AHA CHD Control analyses
A Thorpe
02/12/2020
These are the participant numbers. We have 24 participants.
## [1] "5" "8" "9" "10" "11" "12" "19" "21" "22" "36" "38" "42" "43" "44" "49"
## [16] "50" "55" "57" "59" "60" "61" "65" "66"Demographics
Age. For two we have missing data. The mean = 26.75 (SD=4.12), median = 26, min = 19 and max = 37.
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 20 26.75 4.12 26 26.56 2.97 19 37 18 0.55 0.23 0.92Gender. Majority (78%) female.
## # A tibble: 2 x 3
## dem_genCHR n percent
## <chr> <int> <chr>
## 1 Female 18 78.3%
## 2 Male 5 21.7%Race/Ethnicity. Majority (83%) non-Hispanic white.
## # A tibble: 3 x 4
## # Groups: dem_latinoCHR [2]
## dem_latinoCHR Race_comb n percent
## <labelled> <chr> <int> <chr>
## 1 Hispanic Other 1 4.3%
## 2 Hispanic White 3 13.0%
## 3 Non-hispanic White 19 82.6%Education. Majority (74%) have some college education.
## The following `from` values were not present in `x`: 1## # A tibble: 5 x 3
## dem_educ1 n percent
## <fct> <int> <chr>
## 1 some high school but did not graduate 1 4.3%
## 2 High school graduate or GED 3 13.0%
## 3 Some college or 2-year degree 8 34.8%
## 4 4-year college graduate 9 39.1%
## 5 More than a 4-year college degree 2 8.7%Marital status. Most (87%) were currently married.
## The following `from` values were not present in `x`: 2, 3, 4, 6, 7## # A tibble: 2 x 3
## marital_stat1 n percent
## <labelled> <int> <chr>
## 1 Never married 3 13.0%
## 2 Now married 20 87.0%Religion. Most were Latter Day Saints.
## The following `from` values were not present in `x`: 1, 4, 5, 6, 7, 8, 9, 12## # A tibble: 5 x 3
## religion1 n percent
## <labelled> <int> <chr>
## 1 Agnostic 1 4.3%
## 2 LDS 16 69.6%
## 3 Nothing in particular 2 8.7%
## 4 Roman catholic 2 8.7%
## 5 Spiritual but not commited to particular faith 2 8.7%Religious attendance. Church or other religious meeting attendance was varied.
## # A tibble: 6 x 3
## rel_attend1 n percent
## <fct> <int> <chr>
## 1 Never 2 8.7%
## 2 Once a year or less 3 13.0%
## 3 A few times a year 3 13.0%
## 4 A few times a month 2 8.7%
## 5 Once a week 8 34.8%
## 6 More than once a week 5 21.7%Religious private. Time spent in private religious activities (e.g., prayer meditation) was varied too but mostly daily.
## # A tibble: 6 x 3
## rel_spend1 n percent
## <fct> <int> <chr>
## 1 Rarely or never 4 17.4%
## 2 a few times a month 3 13.0%
## 3 Once a week 1 4.3%
## 4 Two or more times/week 3 13.0%
## 5 Daily 10 43.5%
## 6 More than once a day 2 8.7%Outcome measures
Literacy. Average health literacy was fairly high at 4.23.
## vars n mean sd median trimmed mad min max range skew kurtosis
## lit_writ 1 23 4.26 0.81 4 4.32 1.48 3 5 2 -0.47 -1.39
## lit_con 2 23 4.22 0.67 4 4.26 0.00 3 5 2 -0.24 -0.94
## lit_help 3 23 4.22 0.95 4 4.37 1.48 2 5 3 -1.02 0.02
## se
## lit_writ 0.17
## lit_con 0.14
## lit_help 0.20## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 23 4.23 0.6 4.33 4.28 0.49 3 5 2 -0.51 -0.54 0.12Numeracy. Avergae for overall numeracy was around 4.38. For ability it was 4.53 and for preference was 4.24.
## vars n mean sd median trimmed mad min max range skew kurtosis
## num_fract 1 23 4.39 1.41 5 4.53 1.48 1 6 5 -0.59 -0.54
## num_perc 2 23 4.48 1.47 5 4.63 1.48 1 6 5 -0.57 -0.72
## num_tip 3 23 4.48 1.50 5 4.63 1.48 1 6 5 -0.57 -0.86
## num_off 4 23 4.74 1.45 5 4.89 1.48 1 6 5 -0.84 -0.39
## num_tab 5 23 4.04 1.46 4 4.16 1.48 1 6 5 -0.49 -0.55
## num_chance 6 23 3.35 1.75 3 3.32 2.97 1 6 5 0.12 -1.49
## num_predict 7 23 4.74 1.66 5 5.00 1.48 1 6 5 -1.21 0.05
## num_useful 8 23 4.83 1.23 5 4.95 1.48 2 6 4 -0.67 -0.80
## se
## num_fract 0.29
## num_perc 0.31
## num_tip 0.31
## num_off 0.30
## num_tab 0.30
## num_chance 0.36
## num_predict 0.35
## num_useful 0.26## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 23 4.38 0.88 4.38 4.41 1.11 2.62 5.75 3.12 -0.1 -0.98 0.18## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 23 4.52 1.36 4.75 4.67 1.48 1 6 5 -0.78 -0.12 0.28## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 23 4.24 0.77 4.25 4.21 0.74 3 6 3 0.18 -0.56 0.16Labels for the pre/postnatal participants, survey timings, and decision.
## The following `from` values were not present in `x`: 3## [1] "3_month_followup_d_arm_1" "3_month_followup_s_arm_1"
## [3] "3_month_followup_s_arm_2" "at_diagnosis_arm_1"
## [5] "at_diagnosis_arm_2" "post_decision_dns_arm_1"
## [7] "post_decision_s_arm_1" "post_decision_s_arm_2"## [1] "At diagnosis prenatal"
## [2] "At diagnosis postnatal"
## [3] "Post-decision prenatal (Survived)"
## [4] "Post-decision postnatal (Survived)"
## [5] "Post-decison prenatal (Did not survive)"
## [6] "3-month follow up prenatal (Survived)"
## [7] "3-month follow up postnatal (Survived)"
## [8] "3-month follow up prenatal (Did not survive)"## [1] "At diagnosis" "Post-decision (Survived)"
## [3] "Post-decison (Did not survive)" "3-month follow up (Survived)"
## [5] "3-month follow up (Did not survive)"Brief Symptom Inventory. I need to figure these out.
## vars n mean sd median trimmed mad min max range skew kurtosis
## glo_nerv 1 69 1.70 1.38 2.0 1.63 1.48 0 4 4 0.18 -1.27
## glo_faint 2 69 0.84 1.26 0.0 0.63 0.00 0 4 4 1.26 0.28
## glo_contr 3 69 0.16 0.53 0.0 0.02 0.00 0 3 3 3.61 13.30
## glo_other 4 69 0.29 0.79 0.0 0.11 0.00 0 4 4 3.37 11.99
## glo_troub 5 69 1.78 1.25 1.0 1.74 1.48 0 4 4 0.41 -0.95
## glo_annoy 6 69 2.13 1.21 2.0 2.14 1.48 0 4 4 0.00 -1.18
## glo_pains 7 69 0.42 1.05 0.0 0.16 0.00 0 4 4 2.51 5.26
## glo_open 8 69 0.23 0.60 0.0 0.09 0.00 0 3 3 2.73 7.30
## glo_endlif 9 68 0.13 0.49 0.0 0.00 0.00 0 2 2 3.41 10.01
## glo_trust 10 67 0.60 0.97 0.0 0.42 0.00 0 4 4 1.74 2.70
## glo_poor 11 67 1.19 1.18 1.0 1.07 1.48 0 4 4 0.71 -0.53
## glo_scared 12 67 0.61 0.87 0.0 0.47 0.00 0 4 4 1.50 2.22
## glo_temp 13 67 0.88 1.12 0.0 0.69 0.00 0 4 4 1.18 0.60
## glo_lonewo 14 67 1.24 1.23 1.0 1.09 1.48 0 4 4 0.79 -0.37
## glo_block 15 67 1.84 1.48 2.0 1.80 1.48 0 4 4 0.17 -1.43
## glo_lone 16 67 1.40 1.22 1.0 1.27 1.48 0 4 4 0.64 -0.45
## glo_blue 17 67 1.72 1.24 1.0 1.65 1.48 0 4 4 0.35 -0.96
## glo_noint 18 67 1.43 1.29 1.0 1.33 1.48 0 4 4 0.54 -0.99
## glo_fear 19 68 1.35 1.26 1.0 1.25 1.48 0 4 4 0.49 -0.93
## glo_hurt 20 68 1.09 1.14 1.0 0.95 1.48 0 4 4 0.90 -0.07
## glo_peop 21 68 0.71 0.90 0.0 0.59 0.00 0 3 3 0.96 -0.22
## glo_infer 22 68 0.78 0.99 0.5 0.61 0.74 0 4 4 1.26 0.90
## glo_nause 23 68 1.01 1.31 0.0 0.82 0.00 0 4 4 0.99 -0.31
## glo_watch 24 68 0.68 1.09 0.0 0.45 0.00 0 4 4 1.69 2.07
## glo_sleep 25 68 1.65 1.34 1.0 1.57 1.48 0 4 4 0.36 -1.23
## glo_check 26 68 1.31 1.34 1.0 1.16 1.48 0 4 4 0.68 -0.86
## glo_decis 27 68 1.76 1.34 1.0 1.71 1.48 0 4 4 0.28 -1.21
## glo_trave 28 68 0.34 0.80 0.0 0.16 0.00 0 4 4 3.08 10.35
## glo_breat 29 68 0.60 1.05 0.0 0.38 0.00 0 4 4 1.74 2.07
## glo_spell 30 68 0.78 1.18 0.0 0.55 0.00 0 4 4 1.49 1.16
## glo_avoid 31 68 0.57 1.04 0.0 0.34 0.00 0 4 4 1.84 2.46
## glo_blank 32 68 1.57 1.19 1.0 1.50 1.48 0 4 4 0.43 -0.78
## glo_numb 33 68 0.57 1.11 0.0 0.32 0.00 0 4 4 1.85 2.30
## glo_sins 34 68 0.43 0.87 0.0 0.23 0.00 0 4 4 2.17 4.32
