Package: smdi 0.3.2

Janick Weberpals

smdi: Perform Structural Missing Data Investigations

An easy to use implementation of routine structural missing data diagnostics with functions to visualize the proportions of missing observations, investigate missing data patterns and conduct various empirical missing data diagnostic tests. Reference: Weberpals J, Raman SR, Shaw PA, Lee H, Hammill BG, Toh S, Connolly JG, Dandreo KJ, Tian F, Liu W, Li J, Hernández-Muñoz JJ, Glynn RJ, Desai RJ. smdi: an R package to perform structural missing data investigations on partially observed confounders in real-world evidence studies. JAMIA Open. 2024 Jan 31;7(1):ooae008. <doi:10.1093/jamiaopen/ooae008>.

Authors:Janick Weberpals [aut, cre, cph]

smdi_0.3.2.tar.gz
smdi_0.3.2.tar.gz(r-4.7-any)smdi_0.3.2.tar.gz(r-4.6-any)
smdi_0.3.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
smdi/json (API)

# Install 'smdi' in R:
install.packages('smdi', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Pkgdown/docs site:https://janickweberpals.gitlab-pages.partners.org

Datasets:

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

3.34 score 44 scripts 280 downloads 13 exports 154 dependencies

Last updated from:0e5be32c4d. Checks:4 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK241
source / vignettesOK432
linux-release-x86_64OK247
wasm-releaseOK180

Exports:gg_miss_upsetmd.patternsmdi_asmdsmdi_check_covarsmdi_diagnosesmdi_hotellingsmdi_littlesmdi_na_indicatorsmdi_outcomesmdi_rfsmdi_style_gtsmdi_summarizesmdi_vis

Dependencies:backportsbase64encbigDbitbit64bitopsbootbroombslibcachemcaretclassclicliprclockcodetoolscommonmarkcorpcorcpp11crayoncurldata.tableDBIdiagramdigestdplyre1071evaluatefarverfastDummiesfastmapfontawesomeforcatsforeachfsfuturefuture.applygdatagenericsggplot2glmnetglobalsgluegmodelsgowergridExtragtgtablegtoolshardhathavenhighrhmsHotellinghtmltoolshtmlwidgetsipredisobanditeratorsjomojquerylibjsonlitejuicyjuiceKernSmoothknitrlabelinglabelledlatticelavalifecyclelistenvlitedownlme4lubridatemagrittrmarkdownMASSMatrixmemoisemicemimeminqamitmlmitoolsModelMetricsnaniarnlmenloptrnnetnormnumDerivordinalpanparallellypillarpkgconfigplyrprettyunitspROCprodlimprogressprogressrproxypurrrR6randomForestrappdirsrbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreactablereactRreadrrecipesreformulasreshape2rlangrmarkdownrpartS7sassscalesshapesparsevctrsSQUAREMstringistringrsurveysurvivaltableonetibbletidyrtidyselecttimechangetimeDatetinytextzdbucminfUpSetRutf8V8vctrsviridisviridisLitevisdatvroomwithrxfunxml2yamlzoo

Data generation
smdi dataset background | Exposure and outcome | Confounders | Missingness | Overview covariates/confounder structure | Simulation of covariates and exposure | Simulate time-to-event | Kaplan-Meier estimates | Cox proportional hazards | Export smdi_data_complete | Introduce missingness | Missing complete at random | Missing at random | Missing not at random - value | Assemble final dataset | Export smdi_data
Rendered froma_data_generation.Rmdusingknitr::rmarkdown

Last update: 2024-10-05
Started: 2023-07-17

Multivariate missingness and monotonicity
Multivariate missing data in smdi | Established taxonomies | How does smdi handle multivariate missingness? | Lab 1 analyzed without Lab 2 | Lab 2 analyzed without Lab 1 | Presented in one table using smdi_style_gt()
Rendered fromc_multivariate_missingness.Rmdusingknitr::rmarkdown

Last update: 2024-07-26
Started: 2023-07-17

NARFCS sensitivity analysis
Sensitivity analysis for MNAR(value) | Illustrative example | Visual comparison | smdi diagnostics | Comparing treatment effect estimates | NARFCS imputation | Tipping point analysis | Multivariate missingness PD-L1 biomarker example | More on sensitivity analyses | References
Rendered fromd_narfcs_sensitivity_analysis.Rmdusingknitr::rmarkdown

Last update: 2024-07-26
Started: 2023-07-17

Routine structural missing data diagnostics
smdi main functionalities | Descriptives | Missingness proportions | Missingness patterns | Upset plot | Pattern matrix | Before we are getting started: data format | Group 1 diagnostics: differences in covariate distributions | Median/average absolute standardized mean differences | Hotelling's and Little's hypothesis tests | Hotteling | Little | Group 2 diagnostics: ability to predict missingness | Group 3 diagnostics: association between missingness and outcome | smdi_diagnose() - one function to rule them all | Publication-ready gt-style table | smdi table export | References
Rendered fromb_routine_diagnostics.Rmdusingknitr::rmarkdown

Last update: 2024-07-26
Started: 2023-07-17

Get started with smdi
smdi_diagnose() - the flagship function
Rendered fromsmdi.Rmdusingknitr::rmarkdown

Last update: 2023-07-17
Started: 2023-07-17