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    • + diff --git a/main/search.json b/main/search.json index 4609943..4e41b0f 100644 --- a/main/search.json +++ b/main/search.json @@ -1 +1 @@ -[{"path":"https://openpharma.github.io/savvyr/articles/introduction.html","id":"example-using-dummy-data","dir":"Articles","previous_headings":"","what":"Example using dummy data","title":"Introduction to `savvyr`","text":"generate dataset \\(S1\\) Stegherr, Beyersmann, et al. (2021) using parameter values Arm . First define sample size range censoring times. set hazard three event types (adverse event, death/hard competing event soft competing event). dataset generated, set \\(\\tau\\) maximum event time. structure dataset looks follows: dataset compute estimators used comparisons Stegherr, Schmoor, Beyersmann, et al. (2021) Stegherr, Schmoor, Lübbert, et al. (2021). start estimators account competing events (incidence proportion, incidence density, Inverse Kaplan Meier), incidence proportion accounting competing events Aalen-Johansen (first death hard competing event, using competing events): AE risks look follows: Finally, estimated probabilities competing events based Aalen-Johansen estimators:","code":"n <- 200 min_cens <- 0 max_cens <- 1000 set.seed(2020) dat1 <- generate_data( n, cens = c(min_cens, max_cens), haz_ae = 0.00265, haz_death = 0.00151, haz_soft = 0.00227 ) tau <- max(dat1[, \"time_to_event\"]) kable(head(dat1, 10), align = c(\"crcr\")) ip <- inc_prop(dat1, tau) id <- prop_trans_inc_dens(dat1, tau) km <- one_minus_kaplan_meier(dat1, tau) idce_2 <- prop_trans_inc_dens_ce(dat1, ce = 2, tau) aj_2 <- aalen_johansen(dat1, ce = 2, tau) idce_3 <- prop_trans_inc_dens_ce(dat1, ce = 3, tau) aj_3 <- aalen_johansen(dat1, ce = 3, tau) tab <- rbind(ip, id, km, idce_2, aj_2[1:2], idce_3, aj_3[1:2]) colnames(tab) <- c( \"estimated AE probability\", \"variance of estimation\" ) rownames(tab) <- c( \"incidence proportion\", \"probability transform incidence density ignoring competing event\", \"1 - Kaplan-Meier\", \"probability transform incidence density (death only)\", \"Aalen-Johansen (death only), AE risk\", \"probability transform incidence density (all CEs)\", \"Aalen-Johansen (all CEs), AE risk\" ) kable(tab, digits = c(3, 5)) tab <- rbind(aj_2[3:4], aj_3[3:4]) colnames(tab) <- c( \"estimated probability\", \"variance of estimation\" ) rownames(tab) <- c( \"Aalen-Johansen (death only), CE risk\", \"Aalen-Johansen (all CEs), CE risk\" ) kable(tab, digits = c(3, 5))"},{"path":[]},{"path":"https://openpharma.github.io/savvyr/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Thomas Kuenzel. Author, maintainer. Kaspar Rufibach. Author. Regina Stegherr. Author. Daniel Sabanés Bové. Author. F. Hoffmann-La Roche AG. Copyright holder, funder. Universität Ulm. Copyright holder, funder.","code":""},{"path":"https://openpharma.github.io/savvyr/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Kuenzel T, Rufibach K, Stegherr R, Sabanés Bové D (2024). savvyr: Survival Analysis AdVerse Events VarYing Follow-Times. R package version 0.1.0, https://openpharma.github.io/savvyr/.","code":"@Manual{, title = {savvyr: Survival Analysis for AdVerse Events with VarYing Follow-Up Times}, author = {Thomas Kuenzel and Kaspar Rufibach and Regina Stegherr and Daniel {Sabanés Bové}}, year = {2024}, note = {R package version 0.1.0}, url = {https://openpharma.github.io/savvyr/}, }"},{"path":"https://openpharma.github.io/savvyr/index.html","id":"savvyr-","dir":"","previous_headings":"","what":"Survival Analysis for AdVerse Events with VarYing Follow-Up Times","title":"Survival Analysis for AdVerse Events with VarYing Follow-Up Times","text":"SAVVY project consortium academic pharmaceutical industry partners aims improve analyses adverse event (AE) data clinical trials use survival techniques appropriately dealing varying follow-times competing events. Although statistical methodologies advanced, AE analyses often incidence proportion, incidence density non-parametric Kaplan-Meier estimator used, either ignore censoring competing events. savvyr package contains functions easily conduct proposed improved AE analyses.","code":""},{"path":[]},{"path":"https://openpharma.github.io/savvyr/index.html","id":"release","dir":"","previous_headings":"Installation","what":"Release","title":"Survival Analysis for AdVerse Events with VarYing Follow-Up Times","text":"can install current release version CRAN :","code":"install.packages(\"savvyr\")"},{"path":"https://openpharma.github.io/savvyr/index.html","id":"development","dir":"","previous_headings":"Installation","what":"Development","title":"Survival Analysis for AdVerse Events with VarYing Follow-Up Times","text":"can install development version savvyr GitHub :","code":"if (!require(\"remotes\")) { install.packages(\"remotes\") } remotes::install_github(\"openpharma/savvyr\")"},{"path":"https://openpharma.github.io/savvyr/index.html","id":"getting-started","dir":"","previous_headings":"","what":"Getting Started","title":"Survival Analysis for AdVerse Events with VarYing Follow-Up Times","text":"See introductory vignette get started.","code":""},{"path":"https://openpharma.github.io/savvyr/index.html","id":"citing-savvyr","dir":"","previous_headings":"","what":"Citing savvyr","title":"Survival Analysis for AdVerse Events with VarYing Follow-Up Times","text":"cite savvyr please see .","code":""},{"path":"https://openpharma.github.io/savvyr/reference/aalen_johansen.html","id":null,"dir":"Reference","previous_headings":"","what":"Aalen Johansen Estimator — aalen_johansen","title":"Aalen Johansen Estimator — aalen_johansen","text":"function calculates Aalen Johansen estimator adverse events observed [0, tau]. Please also refer Stegherr et al. (2021) .","code":""},{"path":"https://openpharma.github.io/savvyr/reference/aalen_johansen.