## glo_hopel 35 68 0.93 1.19 0.0 0.73 0.00 0 4 4 1.14 0.35
## glo_conce 36 68 1.71 1.21 2.0 1.64 1.48 0 4 4 0.37 -0.80
## glo_weak 37 68 0.74 1.09 0.0 0.54 0.00 0 4 4 1.35 0.86
## glo_keyed 38 68 0.59 1.04 0.0 0.36 0.00 0 4 4 1.81 2.38
## glo_death 39 68 0.44 0.74 0.0 0.30 0.00 0 3 3 1.50 1.26
## glo_beat 40 68 0.07 0.26 0.0 0.00 0.00 0 1 1 3.20 8.34
## glo_break 41 67 0.33 0.79 0.0 0.13 0.00 0 3 3 2.32 4.26
## glo_selfc 42 68 1.19 1.22 1.0 1.05 1.48 0 4 4 0.74 -0.51
## glo_uneas 43 68 1.00 1.28 0.0 0.80 0.00 0 4 4 1.05 -0.11
## glo_close 44 68 0.40 0.78 0.0 0.23 0.00 0 4 4 2.42 6.66
## glo_terr 45 68 0.46 0.87 0.0 0.27 0.00 0 4 4 2.07 3.97
## glo_argu 46 68 0.50 0.76 0.0 0.36 0.00 0 3 3 1.49 1.65
## glo_neral 47 68 0.82 1.11 0.0 0.64 0.00 0 4 4 1.26 0.61
## glo_achie 48 67 0.60 1.16 0.0 0.33 0.00 0 4 4 1.91 2.48
## glo_restl 49 68 0.82 1.23 0.0 0.61 0.00 0 4 4 1.27 0.41
## glo_worth 50 68 0.74 1.03 0.0 0.54 0.00 0 4 4 1.50 1.63
## glo_takea 51 68 0.54 1.03 0.0 0.30 0.00 0 4 4 2.11 3.82
## glo_guilt 52 68 1.24 1.32 1.0 1.07 1.48 0 4 4 0.77 -0.51
## glo_wrong 53 68 0.60 1.05 0.0 0.39 0.00 0 4 4 1.66 1.53
## se
## glo_nerv 0.17
## glo_faint 0.15
## glo_contr 0.06
## glo_other 0.09
## glo_troub 0.15
## glo_annoy 0.15
## glo_pains 0.13
## glo_open 0.07
## glo_endlif 0.06
## glo_trust 0.12
## glo_poor 0.14
## glo_scared 0.11
## glo_temp 0.14
## glo_lonewo 0.15
## glo_block 0.18
## glo_lone 0.15
## glo_blue 0.15
## glo_noint 0.16
## glo_fear 0.15
## glo_hurt 0.14
## glo_peop 0.11
## glo_infer 0.12
## glo_nause 0.16
## glo_watch 0.13
## glo_sleep 0.16
## glo_check 0.16
## glo_decis 0.16
## glo_trave 0.10
## glo_breat 0.13
## glo_spell 0.14
## glo_avoid 0.13
## glo_blank 0.14
## glo_numb 0.13
## glo_sins 0.11
## glo_hopel 0.14
## glo_conce 0.15
## glo_weak 0.13
## glo_keyed 0.13
## glo_death 0.09
## glo_beat 0.03
## glo_break 0.10
## glo_selfc 0.15
## glo_uneas 0.16
## glo_close 0.09
## glo_terr 0.11
## glo_argu 0.09
## glo_neral 0.13
## glo_achie 0.14
## glo_restl 0.15
## glo_worth 0.13
## glo_takea 0.12
## glo_guilt 0.16
## glo_wrong 0.13## `summarise()` regrouping output by 'redcap_event_name1' (override with `.groups` argument)## # A tibble: 11 x 5
## # Groups: redcap_event_name1 [8]
## redcap_event_name1 Decision count mean sd
## <fct> <chr> <int> <dbl> <dbl>
## 1 At diagnosis prenatal <NA> 22 0.753 0.525
## 2 At diagnosis postnatal <NA> 1 0.906 NA
## 3 Post-decision prenatal (Survived) Surgery 12 0.719 0.562
## 4 Post-decision postnatal (Survived) Surgery 1 1.23 NA
## 5 Post-decison prenatal (Did not survive) Palliative 6 1.33 0.662
## 6 Post-decison prenatal (Did not survive) Surgery 3 0.881 0.491
## 7 Post-decison prenatal (Did not survive) <NA> 1 0.140 NA
## 8 3-month follow up prenatal (Survived) Surgery 11 0.664 0.491
## 9 3-month follow up postnatal (Survived) Surgery 1 2.33 NA
## 10 3-month follow up prenatal (Did not survive) Palliative 6 1.76 0.384
## 11 3-month follow up prenatal (Did not survive) Surgery 5 0.909 0.701Decision Quality Values . I need to figure these out.
## vars n mean sd median trimmed mad min max range skew kurtosis se
## v_pain 1 37 1.54 0.84 1 1.42 0.00 1 4 3 1.26 0.40 0.14
## v_live 2 36 2.17 1.18 2 2.00 1.48 1 6 5 1.30 1.62 0.20
## v_phys 3 41 4.78 1.11 5 4.91 1.48 1 6 5 -1.08 1.41 0.17
## v_ment 4 40 4.22 1.19 4 4.25 1.48 2 6 4 -0.16 -1.02 0.19
## v_time 5 44 4.41 1.32 4 4.50 1.48 2 6 4 -0.28 -0.97 0.20
## v_try 6 42 3.26 1.73 3 3.21 1.48 1 6 5 0.44 -1.20 0.27Knowledge pt1 fetus questions. I need to figure these out. These were taken at post-decision and 3 month follow up (for survived and did not survive).
## vars n mean sd median trimmed mad min max range skew kurtosis
## k_prim 1 44 2.95 0.21 3 3.00 0 2 3 1 -4.22 16.15
## k_mprob 2 43 1.86 1.13 1 1.71 0 1 4 3 0.76 -1.06
## k_opt1 3 44 1.11 0.32 1 1.03 0 1 2 1 2.35 3.62
## k_opt2 4 44 1.25 0.44 1 1.19 0 1 2 1 1.12 -0.77
## l_opt3 5 44 1.14 0.35 1 1.06 0 1 2 1 2.05 2.24
## k_com 6 41 1.00 0.00 1 1.00 0 1 1 0 NaN NaN
## k_sur 7 40 1.02 0.16 1 1.00 0 1 2 1 5.86 33.15
## k_survive 8 44 1.82 0.45 2 1.86 0 1 3 2 -0.79 0.46
## k_comfort 9 44 0.05 0.21 0 0.00 0 0 1 1 4.22 16.15
## k_cable1 10 44 1.07 0.25 1 1.00 0 1 2 1 3.31 9.17
## k_cable2 11 44 1.00 0.00 1 1.00 0 1 1 0 NaN NaN
## k_cable3 12 43 1.44 0.50 1 1.43 0 1 2 1 0.23 -1.99
## k_cable4 13 44 1.18 0.39 1 1.11 0 1 2 1 1.59 0.55
## k_cable5 14 44 1.16 0.37 1 1.08 0 1 2 1 1.80 1.27
## se
## k_prim 0.03
## k_mprob 0.17
## k_opt1 0.05
## k_opt2 0.07
## l_opt3 0.05
## k_com 0.00
## k_sur 0.02
## k_survive 0.07
## k_comfort 0.03
## k_cable1 0.04
## k_cable2 0.00
## k_cable3 0.08
## k_cable4 0.06
## k_cable5 0.06Knowledge pt1 neonate questions. I need to figure these out.
## vars n mean sd median trimmed mad min max range skew kurtosis se
## k_nprim 1 2 3.0 0.00 3.0 3.0 0.00 3 3 0 NaN NaN 0.0
## k_nmprob 2 2 3.0 1.41 3.0 3.0 1.48 2 4 2 0 -2.75 1.0
## k_ncom 3 2 1.5 0.71 1.5 1.5 0.74 1 2 1 0 -2.75 0.5
## k_nsur 4 2 1.0 0.00 1.0 1.0 0.00 1 1 0 NaN NaN 0.0
## k_nsurvive 5 2 2.0 0.00 2.0 2.0 0.00 2 2 0 NaN NaN 0.0
## k_ncomfort 6 2 0.0 0.00 0.0 0.0 0.00 0 0 0 NaN NaN 0.0
## k_ncable1 7 2 1.5 0.71 1.5 1.5 0.74 1 2 1 0 -2.75 0.5
## k_ncable2 8 2 1.5 0.71 1.5 1.5 0.74 1 2 1 0 -2.75 0.5
## k_ncable3 9 2 1.5 0.71 1.5 1.5 0.74 1 2 1 0 -2.75 0.5
## k_ncable4 10 2 1.5 0.71 1.5 1.5 0.74 1 2 1 0 -2.75 0.5
## k_ncable5 11 2 1.5 0.71 1.5 1.5 0.74 1 2 1 0 -2.75 0.5Comrade questions. These were only measured post decision.
## vars n mean sd median trimmed mad min max range skew kurtosis
## com_aware 1 22 4.18 1.18 5 4.39 0 1 5 4 -1.33 0.63
## com_expres 2 22 4.18 1.14 5 4.39 0 1 5 4 -1.26 0.68
## com_chanc 3 22 4.45 1.06 5 4.72 0 1 5 4 -2.08 3.57
## com_infor 4 22 4.14 1.28 5 4.39 0 1 5 4 -1.40 0.73
## com_expan 5 22 4.18 1.30 5 4.44 0 1 5 4 -1.45 0.79
## com_under 6 22 4.23 1.07 5 4.39 0 1 5 4 -1.33 1.29
## com_advan 7 22 4.32 1.21 5 4.61 0 1 5 4 -1.82 2.26
## com_disad 8 22 4.27 1.20 5 4.56 0 1 5 4 -1.76 2.13
## com_decid 9 22 4.27 1.28 5 4.56 0 1 5 4 -1.53 1.10
## com_invol 10 22 4.14 1.42 5 4.39 0 1 5 4 -1.36 0.33
## se
## com_aware 0.25
## com_expres 0.24
## com_chanc 0.23
## com_infor 0.27
## com_expan 0.28
## com_under 0.23
## com_advan 0.26
## com_disad 0.26
## com_decid 0.27
## com_invol 0.30## `summarise()` regrouping output by 'redcap_event_name1' (override with `.groups` argument)## # A tibble: 8 x 5
## # Groups: redcap_event_name1 [6]
## redcap_event_name1 Decision count mean sd
## <fct> <chr> <int> <dbl> <dbl>
## 1 Post-decision prenatal (Survived) Surgery 12 4.48 0.681
## 2 Post-decision postnatal (Survived) Surgery 1 4.4 NA
## 3 Post-decison prenatal (Did not survive) Palliative 6 4.02 1.22
## 4 Post-decison prenatal (Did not survive) Surgery 3 3.63 2.28
## 5 3-month follow up prenatal (Survived) Surgery 11 NaN NA
## 6 3-month follow up postnatal (Survived) Surgery 1 NaN NA
## 7 3-month follow up prenatal (Did not survive) Palliative 6 NaN NA
## 8 3-month follow up prenatal (Did not survive) Surgery 5 NaN NAPreference for shared decision making questions. Note that one participant (ID=44) did not complete the second survey.
