FAERS-Pharmacovigilance ================ - [Introduction](#introduction) - [Installation](#installation) - [Pharmacovigilance Analysis using FAERS](#pharmacovigilance-analysis-using-faers) - [Check metadata of FAERS](#check-metadata-of-faers) - [Download and Parse quarterly data files from FAERS](#download-and-parse-quarterly-data-files-from-faers) - [Standardize and De-duplication](#standardize-and-de-duplication) - [Pharmacovigilance analysis](#pharmacovigilance-analysis) - [sessionInfo](#sessioninfo) ## Introduction The FDA Adverse Event Reporting System (FAERS) stands as a database dedicated to the monitoring of post-marketing drug safety and exercises a notable influence over FDA safety guidance documents, including the modification of drug labels. The quantity of cases stored within FAERS has experienced an exponential surge due to the refinement of submission techniques and adherence to standardized data protocols, making it a pivotal asset for the realm of regulatory science. While FAERS has predominantly focused on safety signal detection, the faers package acts as the intermediary, seamlessly bridging the gap between the FAERS database and the programming language R. Moreover, the faers package provides a unified methodology for the seamless execution of pharmacovigilance analysis, facilitating the integration of genetic tools in R. With an ultimate ambition towards precision medicine, it aspires to scrutinize the vast expanse of the human genome, revealing drug pathways that may be intricately tied to potentially functional, population-differentiated polymorphisms. ## Installation To install from Bioconductor, use the following code: ``` r if (!requireNamespace("BiocManager", quietly = TRUE)) { install.packages("BiocManager") } BiocManager::install("faers") ``` You can install the development version of `faers` from [GitHub](https://github.com/Yunuuuu/faers) with: ``` r if (!requireNamespace("pak")) { install.packages("pak", repos = sprintf( "https://r-lib.github.io/p/pak/devel/%s/%s/%s", .Platform$pkgType, R.Version()$os, R.Version()$arch ) ) } pak::pkg_install("Yunuuuu/faers") ``` ## Pharmacovigilance Analysis using FAERS FAERS is a database for the spontaneous reporting of adverse events and medication errors involving human drugs and therapeutic biological products. This package accelarate the process of Pharmacovigilance Analysis using FAERS. ``` r library(faers) ``` ### Check metadata of FAERS This will return a data.table reporting years, period, quarter, and file urls and file sizes. By default, this will use the cached file in `tools::R_user_dir("faers", "cache")`. If it doesn’t exist, the internal will parse metadata in <https://fis.fda.gov/extensions/FPD-QDE-FAERS/FPD-QDE-FAERS.html> ``` r faers_meta() ``` An metadata copy was associated with the package, just set `internal = TRUE`. ``` r faers_meta(internal = TRUE) #> → Using internal FAERS metadata #> Snapshot time: 2024-09-19 00:18:06.64623 #> year quarter period #> <int> <char> <char> #> 1: 2024 q2 April - June #> 2: 2024 q1 January - March #> 3: 2023 q4 October - December #> 4: 2023 q3 July - September #> 5: 2023 q2 April - June #> 6: 2023 q1 January - March #> 7: 2022 q4 October - December #> 8: 2022 q3 July - September #> 9: 2022 q2 April - June #> 10: 2022 q1 January - March #> 11: 2021 q4 October - December #> 12: 2021 q3 July - September #> 13: 2021 q2 April - June #> 14: 2021 q1 January - March #> 15: 2020 q4 October - December #> 16: 2020 q3 July - September #> 17: 2020 q2 April - June #> 18: 2020 q1 January - March #> 19: 2019 q4 October - December #> 20: 2019 q3 July - September #> 21: 2019 q2 April - June #> 22: 2019 q1 January - March #> 23: 2018 q4 October - December #> 24: 2018 q3 July - September #> 25: 2018 q2 April - June #> 26: 2018 q1 January - March #> 27: 2017 q4 October - December #> 28: 2017 q3 July - September #> 29: 2017 q2 April - June #> 30: 2017 q1 January - March #> 31: 2016 q4 October - December #> 32: 2016 q3 July - September #> 33: 2016 q2 April - June #> 34: 2016 q1 January - March #> 35: 2015 q4 October - December #> 36: 2015 q3 July - September #> 37: 2015 q2 April - June #> 38: 2015 q1 January - March #> 39: 2014 q4 October - December #> 40: 2014 q3 July - September #> 41: 2014 q2 April - June #> 42: 2014 q1 January - March #> 43: 2013 q4 October - December #> 44: 2013 q3 July - September #> 45: 2013 q2 April - June #> 46: 2013 q1 January - March #> 47: 2012 q4 October - December #> 48: 2012 q3 July - September #> 49: 2012 q2 April - June #> 50: 2012 q1 January - March #> 51: 2011 q4 October - December #> 52: 2011 q3 July - September #> 53: 2011 q2 April - June #> 54: 2011 q1 January - March #> 55: 2010 q4 October - December #> 56: 2010 q3 July - September #> 57: 2010 q2 April - June #> 58: 2010 q1 January - March #> 59: 2009 q4 October - December #> 60: 2009 q3 July - September #> 61: 2009 q2 April - June #> 62: 2009 q1 January - March #> 63: 2008 q4 October - December #> 64: 2008 q3 July - September #> 65: 2008 q2 April - June #> 66: 2008 q1 January - March #> 67: 2007 q4 October - December #> 68: 2007 