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Aalen Johansen Estimator — aalen_johansen","text":"","code":"aalen_johansen(data, ce, tau)"},{"path":"https://openpharma.github.io/savvyr/reference/aalen_johansen.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Aalen Johansen Estimator — aalen_johansen","text":"data (data.frame) columns including time_to_event: Time first AE, death soft competing event. type_of_event: 0 censored, 1 AE, 2 death, 3 soft competing event. ce (number) code competing event. tau (number) milestone Aalen-Johansen computed.","code":""},{"path":"https://openpharma.github.io/savvyr/reference/aalen_johansen.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Aalen Johansen Estimator — aalen_johansen","text":"(vector) following entries: ae_prob: Estimated probability AE. ae_prob_var: Variance estimate. ce_prob: Estimated probability competing events. ce_prob_var: Variance competing events.","code":""},{"path":"https://openpharma.github.io/savvyr/reference/aalen_johansen.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Aalen Johansen Estimator — aalen_johansen","text":"Stegherr R, Schmoor C, Lübbert M, Friede T, Beyersmann J (2021). “Estimating comparing adverse event probabilities presence varying follow-times competing events.” Pharmaceutical Statistics, 20(6), 1125--1146. doi:10.1002/pst.2130 , https://onlinelibrary.wiley.com/doi/abs/10.1002/pst.2130.","code":""},{"path":"https://openpharma.github.io/savvyr/reference/aalen_johansen.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Aalen Johansen Estimator — aalen_johansen","text":"","code":"set.seed(123) dat <- generate_data(n = 5, cens = c(2, 5), haz_ae = 2, haz_death = 3, haz_soft = 5) aalen_johansen(dat, ce = 2, tau = 4) #> ae_prob ae_prob_var ce_prob ce_prob_var #> 0.200 0.032 0.800 0.032"},{"path":"https://openpharma.github.io/savvyr/reference/assert_ae_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Assertion of Adverse Event Data — assert_ae_data","title":"Assertion of Adverse Event Data — assert_ae_data","text":"Custom assertion check adverse event data sets.","code":""},{"path":"https://openpharma.github.io/savvyr/reference/assert_ae_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Assertion of Adverse Event Data — assert_ae_data","text":"","code":"assert_ae_data(data)"},{"path":"https://openpharma.github.io/savvyr/reference/assert_ae_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Assertion of Adverse Event Data — assert_ae_data","text":"data data.frame checked time_to_event type_of_event columns.","code":""},{"path":"https://openpharma.github.io/savvyr/reference/assert_ae_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Assertion of Adverse Event Data — assert_ae_data","text":"None.","code":""},{"path":"https://openpharma.github.io/savvyr/reference/generate_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate Example Data — generate_data","title":"Generate Example Data — generate_data","text":"generates dataset denoted S1 Table 4 Stegherr et al. (2021) , .e. assume constant hazards adverse event (AE) hazard, hazard competing event death, hazard \"soft\" competing events. Censoring uniform given range.","code":""},{"path":"https://openpharma.github.io/savvyr/reference/generate_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate Example Data — generate_data","text":"","code":"generate_data(n, cens, haz_ae, haz_death, haz_soft)"},{"path":"https://openpharma.github.io/savvyr/reference/generate_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate Example Data — generate_data","text":"n (count) number patients. cens (numeric) minimum maximum censoring time. haz_ae (number) constant hazard AE. haz_death (number) constant hazard death. haz_soft (number) constant hazard soft competing event.","code":""},{"path":"https://openpharma.github.io/savvyr/reference/generate_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate Example Data — generate_data","text":"(data.frame) data.frame following columns: id: Patient ID. time_to_event: Time first AE, death soft competing event. type_of_event: 0 censored, 1 AE, 2 death, 3 soft competing event. cens: Censoring time.","code":""},{"path":"https://openpharma.github.io/savvyr/reference/generate_data.