## The following `from` values were not present in `x`: 1## # A tibble: 4 x 2
## cps_time12 n
## <labelled> <int>
## 1 I will make the decision after seriously considering my doctor(s) opini… 9
## 2 I will make the decision with little input from my doctor(s) 2
## 3 My doctor(s) and I will make the decision together 10
## 4 My doctor(s) will make the decision but will seriously consider my opin… 2
## # A tibble: 9 x 4
## # Groups: redcap_event_name1, Decision [4]
## redcap_event_name1 Decision cps_time34 n
## <fct> <chr> <labelled> <int>
## 1 Post-decision prenatal (S… Surgery I will make the decision after ser… 4
## 2 Post-decision prenatal (S… Surgery I will make the decision with litt… 1
## 3 Post-decision prenatal (S… Surgery My doctor(s) and I will make the d… 6
## 4 Post-decision prenatal (S… Surgery My doctor(s) will make the decisio… 1
## 5 Post-decision postnatal (… Surgery My doctor(s) will make the decisio… 1
## 6 Post-decison prenatal (Di… Palliati… I will make the decision after ser… 5
## 7 Post-decison prenatal (Di… Palliati… My doctor(s) will make the decisio… 1
## 8 Post-decison prenatal (Di… Surgery I will make the decision after ser… 2
## 9 Post-decison prenatal (Di… Surgery My doctor(s) will make the decisio… 1
Decision self-efficacy questions. Looking okay. A score of 0 means ‘extremely low self efficacy’ and a score of 100 means ‘extremely high self efficacy’ – “basically confidence in understanding the information enough and to be able to make a choice”
## vars n mean sd median trimmed mad min max range skew kurtosis
## dse_avail 1 44 3.30 1.02 4 3.50 0.00 0 4 4 -1.48 1.45
## dse_bene 2 45 3.38 0.91 4 3.54 0.00 0 4 4 -1.67 2.81
## dse_risk 3 45 3.33 0.90 4 3.49 0.00 0 4 4 -1.58 2.67
## dse_under 4 44 3.36 0.92 4 3.53 0.00 0 4 4 -1.64 2.69
## dse_askq 5 45 3.22 1.13 4 3.43 0.00 0 4 4 -1.46 1.21
## dse_expre 6 45 3.20 1.04 4 3.38 0.00 0 4 4 -1.23 0.76
## dse_aska 7 45 3.31 0.87 4 3.43 0.00 0 4 4 -1.42 2.44
## dse_figu 8 45 3.31 0.90 4 3.46 0.00 0 4 4 -1.54 2.62
## dse_hand 9 45 2.82 1.19 3 2.92 1.48 0 4 4 -0.45 -1.11
## dse_best 10 44 3.32 0.86 3 3.47 1.48 0 4 4 -1.72 3.79
## dse_delay 11 45 3.07 1.14 3 3.22 1.48 0 4 4 -0.95 -0.28
## se
## dse_avail 0.15
## dse_bene 0.14
## dse_risk 0.13
## dse_under 0.14
## dse_askq 0.17
## dse_expre 0.15
## dse_aska 0.13
## dse_figu 0.13
## dse_hand 0.18
## dse_best 0.13
## dse_delay 0.17## `summarise()` regrouping output by 'redcap_event_name1' (override with `.groups` argument)## # A tibble: 11 x 5
## # Groups: redcap_event_name1 [8]
## redcap_event_name1 Decision count mean sd
## <fct> <chr> <int> <dbl> <dbl>
## 1 At diagnosis prenatal <NA> 22 82.23 15.80
## 2 At diagnosis postnatal <NA> 1 100 NA
## 3 Post-decision prenatal (Survived) Surgery 12 80.68 20.48
## 4 Post-decision postnatal (Survived) Surgery 1 93.18 NA
## 5 Post-decison prenatal (Did not survive) Palliative 6 66.67 33.85
## 6 Post-decison prenatal (Did not survive) Surgery 3 83.33 28.87
## 7 Post-decison prenatal (Did not survive) <NA> 1 0 NA
## 8 3-month follow up prenatal (Survived) Surgery 11 0 0
## 9 3-month follow up postnatal (Survived) Surgery 1 0 NA
## 10 3-month follow up prenatal (Did not survive) Palliative 6 0 0
## 11 3-month follow up prenatal (Did not survive) Surgery 5 0 0Decision conflict questions. Looking okay. A score of 0 is ‘no decisional conflict’ and 100 is ‘extremely high decisional conflict’ – “basically clear about the best choice for themselves”
## vars n mean sd median trimmed mad min max range skew kurtosis
## dcs_optio 1 45 4.42 0.92 5 4.59 0.00 1 5 4 -2.29 5.87
## dcs_benef 2 45 4.47 0.89 5 4.62 0.00 1 5 4 -2.51 7.13
## dsc_risks 3 45 4.31 1.00 5 4.51 0.00 1 5 4 -1.85 3.33
## dsc_bmatt 4 45 4.49 0.89 5 4.65 0.00 1 5 4 -2.57 7.36
## dsc_rmatt 5 45 4.42 0.89 5 4.59 0.00 1 5 4 -1.84 3.59
## dsc_impor 6 45 4.38 0.94 5 4.57 0.00 1 5 4 -2.09 4.85
## dcs_suppr 7 45 4.51 0.79 5 4.65 0.00 1 5 4 -2.22 6.56
## dcs_press 8 45 4.29 1.06 5 4.51 0.00 1 5 4 -1.70 2.34
## dcs_advic 9 45 4.38 0.96 5 4.57 0.00 1 5 4 -1.99 4.19
## dcs_clear 10 45 4.33 1.00 5 4.54 0.00 1 5 4 -1.88 3.40
## dcs_sure 11 45 4.29 0.99 5 4.49 0.00 1 5 4 -1.81 3.28
## dcs_easy 12 45 3.29 1.59 3 3.35 2.97 1 5 4 -0.23 -1.50
## dcs_infor 13 45 4.44 0.92 5 4.62 0.00 1 5 4 -2.34 6.03
## dcs_shows 14 45 4.51 0.84 5 4.68 0.00 1 5 4 -2.04 4.72
## dcs_stick 15 45 4.56 0.81 5 4.73 0.00 1 5 4 -2.28 6.11
## dcs_satis 16 45 4.33 1.07 5 4.57 0.00 1 5 4 -1.99 3.57
## se
## dcs_optio 0.14
## dcs_benef 0.13
## dsc_risks 0.15
## dsc_bmatt 0.13
## dsc_rmatt 0.13
## dsc_impor 0.14
## dcs_suppr 0.12
## dcs_press 0.16
## dcs_advic 0.14
## dcs_clear 0.15
## dcs_sure 0.15
## dcs_easy 0.24
## dcs_infor 0.14
## dcs_shows 0.13
## dcs_stick 0.12
## dcs_satis 0.16## vars n mean sd median trimmed mad min max range skew kurtosis
## dcs_optio 1 45 3.42 0.92 4 3.59 0.00 0 4 4 -2.29 5.87
## dcs_benef 2 45 3.47 0.89 4 3.62 0.00 0 4 4 -2.51 7.13
## dsc_risks 3 45 3.31 1.00 4 3.51 0.00 0 4 4 -1.85 3.33
## dsc_bmatt 4 45 3.49 0.89 4 3.65 0.00 0 4 4 -2.57 7.36
## dsc_rmatt 5 45 3.42 0.89 4 3.59 0.00 0 4 4 -1.84 3.59
## dsc_impor 6 45 3.38 0.94 4 3.57 0.00 0 4 4 -2.09 4.85
## dcs_suppr 7 45 3.51 0.79 4 3.65 0.00 0 4 4 -2.22 6.56
## dcs_press 8 45 3.29 1.06 4 3.51 0.00 0 4 4 -1.70 2.34
## dcs_advic 9 45 3.38 0.96 4 3.57 0.00 0 4 4 -1.99 4.19
## dcs_clear 10 45 3.33 1.00 4 3.54 0.00 0 4 4 -1.88 3.40
## dcs_sure 11 45 3.29 0.99 4 3.49 0.00 0 4 4 -1.81 3.28
## dcs_easy 12 45 2.29 1.59 2 2.35 2.97 0 4 4 -0.23 -1.50
## dcs_infor 13 45 3.44 0.92 4 3.62 0.00 0 4 4 -2.34 6.03
## dcs_shows 14 45 3.51 0.84 4 3.68 0.00 0 4 4 -2.04 4.72
## dcs_stick 15 45 3.56 0.81 4 3.73 0.00 0 4 4 -2.28 6.11
## dcs_satis 16 45 3.33 1.07 4 3.57 0.00 0 4 4 -1.99 3.57
## se
## dcs_optio 0.14
## dcs_benef 0.13
## dsc_risks 0.15
## dsc_bmatt 0.13
## dsc_rmatt 0.13
## dsc_impor 0.14
## dcs_suppr 0.12
## dcs_press 0.16
## dcs_advic 0.14
## dcs_clear 0.15
## dcs_sure 0.15
## dcs_easy 0.24
## dcs_infor 0.14
## dcs_shows 0.13
## dcs_stick 0.12
## dcs_satis 0.16## `summarise()` ungrouping output (override with `.groups` argument)## # A tibble: 8 x 4
## redcap_event_name1 count mean sd
## <fct> <int> <dbl> <dbl>
## 1 At diagnosis prenatal 22 0 0
## 2 At diagnosis postnatal 1 0 NA
## 3 Post-decision prenatal (Survived) 12 85.42 13.82
## 4 Post-decision postnatal (Survived) 1 98.44 NA
## 5 Post-decison prenatal (Did not survive) 10 67.03 33.56
## 6 3-month follow up prenatal (Survived) 11 83.52 28.76
## 7 3-month follow up postnatal (Survived) 1 100 NA
## 8 3-month follow up prenatal (Did not survive) 11 85.80 12.45## `summarise()` regrouping output by 'redcap_event_name1' (override with `.groups` argument)## # A tibble: 11 x 5
## # Groups: redcap_event_name1 [8]
## redcap_event_name1 Decision count mean sd
## <fct> <chr> <int> <dbl> <dbl>
## 1 At diagnosis prenatal <NA> 22 0 0
## 2 At diagnosis postnatal <NA> 1 0 NA
## 3 Post-decision prenatal (Survived) Surgery 12 85.42 13.82
## 4 Post-decision postnatal (Survived) Surgery 1 98.44 NA
## 5 Post-decison prenatal (Did not survive) Palliative 6 69.53 30.03
## 6 Post-decison prenatal (Did not survive) Surgery 3 84.38 9.758
## 7 Post-decison prenatal (Did not survive) <NA> 1 0 NA
## 8 3-month follow up prenatal (Survived) Surgery 11 83.52 28.76
## 9 3-month follow up postnatal (Survived) Surgery 1 100 NA
## 10 3-month follow up prenatal (Did not survive) Palliative 6 76.82 8.917
## 11 3-month follow up prenatal (Did not survive) Surgery 5 96.56 4.739Decision regret questions. Looking okay. A score of 0 is ‘no regret’ and 100 is ‘high regret’ – “basically was it a good decision, would they do it again”
## vars n mean sd median trimmed mad min max range skew kurtosis
## drs_right 1 23 4.61 0.89 5 4.79 0.00 1 5 4 -2.89 8.76
## drs_regre 2 23 1.61 0.99 1 1.42 0.00 1 5 4 1.87 3.47
## drs_again 3 23 4.43 0.99 5 4.63 0.00 1 5 4 -1.97 3.70
## drs_harm 4 23 2.17 1.37 2 2.00 1.48 1 5 4 0.82 -0.77
## drs_wise 5 23 4.52 0.90 5 4.68 0.00 1 5 4 -2.58 7.37
## se
## drs_right 0.19
## drs_regre 0.21
## drs_again 0.21
## drs_harm 0.29
## drs_wise 0.19## `summarise()` regrouping output by 'redcap_event_name1' (override with `.groups` argument)## # A tibble: 11 x 5
## # Groups: redcap_event_name1 [8]
## redcap_event_name1 Decision count mean sd
## <fct> <chr> <int> <dbl> <dbl>
## 1 At diagnosis prenatal <NA> 22 NaN NA
## 2 At diagnosis postnatal <NA> 1 NaN NA
## 3 Post-decision prenatal (Survived) Surgery 12 NaN NA
## 4 Post-decision postnatal (Survived) Surgery 1 NaN NA
## 5 Post-decison prenatal (Did not survive) Palliative 6 NaN NA
## 6 Post-decison prenatal (Did not survive) Surgery 3 NaN NA
## 7 Post-decison prenatal (Did not survive) <NA> 1 NaN NA
## 8 3-month follow up prenatal (Survived) Surgery 11 85 30
## 9 3-month follow up postnatal (Survived) Surgery 1 80 NA
## 10 3-month follow up prenatal (Did not survive) Palliative 6 73.33 8.756
## 11 3-month follow up prenatal (Did not survive) Surgery 5 95 7.071Consultation quality questions. Looking at min to max values they look okay.
