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baymedr is an R package with the goal of providing
researchers with easy-to-use tools for the computation of Bayes factors
for common biomedical research designs (see van Ravenzwaaij et al.,
2019). Implemented are functions to test the equivalence
(equiv_bf()), non-inferiority (infer_bf()),
and superiority (super_bf()) of an experimental group
(e.g., a new medication) compared to a control group (e.g., a placebo or
an already existing medication) on a continuous dependent variable. A
special focus of baymedr lies on a user-friendly interface,
so that a wide variety or researchers (i.e., not only statisticians) can
utilize baymedr for their analyses.
To install baymedr use:
install.packages("baymedr")You can install the latest development version of
baymedr from GitHub,
using the devtools package, with:
# install.packages("devtools")
devtools::install_github("maxlinde/baymedr")Subsequently, you can load baymedr, so that it is ready
to use:
library(baymedr)All three functions for the three research designs (i.e.,
equivalence, non-inferiority, and superiority) allow the user to compute
Bayes factors based on raw data (if arguments x and
y are defined) or summary statistics (if arguments
n_x, n_y, mean_x,
mean_y, sd_x, and sd_y are
defined). If summary statistics are used, the user has the option to
specify ci_margin and ci_level instead of
sd_x and sd_y. In general, arguments with ‘x’
as a name or suffix correspond to the control group and those with ‘y’
as a name or suffix refer to the experimental group. Importantly, the
dependent variable must be continuous in order to obtain valid
results.
Usage of the functions for equivalence (equiv_bf()),
non-inferiority (infer_bf()), and superiority designs
(super_bf()), results in S4 objects of classes
baymedrEquivalence, baymedrNonInferiority, and
baymedrSuperiority, respectively. Summary information are
shown in the console by printing the created S4 object. To extract the
Bayes factor from one of the three S4 objects, use the function
get_bf().
The Bayes factors resulting from super_bf() and
infer_bf() quantify evidence in favor of the data under the
alternative hypothesis (i.e., superiority and non-inferiority,
respectively) relative to the data under the null hypothesis. In
contrast, the Bayes factor resulting from equiv_bf()
quantifies evidence in favor of the data under the null hypothesis
(i.e., equivalence) relative to the data under the alternative
hypothesis. In case the evidence for the data under the other hypothesis
is desired, the user can take the reciprocal of the Bayes factor.
Bayesian inference requires the specification of a prior
distribution, which mirrors prior beliefs about the likelihood of
parameter values. For the equivalence, non-inferiority, and superiority
tests, the parameter of interest is the effect size between the
experimental and control conditions (see, e.g., Rouder et al., 2009; van
Ravenzwaaij et al., 2019). If relevant information is available, this
knowledge could be expressed in an idiosyncratic prior distribution.
Most of the time, however, relevant information is missing. In that
case, it is reasonable to define a prior distribution that is as
objective as possible. It has been argued that the Cauchy probability
density function centered on 0 represents such a function (see, e.g.,
Rouder et al., 2009). The standard Cauchy distribution resembles a
standard Normal distribution, except that the Cauchy distribution has
less mass at the center but instead heavier tails. The center of the
distribution is determined by the location parameter, while the width is
specified by the scale parameter. By varying the scale of the Cauchy
prior, the user can change the range of reasonable effect sizes. This is
accomplished with the argument prior_scale.
In order to demonstrate the three functions within
baymedr, we create an example dataset (data). There is a
control group “con” and an experimental group “exp” (condition).
Further, random numbers, sampled from the Normal distribution, within
each group are created, serving as the dependent variable of interest
(dv):
set.seed(123456789)
data <- data.frame(
condition = rep(x = c("con", "exp"),
c(150, 180)),
dv = c(rnorm(n = 150,
mean = 7.3,
sd = 3.4),
rnorm(n = 180,
mean = 8.9,
sd = 3.1))
)
data[c(1:5, 151:155), ]
#> condition dv
#> 1 con 9.016566
#> 2 con 8.645978
#> 3 con 12.112828
#> 4 con 4.844097
#> 5 con 5.197586
#> 151 exp 8.541017
#> 152 exp 10.693952
#> 153 exp 12.782147
#> 154 exp 9.094879
#> 155 exp 7.347376super_bf())With super_bf() we can test whether the experimental
group is better than the control group. Importantly, sometimes low and
sometimes high values on the measure of interest represent superiority,
which can be specified with the argument direction. In the
case where low values represent superiority we have BF-0, indicating
that we quantify evidence for the data under the negative alternative
hypothesis (i.e., H-) relative to the null hypothesis (i.e., H0). In the
case where high values represent superiority we have BF+0, indicating
that we quantify evidence for the data under the positive alternative
hypothesis (i.e., H+) relative to the null hypothesis (i.e., H0). The
default is that high values represent superiority.
