tests/testthat/_snaps/load-checks.md
In OuhscBbmc/trawler: Inspect a dataset
load-checks-biochemical
Code
checks$record_id_name
Output
[1] "record_id"
Code
checks$baseline_date_name
Output
[1] "date_enrolled"
Code
checks$record_id_link
Output
[1] "<a href=\"https://bbmc.ouhsc.edu/redcap/redcap_v%s/DataEntry/index.php?pid=%s&arm=%s&id=%s&page=%s\" target=\"_blank\">%s</a>"
Code
checks$github_file_prefix
Output
[1] "https://github.com/OuhscBbmc/validator-1/tree/main"
Code
checks$redcap_project_id
Output
[1] 1612
Code
checks$redcap_version
Output
[1] "10.5.1"
Code
checks$redcap_default_arm
Output
[1] 1
Code
checks$redcap_codebook
Output
[1] "https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/Design/data_dictionary_codebook.php?pid=1612"
Code
checks$redcap_record_link
Output
[1] "<a href=\"https://bbmc.ouhsc.edu/redcap/redcap_v%s/DataEntry/index.php?pid=%s&arm=%s&id=%s&page=%s\" target=\"_blank\">%s</a>"
Code
checks$smells
Output
# A tibble: 11 x 10
check_name description priority debug bound_lower bound_upper bounds_template
<chr> <chr> <int> <lgl> <dbl> <dbl> <chr>
1 females Proportion~ 2 FALSE 0.4 0.6 [%.2f, %.2f]
2 males Proportion~ 2 FALSE 0.4 0.6 [%.2f, %.2f]
3 age Mean parti~ 2 FALSE 20 60 [%.0f, %.0f]
4 serum_pre~ Mean serum~ 2 FALSE 32 39 [%.0f, %.0f]
5 serum_cre~ Mean serum~ 2 FALSE 3 15 [%.0f, %.0f]
6 bmi_at_ba~ Mean BMI i~ 2 FALSE 18 24 [%.0f, %.0f]
7 serum_cho~ Mean chole~ 1 FALSE 100 140 [%.0f, %.0f]
8 dialysis_~ Mean Kt/V ~ 1 FALSE 1.2 5 [%.1f, %.1f]
9 nutrition~ Most patie~ 2 FALSE 0.85 0.99 [%.2f, %.2f]
10 definitiv~ The propor~ 1 FALSE 1 1 [%.1f, %.1f]
11 normalize~ Mean Norma~ 1 FALSE 0 1.2 [%.1f, %.1f]
# i 3 more variables: value_template <chr>, equation <chr>, boundaries <chr>
Code
as.data.frame(checks$smells)
Output
check_name
1 females
2 males
3 age
4 serum_prealbumin_level_at_baseline
5 serum_creatinine_level_at_baseline
6 bmi_at_baseline
7 serum_cholesterol_levels_at_baseline
8 dialysis_adequacy
9 nutritional_counseling
10 definitive_diagnosis
11 normalized_protein_catabolic_rate
description
1 Proportion of female participants is about half the sample
2 Proportion of male participants is about half the sample
3 Mean participant age is between 20 and 60 years
4 Mean serum pre-albumin level is between 31mg/dl and 39mg/dl at baseline
5 Mean serum creatinine level is between 3mg/dl and 15mg/dl at baseline
6 Mean BMI is between 18 and 24
7 Mean cholesterol levels range is between 100 and 140mg/dl at baseline
8 Mean Kt/V value is within the normal range (*i.e.*, between 1.2 and 5)
9 Most patients agreed to receiving nutritional counseling
10 The proportion of study participants diagnosed with malnutrition associated with Chronic Renal Disease is 1.0 (*i.e.*, everyone)
11 Mean Normalized Protein Catabolic Rate (nPCR) is <= 1.2g/kg/day
priority debug bound_lower bound_upper bounds_template value_template
1 2 FALSE 0.40 0.60 [%.2f, %.2f] %.3f
2 2 FALSE 0.40 0.60 [%.2f, %.2f] %.3f
3 2 FALSE 20.00 60.00 [%.0f, %.0f] %.1f
4 2 FALSE 32.00 39.00 [%.0f, %.0f] %.1f
5 2 FALSE 3.00 15.00 [%.0f, %.0f] %.1f
6 2 FALSE 18.00 24.00 [%.0f, %.0f] %.1f