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 45 5.24 1 6 5.38 0 2 6 4 -1.15 0.64 0.15## # A tibble: 12 x 4
## # Groups: redcap_event_name1, Decision [6]
## redcap_event_name1 Decision con_option n
## <fct> <chr> <labelled> <int>
## 1 At diagnosis prenatal <NA> Encouraged a medical interventi… 5
## 2 At diagnosis prenatal <NA> Encouraged palliative care 4
## 3 At diagnosis prenatal <NA> Encouraged termination 1
## 4 At diagnosis prenatal <NA> Neither encouraged or discourag… 12
## 5 At diagnosis postnatal <NA> Encouraged a medical interventi… 1
## 6 Post-decision prenatal (Sur… Surgery Encouraged a medical interventi… 2
## 7 Post-decision prenatal (Sur… Surgery Encouraged termination 1
## 8 Post-decision prenatal (Sur… Surgery Neither encouraged or discourag… 9
## 9 Post-decision postnatal (Su… Surgery Encouraged a medical interventi… 1
## 10 Post-decison prenatal (Did … Palliati… Encouraged palliative care 6
## 11 Post-decison prenatal (Did … Surgery Encouraged a medical interventi… 2
## 12 Post-decison prenatal (Did … Surgery Neither encouraged or discourag… 1
SF12 questions. I need to figure these out.
## vars n mean sd median trimmed mad min max range skew kurtosis
## sf_1genhea 1 45 2.47 0.89 3 2.46 1.48 1 4 3 -0.09 -0.84
## sf_2moder 2 44 2.80 0.41 3 2.86 0.00 2 3 1 -1.42 0.00
## sf_3stair 3 45 2.49 0.63 3 2.57 0.00 1 3 2 -0.77 -0.48
## sf_4less 4 45 1.60 0.50 2 1.62 0.00 1 2 1 -0.39 -1.88
## sf_5kind 5 45 1.67 0.48 2 1.70 0.00 1 2 1 -0.68 -1.57
## sf_6like 6 45 1.29 0.46 1 1.24 0.00 1 2 1 0.90 -1.21
## sf_7care 7 45 1.44 0.50 1 1.43 0.00 1 2 1 0.22 -2.00
## sf_8pain 8 45 2.36 1.13 2 2.30 1.48 1 5 4 0.40 -0.97
## sf_9calm 9 45 3.80 1.04 4 3.81 1.48 2 6 4 0.03 -0.63
## sf_10ener 10 45 4.24 0.93 4 4.30 1.48 2 6 4 -0.49 -0.29
## sf_11blue 11 45 3.73 1.25 4 3.70 1.48 1 6 5 0.09 -0.79
## sf_12soc 12 45 3.49 1.06 4 3.54 1.48 1 5 4 -0.48 -0.43
## se
## sf_1genhea 0.13
## sf_2moder 0.06
## sf_3stair 0.09
## sf_4less 0.07
## sf_5kind 0.07
## sf_6like 0.07
## sf_7care 0.07
## sf_8pain 0.17
## sf_9calm 0.15
## sf_10ener 0.14
## sf_11blue 0.19
## sf_12soc 0.16## vars n mean sd median trimmed mad min max range skew kurtosis
## sf_1genhea 1 45 3.53 0.89 3 3.54 1.48 2 5 3 0.09 -0.84
## sf_2moder 2 44 2.80 0.41 3 2.86 0.00 2 3 1 -1.42 0.00
## sf_3stair 3 45 2.49 0.63 3 2.57 0.00 1 3 2 -0.77 -0.48
## sf_4less 4 45 1.60 0.50 2 1.62 0.00 1 2 1 -0.39 -1.88
## sf_5kind 5 45 1.67 0.48 2 1.70 0.00 1 2 1 -0.68 -1.57
## sf_6like 6 45 1.29 0.46 1 1.24 0.00 1 2 1 0.90 -1.21
## sf_7care 7 45 1.44 0.50 1 1.43 0.00 1 2 1 0.22 -2.00
## sf_8pain 8 45 3.64 1.13 4 3.70 1.48 1 5 4 -0.40 -0.97
## sf_9calm 9 45 3.20 1.04 3 3.19 1.48 1 5 4 -0.03 -0.63
## sf_10ener 10 45 2.76 0.93 3 2.70 1.48 1 5 4 0.49 -0.29
## sf_11blue 11 45 3.73 1.25 4 3.70 1.48 1 6 5 0.09 -0.79
## sf_12soc 12 45 3.49 1.06 4 3.54 1.48 1 5 4 -0.48 -0.43
## se
## sf_1genhea 0.13
## sf_2moder 0.06
## sf_3stair 0.09
## sf_4less 0.07
## sf_5kind 0.07
## sf_6like 0.07
## sf_7care 0.07
## sf_8pain 0.17
## sf_9calm 0.15
## sf_10ener 0.14
## sf_11blue 0.19
## sf_12soc 0.16## `summarise()` regrouping output by 'redcap_event_name1' (override with `.groups` argument)## # A tibble: 11 x 5
## # Groups: redcap_event_name1 [8]
## redcap_event_name1 Decision count mean sd
## <fct> <chr> <int> <dbl> <dbl>
## 1 At diagnosis prenatal <NA> 22 2.659 0.3804
## 2 At diagnosis postnatal <NA> 1 2.583 NA
## 3 Post-decision prenatal (Survived) Surgery 12 NaN NA
## 4 Post-decision postnatal (Survived) Surgery 1 NaN NA
## 5 Post-decison prenatal (Did not survive) Palliative 6 NaN NA
## 6 Post-decison prenatal (Did not survive) Surgery 3 NaN NA
## 7 Post-decison prenatal (Did not survive) <NA> 1 NaN NA
## 8 3-month follow up prenatal (Survived) Surgery 11 2.841 0.4401
## 9 3-month follow up postnatal (Survived) Surgery 1 1.583 NA
## 10 3-month follow up prenatal (Did not survive) Palliative 6 2.333 0.4346
## 11 3-month follow up prenatal (Did not survive) Surgery 5 2.683 0.4802## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 45 2.64 0.45 2.67 2.65 0.49 1.5 3.5 2 -0.28 0.02 0.07ICCAP survived questions. I need to figure these out.
## vars n mean sd median trimmed mad min max range skew
## icc_explain_v2 1 24 4.46 0.59 4.5 4.50 0.74 3 5 2 -0.46
## icc_nurse_v2 2 25 4.44 0.65 5.0 4.52 0.00 3 5 2 -0.66
## icc_enough_v2 3 25 4.20 0.96 4.0 4.33 1.48 2 5 3 -0.93
## icc_satis_v2 4 25 4.28 0.98 5.0 4.43 0.00 2 5 3 -1.06
## icc_friend_v2 5 25 4.64 0.57 5.0 4.71 0.00 3 5 2 -1.19
## icc_under_v2 6 25 4.28 1.31 5.0 4.52 0.00 1 5 4 -1.79
## icc_people_v2 7 25 4.72 0.46 5.0 4.76 0.00 4 5 1 -0.92
## icc_pract_v2 8 25 4.52 0.92 5.0 4.71 0.00 1 5 4 -2.38
## icc_share_v2 9 25 4.72 0.46 5.0 4.76 0.00 4 5 1 -0.92
## icc_worry_v2 10 25 4.60 0.58 5.0 4.67 0.00 3 5 2 -1.00
## icc_symp_v2 11 25 4.56 0.87 5.0 4.71 0.00 1 5 4 -2.73
## icc_agree_v2 12 25 4.84 0.37 5.0 4.90 0.00 4 5 1 -1.74
## icc_talk_v2 13 25 4.60 0.76 5.0 4.76 0.00 2 5 3 -1.94
## icc_happy_v2 14 25 4.76 0.44 5.0 4.81 0.00 4 5 1 -1.15
## icc_good_v2 15 25 4.76 0.44 5.0 4.81 0.00 4 5 1 -1.15
## icc_sad_v2 16 25 3.56 0.92 4.0 3.62 0.00 1 5 4 -0.95
## icc_angry_v2 17 25 3.00 1.04 3.0 3.00 1.48 1 5 4 0.00
## icc_blame_v2 18 25 3.56 1.04 4.0 3.62 1.48 1 5 4 -0.57
## icc_guilt_v2 19 25 3.04 1.14 3.0 3.05 1.48 1 5 4 -0.24
## icc_life_v2 20 25 4.12 0.60 4.0 4.14 0.00 3 5 2 -0.03
## icc_func_v2 21 25 4.20 0.96 4.0 4.33 1.48 1 5 4 -1.48
## icc_burd_v2 22 25 3.52 1.00 4.0 3.62 1.48 1 5 4 -0.88
## icc_health_v2 23 25 3.52 0.96 4.0 3.57 1.48 1 5 4 -0.59
## icc_anxious_v2 24 25 3.84 0.90 4.0 3.90 1.48 2 5 3 -0.36
## icc_face_v2 25 25 4.08 0.81 4.0 4.19 0.00 1 5 4 -1.93
## icc_same_v2 26 25 3.20 0.76 3.0 3.19 0.00 2 5 3 0.22
## icc_worgre_v2 27 25 4.48 0.51 4.0 4.48 0.00 4 5 1 0.08
## icc_doubt_v2 