multibias 1.7.2
- Added
multibias_plot()
to visualize sensitivity
analysis results
- When using validation data in
multibias_adjust()
the
function now incorporates uncertainty of the effect estimates from the
validation data by sampling from each estimate’s mean and SE. Now, when
using validation data, the confidence intervals from multibias
bootstrapped results will represent two sources of uncertainty: random
error and systematic error.
- Added FAQ documentation
multibias 1.7.1
- Updated code with dynamic formula construction so that there is no
limit to the number of known confounders one can include when using
bias_params
as an input for
multibias_adjust()
multibias_adjust()
now has built in bootstrapping
- Added
summary()
method to
data_observed
multibias 1.7
- Created
bias_params
class to handle bias parameter
inputs to multibias_adjust()
- Replaced the various
adjust()
functions with a single
multibias_adjust()
function. Users now specify the biases
they want to adjust for in the data_observed
object. Bias
adjustment formulas are now found in the bias_params
documentation.
- The user now specifies biases for adjustment in the
bias
input of data_observed
- Removed
evans
data; now only used in vignette
multibias 1.6.3
- Created a
pkgdown
web page:
www.paulbrendel.com/multibias
- Refined the vignette, including a new NHANES analysis
multibias 1.6.2
- The following functions now accept
data_validation
as
an input for bias adjustment:
adjust_om_sel.R
adjust_uc_sel.R
adjust_uc_em.R
adjust_uc_om.R
adjust_uc_em_sel.R
adjust_uc_om_sel.R
multibias 1.6.1
- The following functions now accept
data_validation
as
an input for bias adjustment:
adjust_em_om.R
adjust_em_sel.R
- Bug fixes for validation data input in
adjust_em.R
and
adjust_om.R
- Bug fixes for data and printing in
data_observed
and
data_validation
multibias 1.6
- Created new class
data_observed
to represent observed
causal data
- All
adjust
functions now take
data_observed
as input
- Created new class
data_validation
to represent causal
data that can be used as validaiton data for bias adjustment
- The following functions now accept
data_validation
as
an input for bias adjustment:
adjust_uc.R
adjust_em.R
adjust_om.R
adjust_sel.R
multibias 1.5.3
- All exposure misclassificaiton naming changed from
emc
changed to em
- All outcome misclassificaiton naming changed from
omc
changed to om
- Added lifecycle badges for above function renames
- Merged
adjust_multinom_uc_em_sel
into
adjust_uc_em_sel
- Merged
adjust_multinom_uc_om_sel
into
adjust_uc_om_sel
- The following functions now support more flexible combinations of
continuous and binary exposure-outcome variables:
adjust_uc_em_sel.R
adjust_uc_om_sel.R
multibias 1.5.2
- Merged
adjust_multinom_emc_omc
into
adjust_emc_omc
- Merged
adjust_multinom_uc_emc
into
adjust_uc_emc
- Merged
adjust_multinom_uc_omc
into
adjust_uc_omc
- The following functions now support more flexible combinations of
continuous and binary exposure-outcome variables:
adjust_emc_sel
(exposure must be binary)
adjust_omc_sel
(outcome must be binary)
adjust_uc_emc
(exposure must be binary)
adjust_uc_omc
(outcome must be binary)
adjust_multinom_uc_emc
(exposure must be binary)
adjust_multinom_uc_omc
(outcome must be binary)
- Expanded the number of known confounders in dataframes:
df_omc_sel
df_omc_sel_source
multibias 1.5.1
- The following functions now support more flexible combinations of
continuous and binary exposure-outcome variables:
adjust_uc
adjust_emc
(exposure must be binary)
adjust_omc
(outcome must be binary)
adjust_sel
adjust_uc_sel
- Expanded the number of known confounders in dataframes:
df_uc_omc
df_uc_omc_source
df_uc_emc
df_uc_emc_source
- Dataframes
df_uc
and df_uc_source
now both
have continuous and binary exposures and outcomes.
multibias 1.5.0
New features
- Added two functions for simultaneous adjustment of uncontrolled
confounding, outcome misclassification, and selection bias:
adjust_uc_omc_sel
&
adjust_multinom_uc_omc_sel
.
- Added dataframes with uncontrolled confounding, outcome
misclassification, and selection bias:
df_uc_omc_sel
and
df_uc_omc_sel_source
.
- Expanded the number of known confounders in dataframes:
df_uc_sel
df_uc_sel_source
multibias 1.4.0
New features
- Added two functions for simultaneous adjustment of exposure
misclassification and outcome misclassification:
adjust_emc_omc
&
adjust_multinom_emc_omc
.
- Added dataframes with exposure misclassification and outcome
misclassification:
df_emc_omc
and
df_emc_omc_source
.
- Expanded the number of known confounders in dataframes:
df_emc_sel
df_emc_sel_source
Bug fixes
- Improved some of the documentation of equations.
multibias 1.3.0
New features
- Added a function for simultaneous adjustment of outcome
misclassification and selection bias:
adjust_omc_sel
.
- Added dataframes with outcome misclassification and selection bias:
df_omc_sel
and df_omc_sel_source
.
- Expanded the number of known confounders in dataframes:
df_uc
df_uc_source
df_emc
df_emc_source
df_omc
df_omc_source
df_sel
df_sel_source
Bug fixes
- Fixed bug in
adjust_omc
that appears when using three
confounders
multibias 1.2.1
- Moved examples from README to vignette.
multibias 1.2.0
New features
- Added two functions for simultaneous adjustment of uncontrolled
confounding and outcome misclassification:
adjust_uc_omc
and adjust_multinom_uc_omc
.
- Added dataframes with uncontrolled confounding and outcome
misclassification:
df_uc_omc
and
df_uc_omc_source
.
Bug fixes
multibias 1.1.0
New features
- Created new function to adjust for outcome misclassification:
adjust_omc
.
- Added dataframes for all single bias scenarios:
df_emc
df_emc_source
df_omc
df_omc_source
df_sel
df_sel_source
df_uc
df_uc_source
Bug fixes
adjust_sel
had been weighing with the probability of
selection instead of the inverse probability of
selection.
multibias 1.0.0