q3 July - September #> 69: 2007 q2 April - June #> 70: 2007 q1 January - March #> ascii_urls ascii_file_size #> <char> <char> #> 1: https://fis.fda.gov/content/Exports/faers_ascii_2024q2.zip 63.9MB #> 2: https://fis.fda.gov/content/Exports/faers_ascii_2024q1.zip 63.1MB #> 3: https://fis.fda.gov/content/Exports/faers_ascii_2023Q4.zip 69.1MB #> 4: https://fis.fda.gov/content/Exports/faers_ascii_2023Q3.zip 60.1MB #> 5: https://fis.fda.gov/content/Exports/faers_ascii_2023q2.zip 64.5MB #> 6: 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https://fis.fda.gov/content/Exports/aers_sgml_2009q2.zip 18MB #> 62: https://fis.fda.gov/content/Exports/aers_sgml_2009q1.zip 16MB #> 63: https://fis.fda.gov/content/Exports/aers_sgml_2008q4.zip 16MB #> 64: https://fis.fda.gov/content/Exports/aers_sgml_2008q3.zip 16MB #> 65: https://fis.fda.gov/content/Exports/aers_sgml_2008q2.zip 16MB #> 66: https://fis.fda.gov/content/Exports/aers_sgml_2008q1.zip 15MB #> 67: https://fis.fda.gov/content/Exports/aers_sgml_2007q4.zip 14MB #> 68: https://fis.fda.gov/content/Exports/aers_sgml_2007q3.zip 13MB #> 69: https://fis.fda.gov/content/Exports/aers_sgml_2007q2.zip 12MB #> 70: https://fis.fda.gov/content/Exports/aers_sgml_2007q1.zip 12MB #> [ reached getOption("max.print") -- omitted 13 rows ] ``` ### Download and Parse quarterly data files from FAERS The FAERS Quarterly Data files contain raw data extracted from the AERS database for the indicated time ranges. The quarterly data files, which are available in ASCII or SGML formats, include: - `demo`: demographic and administrative information - `drug`: drug information from the case reports - `reac`: reaction information from the reports - `outc`: patient outcome information from the reports - `rpsr`: information on the source of the reports - `ther`: drug therapy start dates and end dates for the reported drugs - `indi`: contains all “Medical Dictionary for Regulatory Activities” (MedDRA) terms coded for the indications for use (diagnoses) for the reported drugs Generally, we can use `faers()` function to download and parse all quarterly data files from FAERS. Internally, the `faers()` function seamlessly utilizes `faers_download()` and `faers_parse()` to preprocess each quarterly data file from the FAERS repository. The default `format` was `ascii` and will return a `FAERSascii` object. (xml format would also be okay , but presently, the XML file receives only minimal support in the following process.) Some variables has been added into specific field. See `?faers_parse` for details. ``` r # Please make sure to replace dir with your own directory path, as the file # included in the package is a sampled version. data1 <- faers(2004, "q1", dir = system.file("extdata", package = "faers"), compress_dir = tempdir() ) #> Finding 1 file already downloaded: 'aers_ascii_2004q1.zip' ``` ``` r data1 #> FAERS data from 1 Quarterly ascii file #> Total reports: 100 (with duplicates) ``` Furthermore, in cases where multiple quarterly data files are requisite, the `faers_combine()` function is judiciously employed. ``` r data2 <- faers(c(2004, 2017), c("q1", "q2"), dir = system.file("extdata", package = "faers"), compress_dir = tempdir() ) #> Finding 2 files already downloaded: 'aers_ascii_2004q1.zip' and #> 'faers_ascii_2017q2.zip' #> → Combining all 2 <FAERS> Datas ``` ``` r data2 #> FAERS data from 2 Quarterly ascii files #> Total reports: 200 (with duplicates) ``` You can use `faers_get()` to get specific field data, a data.table will be returned. ``` r faers_get(data2, "demo") #> year quarter primaryid caseid i_f_code foll_seq image event_dt #> <int> <char> <char> <char> <char> <int> <char> <int> #> 1: 2004 q1 4263764 4060920 I NA 4263764-6 20020101 #> 2: 2004 q1 4263927 4064250 I NA 4263927-X NA #> 3: 2004 q1 4264001 4062524 I NA 4264001-9 20031218 #> 4: 2004 q1 4264319 4064506 I NA 4264319-X 20031216 #> 5: 2004 q1 4266745 4056689 I NA 4266745-1 20030529 #> --- #> 196: 2017 q2 136874291 13687429 I NA <NA> NA #> 197: 2017 q2 136987441 13698744 I NA <NA> 201706 #> 198: 2017 q2 137054551 13705455 I NA <NA> 20160103 #> 199: 2017 q2 137055661 13705566 I NA <NA> NA #> 200: 2017 q2 137086221 13708622 I NA <NA> NA #> mfr_dt fda_dt rept_cod mfr_num #> <int> <int> <char> <char> #> 1: 20031219 20040102 EXP USA031255171 #> 2: 20031209 20040102 EXP B0317710A #> 3: 20031219 20040102 EXP JP-JNJFOC-20031204393 #> 4: 20031218 20040105 EXP MEDI-0001221 #> 5: 20040105 20040108 EXP FR-GLAXOSMITHKLINE-B0318977A #> --- #> 196: 20170612 20170624 EXP GB-TORRENT-00015363 #> 197: 20170623 20170628 EXP JP-PFIZER INC-2017277430 #> 198: 20170501 20170630 EXP US-BAYER-2017-084170 #> 199: 20140423 20170630 PER US-IPSEN BIOPHARMACEUTICALS, INC.-2014-2195 #> 200: 20151116 20170630 PER US-IPSEN BIOPHARMACEUTICALS, INC.