28 25 2.52 0.77 3.0 2.52 1.48 1 4 3 -0.06
## icc_fear_v2 29 25 3.36 0.91 3.0 3.38 1.48 1 5 4 -0.40
## icc_handi_v2 30 25 2.84 1.11 3.0 2.81 1.48 1 5 4 0.12
## icc_cant_v2 31 25 3.52 1.00 4.0 3.57 1.48 1 5 4 -0.64
## icc_norma_v2 32 25 3.84 0.62 4.0 3.81 0.00 3 5 2 0.10
## kurtosis se
## icc_explain_v2 -0.90 0.12
## icc_nurse_v2 -0.71 0.13
## icc_enough_v2 -0.24 0.19
## icc_satis_v2 -0.14 0.20
## icc_friend_v2 0.33 0.11
## icc_under_v2 1.81 0.26
## icc_people_v2 -1.19 0.09
## icc_pract_v2 6.01 0.18
## icc_share_v2 -1.19 0.09
## icc_worry_v2 -0.12 0.12
## icc_symp_v2 8.31 0.17
## icc_agree_v2 1.09 0.07
## icc_talk_v2 3.27 0.15
## icc_happy_v2 -0.71 0.09
## icc_good_v2 -0.71 0.09
## icc_sad_v2 0.66 0.18
## icc_angry_v2 -0.48 0.21
## icc_blame_v2 -0.34 0.21
## icc_guilt_v2 -0.79 0.23
## icc_life_v2 -0.43 0.12
## icc_func_v2 2.50 0.19
## icc_burd_v2 0.78 0.20
## icc_health_v2 0.10 0.19
## icc_anxious_v2 -0.74 0.18
## icc_face_v2 5.78 0.16
## icc_same_v2 -0.45 0.15
## icc_worgre_v2 -2.07 0.10
## icc_doubt_v2 -0.55 0.15
## icc_fear_v2 0.19 0.18
## icc_handi_v2 -0.64 0.22
## icc_cant_v2 -0.18 0.20
## icc_norma_v2 -0.65 0.12## vars n mean sd median trimmed mad min max range skew
## icc_explain_v2 1 24 4.46 0.59 4.5 4.50 0.74 3 5 2 -0.46
## icc_nurse_v2 2 25 4.44 0.65 5.0 4.52 0.00 3 5 2 -0.66
## icc_enough_v2 3 25 4.20 0.96 4.0 4.33 1.48 2 5 3 -0.93
## icc_satis_v2 4 25 4.28 0.98 5.0 4.43 0.00 2 5 3 -1.06
## icc_friend_v2 5 25 4.64 0.57 5.0 4.71 0.00 3 5 2 -1.19
## icc_under_v2 6 25 4.28 1.31 5.0 4.52 0.00 1 5 4 -1.79
## icc_people_v2 7 25 4.72 0.46 5.0 4.76 0.00 4 5 1 -0.92
## icc_pract_v2 8 25 4.52 0.92 5.0 4.71 0.00 1 5 4 -2.38
## icc_share_v2 9 25 4.72 0.46 5.0 4.76 0.00 4 5 1 -0.92
## icc_worry_v2 10 25 4.60 0.58 5.0 4.67 0.00 3 5 2 -1.00
## icc_symp_v2 11 25 4.56 0.87 5.0 4.71 0.00 1 5 4 -2.73
## icc_agree_v2 12 25 4.84 0.37 5.0 4.90 0.00 4 5 1 -1.74
## icc_talk_v2 13 25 4.60 0.76 5.0 4.76 0.00 2 5 3 -1.94
## icc_happy_v2 14 25 4.76 0.44 5.0 4.81 0.00 4 5 1 -1.15
## icc_good_v2 15 25 4.76 0.44 5.0 4.81 0.00 4 5 1 -1.15
## icc_sad_v2 16 25 3.56 0.92 4.0 3.62 0.00 1 5 4 -0.95
## icc_angry_v2 17 25 3.00 1.04 3.0 3.00 1.48 1 5 4 0.00
## icc_blame_v2 18 25 3.56 1.04 4.0 3.62 1.48 1 5 4 -0.57
## icc_guilt_v2 19 25 3.04 1.14 3.0 3.05 1.48 1 5 4 -0.24
## kurtosis se
## icc_explain_v2 -0.90 0.12
## icc_nurse_v2 -0.71 0.13
## icc_enough_v2 -0.24 0.19
## icc_satis_v2 -0.14 0.20
## icc_friend_v2 0.33 0.11
## icc_under_v2 1.81 0.26
## icc_people_v2 -1.19 0.09
## icc_pract_v2 6.01 0.18
## icc_share_v2 -1.19 0.09
## icc_worry_v2 -0.12 0.12
## icc_symp_v2 8.31 0.17
## icc_agree_v2 1.09 0.07
## icc_talk_v2 3.27 0.15
## icc_happy_v2 -0.71 0.09
## icc_good_v2 -0.71 0.09
## icc_sad_v2 0.66 0.18
## icc_angry_v2 -0.48 0.21
## icc_blame_v2 -0.34 0.21
## icc_guilt_v2 -0.79 0.23## Warning: `funs()` is deprecated as of dplyr 0.8.0.
## Please use a list of either functions or lambdas:
##
## # Simple named list:
## list(mean = mean, median = median)
##
## # Auto named with `tibble::lst()`:
## tibble::lst(mean, median)
##
## # Using lambdas
## list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.## vars n mean sd median trimmed mad min max range skew
## icc_explain_v2 1 24 3.46 0.59 3.5 3.50 0.74 2 4 2 -0.46
## icc_nurse_v2 2 25 3.44 0.65 4.0 3.52 0.00 2 4 2 -0.66
## icc_enough_v2 3 25 3.20 0.96 3.0 3.33 1.48 1 4 3 -0.93
## icc_satis_v2 4 25 3.28 0.98 4.0 3.43 0.00 1 4 3 -1.06
## icc_friend_v2 5 25 3.64 0.57 4.0 3.71 0.00 2 4 2 -1.19
## icc_under_v2 6 22 3.73 0.46 4.0 3.78 0.00 3 4 1 -0.95
## icc_people_v2 7 25 3.72 0.46 4.0 3.76 0.00 3 4 1 -0.92
## icc_pract_v2 8 24 3.67 0.56 4.0 3.75 0.00 2 4 2 -1.34
## icc_share_v2 9 25 3.72 0.46 4.0 3.76 0.00 3 4 1 -0.92
## icc_worry_v2 10 25 3.60 0.58 4.0 3.67 0.00 2 4 2 -1.00
## icc_symp_v2 11 24 3.71 0.46 4.0 3.75 0.00 3 4 1 -0.86
## icc_agree_v2 12 25 3.84 0.37 4.0 3.90 0.00 3 4 1 -1.74
## icc_talk_v2 13 25 3.60 0.76 4.0 3.76 0.00 1 4 3 -1.94
## icc_happy_v2 14 25 3.76 0.44 4.0 3.81 0.00 3 4 1 -1.15
## icc_good_v2 15 25 3.76 0.44 4.0 3.81 0.00 3 4 1 -1.15
## icc_sad_v2 16 23 1.57 0.84 1.0 1.42 0.00 1 4 3 1.33
## icc_angry_v2 17 23 2.17 0.89 2.0 2.11 1.48 1 4 3 0.43
## icc_blame_v2 18 21 1.71 0.90 1.0 1.59 0.00 1 4 3 0.94
## icc_guilt_v2 19 23 2.13 1.01 2.0 2.05 1.48 1 4 3 0.51
## kurtosis se
## icc_explain_v2 -0.90 0.12
## icc_nurse_v2 -0.71 0.13
## icc_enough_v2 -0.24 0.19
## icc_satis_v2 -0.14 0.20
## icc_friend_v2 0.33 0.11
## icc_under_v2 -1.14 0.10
## icc_people_v2 -1.19 0.09
## icc_pract_v2 0.73 0.12
## icc_share_v2 -1.19 0.09
## icc_worry_v2 -0.12 0.12
## icc_symp_v2 -1.31 0.09
## icc_agree_v2 1.09 0.07
## icc_talk_v2 3.27 0.15
## icc_happy_v2 -0.71 0.09
## icc_good_v2 -0.71 0.09
## icc_sad_v2 0.89 0.18
## icc_angry_v2 -0.61 0.18
## icc_blame_v2 -0.24 0.20
## icc_guilt_v2 -0.93 0.21## `summarise()` regrouping output by 'redcap_event_name1' (override with `.groups` argument)## # A tibble: 11 x 5
## # Groups: redcap_event_name1 [8]
## redcap_event_name1 Decision count mean sd
## <fct> <chr> <int> <dbl> <dbl>
## 1 At diagnosis prenatal <NA> 22 NaN NA
## 2 At diagnosis postnatal <NA> 1 NaN NA
## 3 Post-decision prenatal (Survived) Surgery 12 3.297 0.3173
## 4 Post-decision postnatal (Survived) Surgery 1 3.421 NA
## 5 Post-decison prenatal (Did not survive) Palliative 6 NaN NA
## 6 Post-decison prenatal (Did not survive) Surgery 3 NaN NA
## 7 Post-decison prenatal (Did not survive) <NA> 1 NaN NA
## 8 3-month follow up prenatal (Survived) Surgery 11 3.235 0.4878
## 9 3-month follow up postnatal (Survived) Surgery 1 3.176 NA
## 10 3-month follow up prenatal (Did not survive) Palliative 6 NaN NA
## 11 3-month follow up prenatal (Did not survive) Surgery 5 NaN NAICCAP did not survive questions. I need to figure these out.