7 1 FALSE 100.00 140.00 [%.0f, %.0f] %.1f
8 1 FALSE 1.20 5.00 [%.1f, %.1f] %.2f
9 2 FALSE 0.85 0.99 [%.2f, %.2f] %.3f
10 1 FALSE 1.00 1.00 [%.1f, %.1f] %.2f
11 1 FALSE 0.00 1.20 [%.1f, %.1f] %.3f
equation
1 function (d) {\n mean(d$sex == "female", na.rm = TRUE)\n}\n
2 function (d) {\n mean(d$sex == "male", na.rm = TRUE)\n}\n
3 function (d) {\n mean(d$age, na.rm = TRUE)\n}\n
4 function (d) {\n mean(d$baseline_prealbumin_level, na.rm = TRUE)\n}\n
5 function (d) {\n mean(d$baseline_creatinine_level, na.rm = TRUE)\n}\n
6 function (d) {\n mean(d$baseline_bmi, na.rm = TRUE)\n}\n
7 function (d) {\n mean(d$baseline_cholesterol, na.rm = TRUE)\n}\n
8 function (d) {\n mean(d$completion_project_questionnaire_ktv_value, na.rm = TRUE)\n}\n
9 function (d) {\n mean(d$nutritional_counseling, na.rm = TRUE)\n}\n
10 function (d) {\n mean(d$differential_diagnoses_malnutrition, na.rm = TRUE)\n}\n
11 function (d) {\n mean(d$baseline_normalized_protein_catabolic_rate, na.rm = TRUE)\n}\n
boundaries
1 [0.40, 0.60]
2 [0.40, 0.60]
3 [20, 60]
4 [32, 39]
5 [3, 15]
6 [18, 24]
7 [100, 140]
8 [1.2, 5.0]
9 [0.85, 0.99]
10 [1.0, 1.0]
11 [0.0, 1.2]
Code
checks$smells_inactive
Output
# A tibble: 1 x 9
check_name description priority debug bound_lower bound_upper bounds_template
<chr> <chr> <int> <lgl> <dbl> <dbl> <chr>
1 average_se~ ??Units co~ 1 FALSE 501 1200 [%.0f, %.0f]
# i 2 more variables: value_template <chr>, equation <chr>
Code
cat(checks$smell_names_md)
Output
11 smells have been defined:
1. females;
2. males;
3. age;
4. serum_prealbumin_level_at_baseline;
5. serum_creatinine_level_at_baseline;
6. bmi_at_baseline;
7. serum_cholesterol_levels_at_baseline;
8. dialysis_adequacy;
9. nutritional_counseling;
10. definitive_diagnosis;
11. normalized_protein_catabolic_rate;
Code
checks$rules
Output
# A tibble: 14 x 6
check_name error_message priority debug redcap_instrument passing_test
<chr> <chr> <int> <lgl> <chr> <chr>
1 baseline_prealbu~ Serum pre-al~ 1 FALSE baseline_data "function (~
2 missing_serum_ma~ Relevant nut~ 1 FALSE baseline_data "function (~
3 serum_prealbumin~ Baseline pre~ 2 FALSE baseline_data, v~ "function (~
4 serum_prealbumin~ Baseline pre~ 2 FALSE baseline_data, v~ "function (~
5 serum_prealbumin~ Baseline pre~ 2 FALSE baseline_data, v~ "function (~
6 serum_prealbumin~ serum prealb~ 3 FALSE baseline_data, v~ "function (~
7 baseline_first_v~ Serum prealb~ 3 FALSE baseline_data, v~ "function (~
8 daily_first_visi~ In-addition ~ 3 FALSE baseline_data, p~ "function (~
9 daily_protein_in~ npcr levels ~ 3 FALSE baseline_data, p~ "function (~
10 hospitalization_~ Patient was ~ 1 FALSE completion_proje~ "function (~
11 optimal_daily_pr~ Daily protei~ 2 FALSE completion_proje~ "function (~
12 recommended_npcr~ NPCR values ~ 2 FALSE completion_data "function (~
13 npcr NPCR at comp~ 2 FALSE completion_data "function (~
14 npcr_comparison NPCR at comp~ 3 FALSE completion_data "function (~
Code
as.data.frame(checks$rules)
Output
check_name
1 baseline_prealbumin_levels
2 missing_serum_marker_levels
3 serum_prealbumin_levels_1
4 serum_prealbumin_levels_2
5 serum_prealbumin_levels_completion_data