-2015-08780 #> mfr_sndr age age_cod gender e_sub wt #> <char> <num> <char> <char> <char> <num> #> 1: ELI LILLY AND COMPANY 68 YR F N 82.0 #> 2: GLAXOSMITHKLINE GLOBAL CLINICAL SAFETY 58 YR F N NA #> 3: CENTOCOR, INC. 53 YR F N 36.8 #> 4: MEDIMUNE, INC. NA <NA> F N NA #> 5: GLAXOSMITHKLINE 48 YR F Y NA #> --- #> 196: TORRENT NA <NA> <NA> Y NA #> 197: PFIZER NA <NA> <NA> Y NA #> 198: BAYER 84 YR M Y NA #> 199: IPSEN NA <NA> F Y NA #> 200: IPSEN 52 YR F Y NA #> wt_cod rept_dt occp_cod death_dt to_mfr confid v23 caseversion #> <char> <int> <char> <lgcl> <char> <char> <lgcl> <int> #> 1: KG 20031223 <NA> NA <NA> <NA> NA 0 #> 2: <NA> 20031219 <NA> NA <NA> <NA> NA 0 #> 3: KG 20031231 MD NA <NA> <NA> NA 0 #> 4: <NA> 20031231 MD NA <NA> <NA> NA 0 #> 5: <NA> 20040108 CN NA <NA> <NA> NA 0 #> --- #> 196: <NA> 20170624 CN NA <NA> <NA> NA 1 #> 197: <NA> 20170628 MD NA <NA> <NA> NA 1 #> 198: <NA> 20170630 LW NA <NA> <NA> NA 1 #> 199: <NA> 20170630 OT NA <NA> <NA> NA 1 #> 200: <NA> 20170630 MD NA <NA> <NA> NA 1 #> age_in_years country_code sex init_fda_dt auth_num lit_ref #> <num> <char> <char> <int> <char> <char> #> 1: 68 <NA> Female NA <NA> <NA> #> 2: 58 <NA> Female NA <NA> <NA> #> 3: 53 <NA> Female NA <NA> <NA> #> 4: NA <NA> Female NA <NA> <NA> #> 5: 48 <NA> Female NA <NA> <NA> #> --- #> 196: NA GB <NA> 20170624 GB-MHRA-ADR 24016450 <NA> #> 197: NA JP <NA> 20170628 <NA> <NA> #> 198: 84 US Male 20170630 <NA> <NA> #> 199: NA US Female 20170630 <NA> <NA> #> 200: 52 US Female 20170630 <NA> <NA> #> age_grp reporter_country occr_country #> <char> <char> <char> #> 1: <NA> <NA> <NA> #> 2: <NA> <NA> <NA> #> 3: <NA> <NA> <NA> #> 4: <NA> <NA> <NA> #> 5: <NA> <NA> <NA> #> --- #> 196: N GB GB #> 197: <NA> JP JP #> 198: E US US #> 199: <NA> US US #> 200: <NA> US US ``` ### Standardize and De-duplication The `reac` file provides the adverse drug reactions, where it includes the “P.T.” field or the “Preferred Term” level terminology from the Medical Dictionary for Regulatory Activities (MedDRA). The `indi` file contains the drug indications, which also uses the “P.T.” level of MedDRA as a descriptor for the drug indication. In this way, `MedDRA` was necessary to standardize this field and add additional informations, such as `System Organ Classes`. ``` r # you must replace `meddra_path` with the path of uncompressed meddra data data <- faers_standardize(data2, meddra_path) ``` To proceed following steps, we just read a standardized data. ``` r data <- readRDS(system.file("extdata", "standardized_data.rds", package = "faers" )) data #> Standardized FAERS data from 2 Quarterly ascii files #> Total reports: 200 (with duplicates) ``` The internal will save the complete MedDRA data in the `@meddra` slot, MedDRA consists of two components: hierarchy and SMQ data. We can specify these components using the use argument. ``` r faers_meddra(data) #> Hierarchy data for MedDRA (version 26.1) ``` ``` r faers_meddra(data, use = "hierarchy") #> Index: <primary_soc_fg> #> llt_code #> <int> #> 1: 10000001 #> 2: 10000002 #> 3: 10000003 #> 4: 10000004 #> 5: 10000005 #> --- #> 87588: 10089903 #> 87589: 10089904 #> 87590: 10089905 #> 87591: 10089906 #> 87592: 10089907 #> llt_name #> <char> #> 1: "Ventilation" pneumonitis #> 2: 11-beta-hydroxylase deficiency #> 3: 11-oxysteroid activity incr #> 4: 11-oxysteroid activity increased #> 5: 17 ketosteroids urine #> --- #> 87588: Unintentional exposure to product #> 87589: Unintentional exposure to product by child #> 87590: Smouldering systemic mastocytosis #> 87591: Systemic mastocytosis with an associated haematological neoplasm #> 87592: Smouldering myeloma #> pt_code pt_name hlt_code #> <int> <char> <int> #> 1: 10081988 Hypersensitivity pneumonitis 10024972 #> 2: 10000002 11-beta-hydroxylase deficiency 10021608 #> 3: 10033315 Oxycorticosteroids increased 10001339 #> 4: 10033315 Oxycorticosteroids increased 10001339 #> 5: 10000005 17 ketosteroids urine 10038589 #> --- #> 87588: 10073317 Accidental exposure to product 10073316 #> 87589: 10073318 Accidental exposure to product by child 10073316 #> 87590: 10089905 Smouldering systemic mastocytosis 10018845 #> 87591: 10089805 Advanced systemic mastocytosis 10018845 #> 87592: 10035226 Plasma cell myeloma 10074470 #> hlt_name #> <char> #> 1: Lower respiratory tract inflammatory and immunologic conditions #> 2: Inborn errors of steroid synthesis #> 3: Adrenal cortex tests #> 4: Adrenal cortex tests #> 5: Reproductive hormone analyses #> --- #> 87588: Accidental exposures to product #> 87589: Accidental exposures to product #> 87590: Haematologic neoplasms NEC #> 87591: Haematologic neoplasms NEC #> 87592: Plasma cell myelomas #> hlgt_code #> <int> #> 1: 10024967 #> 2: 10027424 #> 3: 10014706 #> 4: 10014706 #> 5: 10014706 #> --- #> 87588: 10079145 #> 87589: 10079145 #> 87590: 10018865 #> 87591: 10018865 #> 87592: 10035227 #> hlgt_name #> <char> #> 1: Lower respiratory tract disorders (excl obstruction and infection) #> 2: Metabolic and nutritional disorders congenital #> 3: Endocrine investigations (incl sex hormones) #> 4: Endocrine investigations (incl sex hormones) #> 5: Endocrine investigations (incl sex hormones) #> --- #> 87588: Medication errors and other product use errors and issues #> 87589: Medication errors and other product use errors and issues #> 87590: Haematopoietic neoplasms (excl leukaemias and lymphomas) #> 87591: Haematopoietic neoplasms (excl leukaemias and lymphomas) #> 