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Generate Example Data — generate_data","text":"Stegherr R, Schmoor C, Lübbert M, Friede T, Beyersmann J (2021). “Estimating comparing adverse event probabilities presence varying follow-times competing events.” Pharmaceutical Statistics, 20(6), 1125--1146. doi:10.1002/pst.2130 , https://onlinelibrary.wiley.com/doi/abs/10.1002/pst.2130.","code":""},{"path":"https://openpharma.github.io/savvyr/reference/generate_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate Example Data — generate_data","text":"","code":"set.seed(123) generate_data(n = 5, cens = c(2, 5), haz_ae = 2, haz_death = 3, haz_soft = 5) #> id time_to_event type_of_event cens #> 1 1 0.084345726 2 4.032712 #> 2 2 0.057661027 2 3.717900 #> 3 3 0.132905487 2 2.308774 #> 4 4 0.003157736 2 4.699475 #> 5 5 0.005621098 1 2.738263"},{"path":"https://openpharma.github.io/savvyr/reference/inc_prop.html","id":null,"dir":"Reference","previous_headings":"","what":"Computing the Incidence Proportion — inc_prop","title":"Computing the Incidence Proportion — inc_prop","text":"Computing Incidence Proportion","code":""},{"path":"https://openpharma.github.io/savvyr/reference/inc_prop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computing the Incidence Proportion — inc_prop","text":"","code":"inc_prop(data, tau)"},{"path":"https://openpharma.github.io/savvyr/reference/inc_prop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computing the Incidence Proportion — inc_prop","text":"data (data.frame) columns including: time_to_event: Time first AE, death soft competing event. type_of_event: 0 censored, 1 AE, 2 death, 3 soft competing event. tau (numeric) milestone incidence proportion computed.","code":""},{"path":"https://openpharma.github.io/savvyr/reference/inc_prop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computing the Incidence Proportion — inc_prop","text":"(vector) following entries: ae_prob: Estimated probability AE. ae_prob_var: Variance estimate.","code":""},{"path":"https://openpharma.github.io/savvyr/reference/inc_prop.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computing the Incidence Proportion — inc_prop","text":"","code":"set.seed(123) dat <- generate_data( n = 5, cens = c(2, 5), haz_ae = 2, haz_death = 3, haz_soft = 5 ) inc_prop(dat, tau = 4) #> ae_prob ae_prob_var #> 0.200 0.032"},{"path":"https://openpharma.github.io/savvyr/reference/one_minus_kaplan_meier.html","id":null,"dir":"Reference","previous_headings":"","what":"One Minus Kaplan-Meier — one_minus_kaplan_meier","title":"One Minus Kaplan-Meier — one_minus_kaplan_meier","text":"function calculates one minus Kaplan-Meier estimator adverse events (censoring competing events) observed [0, tau]. Please also refer formula (4) Stegherr et al. (2021) .","code":""},{"path":"https://openpharma.github.io/savvyr/reference/one_minus_kaplan_meier.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"One Minus Kaplan-Meier — one_minus_kaplan_meier","text":"","code":"one_minus_kaplan_meier(data, tau)"},{"path":"https://openpharma.github.io/savvyr/reference/one_minus_kaplan_meier.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"One Minus Kaplan-Meier — one_minus_kaplan_meier","text":"data (data.frame) columns including time_to_event: Time first AE, death soft competing event. type_of_event: 0 censored, 1 AE, 2 death, 3 soft competing event. tau (number) milestone One Minus Kaplan-Meier computed.","code":""},{"path":"https://openpharma.github.io/savvyr/reference/one_minus_kaplan_meier.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"One Minus Kaplan-Meier — one_minus_kaplan_meier","text":"(vector) following entries: ae_prob: Estimated probability AE. ae_prob_var: Variance estimate.","code":""},{"path":"https://openpharma.github.io/savvyr/reference/one_minus_kaplan_meier.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"One Minus Kaplan-Meier — one_minus_kaplan_meier","text":"Stegherr R, Beyersmann J, Jehl V, Rufibach K, Leverkus F, Schmoor C, Friede T (2021). “Survival analysis AdVerse events VarYing follow-times (SAVVY): Rationale statistical concept meta-analytic study.” Biometrical Journal, 63(3), 650-670. doi:10.1002/bimj.201900347 , https://onlinelibrary.wiley.com/doi/pdf/10.1002/bimj.201900347, https://onlinelibrary.wiley.com/doi/abs/10.1002/bimj.201900347.","code":""},{"path":"https://openpharma.github.io/savvyr/reference/one_minus_kaplan_meier.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"One Minus Kaplan-Meier — one_minus_kaplan_meier","text":"","code":"set.seed(123) dat <- generate_data(n = 5, cens = c(2, 5), haz_ae = 2, haz_death = 3, haz_soft = 5) one_minus_kaplan_meier(dat, tau = 4) #> ae_prob ae_prob_var #> 0.250000 0.046875"},{"path":"https://openpharma.github.io/savvyr/reference/prop_trans_inc_dens.html","id":null,"dir":"Reference","previous_headings":"","what":"Computing the Probability Transform Incidence Density — prop_trans_inc_dens","title":"Computing the Probability Transform Incidence Density — prop_trans_inc_dens","text":"Computing Probability Transform Incidence Density","code":""},{"path":"https://openpharma.github.io/savvyr/reference/prop_trans_inc_dens.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computing the Probability Transform Incidence Density — prop_trans_inc_dens","text":"","code":"prop_trans_inc_dens(data, tau)"},{"path":"https://openpharma.github.io/savvyr/reference/prop_trans_inc_dens.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computing the Probability Transform Incidence Density — prop_trans_inc_dens","text":"data (data.frame) columns including time_to_event: Time first AE, death soft competing event. type_of_event: 0 censored, 1 AE, 2 death, 3 soft competing event. tau (number) milestone Probability Transform Incidence Density computed.","code":""},{"path":"https://openpharma.github.io/savvyr/reference/prop_trans_inc_dens.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computing the Probability Transform Incidence Density — prop_trans_inc_dens","text":"(vector) following entries: ae_prob: Estimated probability AE. ae_prob_var: Variance estimate.","code":""},{"path":"https://openpharma.github.io/savvyr/reference/prop_trans_inc_dens.