## vars n mean sd median trimmed mad min max range skew
## icc_explain_v3 1 19 4.47 0.84 5 4.59 0.00 2 5 3 -1.51
## icc_nurse_v3 2 19 4.68 0.48 5 4.71 0.00 4 5 1 -0.73
## icc_enough_v3 3 19 4.53 0.70 5 4.59 0.00 3 5 2 -1.02
## icc_satis_v3 4 19 4.53 0.61 5 4.59 0.00 3 5 2 -0.78
## icc_friend_v3 5 19 4.63 0.50 5 4.65 0.00 4 5 1 -0.50
## icc_under_v3 6 19 4.32 0.75 4 4.35 1.48 3 5 2 -0.52
## icc_people_v3 7 19 4.58 0.51 5 4.59 0.00 4 5 1 -0.29
## icc_pract_v3 8 19 4.16 1.26 5 4.29 0.00 1 5 4 -1.55
## icc_share_v3 9 19 4.58 0.61 5 4.65 0.00 3 5 2 -0.99
## icc_worry_v3 10 19 4.21 1.08 5 4.35 0.00 1 5 4 -1.39
## icc_symp_v3 11 19 4.79 0.42 5 4.82 0.00 4 5 1 -1.31
## icc_agree_v3 12 19 4.74 0.45 5 4.76 0.00 4 5 1 -0.99
## icc_talk_v3 13 19 4.79 0.42 5 4.82 0.00 4 5 1 -1.31
## icc_happy_v3 14 19 4.84 0.37 5 4.88 0.00 4 5 1 -1.73
## icc_good_v3 15 19 4.84 0.37 5 4.88 0.00 4 5 1 -1.73
## icc_sad_v3 16 19 4.26 0.81 4 4.35 1.48 2 5 3 -1.06
## icc_angry_v3 17 19 3.53 0.84 4 3.53 0.00 2 5 3 -0.61
## icc_blame_v3 18 19 3.68 1.11 4 3.71 1.48 2 5 3 -0.10
## icc_guilt_v3 19 19 3.74 0.93 4 3.76 1.48 2 5 3 -0.28
## kurtosis se
## icc_explain_v3 1.55 0.19
## icc_nurse_v3 -1.54 0.11
## icc_enough_v3 -0.35 0.16
## icc_satis_v3 -0.55 0.14
## icc_friend_v3 -1.84 0.11
## icc_under_v3 -1.16 0.17
## icc_people_v3 -2.01 0.12
## icc_pract_v3 1.31 0.29
## icc_share_v3 -0.18 0.14
## icc_worry_v3 1.44 0.25
## icc_symp_v3 -0.29 0.10
## icc_agree_v3 -1.06 0.10
## icc_talk_v3 -0.29 0.10
## icc_happy_v3 1.06 0.09
## icc_good_v3 1.06 0.09
## icc_sad_v3 0.89 0.18
## icc_angry_v3 -0.70 0.19
## icc_blame_v3 -1.48 0.25
## icc_guilt_v3 -0.93 0.21## vars n mean sd median trimmed mad min max range skew
## icc_explain_v3 1 19 3.47 0.84 4 3.59 0.00 1 4 3 -1.51
## icc_nurse_v3 2 19 3.68 0.48 4 3.71 0.00 3 4 1 -0.73
## icc_enough_v3 3 19 3.53 0.70 4 3.59 0.00 2 4 2 -1.02
## icc_satis_v3 4 19 3.53 0.61 4 3.59 0.00 2 4 2 -0.78
## icc_friend_v3 5 19 3.63 0.50 4 3.65 0.00 3 4 1 -0.50
## icc_under_v3 6 19 3.32 0.75 3 3.35 1.48 2 4 2 -0.52
## icc_people_v3 7 19 3.58 0.51 4 3.59 0.00 3 4 1 -0.29
## icc_pract_v3 8 17 3.53 0.62 4 3.60 0.00 2 4 2 -0.83
## icc_share_v3 9 19 3.58 0.61 4 3.65 0.00 2 4 2 -0.99
## icc_worry_v3 10 18 3.39 0.78 4 3.44 0.00 2 4 2 -0.71
## icc_symp_v3 11 19 3.79 0.42 4 3.82 0.00 3 4 1 -1.31
## icc_agree_v3 12 19 3.74 0.45 4 3.76 0.00 3 4 1 -0.99
## icc_talk_v3 13 19 3.79 0.42 4 3.82 0.00 3 4 1 -1.31
## icc_happy_v3 14 19 3.84 0.37 4 3.88 0.00 3 4 1 -1.73
## icc_good_v3 15 19 3.84 0.37 4 3.88 0.00 3 4 1 -1.73
## icc_sad_v3 16 11 1.27 0.65 1 1.11 0.00 1 3 2 1.80
## icc_angry_v3 17 18 1.56 0.78 1 1.50 0.00 1 3 2 0.87
## icc_blame_v3 18 13 1.92 0.76 2 1.91 1.48 1 3 2 0.11
## icc_guilt_v3 19 15 1.60 0.74 1 1.54 0.00 1 3 2 0.68
## kurtosis se
## icc_explain_v3 1.55 0.19
## icc_nurse_v3 -1.54 0.11
## icc_enough_v3 -0.35 0.16
## icc_satis_v3 -0.55 0.14
## icc_friend_v3 -1.84 0.11
## icc_under_v3 -1.16 0.17
## icc_people_v3 -2.01 0.12
## icc_pract_v3 -0.51 0.15
## icc_share_v3 -0.18 0.14
## icc_worry_v3 -1.07 0.18
## icc_symp_v3 -0.29 0.10
## icc_agree_v3 -1.06 0.10
## icc_talk_v3 -0.29 0.10
## icc_happy_v3 1.06 0.09
## icc_good_v3 1.06 0.09
## icc_sad_v3 1.80 0.19
## icc_angry_v3 -0.90 0.18
## icc_blame_v3 -1.40 0.21
## icc_guilt_v3 -1.00 0.19## `summarise()` regrouping output by 'redcap_event_name1' (override with `.groups` argument)## # A tibble: 11 x 5
## # Groups: redcap_event_name1 [8]
## redcap_event_name1 Decision count mean sd
## <fct> <chr> <int> <dbl> <dbl>
## 1 At diagnosis prenatal <NA> 22 NaN NA
## 2 At diagnosis postnatal <NA> 1 NaN NA
## 3 Post-decision prenatal (Survived) Surgery 12 NaN NA
## 4 Post-decision postnatal (Survived) Surgery 1 NaN NA
## 5 Post-decison prenatal (Did not survive) Palliative 6 3.053 0.1756
## 6 Post-decison prenatal (Did not survive) Surgery 3 3.644 0.2171
## 7 Post-decison prenatal (Did not survive) <NA> 1 NaN NA
## 8 3-month follow up prenatal (Survived) Surgery 11 NaN NA
## 9 3-month follow up postnatal (Survived) Surgery 1 NaN NA
## 10 3-month follow up prenatal (Did not survive) Palliative 6 3.066 0.5142
## 11 3-month follow up prenatal (Did not survive) Surgery 5 3.602 0.1326Use of information questions. I need to figure these out.
## vars n mean sd median trimmed mad min max range skew kurtosis
## use_fam 1 22 3.55 1.22 4.0 3.61 1.48 1 5 4 -0.32 -1.13
## use_friend 2 22 2.23 1.23 2.0 2.06 1.48 1 5 4 0.90 -0.13
## use_par 3 22 2.36 1.22 2.0 2.33 1.48 1 4 3 0.23 -1.59
## use_pro 4 22 4.32 0.84 5.0 4.39 0.00 3 5 2 -0.60 -1.37
## use_supp 5 22 2.32 1.36 2.0 2.22 1.48 1 5 4 0.42 -1.41
## use_book 6 22 1.82 1.22 1.0 1.61 0.00 1 5 4 1.23 0.23
## use_mag 7 22 1.14 0.35 1.0 1.06 0.00 1 2 1 1.98 2.00
## use_jour 8 22 1.82 1.40 1.0 1.56 0.00 1 5 4 1.50 0.72
## use_int 9 22 2.41 1.22 2.5 2.33 1.48 1 5 4 0.28 -1.10
## use_tv 10 22 1.14 0.35 1.0 1.06 0.00 1 2 1 1.98 2.00
## se
## use_fam 0.26
## use_friend 0.26
## use_par 0.26
## use_pro 0.18
## use_supp 0.29
## use_book 0.26
## use_mag 0.07
## use_jour 0.30
## use_int 0.26
## use_tv 0.07## The following `from` values were not present in `x`: 1, 2, 3, 4## Complete?
## [1] NA 2 NA NA 2 NA NA 2 NA NA 2 NA NA 2 NA NA 0 2 NA NA 2 NA NA 2 NA
## [26] NA 2 NA NA 2 NA NA 2 NA NA 2 NA NA 2 NA NA NA NA 2 NA NA 2 NA NA 2
## [51] NA NA 2 NA NA 2 NA NA 2 NA NA 2 NA NA 2 NA NA 2 NA## # A tibble: 5 x 4
## # Groups: redcap_event_name1, Decision [5]
## redcap_event_name1 Decision use_of_information_sources_… n
## <fct> <chr> <labelled> <int>
## 1 Post-decision prenatal (Survive… Surgery 2 12
## 2 Post-decision postnatal (Surviv… Surgery 2 1
## 3 Post-decison prenatal (Did not … Palliative 2 6
## 4 Post-decison prenatal (Did not … Surgery 2 3
## 5 Post-decison prenatal (Did not … <NA> 0 1Perinatal grief survived questions. I need to figure these out.
## vars n mean sd median trimmed mad min max range skew kurtosis
## pgs_depre 1 12 2.67 1.37 2.5 2.6 2.22 1 5 4 0.17 -1.52
## pgs_along 2 12 2.42 1.16 2.0 2.4 1.48 1 4 3 0.18 -1.58
## pgs_empty 3 12 2.25 1.22 2.0 2.1 0.74 1 5 4 0.96 -0.17
## pgs_keep 4 12 2.75 1.54 2.0 2.7 1.48 1 5 4 0.25 -1.71
## pgs_talk 5 12 3.50 1.31 4.0 3.6 0.74 1 5 4 -0.88 -0.52
## pgs_griev 6 12 3.00 1.41 3.0 3.0 1.48 1 5 4 0.00 -1.54
## pgs_frigh 7 12 3.25 1.42 4.0 3.3 1.48 1 5 4 -0.40 -1.44
## pgs_suici 8 12 1.25 0.45 1.0 1.2 0.00 1 2 1 1.01 -1.04
## pgs_medic 9 12 2.33 1.50 2.0 2.2 1.48 1 5 4 0.52 -1.52
## pgs_miss 10 12 3.08 1.38 3.5 3.1 0.74 1 5 4 -0.52 -1.31
## pgs_adjus 11 12 4.08 1.16 4.5 4.2 0.74 2 5 3 -0.78 -1.04
## pgs_recal 12 12 3.42 1.38 4.0 3.5 1.48 1 5 4 -0.32 -1.49
## pgs_upset 13 12 1.67 0.98 1.0 1.5 0.00 1 4 3 1.14 0.03
## pgs_cryth 14 12 2.17 1.27 2.0 2.1 1.48 1 4 3 0.45 -1.59
## pgs_quilt 15 12 2.17 1.40 2.0 2.0 1.48 1 5 4 0.82 -0.93
## pgs_phsyc 16 12 1.67 1.23 1.0 1.4 0.00 1 5 4 1.66 1.65
## pgs_unpro 17 12 1.92 1.38 1.0 1.7 0.00 1 5 4 1.09 -0.34
## pgs_laugh 18 12 1.58 0.90 1.0 1.4 0.00 1 4 3 1.49 1.44
## pgs_slow 19 12 2.17 1.34 1.5 2.1 0.74 1 4 3 0.35 -1.80
## pgs_part 20 12 1.92 1.16 1.5 1.8 0.74 1 4 3 0.78 -1.04
## pgs_down 21 12 2.08 1.31 1.5 2.0 0.74 1 4 3 0.53 -1.61
## pgs_worth 22 12 1.50 0.90 1.0 1.3 0.00 1 4 3 1.69 1.95
## pgs_blame 23 12 2.00 0.95 2.0 1.9 1.48 1 4 3 0.58 -0.78
## pgs_cross 24 12 2.00 1.21 1.5 1.9 0.74 1 4 3 0.57 -1.42
## pgs_profc 25 12 2.75 1.42 2.5 2.7 2.22 1 5 4 0.05 -1.69
## pgs_exist 26 12 2.25 1.54 1.5 2.1 0.74 1 5 4 0.57 -1.54
## pgs_lone 27 12 1.92 1.31 1.5 1.7 0.74 1 5 4 1.25 0.20
## se
## pgs_depre 0.40
## pgs_along 0.34
## pgs_empty 0.35
## pgs_keep 0.45
## pgs_talk 0.38
## pgs_griev 0.41
## pgs_frigh 0.41
## pgs_suici 0.13
## pgs_medic 0.43
## pgs_miss 0.40
## pgs_adjus 0.34
## pgs_recal 0.40
## pgs_upset 0.28
## pgs_cryth 0.37
## pgs_quilt 0.41
## pgs_phsyc 0.36
## pgs_unpro 0.40
## pgs_laugh 0.26
## pgs_slow 0.39
## pgs_part 0.34
## pgs_down 0.38
## pgs_worth 0.26
## pgs_blame 0.28
## pgs_cross 0.35
## pgs_profc 0.41
## pgs_exist 0.45
## pgs_lone 0.38## vars n mean sd median trimmed mad min max range skew kurtosis
## pgs_depre 1 12 2.67 1.37 2.5 2.6 2.22 1 5 4 0.17 -1.52
## pgs_along 2 