87592: Plasma cell neoplasms #> soc_code #> <int> #> 1: 10038738 #> 2: 10010331 #> 3: 10022891 #> 4: 10022891 #> 5: 10022891 #> --- #> 87588: 10022117 #> 87589: 10022117 #> 87590: 10029104 #> 87591: 10029104 #> 87592: 10029104 #> soc_name #> <char> #> 1: Respiratory, thoracic and mediastinal disorders #> 2: Congenital, familial and genetic disorders #> 3: Investigations #> 4: Investigations #> 5: Investigations #> --- #> 87588: Injury, poisoning and procedural complications #> 87589: Injury, poisoning and procedural complications #> 87590: Neoplasms benign, malignant and unspecified (incl cysts and polyps) #> 87591: Neoplasms benign, malignant and unspecified (incl cysts and polyps) #> 87592: Neoplasms benign, malignant and unspecified (incl cysts and polyps) #> soc_abbrev primary_soc_fg #> <char> <char> #> 1: Resp Y #> 2: Cong Y #> 3: Inv Y #> 4: Inv Y #> 5: Inv Y #> --- #> 87588: Inj&P Y #> 87589: Inj&P Y #> 87590: Neopl Y #> 87591: Neopl Y #> 87592: Neopl Y ``` The internal will include a `meddra_hierarchy_idx` column that represents the index of the MedDRA hierarchy data in the `indi` and `reac` field when standardized. Additionally, the columns `meddra_hierarchy_from`, `meddra_code`, and `meddra_pt` will also be added which provide standardized names of the original PT (indi: indi_pt; reac: pt) (refer to `ASC_NTS.pdf` or `ASC_NTS.docx` in the FAERS quarterly file for the meanings of the original names, most original names will remain unchanged except for some names different between FAERS quarterly files, see `?faers_parse` for details). We can retrieve this data using the `faers_meddra()` function. When we use `faers_get()` to retrieve `indi` or `reac` data from the standardized `FAERSascii` object, the meddra hierarchy columns are automatically added to the returned data.table. ``` r faers_get(data, "indi") #> year quarter primaryid indi_drug_seq #> <int> <char> <char> <int> #> 1: 2004 q1 4263927 1004493661 #> 2: 2004 q1 4264001 1004493811 #> 3: 2004 q1 4264001 1004520441 #> 4: 2004 q1 4264001 1004520538 #> 5: 2004 q1 4264319 1004494389 #> --- #> 376: 2017 q2 137054551 15 #> 377: 2017 q2 137054551 16 #> 378: 2017 q2 137054551 17 #> 379: 2017 q2 137055661 1 #> 380: 2017 q2 137086221 1 #> indi_pt caseid meddra_hierarchy_from #> <char> <char> <char> #> 1: DIABETES MELLITUS NON-INSULIN-DEPENDENT <NA> llt #> 2: RHEUMATOID ARTHRITIS <NA> llt #> 3: RHEUMATOID ARTHRITIS <NA> llt #> 4: RHEUMATOID ARTHRITIS <NA> llt #> 5: ANTIVIRAL PROPHYLAXIS <NA> llt #> --- #> 376: Gastrooesophageal reflux disease 13705455 llt #> 377: Colitis ischaemic 13705455 llt #> 378: Blood cholesterol increased 13705455 llt #> 379: Acromegaly 13705566 llt #> 380: Product used for unknown indication 13708622 llt #> meddra_code meddra_pt llt_code #> <char> <char> <int> #> 1: 10012613 Diabetes mellitus non-insulin-dependent 10012613 #> 2: 10039073 Rheumatoid arthritis 10039073 #> 3: 10039073 Rheumatoid arthritis 10039073 #> 4: 10039073 Rheumatoid arthritis 10039073 #> 5: 10049087 Antiviral prophylaxis 10049087 #> --- #> 376: 10017885 Gastrooesophageal reflux disease 10017885 #> 377: 10009895 Colitis ischaemic 10009895 #> 378: 10005425 Blood cholesterol increased 10005425 #> 379: 10000599 Acromegaly 10000599 #> 380: 10070592 Product used for unknown indication 10070592 #> llt_name pt_code #> <char> <int> #> 1: Diabetes mellitus non-insulin-dependent 10067585 #> 2: Rheumatoid arthritis 10039073 #> 3: Rheumatoid arthritis 10039073 #> 4: Rheumatoid arthritis 10039073 #> 5: Antiviral prophylaxis 10049087 #> --- #> 376: Gastrooesophageal reflux disease 10017885 #> 377: Colitis ischaemic 10009895 #> 378: Blood cholesterol increased 10005425 #> 379: Acromegaly 10000599 #> 380: Product used for unknown indication 10070592 #> pt_name hlt_code #> <char> <int> #> 1: Type 2 diabetes mellitus 10012602 #> 2: Rheumatoid arthritis 10039078 #> 3: Rheumatoid arthritis 10039078 #> 4: Rheumatoid arthritis 10039078 #> 5: Antiviral prophylaxis 10002790 #> --- #> 376: Gastrooesophageal reflux disease 10017933 #> 377: Colitis ischaemic 10009888 #> 378: Blood cholesterol increased 10008651 #> 379: Acromegaly 10002700 #> 380: Product used for unknown indication 10027700 #> hlt_name hlgt_code #> <char> <int> #> 1: Diabetes mellitus (incl subtypes) 10018424 #> 2: Rheumatoid arthropathies 10023213 #> 3: Rheumatoid arthropathies 10023213 #> 4: Rheumatoid arthropathies 10023213 #> 5: Antiinfective therapies 10043413 #> --- #> 376: Gastrointestinal atonic and hypomotility disorders NEC 10017977 #> 377: Colitis (excl infective) 10017969 #> 378: Cholesterol analyses 10024580 #> 379: Anterior pituitary hyperfunction 10021112 #> 380: Therapeutic procedures NEC 10043413 #> hlgt_name soc_code #> <char> <int> #> 1: Glucose metabolism disorders (incl diabetes mellitus) 10027433 #> 2: Joint disorders 10028395 #> 3: Joint disorders 10028395 #> 4: Joint disorders 10028395 #> 5: Therapeutic procedures and supportive care NEC 10042613 #> --- #> 376: Gastrointestinal motility and defaecation conditions 10017947 #> 377: Gastrointestinal inflammatory conditions 10017947 #> 378: Lipid