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computing the Probability Transform Incidence Density — prop_trans_inc_dens","text":"","code":"set.seed(123) dat <- generate_data(n = 5, cens = c(2, 5), haz_ae = 2, haz_death = 3, haz_soft = 5) prop_trans_inc_dens(dat, tau = 4) #> ae_prob ae_prob_var #> 9.999992e-01 1.125795e-10"},{"path":"https://openpharma.github.io/savvyr/reference/prop_trans_inc_dens_ce.html","id":null,"dir":"Reference","previous_headings":"","what":"Probability Transform Incidence Density Accounting for Competing Events — prop_trans_inc_dens_ce","title":"Probability Transform Incidence Density Accounting for Competing Events — prop_trans_inc_dens_ce","text":"function calculates incidence density adverse events specified competing events observed [0, tau] combines transforms incidence densities probability scale. Please also refer formulas (4) (5) Stegherr et al. (2021) .","code":""},{"path":"https://openpharma.github.io/savvyr/reference/prop_trans_inc_dens_ce.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Probability Transform Incidence Density Accounting for Competing Events — prop_trans_inc_dens_ce","text":"","code":"prop_trans_inc_dens_ce(data, ce, tau)"},{"path":"https://openpharma.github.io/savvyr/reference/prop_trans_inc_dens_ce.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Probability Transform Incidence Density Accounting for Competing Events — prop_trans_inc_dens_ce","text":"data (data.frame) columns including time_to_event: Time first AE, death soft competing event. type_of_event: 0 censored, 1 AE, 2 death, 3 soft competing event. ce (number) code competing event. tau (number) milestone Probability Transform Incidence Density computed.","code":""},{"path":"https://openpharma.github.io/savvyr/reference/prop_trans_inc_dens_ce.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Probability Transform Incidence Density Accounting for Competing Events — prop_trans_inc_dens_ce","text":"(vector) following entries: ae_prob: Estimated probability AE. ae_prob_var: Variance estimate.","code":""},{"path":"https://openpharma.github.io/savvyr/reference/prop_trans_inc_dens_ce.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Probability Transform Incidence Density Accounting for Competing Events — prop_trans_inc_dens_ce","text":"Stegherr R, Schmoor C, Beyersmann J, Rufibach K, Jehl V, Brückner , Eisele L, Künzel T, Kupas K, Langer F, Leverkus F, Loos , Norenberg C, Voss F, Friede T (2021). “Survival analysis AdVerse events VarYing follow-times (SAVVY)—estimation adverse event risks.” Trials, 22(1), 420. ISSN 1745-6215, doi:10.1186/s13063-021-05354-x .","code":""},{"path":"https://openpharma.github.io/savvyr/reference/prop_trans_inc_dens_ce.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Probability Transform Incidence Density Accounting for Competing Events — prop_trans_inc_dens_ce","text":"","code":"set.seed(123) dat <- generate_data(n = 5, cens = c(2, 5), haz_ae = 2, haz_death = 3, haz_soft = 5) prop_trans_inc_dens_ce(dat, ce = 2, tau = 4) #> ae_prob ae_prob_var #> 0.200 0.032"},{"path":"https://openpharma.github.io/savvyr/reference/savvyr-package.html","id":null,"dir":"Reference","previous_headings":"","what":"savvyr: Survival Analysis for AdVerse Events with VarYing Follow-Up Times — savvyr-package","title":"savvyr: Survival Analysis for AdVerse Events with VarYing Follow-Up Times — savvyr-package","text":"SAVVY (Survival Analysis AdVerse Events VarYing Follow-Times) project consortium academic pharmaceutical industry partners aims improve analyses adverse event (AE) data clinical trials use survival techniques appropriately dealing varying follow-times competing events, see Stegherr, Schmoor, Beyersmann, et al. (2021) doi:10.1186/s13063-021-05354-x . Although statistical methodologies advanced, AE analyses often incidence proportion, incidence density non-parametric Kaplan-Meier estimator used, either ignore censoring competing events. package contains functions easily conduct proposed improved AE analyses.","code":""},{"path":[]},{"path":"https://openpharma.github.io/savvyr/reference/savvyr-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"savvyr: Survival Analysis for AdVerse Events with VarYing Follow-Up Times — savvyr-package","text":"Maintainer: Thomas Kuenzel thomas.kuenzel@roche.com Authors: Kaspar Rufibach kaspar.rufibach@roche.com Regina Stegherr regina.stegherr@uni-ulm.de Daniel Sabanés Bové daniel.sabanes_bove@roche.com contributors: F. Hoffmann-La Roche AG [copyright holder, funder] Universität Ulm [copyright holder, funder]","code":""},{"path":"https://openpharma.github.io/savvyr/news/index.html","id":"savvyr-010","dir":"Changelog","previous_headings":"","what":"savvyr 0.1.0","title":"savvyr 0.1.0","text":"First CRAN version package. package provides functions easily conduct improved AE analyses proposed SAVVY framework.","code":""},{"path":"https://openpharma.github.io/savvyr/news/index.html","id":"new-features-0-1-0","dir":"Changelog","previous_headings":"","what":"New Features","title":"savvyr 0.1.0","text":"Estimators account competing events (incidence proportion, incidence density, Inverse Kaplan Meier). Estimators accounting competing events (incidence proportion accounting competing events Aalen-Johansen, first death hard competing event, using competing