Enhanced Interpersonal Therapy: Study of the adaptation of the IPT technique proposed by TELEPSI

Data analysis code

Authors

Affiliation

Bruno Braga Montezano

Federal University of Rio Grande do Sul

Ana Luiza da Silva Ache

Marcelo Fleck

Intro

TODO.

Descriptive data

df <- suppressWarnings(readxl::read_xlsx("./data/df_complete.xlsx"))

tip_df <- df |>
  dplyr::filter(RND_RAND_TTO == "TIP") |>
  dplyr::mutate(
    Sexo = table1::setLabel(Sexo, "Gender"),
    Idade = table1::setLabel(Idade, "Age"),
    Essenciais = table1::setLabel(Essenciais, "Occupational category"),
    G_NPS1 = table1::setLabel(
      G_NPS1,
      "How likely are you to recommend this treatment to a friend or colleague?"
    ),
    G_TRAT = table1::setLabel(
      G_TRAT,
      "How satisfied are you with the treatment?"
    ),
    G_PERC = table1::setLabel(
      G_PERC,
      paste0(
        "Considering your perception of your emotional state, how do you ",
        "feel today about starting treatment?"
      )
    ),
  )

table1::table1(
  ~ Sexo + Idade + Essenciais + G_NPS1 + G_TRAT + G_PERC,
  data = tip_df
)

	Overall (N=448)
Gender
1 Feminino	388 (86.6%)
2 Masculino	60 (13.4%)
Age
Mean (SD)	35.4 (9.03)
Median [Min, Max]	35.0 [19.0, 67.0]
Missing	15 (3.3%)
Occupational category
1 Profissional da Saúde	361 (80.6%)
2 Outro Profissional de Serviços Essenciais	32 (7.1%)
3 Professor(a)	38 (8.5%)
4 Outra categoria profissional	16 (3.6%)
Missing	1 (0.2%)
How likely are you to recommend this treatment to a friend or colleague?
Mean (SD)	9.65 (1.17)
Median [Min, Max]	10.0 [0, 10.0]
Missing	116 (25.9%)
How satisfied are you with the treatment?
1 Muito satisfeito	195 (43.5%)
2 Satisfeito	90 (20.1%)
3 Um pouco satisfeito	11 (2.5%)
4 Nem satisfeito, nem insatisfeito	5 (1.1%)
5 Um pouco insatisfeito	2 (0.4%)
6 Insatisfeito	5 (1.1%)
7 Muito insatisfeito	24 (5.4%)
Missing	116 (25.9%)
Considering your perception of your emotional state, how do you feel today about starting treatment?
2 Moderadamente pior	4 (0.9%)
3 Levemente pior	6 (1.3%)
4 Igual	16 (3.6%)
5 Levemente melhor	70 (15.6%)
6 Moderadamente melhor	135 (30.1%)
7 Muito melhor	101 (22.5%)
Missing	116 (25.9%)

PROMIS outcomes

promis_df <- tip_df |>
  dplyr::select(
    ID,
    dplyr::matches("(B|G|H|I)_PROMIS_(DEP|ANX|IRR|SLEEP)$")
  ) |>
  tidyr::pivot_longer(
    cols = c(dplyr::everything(), -ID),
    names_pattern = "(.)_PROMIS_(.*)$",
    names_to = c("time", "outcome"),
    values_to = "score"
  ) |>
  dplyr::mutate(
    time = factor(time, levels = c("B", "G", "H", "I")),
    outcome = as.factor(outcome)
  )

Depression

All rows

rcompanion::groupwiseMean(
  score ~ time,
  data = promis_df |> dplyr::filter(outcome == "DEP"),
  na.rm = TRUE
)

  time   n Mean Conf.level Trad.lower Trad.upper
1    B 447 27.7       0.95       27.1       28.2
2    G 332 17.9       0.95       17.2       18.6
3    H 294 19.1       0.95       18.2       20.0
4    I 249 19.8       0.95       18.8       20.9