12 2.42 1.16 2.0 2.4 1.48 1 4 3 0.18 -1.58
## pgs_empty 3 12 2.25 1.22 2.0 2.1 0.74 1 5 4 0.96 -0.17
## pgs_keep 4 12 2.75 1.54 2.0 2.7 1.48 1 5 4 0.25 -1.71
## pgs_talk 5 12 3.50 1.31 4.0 3.6 0.74 1 5 4 -0.88 -0.52
## pgs_griev 6 12 3.00 1.41 3.0 3.0 1.48 1 5 4 0.00 -1.54
## pgs_frigh 7 12 3.25 1.42 4.0 3.3 1.48 1 5 4 -0.40 -1.44
## pgs_suici 8 12 1.25 0.45 1.0 1.2 0.00 1 2 1 1.01 -1.04
## pgs_medic 9 12 2.33 1.50 2.0 2.2 1.48 1 5 4 0.52 -1.52
## pgs_miss 10 12 3.08 1.38 3.5 3.1 0.74 1 5 4 -0.52 -1.31
## pgs_adjus 11 12 1.92 1.16 1.5 1.8 0.74 1 4 3 0.78 -1.04
## pgs_recal 12 12 3.42 1.38 4.0 3.5 1.48 1 5 4 -0.32 -1.49
## pgs_upset 13 12 1.67 0.98 1.0 1.5 0.00 1 4 3 1.14 0.03
## pgs_cryth 14 12 2.17 1.27 2.0 2.1 1.48 1 4 3 0.45 -1.59
## pgs_quilt 15 12 2.17 1.40 2.0 2.0 1.48 1 5 4 0.82 -0.93
## pgs_phsyc 16 12 1.67 1.23 1.0 1.4 0.00 1 5 4 1.66 1.65
## pgs_unpro 17 12 1.92 1.38 1.0 1.7 0.00 1 5 4 1.09 -0.34
## pgs_laugh 18 12 1.58 0.90 1.0 1.4 0.00 1 4 3 1.49 1.44
## pgs_slow 19 12 2.17 1.34 1.5 2.1 0.74 1 4 3 0.35 -1.80
## pgs_part 20 12 1.92 1.16 1.5 1.8 0.74 1 4 3 0.78 -1.04
## pgs_down 21 12 2.08 1.31 1.5 2.0 0.74 1 4 3 0.53 -1.61
## pgs_worth 22 12 1.50 0.90 1.0 1.3 0.00 1 4 3 1.69 1.95
## pgs_blame 23 12 2.00 0.95 2.0 1.9 1.48 1 4 3 0.58 -0.78
## pgs_cross 24 12 2.00 1.21 1.5 1.9 0.74 1 4 3 0.57 -1.42
## pgs_profc 25 12 2.75 1.42 2.5 2.7 2.22 1 5 4 0.05 -1.69
## pgs_exist 26 12 2.25 1.54 1.5 2.1 0.74 1 5 4 0.57 -1.54
## pgs_lone 27 12 1.92 1.31 1.5 1.7 0.74 1 5 4 1.25 0.20
## se
## pgs_depre 0.40
## pgs_along 0.34
## pgs_empty 0.35
## pgs_keep 0.45
## pgs_talk 0.38
## pgs_griev 0.41
## pgs_frigh 0.41
## pgs_suici 0.13
## pgs_medic 0.43
## pgs_miss 0.40
## pgs_adjus 0.34
## pgs_recal 0.40
## pgs_upset 0.28
## pgs_cryth 0.37
## pgs_quilt 0.41
## pgs_phsyc 0.36
## pgs_unpro 0.40
## pgs_laugh 0.26
## pgs_slow 0.39
## pgs_part 0.34
## pgs_down 0.38
## pgs_worth 0.26
## pgs_blame 0.28
## pgs_cross 0.35
## pgs_profc 0.41
## pgs_exist 0.45
## pgs_lone 0.38## `summarise()` regrouping output by 'redcap_event_name1' (override with `.groups` argument)## # A tibble: 11 x 5
## # Groups: redcap_event_name1 [8]
## redcap_event_name1 Decision count mean sd
## <fct> <chr> <int> <dbl> <dbl>
## 1 At diagnosis prenatal <NA> 22 0 0
## 2 At diagnosis postnatal <NA> 1 0 NA
## 3 Post-decision prenatal (Survived) Surgery 12 0 0
## 4 Post-decision postnatal (Survived) Surgery 1 0 NA
## 5 Post-decison prenatal (Did not survive) Palliative 6 0 0
## 6 Post-decison prenatal (Did not survive) Surgery 3 0 0
## 7 Post-decison prenatal (Did not survive) <NA> 1 0 NA
## 8 3-month follow up prenatal (Survived) Surgery 11 56.73 18.68
## 9 3-month follow up postnatal (Survived) Surgery 1 115 NA
## 10 3-month follow up prenatal (Did not survive) Palliative 6 0 0
## 11 3-month follow up prenatal (Did not survive) Surgery 5 0 0Perinatal grief did not survive questions. I need to figure these out.
## vars n mean sd median trimmed mad min max range skew kurtosis
## pgd_depre 1 11 3.55 1.04 4 3.56 1.48 2 5 3 -0.11 -1.36
## pgd_along 2 11 3.36 1.21 4 3.44 0.00 1 5 4 -0.64 -1.01
## pgd_empty 3 11 4.00 0.89 4 4.11 0.00 2 5 3 -0.76 -0.16
## pgd_keep 4 11 3.64 0.81 4 3.67 0.00 2 5 3 -0.40 -0.62
## pgd_need 5 11 4.55 0.69 5 4.67 0.00 3 5 2 -0.98 -0.45
## pgd_griev 6 11 4.64 0.50 5 4.67 0.00 4 5 1 -0.49 -1.91
## pgd_frigh 7 11 3.55 0.93 3 3.56 1.48 2 5 3 0.22 -1.18
## pgd_suici 8 11 1.91 1.22 1 1.78 0.00 1 4 3 0.75 -1.21
## pgd_medic 9 11 2.45 1.57 2 2.33 1.48 1 5 4 0.29 -1.79
## pgd_miss 10 11 4.55 1.21 5 4.89 0.00 1 5 4 -2.23 3.64
## pgd_adjus 11 11 2.82 0.98 3 2.89 1.48 1 4 3 -0.26 -1.23
## pgd_recal 12 11 3.36 1.21 4 3.33 1.48 2 5 3 -0.02 -1.76
## pgd_upset 13 11 3.00 1.18 3 3.00 1.48 1 5 4 0.00 -1.24
## pgd_cryth 14 11 3.91 0.94 4 4.00 1.48 2 5 3 -0.49 -0.84
## pgd_guilt 15 11 2.91 1.22 3 2.89 1.48 1 5 4 0.15 -1.39
## pgd_physc 16 11 2.64 1.21 2 2.56 0.00 1 5 4 0.64 -1.01
## pgd_unpro 17 11 2.36 1.29 3 2.22 1.48 1 5 4 0.40 -0.91
## pgd_laugh 18 11 2.91 1.14 2 2.78 0.00 2 5 3 0.53 -1.50
## pgd_slow 19 11 3.27 1.35 3 3.22 1.48 2 5 3 0.22 -1.89
## pgd_part 20 11 3.18 1.40 3 3.22 1.48 1 5 4 0.11 -1.54
## pgd_down 21 11 3.09 1.30 3 3.11 1.48 1 5 4 -0.40 -1.23
## pgd_worth 22 11 2.82 1.17 3 2.78 1.48 1 5 4 0.31 -1.08
## pgd_blame 23 11 3.09 1.30 4 3.11 1.48 1 5 4 -0.15 -1.68
## pgd_cross 24 11 2.82 1.33 3 2.78 1.48 1 5 4 0.07 -1.49
## pgd_profc 25 11 3.27 1.10 4 3.22 1.48 2 5 3 -0.08 -1.73
## pgd_exist 26 11 3.64 1.03 4 3.67 1.48 2 5 3 -0.33 -1.23
## pgd_lone 27 11 3.55 1.13 4 3.56 1.48 2 5 3 -0.29 -1.52
## se
## pgd_depre 0.31
## pgd_along 0.36
## pgd_empty 0.27
## pgd_keep 0.24
## pgd_need 0.21
## pgd_griev 0.15
## pgd_frigh 0.28
## pgd_suici 0.37
## pgd_medic 0.47
## pgd_miss 0.37
## pgd_adjus 0.30
## pgd_recal 0.36
## pgd_upset 0.36
## pgd_cryth 0.28
## pgd_guilt 0.37
## pgd_physc 0.36
## pgd_unpro 0.39
## pgd_laugh 0.34
## pgd_slow 0.41
## pgd_part 0.42
## pgd_down 0.39
## pgd_worth 0.35
## pgd_blame 0.39
## pgd_cross 0.40
## pgd_profc 0.33
## pgd_exist 0.31
## pgd_lone 0.34## vars n mean sd median trimmed mad min max range skew kurtosis
## pgd_depre 1 11 3.55 1.04 4 3.56 1.48 2 5 3 -0.11 -1.36
## pgd_along 2 11 3.36 1.21 4 3.44 0.00 1 5 4 -0.64 -1.01
## pgd_empty 3 11 4.00 0.89 4 4.11 0.00 2 5 3 -0.76 -0.16
## pgd_keep 4 11 3.64 0.81 4 3.67 0.00 2 5 3 -0.40 -0.62
## pgd_need 5 11 4.55 0.69 5 4.67 0.00 3 5 2 -0.98 -0.45
## pgd_griev 6 11 4.64 0.50 5 4.67 0.00 4 5 1 -0.49 -1.91
## pgd_frigh 7 11 3.55 0.93 3 3.56 1.48 2 5 3 0.22 -1.18
## pgd_suici 8 11 1.91 1.22 1 1.78 0.00 1 4 3 0.75 -1.21
## pgd_medic 9 11 2.45 1.57 2 2.33 1.48 1 5 4 0.29 -1.79
## pgd_miss 10 11 4.55 1.21 5 4.89 0.00 1 5 4 -2.23 3.64
## pgd_adjus 11 11 3.18 0.98 3 3.11 1.48 2 5 3 0.26 -1.23
## pgd_recal 12 11 3.36 1.21 4 3.33 1.48 2 5 3 -0.02 -1.76
## pgd_upset 13 11 3.00 1.18 3 3.00 1.48 1 5 4 0.00 -1.24
## pgd_cryth 14 11 3.91 0.94 4 4.00 1.48 2 5 3 -0.49 -0.84
## pgd_guilt 15 11 2.91 1.22 3 2.89 1.48 1 5 4 0.15 -1.39
## pgd_physc 16 11 2.64 1.21 2 2.56 0.00 1 5 4 0.64 -1.01
## pgd_unpro 17 11 2.36 1.29 3 2.22 1.48 1 5 4 0.40 -0.91
## pgd_laugh 18 11 2.91 1.14 2 2.78 0.00 2 5 3 0.53 -1.50
## pgd_slow 19 11 3.27 1.35 3 3.22 1.48 2 5 3 0.22 -1.89
## pgd_part 20 11 3.18 1.40 3 3.22 1.48 1 5 4 0.11 -1.54
## pgd_down 21 11 3.09 1.30 3 3.11 1.48 1 5 4 -0.40 -1.23
## pgd_worth 22 11 2.82 1.17 3 2.78 1.48 1 5 4 0.31 -1.08
## pgd_blame 23 11 3.09 1.30 4 3.11 1.48 1 5 4 -0.15 -1.68
## pgd_cross 24 11 2.82 1.33 3 2.78 1.48 1 5 4 0.07 -1.49
## pgd_profc 25 11 3.27 1.10 4 3.22 1.48 2 5 3 -0.08 -1.73
## pgd_exist 26 11 3.64 1.03 4 3.67 1.48 2 5 3 -0.33 -1.23
## pgd_lone 27 11 3.55 1.13 4 3.56 1.48 2 5 3 -0.29 -1.52
## se
## pgd_depre 0.31
## pgd_along 0.36
## pgd_empty 0.27
## pgd_keep 0.24
## pgd_need 0.21
## pgd_griev 0.15
## pgd_frigh 0.28
## pgd_suici 0.37
## pgd_medic 0.47
## pgd_miss 0.37
## pgd_adjus 0.30
## pgd_recal 0.36
## pgd_upset 0.36
## pgd_cryth 0.28
## pgd_guilt 0.37
## pgd_physc 0.36
## pgd_unpro 0.39
## pgd_laugh 0.34
## pgd_slow 0.41
## pgd_part 0.42
## pgd_down 0.39
## pgd_worth 0.35
## pgd_blame 0.39
## pgd_cross 0.40
## pgd_profc 0.33
## pgd_exist 0.31
## pgd_lone 0.34## `summarise()` regrouping output by 'redcap_event_name1' (override with `.groups` argument)## # A tibble: 11 x 5
## # Groups: redcap_event_name1 [8]
## redcap_event_name1 Decision count mean sd
## <fct> <chr> <int> <dbl> <dbl>
## 1 At diagnosis prenatal <NA> 22 0 0
## 2 At diagnosis postnatal <NA> 1 0 NA
## 3 Post-decision prenatal (Survived) Surgery 12 0 0
## 4 Post-decision postnatal (Survived) Surgery 1 0 NA
## 5 Post-decison prenatal (Did not survive) Palliative 6 0 0
## 6 Post-decison prenatal (Did not survive) Surgery 3 0 0
## 7 Post-decison prenatal (Did not survive) <NA> 1 0 NA
## 8 3-month follow up prenatal (Survived) Surgery 11 0 0
## 9 3-month follow up postnatal (Survived) Surgery 1 0 NA
## 10 3-month follow up prenatal (Did not survive) Palliative 6 98.5 16.18
## 11 3-month follow up prenatal (Did not survive) Surgery 5 78 16.14Assessing burden questions. I need to figure these out.
