-
Notifications
You must be signed in to change notification settings - Fork 2
/
README.Rmd
94 lines (66 loc) · 5.26 KB
/
README.Rmd
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# Register studies
<!-- badges: start -->
<!-- badges: end -->
Data utilised in register studies, often called as register-based data or administrative data, typically consists of id's, time stamp variables and codes describing different events. This package has been designed specifically for handling of such data.
Package is based on tidyverse and examples utilise widely tidyverse programming style. The functions are designed with ICD codes in mind, but can be used with any kind of string formatted codes and are thus not necessarily limited to healthcare data. Users are allowed to make their own definitions of code classifiers, so the package suits needs of different countries having their own code definitions of disease codes for example.
Authors: [Juho Kopra](https://github.com/jukop), [Jani Miettinen](https://github.com/janikmiet), [Reijo Sund](https://github.com/rsund)
## Installation
You can install the released version of regstudies from [CRAN](https://CRAN.R-project.org) with:
``` r
install.packages("regstudies") # maybe some day!!
```
And the development version from [GitHub](https://github.com/) with:
``` r
# install.packages("devtools")
devtools::install_github("pohjois-savon-tietoallas/regstudies")
```
## Example
This is a basic example which shows you how to solve a common problem:
```{r example, eval=FALSE}
library(regstudies)
# ´regstudies´ makes it easy to classify codes such as ICD-codes to groups and also to sum scores based on the groupings.
sample_data <- left_join(sample_cohort,sample_regdata)
sample_data %>%
classify_charlson(icd_codes = CODE1) %>%
sum_score(score_charlson)
# Users can define the code groups themselves using simple regular expression syntax.
my_classes <-
tribble(~class_myclass, ~label_myclass, ~icd10, ~icd9, ~score,
"aids", "AIDS or HIV", "^B20|^B21|^B22|^B24", "^042|^043|^044", 7,
"dementia", "Dementia", "^F00|^F01|^F02|^F03|^F051|^G30|^G311", "^290|^2941|^3312", 3,
"pud", "Peptic ulcer disease", "^K25|^K26|^K27|^K28", "^531|^532|^533|^534", 1
)
# This classification definition has two different type of codes.
head(my_classes)
# Data has three different types of codes.
head(sample_data)
# Different groups types can are automatically handled simultaneously.
sample_data %>%
classify_codes(codes=CODE1,diag_tbl = read_classes(my_classes)) %>%
sum_score(score)
```
## Citation
```{r echo=FALSE}
citation("regstudies")
```
## Developers, check [contributing guide](CONTRIBUTING.html)
## References
The current version (0.5.2) contains classification tables for Elixhauser and Charlson comorbidity indices. The Elixhauser and Charlson classifications for ICD-9-CM and ICD-10 versions were originally implemented as regular expressions in [`comorbidity`](https://ellessenne.github.io/comorbidity/) by Gasparini (2018). They have been extended based on Pylvänäinen et al. (2019) for Social Security Institution (SII) entitlement and medication reimbursement codes. The key references for the Elixhauser and Charlson comorbidity indices are Charlson et al. (1987), Elixhauser et al. (1998) and Quan et al. (2015). The scores utilised in the same tables are based on van Walraven et al. (2009) and Quan et al. (2015). All the source code in `regstudies` are written by original authors.
- Charlson, Pompei, Ales & MacKenzie (1987) *A new method of classifying prognostic comorbidity in longitudinal studies: development and validation*. Journal of Chronic Diseases. 40(5), 373-83. DOI: [10.1016/0021-9681(87)90171-8](https://doi.org/10.1016/0021-9681\(87\)90171-8)
- Elixhauser, Steiner, Harris and Coffey (1998). *Comorbidity measures for use with administrative data*. Medical Care, 36(1), 8-27. DOI: [10.1097/00005650-199801000-00004](https://doi.org/10.1097/00005650-199801000-00004)
- Gasparini (2018) *comorbidity: An R package for computing comorbidity scores* Journal of Open Source Software, 3(23), 648. DOI: [10.21105/joss.00648](https://doi.org/10.21105/joss.00648)
- Pylväläinen, Talala, Murtola, Taari, Raitanen, Tammela, & Auvinen (2019). *Charlson Comorbidity Index Based On Hospital Episode Statistics Performs Adequately In Predicting Mortality, But Its Discriminative Ability Diminishes Over Time*. Clinical epidemiology, 11, 923. DOI: [10.2147/CLEP.S218697](https://dx.doi.org/10.2147%2FCLEP.S218697)
- Quan, Sundararajan, Halfon, Fong, Burnand, Luthi, Saunders, Beck, Feasby & Ghali (2015) *Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data*. Medical Care, 43(11): pp. 1130-39. DOI: [10.1097/01.mlr.0000182534.19832.83](https://doi.org/10.1097/01.mlr.0000182534.19832.83)
- van Walraven, Austin, Jennings, Quan & Forster (2009). *A modification of the Elixhauser comorbidity measures into a point system for hospital death using administrative data*. Medical Care, 47(6), 626-33. DOI: [10.1097/mlr.0b013e31819432e5](https://doi.org/10.1097/mlr.0b013e31819432e5)