We can use the raw data to compute a Bayes factor:
mod_super_raw <- super_bf(
x = data$dv[data$condition == "con"],
y = data$dv[data$condition == "exp"]
)
mod_super_raw
#> ******************************
#> Superiority analysis
#> --------------------
#> Data: raw data
#> H0 (non-superiority): mu_y == mu_x
#> H+ (superiority): mu_y > mu_x
#> Cauchy prior scale: 0.707
#>
#> BF+0 (superiority) = 44.00
#> ******************************
get_bf(object = mod_super_raw)
#> [1] 44.00176Alternatively, if the raw data are not available, we can use summary statistics to compute a Bayes factor (cf. van Ravenzwaaij et al., 2019). The data were obtained from Skjerven et al. (2013):
mod_super_sum <- super_bf(
n_x = 201,
n_y = 203,
mean_x = 68.1,
mean_y = 63.6,
ci_margin = (15.5 - (-6.5)) / 2,
ci_level = 0.95,
direction = "low"
)
mod_super_sum
#> ******************************
#> Superiority analysis
#> --------------------
#> Data: summary data
#> H0 (non-superiority): mu_y == mu_x
#> H- (superiority): mu_y < mu_x
#> Cauchy prior scale: 0.707
#>
#> BF-0 (superiority) = 0.24
#> ******************************
get_bf(object = mod_super_sum)
#> [1] 0.2364177equiv_bf())With equiv_bf() we can test whether the experimental and
the control groups are (practically) equivalent. With the argument
interval, an equivalence interval can be specified. The
argument interval_std can be used to specify whether the
equivalence interval is given in standardized (TRUE; the default) or
unstandardized (FALSE) units. However, in contrast to the frequentist
equivalence test, equiv_bf() can also incorporate a point
null hypothesis, which constitutes the default in
equiv_bf() (i.e., interval = 0). The Bayes
factor (i.e., BF01) resulting from equiv_bf() quantifies
evidence for the data under the null hypothesis (i.e., H0) relative to
the two-sided alternative hypothesis (i.e., H1).
We can use the raw data to compute a Bayes factor:
mod_equiv_raw <- equiv_bf(
x = data$dv[data$condition == "con"],
y = data$dv[data$condition == "exp"],
interval = 0.1,
)
mod_equiv_raw
#> ******************************
#> Equivalence analysis
#> --------------------
#> Data: raw data
#> H0 (equivalence): mu_y - mu_x > c_low AND mu_y - mu_x < c_high
#> H1 (non-equivalence): mu_y - mu_x < c_low OR mu_y - mu_x > c_high
#> Equivalence interval: Lower = -0.10; Upper = 0.10 (standardised)
#> Lower = -0.33; Upper = 0.33 (unstandardised)
#> Cauchy prior scale: 0.707
#>
#> BF01 (equivalence) = 0.11
#> ******************************
get_bf(object = mod_equiv_raw)
#> [1] 0.108721Alternatively, if the raw data are not available, we can use summary statistics to compute a Bayes factor (cf. van Ravenzwaaij et al., 2019). The data were obtained from Steiner et al. (2015):
mod_equiv_sum <- equiv_bf(
n_x = 560,
n_y = 538,
mean_x = 8.683,
mean_y = 8.516,
sd_x = 3.6,
sd_y = 3.6
)
mod_equiv_sum
#> ******************************
#> Equivalence analysis
#> --------------------
#> Data: summary data
#> H0 (equivalence): mu_y == mu_x
#> H1 (non-equivalence): mu_y != mu_x
#> Equivalence interval: Lower = -0.00; Upper = 0.00 (standardised)
#> Lower = -0.00; Upper = 0.00 (unstandardised)
#> Cauchy prior scale: 0.707
#>
#> BF01 (equivalence) = 11.05
#> ******************************
get_bf(object = mod_equiv_sum)
#> [1] 11.04945infer_bf())With infer_bf() we can test whether the experimental
group is not worse by a certain amount–which is given by the
non-inferiority margin–than the control group. Importantly, sometimes
low and sometimes high values on the dependent variable represent
non-inferiority, which can be specified with the argument
direction. In the case where low values represent
non-inferiority we have BF-+, indicating that we quantify evidence for
the data under the negative alternative hypothesis (i.e., H-) relative
to the positive null hypothesis (i.e., H+). In the case where high
values represent non-superiority we have BF+-, indicating that we
quantify evidence for the data under the positive alternative hypothesis
(i.e., H+) relative to the negative null hypothesis (i.e., H-). The
default is that high values represent non-inferiority. The
non-inferiority margin can be specified with the argument
ni_margin. The argument ni_margin_std can be
used to specify whether the non-inferiority margin is given in
standardized (TRUE; the default) or unstandardized (FALSE) units.