## vars n mean sd median trimmed mad min max range skew kurtosis
## bur_consum 1 68 3.49 1.13 4 3.55 1.48 1 5 4 -0.40 -0.55
## bur_burden 2 68 4.07 1.01 4 4.20 1.48 1 5 4 -0.83 -0.14
## bur_number 3 68 0.28 0.45 0 0.23 0.00 0 1 1 0.96 -1.09
## bur_upset 4 68 0.18 0.38 0 0.11 0.00 0 1 1 1.66 0.77
## bur_mess 5 68 0.12 0.32 0 0.04 0.00 0 1 1 2.32 3.44
## bur_compli 6 68 0.07 0.26 0 0.00 0.00 0 1 1 3.20 8.34
## bur_match 7 68 0.07 0.26 0 0.00 0.00 0 1 1 3.20 8.34
## bur_diffic 8 67 0.04 0.21 0 0.00 0.00 0 1 1 4.30 16.78
## bur_perceiv 9 67 3.79 0.81 4 3.76 1.48 2 5 3 0.22 -1.14
## se
## bur_consum 0.14
## bur_burden 0.12
## bur_number 0.05
## bur_upset 0.05
## bur_mess 0.04
## bur_compli 0.03
## bur_match 0.03
## bur_diffic 0.03
## bur_perceiv 0.10## `summarise()` ungrouping output (override with `.groups` argument)## # A tibble: 8 x 4
## redcap_event_name1 count mean sd
## <fct> <int> <dbl> <dbl>
## 1 At diagnosis prenatal 22 1.333 0.1878
## 2 At diagnosis postnatal 1 1.556 NA
## 3 Post-decision prenatal (Survived) 12 1.297 0.1781
## 4 Post-decision postnatal (Survived) 1 1.444 NA
## 5 Post-decison prenatal (Did not survive) 10 1.358 0.3415
## 6 3-month follow up prenatal (Survived) 11 1.313 0.2374
## 7 3-month follow up postnatal (Survived) 1 1.778 NA
## 8 3-month follow up prenatal (Did not survive) 11 1.369 0.2609ICCAP scale questions. I need to figure these out.
## vars n mean sd median trimmed mad min max range skew kurtosis se
## icc_explain 1 1 2 NA 2 2 0 2 2 0 NA NA NA
## icc_nurse 2 1 5 NA 5 5 0 5 5 0 NA NA NA
## icc_enough 3 1 2 NA 2 2 0 2 2 0 NA NA NA
## icc_satis 4 1 2 NA 2 2 0 2 2 0 NA NA NA
## icc_friend 5 1 5 NA 5 5 0 5 5 0 NA NA NA
## icc_under 6 1 5 NA 5 5 0 5 5 0 NA NA NA
## icc_people 7 1 5 NA 5 5 0 5 5 0 NA NA NA
## icc_pract 8 1 2 NA 2 2 0 2 2 0 NA NA NA
## icc_share 9 1 2 NA 2 2 0 2 2 0 NA NA NA
## icc_worry 10 1 2 NA 2 2 0 2 2 0 NA NA NA
## icc_symp 11 1 5 NA 5 5 0 5 5 0 NA NA NA
## icc_agree 12 1 5 NA 5 5 0 5 5 0 NA NA NA
## icc_talk 13 1 5 NA 5 5 0 5 5 0 NA NA NA
## icc_happy 14 1 5 NA 5 5 0 5 5 0 NA NA NA
## icc_good 15 1 5 NA 5 5 0 5 5 0 NA NA NA
## icc_sad 16 1 5 NA 5 5 0 5 5 0 NA NA NA
## icc_angry 17 1 5 NA 5 5 0 5 5 0 NA NA NA
## icc_blame 18 1 5 NA 5 5 0 5 5 0 NA NA NA
## icc_guilt 19 1 5 NA 5 5 0 5 5 0 NA NA NA
## icc_life 20 1 5 NA 5 5 0 5 5 0 NA NA NA
## icc_func 21 1 1 NA 1 1 0 1 1 0 NA NA NA
## icc_burd 22 1 1 NA 1 1 0 1 1 0 NA NA NA
## icc_health 23 1 1 NA 1 1 0 1 1 0 NA NA NA
## icc_anxious 24 1 1 NA 1 1 0 1 1 0 NA NA NA
## icc_face 25 1 1 NA 1 1 0 1 1 0 NA NA NA
## icc_same 26 1 1 NA 1 1 0 1 1 0 NA NA NA
## icc_worgre 27 1 1 NA 1 1 0 1 1 0 NA NA NA
## icc_doubt 28 1 1 NA 1 1 0 1 1 0 NA NA NA
## icc_fear 29 1 1 NA 1 1 0 1 1 0 NA NA NA
## icc_handi 30 1 1 NA 1 1 0 1 1 0 NA NA NA
## icc_cant 31 1 1 NA 1 1 0 1 1 0 NA NA NA
## icc_norma 32 1 1 NA 1 1 0 1 1 0 NA NA NA## `summarise()` ungrouping output (override with `.groups` argument)## # A tibble: 8 x 4
## redcap_event_name1 count mean sd
## <fct> <int> <dbl> <dbl>
## 1 At diagnosis prenatal 22 NaN NA
## 2 At diagnosis postnatal 1 NaN NA
## 3 Post-decision prenatal (Survived) 12 NaN NA
## 4 Post-decision postnatal (Survived) 1 NaN NA
## 5 Post-decison prenatal (Did not survive) 10 2.938 NA
## 6 3-month follow up prenatal (Survived) 11 NaN NA
## 7 3-month follow up postnatal (Survived) 1 NaN NA
## 8 3-month follow up prenatal (Did not survive) 11 NaN NADecision quality values scale questions. I need to figure these out.
## vars n mean sd median trimmed mad min max range skew kurtosis se
## v_littlepd 1 2 1.0 0.00 1.0 1.0 0.00 1 1 0 NaN NaN 0.0
## v_anytreat 2 2 1.0 0.00 1.0 1.0 0.00 1 1 0 NaN NaN 0.0
## v_avoidphy 3 2 3.5 3.54 3.5 3.5 3.71 1 6 5 0 -2.75 2.5
## v_avoidmen 4 2 3.5 3.54 3.5 3.5 3.71 1 6 5 0 -2.75 2.5
## v_avoidtre 5 2 5.0 1.41 5.0 5.0 1.48 4 6 2 0 -2.75 1.0
## v_everytre 6 2 1.0 0.00 1.0 1.0 0.00 1 1 0 NaN NaN 0.0## # A tibble: 1 x 3
## # Groups: redcap_event_name1 [1]
## redcap_event_name1 v_littlepd n
## <fct> <dbl> <int>
## 1 Post-decison prenatal (Did not survive) 1 2## Complete?
## [1] NA 2 NA NA 0 NA NA 0 NA NA NA NA NA 0 NA NA 0 NA NA NA 0 NA NA NA NA
## [26] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 0 NA NA 0 NA NA NA
## [51] NA NA NA NA NA NA NA NA NA NA NA NA NA NA 0 NA NA 0 NA## `summarise()` ungrouping output (override with `.groups` argument)## # A tibble: 8 x 4
## redcap_event_name1 count mean sd
## <fct> <int> <dbl> <dbl>
## 1 At diagnosis prenatal 22 NaN NA
## 2 At diagnosis postnatal 1 NaN NA
## 3 Post-decision prenatal (Survived) 12 NaN NA
## 4 Post-decision postnatal (Survived) 1 NaN NA
## 5 Post-decison prenatal (Did not survive) 10 2.5 0.9428
## 6 3-month follow up prenatal (Survived) 11 NaN NA
## 7 3-month follow up postnatal (Survived) 1 NaN NA
## 8 3-month follow up prenatal (Did not survive) 11 NaN NAWho died questions. I need to figure these out.
## # A tibble: 11 x 3
## # Groups: redcap_event_name1 [8]
## redcap_event_name1 Decision n
## <fct> <chr> <int>
## 1 At diagnosis prenatal <NA> 22
## 2 At diagnosis postnatal <NA> 1
## 3 Post-decision prenatal (Survived) Surgery 12
## 4 Post-decision postnatal (Survived) Surgery 1
## 5 Post-decison prenatal (Did not survive) Palliative 6
## 6 Post-decison prenatal (Did not survive) Surgery 3
## 7 Post-decison prenatal (Did not survive) <NA> 1
## 8 3-month follow up prenatal (Survived) Surgery 11
## 9 3-month follow up postnatal (Survived) Surgery 1
## 10 3-month follow up prenatal (Did not survive) Palliative 6
## 11 3-month follow up prenatal (Did not survive) Surgery 5## # A tibble: 6 x 3
## # Groups: redcap_event_name1 [4]
## redcap_event_name1 Decision n
## <fct> <chr> <int>
## 1 Post-decision prenatal (Survived) Surgery 12
## 2 Post-decison prenatal (Did not survive) Palliative 6
## 3 Post-decison prenatal (Did not survive) Surgery 3
## 4 3-month follow up prenatal (Survived) Surgery 11
## 5 3-month follow up prenatal (Did not survive) Palliative 6
## 6 3-month follow up prenatal (Did not survive) Surgery 5