events).","code":""}] +[{"path":"https://openpharma.github.io/savvyr/main/articles/introduction.html","id":"example-using-dummy-data","dir":"Articles","previous_headings":"","what":"Example using dummy data","title":"Introduction to `savvyr`","text":"generate dataset \\(S1\\) Stegherr, Beyersmann, et al. (2021) using parameter values Arm . First define sample size range censoring times. set hazard three event types (adverse event, death/hard competing event soft competing event). dataset generated, set \\(\\tau\\) maximum event time. structure dataset looks follows: dataset compute estimators used comparisons Stegherr, Schmoor, Beyersmann, et al. (2021) Stegherr, Schmoor, Lübbert, et al. (2021). start estimators account competing events (incidence proportion, incidence density, Inverse Kaplan Meier), incidence proportion accounting competing events Aalen-Johansen (first death hard competing event, using competing events): AE risks look follows: Finally, estimated probabilities competing events based Aalen-Johansen estimators:","code":"n <- 200 min_cens <- 0 max_cens <- 1000 set.seed(2020) dat1 <- generate_data( n, cens = c(min_cens, max_cens), haz_ae = 0.00265, haz_death = 0.00151, haz_soft = 0.00227 ) tau <- max(dat1[, \"time_to_event\"]) kable(head(dat1, 10), align = c(\"crcr\")) ip <- inc_prop(dat1, tau) id <- prop_trans_inc_dens(dat1, tau) km <- one_minus_kaplan_meier(dat1, tau) idce_2 <- prop_trans_inc_dens_ce(dat1, ce = 2, tau) aj_2 <- aalen_johansen(dat1, ce = 2, tau) idce_3 <- prop_trans_inc_dens_ce(dat1, ce = 3, tau) aj_3 <- aalen_johansen(dat1, ce = 3, tau) tab <- rbind(ip, id, km, idce_2, aj_2[1:2], idce_3, aj_3[1:2]) colnames(tab) <- c( \"estimated AE probability\", \"variance of estimation\" ) rownames(tab) <- c( \"incidence proportion\", \"probability transform incidence density ignoring competing event\", \"1 - Kaplan-Meier\", \"probability transform incidence density (death only)\", \"Aalen-Johansen (death only), AE risk\", \"probability transform incidence density (all CEs)\", \"Aalen-Johansen (all CEs), AE risk\" ) kable(tab, digits = c(3, 5)) tab <- rbind(aj_2[3:4], aj_3[3:4]) colnames(tab) <- c( \"estimated probability\", \"variance of estimation\" ) rownames(tab) <- c( \"Aalen-Johansen (death only), CE risk\", \"Aalen-Johansen (all CEs), CE risk\" ) kable(tab, digits = c(3, 5))"},{"path":[]},{"path":"https://openpharma.github.io/savvyr/main/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Thomas Kuenzel. Author, maintainer. Kaspar Rufibach. Author. Regina Stegherr. Author. Daniel Sabanés Bové. Author. F. Hoffmann-La Roche AG. Copyright holder, funder. Universität Ulm. Copyright holder, funder.","code":""},{"path":"https://openpharma.github.io/savvyr/main/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Kuenzel T, Rufibach K, Stegherr R, Sabanés Bové D (2024). savvyr: Survival Analysis AdVerse Events VarYing Follow-Times. R package version 0.1.0, https://openpharma.github.io/savvyr/main/.","code":"@Manual{, title = {savvyr: Survival Analysis for AdVerse Events with VarYing Follow-Up Times}, author = {Thomas Kuenzel and Kaspar Rufibach and Regina Stegherr and Daniel {Sabanés Bové}}, year = {2024}, note = {R package version 0.1.0}, url = {https://openpharma.github.io/savvyr/main/}, }"},{"path":"https://openpharma.github.io/savvyr/main/index.html","id":"savvyr-","dir":"","previous_headings":"","what":"Survival Analysis for AdVerse Events with VarYing Follow-Up Times","title":"Survival Analysis for AdVerse Events with VarYing Follow-Up Times","text":"SAVVY project consortium academic pharmaceutical industry partners aims improve analyses adverse event (AE) data clinical trials use survival techniques appropriately dealing varying follow-times competing events. Although statistical methodologies advanced, AE analyses often incidence proportion, incidence density non-parametric Kaplan-Meier estimator used, either ignore censoring competing events. savvyr package contains functions easily conduct proposed improved AE analyses.","code":""},{"path":[]},{"path":"https://openpharma.github.io/savvyr/main/index.html","id":"release","dir":"","previous_headings":"Installation","what":"Release","title":"Survival Analysis for AdVerse Events with VarYing Follow-Up Times","text":"can install current release version CRAN :","code":"install.packages(\"savvyr\")"},{"path":"https://openpharma.github.io/savvyr/main/index.html","id":"development","dir":"","previous_headings":"Installation","what":"Development","title":"Survival Analysis for AdVerse Events with VarYing Follow-Up Times","text":"can install development version savvyr GitHub :","code":"if (!require(\"remotes\")) { install.packages(\"remotes\") } remotes::install_github(\"openpharma/savvyr\")"},{"path":"https://openpharma.github.io/savvyr/main/index.html","id":"getting-started","dir":"","previous_headings":"","what":"Getting Started","title":"Survival Analysis for AdVerse Events with VarYing Follow-Up Times","text":"See introductory vignette get started.","code":""},{"path":"https://openpharma.github.io/savvyr/main/index.html","id":"citing-savvyr","dir":"","previous_headings":"","what":"Citing savvyr","title":"Survival Analysis for AdVerse Events with VarYing Follow-Up Times","text":"cite savvyr please see .","code":""},{"path":"https://openpharma.github.io/savvyr/main/reference/aalen_johansen.html","id":null,"dir":"Reference","previous_headings":"","what":"Aalen Johansen Estimator — aalen_johansen","title":"Aalen Johansen Estimator — aalen_johansen","text":"function calculates Aalen Johansen estimator adverse events observed [0, tau]. Please also refer Stegherr et al. (2021) .","code":""},{"path":"https://openpharma.github.io/savvyr/main/reference/aalen_johansen.