We can use the raw data to compute a Bayes factor:
mod_infer_raw <- infer_bf(
x = data$dv[data$condition == "con"],
y = data$dv[data$condition == "exp"],
ni_margin = 1.5,
ni_margin_std = FALSE
)
mod_infer_raw
#> ******************************
#> Non-inferiority analysis
#> ------------------------
#> Data: raw data
#> H- (inferiority): mu_y - mu_x < -ni_margin
#> H+ (non-inferiority): mu_y - mu_x > -ni_margin
#> Non-inferiority margin: 0.45 (standardised)
#> 1.50 (unstandardised)
#> Cauchy prior scale: 0.707
#>
#> BF+- (non-inferiority) = 6.85e+10
#> ******************************
get_bf(object = mod_infer_raw)
#> [1] 68546673627Alternatively, if the raw data are not available, we can use summary statistics to compute a Bayes factor (cf. van Ravenzwaaij et al., 2019). The data were obtained from Andersson et al. (2013):
mod_infer_sum <- infer_bf(
n_x = 33,
n_y = 32,
mean_x = 17.1,
mean_y = 13.6,
sd_x = 8,
sd_y = 9.8,
ni_margin = 2,
ni_margin_std = FALSE,
direction = "low"
)
mod_infer_sum
#> ******************************
#> Non-inferiority analysis
#> ------------------------
#> Data: summary data
#> H+ (inferiority): mu_y - mu_x > ni_margin
#> H- (non-inferiority): mu_y - mu_x < ni_margin
#> Non-inferiority margin: 0.22 (standardised)
#> 2.00 (unstandardised)
#> Cauchy prior scale: 0.707
#>
#> BF-+ (non-inferiority) = 79.59
#> ******************************
get_bf(object = mod_infer_sum)
#> [1] 79.59441Gronau, Q. F., Ly, A., & Wagenmakers, E.-J. (2020). Informed Bayesian t-tests. The American Statistician, 74(2), 137-143.
Rouder, J. N., Speckman, P. L., Sun, D., Morey, R. D., & Iverson, G. (2009). Bayesian t tests for accepting and rejecting the null hypothesis. Psychonomic Bulletin & Review, 16(2), 225-237.
Schönbrodt, F. D., Wagenmakers, E.-J., Zehetleitner, M., & Perugini, M. (2017). Sequential hypothesis testing with Bayes factors: Efficiently testing mean differences. Psychological Methods, 22(2), 322-339.
van Ravenzwaaij, D., Monden, R., Tendeiro, J. N., & Ioannidis, J. P. A. (2019). Bayes factors for superiority, non-inferiority, and equivalence designs. BMC Medical Research Methodology, 19(1), 71.
Wagenmakers, E.-J., Marsman, M., Jamil, T., Ly, A., Verhagen, J., Love, J., … Morey, R. D. (2018). Bayesian inference for psychology. Part I: Theoretical advantages and practical ramifications. Psychonomic Bulletin & Review, 25(1), 35-57.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.