rifttable 0.7.2

• Bug fixes:
  • survdiff_ci() now uses the conf.level argument correctly in confidence interval estimation using the MOVER approach (#8).
• Internal:
  • Anticipate changes in {dplyr} 1.2.0 in label handling in unit tests (thanks to @DavisVaughan, #10).

rifttable 0.7.1

• Housekeeping for CRAN release

rifttable 0.7.0

• Breaking changes:
  • Require base R pipe |> and thus R >= 4.1.
  • Make the id variable identifying clustered observations within the same individual a global rifttable() option for the entire data set, not only for specific estimators.
• New functionality:
  • Expand input checks to missing values in time/event variables, to missing effect modifiers for joint models and their levels, to nonexistent custom estimators, and to empty input data sets.
  • Add type = "sum" estimator.
• Internal and bug fixes:
  • Cover entire package by unit tests.
  • Return exposure consistently as a character.
  • Let the design accept weight in addition to weights.
  • Require {risks} >= 0.4.3.
  • Use modern tidyselect, anonymous functions, and code style.

rifttable 0.6.3

• New functionality:
  • Provide easier interface to code competing events by directly providing event = "event_variable@Event_Type_One" in the design. If multiple event types are present, estimate cumulative incidence and differences/ratios of cumulative incidence in a competing-event setting.
  • Allow for clustered observations in survival data, e.g., multiple rows per person.
  • Estimate ratios of survival and cumulative incidence, i.e., x-year risk ratios. Use MOVER estimation for confidence intervals of both ratios and differences in survival and cumulative incidence by default.
  • Support weights, e.g., inverse-probability weights, directly via a weights argument in the design. Weighted estimates are currently available for many survival estimators: type = "cuminc", "surv", their differences and ratios (e.g., "cumincdiff"), and "hr". This is a breaking change for Cox models (type = "hr"), where providing weights in the arguments list now generates an error.
• Bug fixes:
  • Allow for @ in factor levels for a table1_design().
  • rt_gt(): Output knitr-formatted tables for GitHub-flavored markdown also in Quarto .qmd, similar to .Rmd.
  • Better handling of edge cases, e.g., ratios of 0, when rounding estimates.
• Expanded documentation
  • Restructure site.
  • Separate documentation of estimators by outcome type.
  • New FAQs on confidence levels, reference levels, custom functions, and joint models.

rifttable 0.6.2

• New functionality:
  • Add overall argument exposure_levels to let user control handling of missing exposure levels (NA) or factors with empty levels as the exposure.
  • type = "geomean" for geometric means.
• Documentation: Expand FAQs.
• Internal and bug fixes:
  • Consider exposure or trend of "" as missing, and stratum = "" as no subsetting by the effect_modifier, instead of subsetting to effect modifier being an empty string. Input check that a stratum must be provided for joint models and strata are not empty.
  • Consider missing type as "blank".
  • Do not add empty rows/columns if type2 has empty results for some cells or if only a trend variable and no exposure is given.
  • Rounding works even if result vector contains strings (e.g., no estimate).
  • More safeguards for all-NA outcome variables. More input checks.
  • Do not warn about non-0/1 outcomes in log-linear models for ratios of continuous variables.
  • Add initial set of unit tests.

rifttable 0.6.1

• New functionality:
  • Cox models (type = "hr") allow for weights, clustering, and robust standard errors.
  • Argument ratio_digits_decrease: By default, decrease number of decimal digits shown for ratios by 1 digit for ratios > 3 and by 2 digits for ratios > 10. Leads to rounded ratios and confidence intervals of 1.23, 3.4, and 11.
  • rt_gt() now indents the first column and applies markdown formatting to it by default.
• New FAQ vignette.
• Bug fixes:
  • Binary outcomes returned NA instead of 0 in unstratified tables with all-null outcome.
  • type = "maxfu" ignored digits and diff_digits.
  • Allow for different exposure (strata labels) and arguments in one table.
  • Show unstratified estimate if exposure is "", not just for NA.
• Internal:
  • rt_gt(): suppress random id of gt tables to keep git diff slim.
  • Keep variables .event, .outcome, etc. available under their original names.
  • Require {risks} >= 0.4.2.
  • Examples load the breastcancer dataset from the risks package.

rifttable 0.6.0

• Breaking changes:
  • to is set to ", " by default, instead of "-" for ratio variables and " to " for difference variables
  • Custom functions are now directly called via the type variable, following a restructuring of all estimation functions with greater flexibility.
  • design$type no longer accepts additional arguments, such as time points. Supply list instead via design$arguments.
  • Suppression of strata with sparse or re-identifiable data with design$nmin now differentiates between counts of total observations or outcomes, depending on estimator.
• New function table1_design(): Generate design of a descriptive “Table 1.”
• New outcome option "variable@level" for categorical variables that displays level as a binary outcome. Used by table1_design().
• Support unstratified tables displaying the trend/linear slope (trend variable in the design) without an exposure.
• More customization:
  • rifttable(reference = ...): Label for the reference category.
  • design$ci: Width of confidence intervals.
  • design$na_rm: Omitting observations with missing outcome data.
  • design$arguments: Flexibly passing along any argument to estimation functions.
• New vignette describing all estimators.
• Internal:
  • Drop dependency on R >= 4.1 and native pipe.
  • Require {risks} >= 0.4.0.
  • Remove dependency on {labelled} package.
  • The {gt} and {quantreg} packages are now optional as soft dependencies.
  • Compatible with {dplyr} 1.1.0, {tidyselect} 1.2.0

rifttable 0.5.0

• khsmisc::table2() “graduated” into its own package. See {khsmisc} Changelog for earlier versions.
• Add breastcancer() dataset
• Use R >= 4.1 native pipe, |>
• Remove RMTL estimators