analyses 10022891 #> 379: Hypothalamus and pituitary gland disorders 10014698 #> 380: Therapeutic procedures and supportive care NEC 10042613 #> soc_name soc_abbrev primary_soc_fg #> <char> <char> <char> #> 1: Metabolism and nutrition disorders Metab Y #> 2: Musculoskeletal and connective tissue disorders Musc Y #> 3: Musculoskeletal and connective tissue disorders Musc Y #> 4: Musculoskeletal and connective tissue disorders Musc Y #> 5: Surgical and medical procedures Surg Y #> --- #> 376: Gastrointestinal disorders Gastr Y #> 377: Gastrointestinal disorders Gastr Y #> 378: Investigations Inv Y #> 379: Endocrine disorders Endo Y #> 380: Surgical and medical procedures Surg Y ``` ``` r faers_get(data, "reac") #> year quarter primaryid pt #> <int> <char> <char> <char> #> 1: 2004 q1 4263764 BLOOD PRESSURE INCREASED #> 2: 2004 q1 4263764 DIABETES MELLITUS INADEQUATE CONTROL #> 3: 2004 q1 4263927 ACCELERATED HYPERTENSION #> 4: 2004 q1 4263927 DIZZINESS #> 5: 2004 q1 4263927 FATIGUE #> --- #> 638: 2017 q2 136987441 Interstitial lung disease #> 639: 2017 q2 137054551 Haemothorax #> 640: 2017 q2 137055661 Diarrhoea #> 641: 2017 q2 137055661 Inappropriate schedule of drug administration #> 642: 2017 q2 137086221 Menstrual disorder #> v3 caseid drug_rec_act meddra_hierarchy_from meddra_code #> <lgcl> <char> <lgcl> <char> <char> #> 1: NA <NA> NA llt 10005750 #> 2: NA <NA> NA llt 10012607 #> 3: NA <NA> NA llt 10000358 #> 4: NA <NA> NA llt 10013573 #> 5: NA <NA> NA llt 10016256 #> --- #> 638: NA 13698744 NA llt 10022611 #> 639: NA 13705455 NA llt 10019027 #> 640: NA 13705566 NA llt 10012735 #> 641: NA 13705566 NA llt 10021597 #> 642: NA 13708622 NA llt 10027327 #> meddra_pt llt_code #> <char> <int> #> 1: Blood pressure increased 10005750 #> 2: Diabetes mellitus inadequate control 10012607 #> 3: Accelerated hypertension 10000358 #> 4: Dizziness 10013573 #> 5: Fatigue 10016256 #> --- #> 638: Interstitial lung disease 10022611 #> 639: Haemothorax 10019027 #> 640: Diarrhoea 10012735 #> 641: Inappropriate schedule of drug administration 10021597 #> 642: Menstrual disorder 10027327 #> llt_name pt_code #> <char> <int> #> 1: Blood pressure increased 10005750 #> 2: Diabetes mellitus inadequate control 10012607 #> 3: Accelerated hypertension 10000358 #> 4: Dizziness 10013573 #> 5: Fatigue 10016256 #> --- #> 638: Interstitial lung disease 10022611 #> 639: Haemothorax 10019027 #> 640: Diarrhoea 10012735 #> 641: Inappropriate schedule of drug administration 10081572 #> 642: Menstrual disorder 10027327 #> pt_name hlt_code #> <char> <int> #> 1: Blood pressure increased 10047110 #> 2: Diabetes mellitus inadequate control 10012602 #> 3: Accelerated hypertension 10000356 #> 4: Dizziness 10029306 #> 5: Fatigue 10003550 #> --- #> 638: Interstitial lung disease 10033979 #> 639: Haemothorax 10035761 #> 640: Diarrhoea 10012736 #> 641: Inappropriate schedule of product administration 10079147 #> 642: Menstrual disorder 10027335 #> hlt_name hlgt_code #> <char> <int> #> 1: Vascular tests NEC (incl blood pressure) 10007512 #> 2: Diabetes mellitus (incl subtypes) 10018424 #> 3: Accelerated and malignant hypertension 10057166 #> 4: Neurological signs and symptoms NEC 10029305 #> 5: Asthenic conditions 10018073 #> --- #> 638: Parenchymal lung disorders NEC 10024967 #> 639: Pneumothorax and pleural effusions NEC 10035597 #> 640: Diarrhoea (excl infective) 10017977 #> 641: Product administration errors and issues 10079145 #> 642: Menstruation and uterine bleeding NEC 10013326 #> hlgt_name #> <char> #> 1: Cardiac and vascular investigations (excl enzyme tests) #> 2: Glucose metabolism disorders (incl diabetes mellitus) #> 3: Vascular hypertensive disorders #> 4: Neurological disorders NEC #> 5: General system disorders NEC #> --- #> 638: Lower respiratory tract disorders (excl obstruction and infection) #> 639: Pleural disorders #> 640: Gastrointestinal motility and defaecation conditions #> 641: Medication errors and other product use errors and issues #> 642: Menstrual cycle and uterine bleeding disorders #> soc_code soc_name soc_abbrev #> <int> <char> <char> #> 1: 10022891 Investigations Inv #> 2: 10027433 Metabolism and nutrition disorders Metab #> 3: 10047065 Vascular disorders Vasc #> 4: 10029205 Nervous system disorders Nerv #> 5: 10018065 General disorders and administration site conditions Genrl #> --- #> 638: 10038738 Respiratory, thoracic and mediastinal disorders Resp #> 639: 10038738 Respiratory, thoracic and mediastinal disorders Resp #> 640: 10017947 Gastrointestinal disorders Gastr #> 641: 10022117 Injury, poisoning and procedural complications Inj&P #> 642: 10038604 Reproductive system and breast disorders Repro #> primary_soc_fg #> <char> #> 1: Y #> 2: Y #> 3: Y #> 4: Y #> 5: Y #> --- #> 638: Y #> 639: Y #> 640: Y #> 641: Y #> 642: Y ``` One limitation of FAERS database is duplicate and incomplete reports. There are many instances of duplicative reports and some reports do not contain all the necessary information. We deemed two cases to be identical if they exhibited a full concordance across drugs administered, and adverse reactions and but