Complete rows (per-protocol)

rcompanion::groupwiseMean(
  score ~ time,
  data = promis_df |>
    dplyr::filter(outcome == "DEP") |>
    dplyr::group_by(ID) |>
    dplyr::filter(all(!is.na(score))) |>
    dplyr::ungroup(),
  na.rm = TRUE
)

  time   n Mean Conf.level Trad.lower Trad.upper
1    B 201 27.4       0.95       26.5       28.2
2    G 201 17.5       0.95       16.6       18.5
3    H 201 18.9       0.95       17.8       20.0
4    I 201 19.7       0.95       18.5       20.8

Anxiety

All rows

rcompanion::groupwiseMean(
  score ~ time,
  data = promis_df |> dplyr::filter(outcome == "ANX"),
  na.rm = TRUE
)

  time   n Mean Conf.level Trad.lower Trad.upper
1    B 447 34.7       0.95       34.4       35.1
2    G 332 23.9       0.95       23.1       24.7
3    H 294 25.6       0.95       24.7       26.5
4    I 249 25.5       0.95       24.4       26.5

Complete rows (per-protocol)

rcompanion::groupwiseMean(
  score ~ time,
  data = promis_df |>
    dplyr::filter(outcome == "ANX") |>
    dplyr::group_by(ID) |>
    dplyr::filter(all(!is.na(score))) |>
    dplyr::ungroup(),
  na.rm = TRUE
)

  time   n Mean Conf.level Trad.lower Trad.upper
1    B 201 34.4       0.95       33.9       34.9
2    G 201 23.8       0.95       22.8       24.7
3    H 201 25.3       0.95       24.2       26.5
4    I 201 25.5       0.95       24.4       26.7

Irritability

All rows

rcompanion::groupwiseMean(
  score ~ time,
  data = promis_df |> dplyr::filter(outcome == "IRR"),
  na.rm = TRUE
)

  time   n Mean Conf.level Trad.lower Trad.upper
1    B 447 18.9       0.95       18.5       19.2
2    G 332 12.3       0.95       11.8       12.9
3    H 294 14.0       0.95       13.4       14.6
4    I 249 13.8       0.95       13.2       14.5

Complete rows (per-protocol)

rcompanion::groupwiseMean(
  score ~ time,
  data = promis_df |>
    dplyr::filter(outcome == "IRR") |>
    dplyr::group_by(ID) |>
    dplyr::filter(all(!is.na(score))) |>
    dplyr::ungroup(),
  na.rm = TRUE
)

  time   n Mean Conf.level Trad.lower Trad.upper
1    B 201 18.4       0.95       17.8       18.9
2    G 201 12.3       0.95       11.6       12.9
3    H 201 13.9       0.95       13.1       14.6
4    I 201 13.7       0.95       13.0       14.4

Sleep

All rows

rcompanion::groupwiseMean(
  score ~ time,
  data = promis_df |> dplyr::filter(outcome == "SLEEP"),
  na.rm = TRUE
)

  time   n Mean Conf.level Trad.lower Trad.upper
1    B 447 29.0       0.95       28.4       29.5
2    G 332 22.8       0.95       22.1       23.6
3    H 294 24.4       0.95       23.5       25.2
4    I 249 24.3       0.95       23.4       25.2

Complete rows (per-protocol)

rcompanion::groupwiseMean(
  score ~ time,
  data = promis_df |>
    dplyr::filter(outcome == "SLEEP") |>
    dplyr::group_by(ID) |>
    dplyr::filter(all(!is.na(score))) |>
    dplyr::ungroup(),
  na.rm = TRUE
)

  time   n Mean Conf.level Trad.lower Trad.upper
1    B 201 29.0       0.95       28.1       29.8
2    G 201 23.0       0.95       22.1       24.0
3    H 201 24.2       0.95       23.2       25.2
4    I 201 24.5       0.95       23.5       25.5