Select sample providing enough data to identify sudden gains

Select sample for further sudden gains analyses depending on specified methods.

The following table shows the different data patterns that get selected when method = "pattern". This function goes through the data and selects all cases with at least one of the following data patterns.

Pattern	x1	x2	x3	x4	x5	x6
1.	x	N	x	x	.	.
2.	x	N	x	.	x	.
3.	x	.	N	x	x	.
4.	x	.	N	x	.	x

Note. x1 to x6 are consecutive data points of the primary outcome measure. 'x' = Available data; '.' = Missing data. 'N' represents available data to be examined as a possible pregain session.

Usage

select_cases(
  data,
  id_var_name,
  sg_var_list,
  method = c("pattern", "min_sess"),
  min_sess_num = NULL,
  return_id_lgl = FALSE
)

Arguments

data: A dataset in wide format with an id variable and the sudden gains variables.
id_var_name: String, specifying the name of the ID variable. Each row should have a unique value.
sg_var_list: Vector, specifying the variable names of each measurement point sequentially.
method: String, specifying the method used to select cases: pattern or min_sess.
min_sess_num: Numeric, minimum number of available sessions to be selected. This argument needs to be specified if method = min_sess.
return_id_lgl: Logical, if TRUE the function returns the ID variable and a new variable sg_select indicating whether there is enough data available to identify sudden gains. If set to FALSE this function returns the input data together with the new variable sg_select.

Value

A wide dataset indicating with all cases and a variable indicating whether each cases provides enough data to identify sudden gains.

Examples

# 1. method = "pattern"
select_cases(data = sgdata,
             id_var_name = "id",
             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"),
             method = "pattern",
             return_id_lgl = FALSE)
#> The method 'pattern' was used to select cases.
#> See help('select_cases') for more information.
#> # A tibble: 43 × 32
#>       id bdi_s0 bdi_s1 bdi_s2 bdi_s3 bdi_s4 bdi_s5 bdi_s6 bdi_s7 bdi_s8 bdi_s9
#>    <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>
#>  1     1     33     35     37     34     23     24     21     29     17     11
#>  2     2     26     26     NA     26     NA     27     17     19     11     10
#>  3     3     40     35     38     35     37     36     37     NA     35     26
#>  4     4     33     31     30     32     22     22     20     21     19     24
#>  5     5     39     39     37     36     26     26     23     21     19      6
#>  6     6     35     37     NA     23     NA     23     21     NA     NA     19
#>  7     7     NA     37     NA     23     21     NA     21     NA     NA     NA
#>  8     8     40     41     37     NA     NA     NA     NA     NA     NA     NA
#>  9     9     33     35     34     32     23     24     22     21     17     14
#> 10    10     34     35     35     25     25     17     17     16     16     11
#> # … with 33 more rows, and 21 more variables: 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>,
#> #   rq_s7 <dbl>, rq_s8 <dbl>, rq_s9 <dbl>, rq_s10 <dbl>, rq_s11 <dbl>,
#> #   rq_s12 <dbl>, rq_fu1 <dbl>, rq_fu2 <dbl>, sg_select <lgl>

# 2. method = "min_sess"
select_cases(data = sgdata,
             id_var_name = "id",
             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"),
             method = "min_sess",
             min_sess_num = 9,
             return_id_lgl = TRUE)
#> The method 'min_sess' was used to select cases.
#> # A tibble: 43 × 2
#>       id sg_select
#>    <dbl> <lgl>    
#>  1     1 TRUE     
#>  2     2 TRUE     
#>  3     3 TRUE     
#>  4     4 TRUE     
#>  5     5 TRUE     
#>  6     6 FALSE    
#>  7     7 FALSE    
#>  8     8 FALSE    
#>  9     9 TRUE     
#> 10    10 TRUE     
#> # … with 33 more rows