == .x) %>%
smr_minor(us_119ucod_19602021)
}) %>%
setNames(lvls)
smr_majors <-
map(smr_minors,
~ smr_major(., us_119ucod_19602021))%>%
setNames(names(smr_minors))
#Adjust names of sheets
names(smr_minors) <- str_replace_all(names(smr_minors), "\\[|\\]", "_")
names(smr_majors) <- str_replace_all(names(smr_majors), "\\[|\\]", "_")
#Save results
library(writexl)
write_xlsx(smr_minors, 'C:/SMR_Minors_by_exp.xlsx')
write_xlsx(smr_majors, 'C:/SMR_Majors_by_exp.xlsx')These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.