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Aalen Johansen Estimator — aalen_johansen","text":"","code":"aalen_johansen(data, ce, tau)"},{"path":"https://openpharma.github.io/savvyr/main/reference/aalen_johansen.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Aalen Johansen Estimator — aalen_johansen","text":"data (data.frame) columns including time_to_event: Time first AE, death soft competing event. type_of_event: 0 censored, 1 AE, 2 death, 3 soft competing event. ce (number) code competing event. tau (number) milestone Aalen-Johansen computed.","code":""},{"path":"https://openpharma.github.io/savvyr/main/reference/aalen_johansen.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Aalen Johansen Estimator — aalen_johansen","text":"(vector) following entries: ae_prob: Estimated probability AE. ae_prob_var: Variance estimate. ce_prob: Estimated probability competing events. ce_prob_var: Variance competing events.","code":""},{"path":"https://openpharma.github.io/savvyr/main/reference/aalen_johansen.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Aalen Johansen Estimator — aalen_johansen","text":"Stegherr R, Schmoor C, Lübbert M, Friede T, Beyersmann J (2021). “Estimating comparing adverse event probabilities presence varying follow-times competing events.” Pharmaceutical Statistics, 20(6), 1125--1146. doi:10.1002/pst.2130 , https://onlinelibrary.wiley.com/doi/abs/10.1002/pst.2130.","code":""},{"path":"https://openpharma.github.io/savvyr/main/reference/aalen_johansen.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Aalen Johansen Estimator — aalen_johansen","text":"","code":"set.seed(123) dat <- generate_data(n = 5, cens = c(2, 5), haz_ae = 2, haz_death = 3, haz_soft = 5) aalen_johansen(dat, ce = 2, tau = 4) #> ae_prob ae_prob_var ce_prob ce_prob_var #> 0.200 0.032 0.800 0.032"},{"path":"https://openpharma.github.io/savvyr/main/reference/assert_ae_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Assertion of Adverse Event Data — assert_ae_data","title":"Assertion of Adverse Event Data — assert_ae_data","text":"Custom assertion check adverse event data sets.","code":""},{"path":"https://openpharma.github.io/savvyr/main/reference/assert_ae_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Assertion of Adverse Event Data — assert_ae_data","text":"","code":"assert_ae_data(data)"},{"path":"https://openpharma.github.io/savvyr/main/reference/assert_ae_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Assertion of Adverse Event Data — assert_ae_data","text":"data data.frame checked time_to_event type_of_event columns.","code":""},{"path":"https://openpharma.github.io/savvyr/main/reference/assert_ae_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Assertion of Adverse Event Data — assert_ae_data","text":"None.","code":""},{"path":"https://openpharma.github.io/savvyr/main/reference/generate_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate Example Data — generate_data","title":"Generate Example Data — generate_data","text":"generates dataset denoted S1 Table 4 Stegherr et al. (2021) , .e. assume constant hazards adverse event (AE) hazard, hazard competing event death, hazard \"soft\" competing events. Censoring uniform given range.","code":""},{"path":"https://openpharma.github.io/savvyr/main/reference/generate_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate Example Data — generate_data","text":"","code":"generate_data(n, cens, haz_ae, haz_death, haz_soft)"},{"path":"https://openpharma.github.io/savvyr/main/reference/generate_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate Example Data — generate_data","text":"n (count) number patients. cens (numeric) minimum maximum censoring time. haz_ae (number) constant hazard AE. haz_death (number) constant hazard death. haz_soft (number) constant hazard soft competing event.","code":""},{"path":"https://openpharma.github.io/savvyr/main/reference/generate_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate Example Data — generate_data","text":"(data.frame) data.frame following columns: id: Patient ID. time_to_event: Time first AE, death soft competing event. type_of_event: 0 censored, 1 AE, 2 death, 3 soft competing event. cens: Censoring time.","code":""},{"path":"https://openpharma.github.io/savvyr/main/reference/generate_data.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Generate Example Data — generate_data","text":"Stegherr R, Schmoor C, Lübbert M, Friede T, Beyersmann J (2021). “Estimating comparing adverse event probabilities presence varying follow-times competing events.” Pharmaceutical Statistics, 20(6), 1125--1146. doi:10.1002/pst.2130 , https://onlinelibrary.wiley.com/doi/abs/10.1002/pst.2130.","code":""},{"path":"https://openpharma.github.io/savvyr/main/reference/generate_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate Example Data — generate_data","text":"","code":"set.seed(123) generate_data(n = 5, cens = c(2, 5), haz_ae = 2, haz_death = 3, haz_soft = 5) #> id time_to_event type_of_event cens #> 1 1 0.084345726 2 4.032712 #> 2 2 0.057661027 2 3.717900 #> 3 3 0.132905487 2 2.308774 #> 4 4 0.003157736 2 4.699475 #> 5 5 0.005621098 1 