View source: R/non-overlap-measures.R
Tau_BC | R Documentation |
Calculates the baseline-corrected Tau index (Tarlow 2017).
Tau_BC(
A_data,
B_data,
condition,
outcome,
baseline_phase = NULL,
intervention_phase = NULL,
improvement = "increase",
SE = "unbiased",
confidence = 0.95,
trunc_const = FALSE,
Kendall = FALSE,
pretest_trend = FALSE,
report_correction = FALSE,
warn = TRUE
)
A_data |
vector of numeric data for A phase. Missing values are dropped. |
B_data |
vector of numeric data for B phase. Missing values are dropped. |
condition |
vector identifying the treatment condition for each observation in the series. |
outcome |
vector of outcome data for the entire series. |
baseline_phase |
character string specifying which value of
|
intervention_phase |
character string specifying which value of
|
improvement |
character string indicating direction of improvement. Default is "increase". |
SE |
character value indicating which formula to use for calculating the
standard error of Tau-BC, with possible values |
confidence |
confidence level for the reported interval estimate. Set to
|
trunc_const |
logical value indicating whether to return the truncation constant used to calculate the standard error. |
Kendall |
logical value indicating whether to use Kendall's rank
correlation to calculate the Tau effect size measure. If |
pretest_trend |
significance level for the initial baseline trend test.
The raw data are corrected and |
report_correction |
logical value indicating whether to report the
baseline corrected slope and intercept values. Default is |
warn |
logical value indicating whether to print a message regarding the
outcome of the baseline trend test. Default is |
Tau-BC is an elaboration of the Tau
that includes a
correction for baseline trend. The calculation of Tau-BC involves two or
three steps, depending on the pretest_trend
argument.
If pretest_trend = FALSE
(the default), the first step involves
adjusting the outcomes for baseline trend estimated using Theil-Sen
regression. In the second step, the residuals from Theil-Sen regression are
used to calculate the Tau
(using either Kendall's rank correlation,
with adjustment for ties, or computing Tau directly, without adjustment for
ties).
Alternately, pretest_trend
can be set equal to a significance level
between 0 and 1 (e.g. pretest_trend = .05
, as suggested by Tarlow
(2017). In this case, the first step involves a significance test for the
slope of the baseline trend based on Kendall's rank correlation. If the
slope is not significantly different from zero, then no baseline trend
adjustment is made and Tau-BC is set equal to Tau
index. If the
slope is significantly different from zero, then in the second step, the
outcomes are adjusted for baseline trend using Theil-Sen regression. Then,
in the third step, the residuals from Theil-Sen regression are used to
calculate the Tau
index. If Kendall = FALSE
(the default),
then Tau
(non-overlap) index is calculated. If Kendall
= TRUE
, then Kendall's rank correlation is calculated, including
adjustment for ties, as in Tarlow (2017).
Note that the standard error formulas are based on the standard errors for
Tau
(non-overlap) and they do not account for the additional
uncertainty due to use of the baseline trend correction (nor to the
pre-test for statistical significance of baseline trend, if used).
A list containing the estimate, standard error, and/or confidence interval.
Tarlow, K. R. (2017). An improved rank correlation effect size statistic for single-case designs: Baseline corrected Tau. Behavior modification, 41(4), 427-467. doi:\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1177/0145445516676750")}
Tau
, Tau_U
A <- c(20, 20, 26, 25, 22, 23)
B <- c(28, 25, 24, 27, 30, 30, 29)
Tau_BC(A_data = A, B_data = B)
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