showed discrepancies in one or none of the following fields: gender, age, reporting country, event date, start date, and drug indications. ``` r data <- faers_dedup(data) #> → deduplication from the same source by retain the most recent report #> → merging `drug`, `indi`, `ther`, and `reac` data #> → deduplication from multiple sources by matching sex, age, reporting country, event date, start date, drug indications, drugs administered, and adverse reactions ``` ``` r data #> Standardized and De-duplicated FAERS data from 2 Quarterly ascii files #> Total unique reports: 200 ``` ### Pharmacovigilance analysis Pharmacovigilance is the science and activities relating to the detection, assessment, understanding and prevention of adverse effects or any other medicine/vaccine related problem. To mine the signals of “insulin”, we start by using the `faers_filter()` function. In this function, the `.fn` argument should be a function that accepts data specified in `.field`. It is important to note that `.fn` should always return the `primaryid` that you want to keep. To enhance our analysis, it would be advantageous to include all drug synonym names for `insulin`. These synonyms can be obtained by querying sources such as <https://go.drugbank.com/> or alternative databases. Furthermore, we extract the brand names of insulin from the [Drugs@FDA](https://www.fda.gov/drugs/drug-approvals-and-databases/drugsfda-data-files) dataset, which can be easily obtained using the `fda_drugs()` function. ``` r insulin_names <- "insulin" insulin_pattern <- paste(insulin_names, collapse = "|") fda_insulin <- fda_drugs()[ grepl(insulin_pattern, ActiveIngredient, ignore.case = TRUE) ] #> → Using Drugs@FDA data from cached #> '/home/yun/.cache/R/faers/faers/fdadrugs/fda_drugs_data_2024-09-19.zip' #> Snapshot date: 2024-09-19 #> Warning: One or more parsing issues, call `problems()` on your data frame for details, #> e.g.: #> dat <- vroom(...) #> problems(dat) ``` ``` r insulin_pattern <- paste0( unique(tolower(c(insulin_names, fda_insulin$DrugName))), collapse = "|" ) insulin_data <- faers_filter(data, .fn = function(x) { idx <- grepl(insulin_pattern, x$drugname, ignore.case = TRUE) | grepl(insulin_pattern, x$prod_ai, ignore.case = TRUE) x[idx, primaryid] }, .field = "drug") insulin_data #> Standardized and De-duplicated FAERS data from 2 Quarterly ascii files #> Total unique reports: 9 ``` Then, signal can be easily obtained with `faers_phv_signal()` which internally use `faers_phv_table()` to create a contingency table and use `phv_signal()` to do signal analysis specified in `.methods` argument. By default, all supported signal analysis methods will be run, including “ror”, “prr”, “chisq”, “bcpnn_norm”, “bcpnn_mcmc”, “obsexp_shrink”, “fisher”, and “ebgm”. The most important argument for this function is `.object`, which should be a de-duplicated FAERSascii object containing the data for the drugs or traits of interest. Additionally, you must specify either `.full`, which represents the background distributions data (usually the entire FAERS data), or you can specify `.object2`, which should be the control data or another drug of interest for comparison. ``` r insulin_signals <- faers_phv_signal(insulin_data, .full = data, BPPARAM = BiocParallel::SerialParam(RNGseed = 1L) ) #> ℹ Running `phv_ror()` #> ℹ Running `phv_prr()` #> ℹ Running `phv_chisq()` #> ℹ Running `phv_bcpnn_norm()` #> ℹ Running `phv_bcpnn_mcmc()` #> ℹ Running `phv_obsexp_shrink()` #> ℹ Running `phv_fisher()` #> ℹ Running `phv_ebgm()` ``` ``` r insulin_signals #> Key: <soc_name> #> soc_name a #> <char> <int> #> 1: Blood and lymphatic system disorders 1 #> 2: Cardiac disorders 2 #> 3: Congenital, familial and genetic disorders 0 #> 4: Ear and labyrinth disorders 0 #> 5: Endocrine disorders 0 #> 6: Eye disorders 0 #> 7: Gastrointestinal disorders 1 #> 8: General disorders and administration site conditions 3 #> 9: Hepatobiliary disorders 0 #> 10: Immune system disorders 1 #> 11: Infections and infestations 1 #> 12: Injury, poisoning and procedural complications 2 #> 13: Investigations 5 #> 14: Metabolism and nutrition disorders 2 #> 15: Musculoskeletal and connective tissue disorders 2 #> b c d expected ror ror_ci_low ror_ci_high prr #> <int> <int> <int> <num> <num> <num> <num> <num> #> 1: 8 10 181 0.495 2.2625000 0.25725215 19.898400 2.1222222 #> 2: 7 14 177 0.720 3.6122449 0.68476430 19.055189 3.0317460 #> 3: 9 1 190 0.045 0.0000000 0.00000000 NaN 0.0000000 #> 4: 9 1 190 0.045 0.0000000 0.00000000 NaN 0.0000000 #> 5: 9 1 190 0.045 0.0000000 0.00000000 NaN 0.0000000 #> 6: 9 3 188 0.135 0.0000000 0.00000000 NaN 0.0000000 #> 7: 8 33 158 1.530 0.5984848 0.07238296 4.948459 0.6430976 #> 8: 6 63 128 2.970 1.0158730 0.24595627 4.195860 1.0105820 #> 9: 9 7 184 0.315 0.0000000 0.00000000 NaN 0.0000000 #> 10: 8 5 186 0.270 4.6500000 0.48490936 44.590808 4.2444444 #> 11: 8 20 171 0.945 1.0687500 0.12702886 8.991866 1.0611111 #> 12: 7 30 161 1.440 1.5333333 0.30372417 7.740942 1.4148148 #> 13: 4 23 168 1.260 9.1304348 2.28530339 36.478675 4.6135266 #> 14: 7 16 175 0.810 3.1250000 0.59851341 