2.738263"},{"path":"https://openpharma.github.io/savvyr/main/reference/inc_prop.html","id":null,"dir":"Reference","previous_headings":"","what":"Computing the Incidence Proportion — inc_prop","title":"Computing the Incidence Proportion — inc_prop","text":"Computing Incidence Proportion","code":""},{"path":"https://openpharma.github.io/savvyr/main/reference/inc_prop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computing the Incidence Proportion — inc_prop","text":"","code":"inc_prop(data, tau)"},{"path":"https://openpharma.github.io/savvyr/main/reference/inc_prop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computing the Incidence Proportion — inc_prop","text":"data (data.frame) columns including: time_to_event: Time first AE, death soft competing event. type_of_event: 0 censored, 1 AE, 2 death, 3 soft competing event. tau (numeric) milestone incidence proportion computed.","code":""},{"path":"https://openpharma.github.io/savvyr/main/reference/inc_prop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computing the Incidence Proportion — inc_prop","text":"(vector) following entries: ae_prob: Estimated probability AE. ae_prob_var: Variance estimate.","code":""},{"path":"https://openpharma.github.io/savvyr/main/reference/inc_prop.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computing the Incidence Proportion — inc_prop","text":"","code":"set.seed(123) dat <- generate_data( n = 5, cens = c(2, 5), haz_ae = 2, haz_death = 3, haz_soft = 5 ) inc_prop(dat, tau = 4) #> ae_prob ae_prob_var #> 0.200 0.032"},{"path":"https://openpharma.github.io/savvyr/main/reference/one_minus_kaplan_meier.html","id":null,"dir":"Reference","previous_headings":"","what":"One Minus Kaplan-Meier — one_minus_kaplan_meier","title":"One Minus Kaplan-Meier — one_minus_kaplan_meier","text":"function calculates one minus Kaplan-Meier estimator adverse events (censoring competing events) observed [0, tau]. Please also refer formula (4) Stegherr et al. (2021) .","code":""},{"path":"https://openpharma.github.io/savvyr/main/reference/one_minus_kaplan_meier.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"One Minus Kaplan-Meier — one_minus_kaplan_meier","text":"","code":"one_minus_kaplan_meier(data, tau)"},{"path":"https://openpharma.github.io/savvyr/main/reference/one_minus_kaplan_meier.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"One Minus Kaplan-Meier — one_minus_kaplan_meier","text":"data (data.frame) columns including time_to_event: Time first AE, death soft competing event. type_of_event: 0 censored, 1 AE, 2 death, 3 soft competing event. tau (number) milestone One Minus Kaplan-Meier computed.","code":""},{"path":"https://openpharma.github.io/savvyr/main/reference/one_minus_kaplan_meier.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"One Minus Kaplan-Meier — one_minus_kaplan_meier","text":"(vector) following entries: ae_prob: Estimated probability AE. ae_prob_var: Variance estimate.","code":""},{"path":"https://openpharma.github.io/savvyr/main/reference/one_minus_kaplan_meier.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"One Minus Kaplan-Meier — one_minus_kaplan_meier","text":"Stegherr R, Beyersmann J, Jehl V, Rufibach K, Leverkus F, Schmoor C, Friede T (2021). “Survival analysis AdVerse events VarYing follow-times (SAVVY): Rationale statistical concept meta-analytic study.” Biometrical Journal, 63(3), 650-670. doi:10.1002/bimj.201900347 , https://onlinelibrary.wiley.com/doi/pdf/10.1002/bimj.201900347, https://onlinelibrary.wiley.com/doi/abs/10.1002/bimj.201900347.","code":""},{"path":"https://openpharma.github.io/savvyr/main/reference/one_minus_kaplan_meier.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"One Minus Kaplan-Meier — one_minus_kaplan_meier","text":"","code":"set.seed(123) dat <- generate_data(n = 5, cens = c(2, 5), haz_ae = 2, haz_death = 3, haz_soft = 5) one_minus_kaplan_meier(dat, tau = 4) #> ae_prob ae_prob_var #> 0.250000 0.046875"},{"path":"https://openpharma.github.io/savvyr/main/reference/prop_trans_inc_dens.html","id":null,"dir":"Reference","previous_headings":"","what":"Computing the Probability Transform Incidence Density — prop_trans_inc_dens","title":"Computing the Probability Transform Incidence Density — prop_trans_inc_dens","text":"Computing Probability Transform Incidence Density","code":""},{"path":"https://openpharma.github.io/savvyr/main/reference/prop_trans_inc_dens.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Computing the Probability Transform Incidence Density — prop_trans_inc_dens","text":"","code":"prop_trans_inc_dens(data, tau)"},{"path":"https://openpharma.github.io/savvyr/main/reference/prop_trans_inc_dens.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Computing the Probability Transform Incidence Density — prop_trans_inc_dens","text":"data (data.frame) columns including time_to_event: Time first AE, death soft competing event. type_of_event: 0 censored, 1 AE, 2 death, 3 soft competing event. tau (number) milestone Probability Transform Incidence Density computed.","code":""},{"path":"https://openpharma.github.io/savvyr/main/reference/prop_trans_inc_dens.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Computing the Probability Transform Incidence Density — prop_trans_inc_dens","text":"(vector) following entries: ae_prob: Estimated probability AE. ae_prob_var: Variance estimate.","code":""},{"path":"https://openpharma.github.io/savvyr/main/reference/prop_trans_inc_dens.