6 serum_prealbumin_levels_expectations
7 baseline_first_visit_lab_parameters
8 daily_first_visit_lab_and_workup_parameters
9 daily_protein_intake
10 hospitalization_reason
11 optimal_daily_protein_intake
12 recommended_npcr_range
13 npcr
14 npcr_comparison
error_message
1 Serum pre-albumin level of all enrolled patients do not meet the study criterion
2 Relevant nutritional serum markers are missing
3 Baseline prealbumin levels are not missing however levels were not carefully monitored in the subsequent visit
4 Baseline prealbumin levels and pre-albumin levels during the 1st visit are not missing however levels in the next reading are missing
5 Baseline prealbumin levels are not missing however readings are not carefully monitored at completion
6 serum prealbumin levels are not missing however subsequent readings did not come as expected
7 Serum prealbumin levels are low and protein intake at baseline is less than optimal
8 In-addition to baseline & visit lab protein parameters, blood work-up npcr levels are also low
9 npcr levels in study have not improved as intended
10 Patient was hospitalized but reason and the date of hospitalization is missing
11 Daily protein intake is optimal but one of the nutritional marker is not within the normal range
12 NPCR values are not within the recommended range at completion
13 NPCR at completion is missing
14 NPCR at completion is not greater than npcr at baseline
priority debug
1 1 FALSE
2 1 FALSE
3 2 FALSE
4 2 FALSE
5 2 FALSE
6 3 FALSE
7 3 FALSE
8 3 FALSE
9 3 FALSE
10 1 FALSE
11 2 FALSE
12 2 FALSE
13 2 FALSE
14 3 FALSE
redcap_instrument
1 baseline_data
2 baseline_data
3 baseline_data, visit_lab_date
4 baseline_data, visit_lab_date, visit_blood_workup
5 baseline_data, visit_lab_date, visit_blood_workup, completion_date
6 baseline_data, visit_lab_date, visit_blood_workup, completion_date
7 baseline_data, visit_lab_data
8 baseline_data, patient_morale_questionnaire
9 baseline_data, patient_morale_questionnaire
10 completion_project_questionnaire
11 completion_project_questionnaire, completion_data
12 completion_data
13 completion_data
14 completion_data
passing_test
1 function (d) {\n dplyr::if_else(\n !is.na(d$date_enrolled),\n dplyr::between(d$baseline_prealbumin_level, 30, 40),\n TRUE\n )\n}\n
2 function (d) {\n dplyr::if_else(\n !is.na(d$date_enrolled),\n (\n !is.na(d$baseline_prealbumin_level) &\n !is.na(d$baseline_creatinine_level) &\n !is.na(d$baseline_transferrin_level)\n ),\n TRUE\n )\n}\n
3 function (d) {\n events_to_check <- c("enrollment_arm_1", "visit_1_arm_1")\n dplyr::if_else(\n (\n d$redcap_event_name %in% events_to_check &\n !is.na(d$baseline_prealbumin_level)\n ),\n !is.na(d$visit_lab_prealbumin_level),\n TRUE\n )\n}\n
4 function (d) {\n events_to_check <- c("visit_1_arm_1", "visit_2_arm_1")\n dplyr::if_else(\n (\n d$redcap_event_name %in% events_to_check &\n !is.na(d$baseline_prealbumin_level) &\n !is.na(d$visit_lab_prealbumin_level)\n ),\n !is.na(d$visit_blood_workup_prealbumin_level),\n TRUE\n )\n}\n