16.316468 2.6527778 #> 15: 7 16 175 0.810 3.1250000 0.59851341 16.316468 2.6527778 #> prr_ci_low prr_ci_high chisq chisq_pvalue bcpnn_norm_ic #> <num> <num> <num> <num> <num> #> 1: 0.30379118 14.825405 5.596283e-05 0.994031217 0.07104720 #> 2: 0.80811718 11.373950 9.617573e-01 0.326744514 0.56756743 #> 3: 0.00000000 NaN 3.656454e-27 1.000000000 -0.51110211 #> 4: 0.00000000 NaN 3.656454e-27 1.000000000 -0.51110211 #> 5: 0.00000000 NaN 3.656454e-27 1.000000000 -0.51110211 #> 6: 0.00000000 NaN 6.503567e-28 1.000000000 -0.83645056 #> 7: 0.09874593 4.188270 7.421123e-04 0.978266926 -0.74210295 #> 8: 0.39248199 2.602096 7.473582e-32 1.000000000 -0.20701337 #> 9: 0.00000000 NaN 1.786975e-29 1.000000000 -1.15078008 #> 10: 0.55173607 32.652041 2.115031e-01 0.645591807 0.35698592 #> 11: 0.15969124 7.050836 4.127312e-31 1.000000000 -0.34437600 #> 12: 0.39906207 5.016014 3.116430e-03 0.955481225 0.03350040 #> 13: 2.29367967 9.279686 1.014420e+01 0.001447562 1.27887175 #> 14: 0.71637974 9.823324 6.763452e-01 0.410848097 0.48796446 #> 15: 0.71637974 9.823324 6.763452e-01 0.410848097 0.48796446 #> bcpnn_norm_ic_ci_low bcpnn_norm_ic_ci_high bcpnn_mcmc_ic #> <num> <num> <num> #> 1: -2.4976231 2.639718 0.59289239 #> 2: -1.5265734 2.661708 1.03548539 #> 3: -4.8776285 3.855424 -0.12173930 #> 4: -4.8776285 3.855424 -0.12173930 #> 5: -4.8776285 3.855424 -0.12173930 #> 6: -4.8584283 3.185527 -0.34296004 #> 7: -3.2168766 1.732671 -0.43639278 #> 8: -1.9734584 1.559432 0.01244120 #> 9: -5.0194729 2.717913 -0.70378441 #> 10: -2.3142523 3.028224 0.96326361 #> 11: -2.8481952 2.159443 0.05418696 #> 12: -2.0013075 2.068308 0.36601183 #> 13: -0.2922032 2.849947 1.64403679 #> 14: -1.5933747 2.569304 0.93273250 #> 15: -1.5933747 2.569304 0.93273250 #> bcpnn_mcmc_ic_ci_low bcpnn_mcmc_ic_ci_high oe_ratio oe_ratio_ci_low #> <num> <num> <num> <num> #> 1: -3.0102202 2.0880732 0.59219407 -3.1909068 #> 2: -1.3187573 2.2101554 1.03504695 -1.5580207 #> 3: -10.0407092 2.1339131 -0.12432814 -9.9495140 #> 4: -9.9798292 2.1441716 -0.12432814 -9.9461122 #> 5: -10.0105588 2.1297171 -0.12432814 -10.0407092 #> 6: -10.3155180 1.9044035 -0.34482850 -10.2035257 #> 7: -4.0344771 1.0414647 -0.43651723 -4.2196181 #> 8: -1.7245681 0.9206723 0.01241926 -2.0569464 #> 9: -10.5755765 1.5508494 -0.70487196 -10.5739398 #> 10: -2.6472043 2.4553831 0.96203215 -2.8210687 #> 11: -3.5870730 1.5310781 0.05389301 -3.7292078 #> 12: -1.9666079 1.5187159 0.36587144 -2.2271962 #> 13: 0.4991892 2.3671228 1.64385619 0.0816764 #> 14: -1.3982876 2.0999196 0.93236128 -1.6607064 #> 15: -1.4095817 2.0956930 0.93236128 -1.6607064 #> oe_ratio_ci_high odds_ratio odds_ratio_ci_low odds_ratio_ci_high #> <num> <num> <num> <num> #> 1: 2.2796203 2.2497947 0.04640325 19.939227 #> 2: 2.4264491 3.5751799 0.33257276 21.377226 #> 3: 2.1496123 0.0000000 0.00000000 817.112299 #> 4: 2.1269540 0.0000000 0.00000000 817.112299 #> 5: 2.1339131 0.0000000 0.00000000 817.112299 #> 6: 1.9277455 0.0000000 0.00000000 55.536645 #> 7: 1.2509090 0.5998010 0.01309984 4.736220 #> 8: 1.2189129 1.0158008 0.15919644 4.943911 #> 9: 1.5433315 0.0000000 0.00000000 16.615730 #> 10: 2.6494584 4.5816265 0.08749672 48.962674 #> 11: 1.7413193 1.0684062 0.02295250 8.703699 #> 12: 1.7572736 1.5296110 0.14818709 8.572929 #> 13: 2.6284559 8.9547789 1.78787699 48.608711 #> 14: 2.3237635 3.0984073 0.29094788 18.237662 #> 15: 2.3237635 3.0984073 0.29094788 18.237662 #> fisher_pvalue ebgm ebgm_ci_low ebgm_ci_high #> <num> <num> <num> <num> #> 1: 0.40543496 1.395816 0.91 2.05 #> 2: 0.15526163 1.435648 0.95 2.09 #> 3: 1.00000000 1.373343 0.89 2.03 #> 4: 1.00000000 1.373343 0.89 2.03 #> 5: 1.00000000 1.373343 0.89 2.03 #> 6: 1.00000000 1.365913 0.89 2.02 #> 7: 1.00000000 1.315687 0.86 1.93 #> 8: 1.00000000 1.321095 0.88 1.91 #> 9: 1.00000000 1.351290 0.88 2.00 #> 10: 0.24411452 1.414544 0.93 2.08 #> 11: 1.00000000 1.359809 0.89 1.99 #> 12: 0.63824281 1.378028 0.91 2.00 #> 13: 0.00322619 1.560910 1.06 2.22 #> 14: 0.18867787 1.428183 0.94 2.08 #> 15: 0.18867787 1.428183 0.94 2.08 #> [ reached getOption("max.print") -- omitted 13 rows ] ``` The column containing the events of interest can be specified using an atomic character in the `.events` (default: “soc_name”) argument. The combination of all specified columns will define the unique event. Additionally, we can control which field data to find the columns in the `.field` (default: “reac”) argument. ``` r insulin_signals_hlgt <- faers_phv_signal( insulin_data, .events = "hlgt_name", .full = data, BPPARAM = BiocParallel::SerialParam(RNGseed = 1L) ) #> ℹ Running `phv_ror()` #> ℹ Running `phv_prr()` #> ℹ Running `phv_chisq()` #> ℹ Running `phv_bcpnn_norm()` #> ℹ Running `phv_bcpnn_mcmc()` #> ℹ Running `phv_obsexp_shrink()` #> ℹ Running `phv_fisher()` #> ℹ Running `phv_ebgm()` ``` ``` r insulin_signals_hlgt #> Key: <hlgt_name> #> hlgt_name a b #> <char> <int> <int> #> 1: Abortions and stillbirth 0 9 #> 2: Acid-base disorders 0 9 #> 3: Administration site reactions 0 9 #> 4: Allergic conditions 1 8 #> 5: Anaemias nonhaemolytic and marrow depression 1 8 #> --- #> 136: Viral infectious disorders 0 9 #> 137: Vision disorders 0 9 #> 138: Vitamin related disorders 0 9 #> 139: Vulvovaginal disorders (excl infections and inflammations) 0 9 #> 140: White blood cell disorders 0 9 #> c d expected ror ror_ci_low ror_ci_high prr prr_ci_low #> <int> <int> <num> <num> <num> <num> <num> <num> #> 1: 1 190 0.045 0.000000 0.0000000 NaN 0.000000 0.0000000 #> 2: 1 190 0.045 0.000000 0.0000000 NaN 0.000000 0.0000000 #> 3: 7 184 0.315 0.000000 0.0000000 NaN 0.000000 0.0000000 #> 4: 3 188 0.180 7.833333 0.7313919 83.89635 7.074074 0.8140454 #> 5: 2 189 0.135 11.812500 0.9671714 144.27139 10.611111 1.0580416 #> --- #> 136: 5 186 0.225 0.000000 0.0000000 NaN 0.000000 0.0000000 #> 137: 2 189 0.090 0.000000 0.0000000 NaN 0.000000 0.0000000 #> 138: 1 190 0.045 0.000000 0.0000000 NaN 0.000000 0.0000000 #> 139: 1 190 0.045 0.000000 0.0000000 NaN 0.000000 0.0000000 #> 140: 4 187 0.180 0.000000 0.0000000 NaN 0.000000 0.0000000 #> prr_ci_high chisq chisq_pvalue bcpnn_norm_ic bcpnn_norm_ic_ci_low #> <num> <num> <num> <num> <num> #> 1: NaN 3.656454e-27 1.0000000 -0.5111021 -4.877628 #> 2: NaN 3.656454e-27 1.0000000 -0.5111021 -4.877628 #> 3: NaN 1.786975e-29 1.0000000 -1.1507801 -5.019473 #> 4: 61.47388 6.078522e-01 0.4355976 0.5096749 -2.261566 #> 5: 106.41895 1.049089e+00 0.3057170 0.6062445 -2.253967 #> --- #> 136: NaN 1.530722e-28 1.0000000 -1.0141202 -4.932568 #> 137: NaN 3.065558e-31 1.0000000 -0.7103510 -4.841467 #> 138: NaN 3.656454e-27 1.0000000 -0.5111021 -4.877628 #> 139: NaN 3.656454e-27 1.0000000 -0.5111021 -4.877628 #> 140: NaN 7.493363e-31 1.0000000 -0.9330202 -4.892221 #> bcpnn_norm_ic_ci_high bcpnn_mcmc_ic bcpnn_mcmc_ic_ci_low #> <num> <num> <num> #> 1: 3.855424 -0.1217393 -9.949514 #> 2: 3.855424 -0.1217393 -9.946112 #> 3: 2.717913 -0.7037844 -10.647371 #> 4: 3.280916 1.1429684 -2.451903 #> 5: 3.466456 1.2420025 -2.377199 #> --- #> 136: 2.904328 -0.5346491 -10.419653 #> 137: 3.420765 -0.2366002 -10.059217 #> 138: 3.855424 -0.1217393 -10.109186 #> 139: 3.855424 -0.1217393 -9.993573 #> 140: 3.026180 -0.4419941 -10.270609 #> bcpnn_mcmc_ic_ci_high oe_ratio oe_ratio_ci_low oe_ratio_ci_high #> <num> <num> <num> <num> #> 1: 2.149612 -0.1243281 -9.949514 2.149612 #> 2: 2.126954 -0.1243281 -9.946112 2.126954 #> 3: 1.546410 -0.7048720 -10.647371 1.546410 #> 4: 2.637610 1.1413558 -2.641745 2.828782 #> 5: 2.723221 1.2401340 -2.542967 2.927560 #> --- #> 136: 1.718654 -0.5360529 -10.393320 1.725435 #> 137: 2.016519 -0.2387869 -10.152698 2.033643 #> 138: 2.135864 -0.1243281 -10.057893 2.136312 #> 139: 2.140545 -0.1243281 -10.025352 2.129330 #> 140: 1.813233 -0.4436067 -10.299000 1.794391 #> odds_ratio odds_ratio_ci_low odds_ratio_ci_high fisher_pvalue ebgm #> <num> <num> <num> <num> <num> #> 1: 0.000000 0.0000000 817.11230 1.0000000 1.118622 #> 2: 0.000000 0.0000000 817.11230 1.0000000 1.118622 #> 3: 0.000000 0.0000000 16.61573 1.0000000 0.998863 #> 4: 7.641475 0.1327801 108.45121 0.1693996 1.468797 #> 5: 11.402720 0.1781480 241.08148 0.1296368 1.553892 #> --- #> 136: 0.000000 0.0000000 25.82927 1.0000000 1.026924 #> 137: 0.000000 0.0000000 118.46131 1.0000000 1.090039 #> 138: 0.000000 0.0000000 817.11230 1.0000000 1.118622 #> 139: 0.000000 0.0000000 817.11230 1.0000000 1.118622 #> 140: 0.000000 0.0000000 35.42367 1.0000000 1.044722 #> ebgm_ci_low ebgm_ci_high #> <num> <num> #> 1: 0.92 4.64 #> 2: 0.92 4.64 #> 3: 0.92 4.61 #> 4: 0.92 4.66 #> 5: 0.92 4.66 #> --- #> 136: 0.92 4.62 #> 137: 0.92 4.64 #> 138: 0.92 4.64 #> 139: 0.92 4.64 #> 140: 0.92 4.63 ``` ## sessionInfo ``` r sessionInfo() #> R version 4.4.0 (2024-04-24) #> Platform: x86_64-pc-linux-gnu #> Running under: Ubuntu 24.04 LTS #> #> Matrix products: default #> BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/libmkl_rt.so; LAPACK version 3.8.0 #> #> locale: #> [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8 #> [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8 #> [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C #> [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C #> #> time zone: Asia/Shanghai #> tzcode source: system (glibc) #> #> attached base packages: #> [1] stats graphics grDevices utils datasets methods base #> #> other attached packages: #> [1] faers_1.1.6 #> #> loaded via a namespace (and not attached): #> [1] Matrix_1.7-0 bit_4.0.5 gtable_0.3.5 #> [4] dplyr_1.1.4 compiler_4.4.0 crayon_1.5.3 #> [7] tidyselect_1.2.1 MatrixModels_0.5-3 parallel_4.4.0 #> [10] scales_1.3.0 splines_4.4.0 BiocParallel_1.38.0 #> [13] yaml_2.3.8 fastmap_1.2.0 lattice_0.22-6 #> [16] coda_0.19-4.1 R6_2.5.1 ggplot2_3.5.1 #> [19] generics_0.1.3 MCMCpack_1.7-0 knitr_1.47 #> [22] MASS_7.3-61 tibble_3.2.1 openEBGM_0.9.1 #> [25] munsell_0.5.1 pillar_1.9.0 tzdb_0.4.0 #> [28] rlang_1.1.4 utf8_1.2.4 xfun_0.45 #> [31] bit64_4.0.5 cli_3.6.3 magrittr_2.0.3 #> [34] mcmc_0.9-8 digest_0.6.36 grid_4.4.0 #> [37] vroom_1.6.5 quantreg_5.98 lifecycle_1.0.4 #> [40] vctrs_0.6.5 evaluate_0.24.0 SparseM_1.84 #> [43] glue_1.7.0 data.table_1.15.4 codetools_0.2-20 #> [46] survival_3.7-0 colorspace_2.1-0 fansi_1.0.6 #> [49] rmarkdown_2.27 tools_4.4.0 pkgconfig_2.0.3 #> [52] htmltools_0.5.8.1 ```