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Computing the Probability Transform Incidence Density — prop_trans_inc_dens","text":"","code":"set.seed(123) dat <- generate_data(n = 5, cens = c(2, 5), haz_ae = 2, haz_death = 3, haz_soft = 5) prop_trans_inc_dens(dat, tau = 4) #> ae_prob ae_prob_var #> 9.999992e-01 1.125795e-10"},{"path":"https://openpharma.github.io/savvyr/main/reference/prop_trans_inc_dens_ce.html","id":null,"dir":"Reference","previous_headings":"","what":"Probability Transform Incidence Density Accounting for Competing Events — prop_trans_inc_dens_ce","title":"Probability Transform Incidence Density Accounting for Competing Events — prop_trans_inc_dens_ce","text":"function calculates incidence density adverse events specified competing events observed [0, tau] combines transforms incidence densities probability scale. Please also refer formulas (4) (5) Stegherr et al. (2021) .","code":""},{"path":"https://openpharma.github.io/savvyr/main/reference/prop_trans_inc_dens_ce.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Probability Transform Incidence Density Accounting for Competing Events — prop_trans_inc_dens_ce","text":"","code":"prop_trans_inc_dens_ce(data, ce, tau)"},{"path":"https://openpharma.github.io/savvyr/main/reference/prop_trans_inc_dens_ce.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Probability Transform Incidence Density Accounting for Competing Events — prop_trans_inc_dens_ce","text":"data (data.frame) columns including time_to_event: Time first AE, death soft competing event. type_of_event: 0 censored, 1 AE, 2 death, 3 soft competing event. ce (number) code competing event. tau (number) milestone Probability Transform Incidence Density computed.","code":""},{"path":"https://openpharma.github.io/savvyr/main/reference/prop_trans_inc_dens_ce.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Probability Transform Incidence Density Accounting for Competing Events — prop_trans_inc_dens_ce","text":"(vector) following entries: ae_prob: Estimated probability AE. ae_prob_var: Variance estimate.","code":""},{"path":"https://openpharma.github.io/savvyr/main/reference/prop_trans_inc_dens_ce.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Probability Transform Incidence Density Accounting for Competing Events — prop_trans_inc_dens_ce","text":"Stegherr R, Schmoor C, Beyersmann J, Rufibach K, Jehl V, Brückner , Eisele L, Künzel T, Kupas K, Langer F, Leverkus F, Loos , Norenberg C, Voss F, Friede T (2021). “Survival analysis AdVerse events VarYing follow-times (SAVVY)—estimation adverse event risks.” Trials, 22(1), 420. ISSN 1745-6215, doi:10.1186/s13063-021-05354-x .","code":""},{"path":"https://openpharma.github.io/savvyr/main/reference/prop_trans_inc_dens_ce.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Probability Transform Incidence Density Accounting for Competing Events — prop_trans_inc_dens_ce","text":"","code":"set.seed(123) dat <- generate_data(n = 5, cens = c(2, 5), haz_ae = 2, haz_death = 3, haz_soft = 5) prop_trans_inc_dens_ce(dat, ce = 2, tau = 4) #> ae_prob ae_prob_var #> 0.200 0.032"},{"path":"https://openpharma.github.io/savvyr/main/reference/savvyr-package.html","id":null,"dir":"Reference","previous_headings":"","what":"savvyr: Survival Analysis for AdVerse Events with VarYing Follow-Up Times — savvyr-package","title":"savvyr: Survival Analysis for AdVerse Events with VarYing Follow-Up Times — savvyr-package","text":"SAVVY (Survival Analysis AdVerse Events VarYing Follow-Times) project consortium academic pharmaceutical industry partners aims improve analyses adverse event (AE) data clinical trials use survival techniques appropriately dealing varying follow-times competing events, see Stegherr, Schmoor, Beyersmann, et al. (2021) doi:10.1186/s13063-021-05354-x . Although statistical methodologies advanced, AE analyses often incidence proportion, incidence density non-parametric Kaplan-Meier estimator used, either ignore censoring competing events. package contains functions easily conduct proposed improved AE analyses.","code":""},{"path":[]},{"path":"https://openpharma.github.io/savvyr/main/reference/savvyr-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"savvyr: Survival Analysis for AdVerse Events with VarYing Follow-Up Times — savvyr-package","text":"Maintainer: Thomas Kuenzel thomas.kuenzel@roche.com Authors: Kaspar Rufibach kaspar.rufibach@roche.com Regina Stegherr regina.stegherr@uni-ulm.de Daniel Sabanés Bové daniel.sabanes_bove@roche.com contributors: F. Hoffmann-La Roche AG [copyright holder, funder] Universität Ulm [copyright holder, funder]","code":""},{"path":"https://openpharma.github.io/savvyr/main/news/index.html","id":"savvyr-010","dir":"Changelog","previous_headings":"","what":"savvyr 0.1.0","title":"savvyr 0.1.0","text":"First CRAN version package. package provides functions easily conduct improved AE analyses proposed SAVVY framework.","code":""},{"path":"https://openpharma.github.io/savvyr/main/news/index.html","id":"new-features-0-1-0","dir":"Changelog","previous_headings":"","what":"New Features","title":"savvyr 0.1.0","text":"Estimators account competing events (incidence proportion, incidence density, Inverse Kaplan Meier). Estimators accounting competing events (incidence proportion accounting competing events Aalen-Johansen, first death hard competing event, using competing events).","code":""}]