5 function (d) {\n events_to_check <- c("visit_1_arm_1", "visit_2_arm_1", "final_visit_arm_1")\n dplyr::if_else(\n (\n d$redcap_event_name %in% events_to_check &\n !is.na(d$baseline_prealbumin_level) &\n !is.na(d$visit_lab_prealbumin_level) &\n !is.na(d$visit_blood_workup_prealbumin_level)\n ),\n !is.na(d$completion_data_prealbumin_level),\n TRUE\n )\n}\n
6 function (d) {\n events_to_check <- c("visit_1_arm_1", "visit_2_arm_1", "final_visit_arm_1")\n dplyr::if_else(\n (\n d$redcap_event_name %in% events_to_check &\n !is.na(d$baseline_prealbumin_level) &\n !is.na(d$visit_lab_prealbumin_level) &\n !is.na(d$visit_blood_workup_prealbumin_level) &\n !is.na(d$completion_data_prealbumin_level)\n ),\n (d$visit_lab_prealbumin_level < d$completion_data_prealbumin_level),\n TRUE\n )\n}\n
7 function (d) {\n events_to_check <- c("enrollment_arm_1", "visit_1_arm_1")\n dplyr::if_else(\n (\n d$redcap_event_name %in% events_to_check &\n (d$baseline_prealbumin_level < 30) &\n (d$baseline_normalized_protein_catabolic_rate < 1.2)\n ),\n (d$baseline_normalized_protein_catabolic_rate <= d$visit_lab_npcr),\n TRUE\n )\n}\n
8 function (d) {\n events_to_check <- c("enrollment_arm_1", "visit_1_arm_1")\n dplyr::if_else(\n (\n (d$redcap_event_name %in% events_to_check) &\n (d$baseline_prealbumin_level < 30) &\n (d$baseline_normalized_protein_catabolic_rate < 1.2) &\n (d$baseline_normalized_protein_catabolic_rate <= d$visit_lab_npcr)\n ),\n d$visit_lab_npcr <= d$visit_blood_workup_npcr,\n TRUE\n )\n}\n
9 function (d) {\n events_to_check <- c("enrollment_arm_1", "visit_1_arm_1", "visit_2_arm_1", "final_visit_arm_1")\n dplyr::if_else(\n (\n (d$redcap_event_name %in% events_to_check) &\n (d$baseline_prealbumin_level < 30) &\n (d$baseline_normalized_protein_catabolic_rate < 1.2) &\n (d$baseline_normalized_protein_catabolic_rate <= d$visit_lab_npcr) &\n (d$visit_lab_npcr <= d$visit_blood_workup_npcr)\n ),\n d$visit_blood_workup_npcr < d$completion_data_npcr,\n TRUE\n )\n}\n
10 function (d) {\n dplyr::if_else(\n d$completion_project_questionnaire_hospitalization == 1L,\n !is.na(d$completion_project_questionnaire_hospitalization_cause) & !is.na(d$completion_project_questionnaire_hospitalization_date),\n TRUE\n )\n}\n
11 function (d) {\n dplyr::if_else(\n d$completion_data_npcr >= 1.2,\n dplyr::between(d$completion_data_prealbumin_level, 30, 40),\n TRUE\n )\n}\n
12 function (d) {\n dplyr::between(d$completion_data_npcr, 1.2, 1.4)\n}\n
13 function (d) {\n events_to_check <- c("final_visit_arm_1")\n dplyr::if_else(\n d$redcap_event_name %in% events_to_check,\n !is.na(d$completion_data_npcr), # If this row exists in the desired event, then check for nonmissingness.\n TRUE # Otherwise, the test passes for rows associated with all other events.\n )\n}\n
14 function (d) {\n dplyr::if_else(\n !is.na(d$completion_data_npcr),\n (d$npcr_at_baseline < d$completion_data_npcr),\n TRUE\n )\n}
Code
checks$rules_inactive
Output
# A tibble: 1 x 6
check_name error_message priority debug redcap_instrument passing_test
<chr> <chr> <int> <lgl> <chr> <chr>
1 pre_albumin_levels pre_albumin ~ 3 FALSE visit_lab_date "function (~
OuhscBbmc/trawler documentation built on Oct. 8, 2024, 11:44 p.m.
