This function returns a wide data set with one row for each case in data.
The data set includes variables indicating whether each case experienced a sudden gain/loss or not,
values around the period of each gain/loss, and descriptives.
For cases with no sudden gain/loss the descriptive variables are coded as missing (NA).
Usage
create_byperson(
data,
sg_crit1_cutoff,
id_var_name,
sg_var_list,
tx_start_var_name,
tx_end_var_name,
sg_measure_name,
multiple_sg_select = c("first", "last", "smallest", "largest"),
data_is_bysg = FALSE,
identify = c("sg", "sl"),
sg_crit2_pct = 0.25,
sg_crit3 = TRUE,
sg_crit3_alpha = 0.05,
sg_crit3_adjust = TRUE,
sg_crit3_critical_value = 2.776,
identify_sg_1to2 = FALSE
)Arguments
- data
A data set in wide format including an ID variable and variables for each measurement point.
- sg_crit1_cutoff
Numeric, specifying the cut-off value to be used for the first sudden gains criterion. The function
define_crit1_cutoffcan be used to calculate a cutoff value based on the Reliable Change Index (RCI; Jacobson & Truax, 1991). If set toNULLthe first criterion wont be applied.- id_var_name
String, specifying the name of the ID variable.
- sg_var_list
Vector, specifying the variable names of each measurement point sequentially.
- tx_start_var_name
String, specifying the variable name of the first measurement point of the intervention.
- tx_end_var_name
String, specifying the variable name of the last measurement point of the intervention.
- sg_measure_name
String, specifying the name of the measure used to identify sudden gains/losses.
- multiple_sg_select
String, specifying which sudden gain/loss to select for this data set if more than one gain/loss was identified per case. Options are:
"first","last","smallest", or"largest". The default is to select the first sudden gain ("first") if someone experienced multiple gains.- data_is_bysg
Logical, specifying whether the data set in the
dataargument is a bysg datasets created using thecreate_bysgfunction.- identify
String, specifying whether to identify sudden gains (
"sg") usingidentify_sgor sudden losses ("sl") usingidentify_sl. The default is to identify sudden gains ("sg").- sg_crit2_pct
Numeric, specifying the percentage change to be used for the second sudden gains/losses criterion. If set to
NULLthe second criterion wont be applied.- sg_crit3
If set to
TRUEthe third criterion will be applied automatically adjusting the critical value for missingness. If set toFALSEthe third criterion wont be applied.- sg_crit3_alpha
Numeric, alpha for the student t-test (two-tailed) to determine the critical value to be used for the third criterion. Degrees of freedom are based on the number of available data in the three sessions preceding the gain and the three sessions following the gain.
- sg_crit3_adjust
Logical, specify whether critical value gets adjusted for missingness, see Lutz et al. (2013) and the documentation of this R package for further details. This argument is set to
TRUEby default adjusting the critical value for missingness as described in the package documentation and Lutz et al. (2013): A critical value of 2.776 is used when all three data points before and after a potential gain are available, where one datapoint is missing either before or after a potential gain a critical value of 3.182 is used, and where one datapoint is missing both before and after the gain a critical value of 4.303 is used (for sg_crit3_alpha = 0.05). If set toFALSEthe critical value specified insg_crit3_critical_valuewill instead be used for all comparisons, regardless of missingnes in the sequence of data points that are investigated for potential sudden gains.- sg_crit3_critical_value
Numeric, specifying the critical value to instead be used for all comparisons, regardless of missingnes in the sequence of data points that are investigated for potential sudden gains.
- identify_sg_1to2
Logical, indicating whether to identify sudden losses from measurement point 1 to 2. If set to TRUE, this implies that the first variable specified in
sg_var_listrepresents a baseline measurement point, e.g. pre-intervention assessment.
References
Tang, T. Z., & DeRubeis, R. J. (1999). Sudden gains and critical sessions in cognitive-behavioral therapy for depression. Journal of Consulting and Clinical Psychology, 67(6), 894–904. doi:10.1037/0022-006X.67.6.894 .
Examples
# Create byperson data set, selecting the largest gain in case of muliple gains
create_byperson(data = sgdata,
sg_crit1_cutoff = 7,
id_var_name = "id",
tx_start_var_name = "bdi_s1",
tx_end_var_name = "bdi_s12",
sg_var_list = c("bdi_s1", "bdi_s2", "bdi_s3",
"bdi_s4", "bdi_s5", "bdi_s6",
"bdi_s7", "bdi_s8", "bdi_s9",
"bdi_s10", "bdi_s11", "bdi_s12"),
sg_measure_name = "bdi",
multiple_sg_select = "largest")
#> First, second, and third sudden gains criteria were applied.
#> The critical value for the third criterion was adjusted for missingness.
#> The largest gain/loss was selected in case of multiple gains/losses.
#> # A tibble: 43 × 46
#> id id_sg sg_cr…¹ sg_se…² sg_fr…³ sg_bd…⁴ sg_bd…⁵ sg_bd…⁶ sg_bd…⁷ sg_bd…⁸
#> <dbl> <chr> <int> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 1_sg_7 1 7 2 24 21 29 17 11
#> 2 2 NA 0 NA 0 NA NA NA NA NA
#> 3 3 3_sg_8 1 8 1 37 NA 35 26 24
#> 4 4 4_sg_3 1 3 1 31 30 32 22 22
#> 5 5 5_sg_8 1 8 2 23 21 19 6 7
#> 6 6 NA 0 NA 0 NA NA NA NA NA
#> 7 7 NA 0 NA 0 NA NA NA NA NA
#> 8 8 NA 0 NA 0 NA NA NA NA NA
#> 9 9 9_sg_3 1 3 1 35 34 32 23 24
#> 10 10 10_sg_2 1 2 2 NA 35 35 25 25
#> # … with 33 more rows, 36 more variables: sg_bdi_n3 <dbl>, sg_magnitude <dbl>,
#> # sg_bdi_tx_change <dbl>, sg_change_proportion <dbl>,
#> # sg_reversal_value <dbl>, sg_reversal <dbl>, bdi_s0 <dbl>, bdi_s1 <dbl>,
#> # bdi_s2 <dbl>, bdi_s3 <dbl>, bdi_s4 <dbl>, bdi_s5 <dbl>, bdi_s6 <dbl>,
#> # bdi_s7 <dbl>, bdi_s8 <dbl>, bdi_s9 <dbl>, bdi_s10 <dbl>, bdi_s11 <dbl>,
#> # bdi_s12 <dbl>, bdi_fu1 <dbl>, bdi_fu2 <dbl>, rq_s0 <dbl>, rq_s1 <dbl>,
#> # rq_s2 <dbl>, rq_s3 <dbl>, rq_s4 <dbl>, rq_s5 <dbl>, rq_s6 <dbl>, …