Create a data set with one row for each sudden gain/loss

This function returns a wide data set with one row for each sudden gain/loss and assigns a unique identifier to each. The data set includes variables indicating values around the period of each gain/loss, and calculates descriptives of each gain/loss.

Usage

create_bysg(
  data,
  sg_crit1_cutoff,
  id_var_name,
  sg_var_list,
  tx_start_var_name,
  tx_end_var_name,
  sg_measure_name,
  sg_crit2_pct = 0.25,
  sg_crit3 = TRUE,
  sg_crit3_alpha = 0.05,
  sg_crit3_adjust = TRUE,
  sg_crit3_critical_value = 2.776,
  identify = c("sg", "sl"),
  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_cutoff can be used to calculate a cutoff value based on the Reliable Change Index (RCI; Jacobson & Truax, 1991). If set to NULL the 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.
sg_crit2_pct: Numeric, specifying the percentage change to be used for the second sudden gains/losses criterion. If set to NULL the second criterion wont be applied.
sg_crit3: If set to TRUE the third criterion will be applied automatically adjusting the critical value for missingness. If set to FALSE the 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 TRUE by 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 to FALSE the critical value set in sg_crit3_critical_value will 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: String, specifying whether to identify sudden gains ("sg") using identify_sg or sudden losses ("sl") using identify_sl. The default is to identify sudden gains ("sg").
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_list represents a baseline measurement point, e.g. pre-intervention assessment.

Value

A wide data set with one row per sudden gain/loss.

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 bypsg data set
create_bysg(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")
#> First, second, and third sudden gains criteria were applied.
#> The critical value for the third criterion was adjusted for missingness.
#> # A tibble: 24 × 28
#>       id id_sg sg_cr…¹ sg_se…² sg_fr…³ bdi_s1 bdi_s2 bdi_s3 bdi_s4 bdi_s5 bdi_s6
#>    <dbl> <chr>   <int>   <dbl>   <int>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>
#>  1     1 1_sg…       1       3       2     35     37     34     23     24     21
#>  2     1 1_sg…       1       7       2     35     37     34     23     24     21
#>  3     3 3_sg…       1       8       1     35     38     35     37     36     37
#>  4     4 4_sg…       1       3       1     31     30     32     22     22     20
#>  5     5 5_sg…       1       3       2     39     37     36     26     26     23
#>  6     5 5_sg…       1       8       2     39     37     36     26     26     23
#>  7     9 9_sg…       1       3       1     35     34     32     23     24     22
#>  8    10 10_s…       1       2       2     35     35     25     25     17     17
#>  9    10 10_s…       1       4       2     35     35     25     25     17     17
#> 10    12 12_s…       1       3       1     38     38     31     23     21     22
#> # … with 14 more rows, 17 more variables: bdi_s7 <dbl>, bdi_s8 <dbl>,
#> #   bdi_s9 <dbl>, bdi_s10 <dbl>, bdi_s11 <dbl>, bdi_s12 <dbl>, sg_bdi_2n <dbl>,
#> #   sg_bdi_1n <dbl>, sg_bdi_n <dbl>, sg_bdi_n1 <dbl>, sg_bdi_n2 <dbl>,
#> #   sg_bdi_n3 <dbl>, sg_magnitude <dbl>, sg_bdi_tx_change <dbl>,
#> #   sg_change_proportion <dbl>, sg_reversal_value <dbl>, sg_reversal <dbl>, and
#> #   abbreviated variable names ¹sg_crit123, ²sg_session_n, ³sg_freq_byperson