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load-checks-biochemical
Code
checks$record_id_name
Output
[1] "record_id"
Code
checks$baseline_date_name
Output
[1] "date_enrolled"
Code
checks$record_id_link
Output
[1] "<a href=\"https://bbmc.ouhsc.edu/redcap/redcap_v%s/DataEntry/index.php?pid=%s&arm=%s&id=%s&page=%s\" target=\"_blank\">%s</a>"
Code
checks$github_file_prefix
Output
[1] "https://github.com/OuhscBbmc/validator-1/tree/main"
Code
checks$redcap_project_id
Output
[1] 1612
Code
checks$redcap_version
Output
[1] "10.5.1"
Code
checks$redcap_default_arm
Output
[1] 1
Code
checks$redcap_codebook
Output
[1] "https://bbmc.ouhsc.edu/redcap/redcap_v10.5.1/Design/data_dictionary_codebook.php?pid=1612"
Code
checks$redcap_record_link
Output
[1] "<a href=\"https://bbmc.ouhsc.edu/redcap/redcap_v%s/DataEntry/index.php?pid=%s&arm=%s&id=%s&page=%s\" target=\"_blank\">%s</a>"
Code
checks$smells
Output
# A tibble: 11 x 10
check_name description priority debug bound_lower bound_upper bounds_template
<chr> <chr> <int> <lgl> <dbl> <dbl> <chr>
1 females Proportion~ 2 FALSE 0.4 0.6 [%.2f, %.2f]
2 males Proportion~ 2 FALSE 0.4 0.6 [%.2f, %.2f]
3 age Mean parti~ 2 FALSE 20 60 [%.0f, %.0f]
4 serum_pre~ Mean serum~ 2 FALSE 32 39 [%.0f, %.0f]
5 serum_cre~ Mean serum~ 2 FALSE 3 15 [%.0f, %.0f]
6 bmi_at_ba~ Mean BMI i~ 2 FALSE 18 24 [%.0f, %.0f]
7 serum_cho~ Mean chole~ 1 FALSE 100 140 [%.0f, %.0f]
8 dialysis_~ Mean Kt/V ~ 1 FALSE 1.2 5 [%.1f, %.1f]
9 nutrition~ Most patie~ 2 FALSE 0.85 0.99 [%.2f, %.2f]
10 definitiv~ The propor~ 1 FALSE 1 1 [%.1f, %.1f]
11 normalize~ Mean Norma~ 1 FALSE 0 1.2 [%.1f, %.1f]
# i 3 more variables: value_template <chr>, equation <chr>, boundaries <chr>
Code
as.data.frame(checks$smells)
Output
check_name
1 females
2 males
3 age
4 serum_prealbumin_level_at_baseline
5 serum_creatinine_level_at_baseline
6 bmi_at_baseline
7 serum_cholesterol_levels_at_baseline
8 dialysis_adequacy
9 nutritional_counseling
10 definitive_diagnosis
11 normalized_protein_catabolic_rate
description
1 Proportion of female participants is about half the sample
2 Proportion of male participants is about half the sample
3 Mean participant age is between 20 and 60 years
4 Mean serum pre-albumin level is between 31mg/dl and 39mg/dl at baseline
5 Mean serum creatinine level is between 3mg/dl and 15mg/dl at baseline
6 Mean BMI is between 18 and 24
7 Mean cholesterol levels range is between 100 and 140mg/dl at baseline
8 Mean Kt/V value is within the normal range (*i.e.*, between 1.2 and 5)
9 Most patients agreed to receiving nutritional counseling
10 The proportion of study participants diagnosed with malnutrition associated with Chronic Renal Disease is 1.0 (*i.e.*, everyone)
11 Mean Normalized Protein Catabolic Rate (nPCR) is <= 1.2g/kg/day
priority debug bound_lower bound_upper bounds_template value_template
1 2 FALSE 0.40 0.60 [%.2f, %.2f] %.3f
2 2 FALSE 0.40 0.60 [%.2f, %.2f] %.3f
3 2 FALSE 20.00 60.00 [%.0f, %.0f] %.1f
4 2 FALSE 32.00 39.00 [%.0f, %.0f] %.1f
5 2 FALSE 3.00 15.00 [%.0f, %.0f] %.1f
6 2 FALSE 18.00 24.00 [%.0f, %.0f] %.1f
7 1 FALSE 100.00 140.00 [%.0f, %.0f] %.1f
8 1 FALSE 1.20 5.00 [%.1f, %.1f] %.2f
9 2 FALSE 0.85 0.99 [%.2f, %.2f] %.3f
10 1 FALSE 1.00 1.00 [%.1f, %.1f] %.2f
11 1 FALSE 0.00 1.20 [%.1f, %.1f] %.3f
equation
1 function (d) {\n mean(d$sex == "female", na.rm = TRUE)\n}\n
2 function (d) {\n mean(d$sex == "male", na.rm = TRUE)\n}\n
3 function (d) {\n mean(d$age, na.rm = TRUE)\n}\n
4 function (d) {\n mean(d$baseline_prealbumin_level, na.rm = TRUE)\n}\n
5 function (d) {\n mean(d$baseline_creatinine_level, na.rm = TRUE)\n}\n
6 function (d) {\n mean(d$baseline_bmi, na.rm = TRUE)\n}\n
7 function (d) {\n mean(d$baseline_cholesterol, na.rm = TRUE)\n}\n
8 function (d) {\n mean(d$completion_project_questionnaire_ktv_value, na.rm = TRUE)\n}\n
9 function (d) {\n mean(d$nutritional_counseling, na.rm = TRUE)\n}\n
10 function (d) {\n mean(d$differential_diagnoses_malnutrition, na.rm = TRUE)\n}\n
11 function (d) {\n mean(d$baseline_normalized_protein_catabolic_rate, na.rm = TRUE)\n}\n
boundaries
1 [0.40, 0.60]
2 [0.40, 0.60]
3 [20, 60]
4 [32, 39]
5 [3, 15]
6 [18, 24]
7 [100, 140]
8 [1.2, 5.0]
9 [0.85, 0.99]
10 [1.0, 1.0]
11 [0.0, 1.2]
Code
checks$smells_inactive
Output
# A tibble: 1 x 9
check_name description priority debug bound_lower bound_upper bounds_template
<chr> <chr> <int> <lgl> <dbl> <dbl> <chr>
1 average_se~ ??Units co~ 1 FALSE 501 1200 [%.0f, %.0f]
# i 2 more variables: value_template <chr>, equation <chr>
Code
cat(checks$smell_names_md)
Output
11 smells have been defined:
1. females;
2. males;
3. age;
4. serum_prealbumin_level_at_baseline;
5. serum_creatinine_level_at_baseline;
6. bmi_at_baseline;
7. serum_cholesterol_levels_at_baseline;
8. dialysis_adequacy;
9. nutritional_counseling;
10. definitive_diagnosis;
11. normalized_protein_catabolic_rate;
Code
checks$rules
Output
# A tibble: 14 x 6
check_name error_message priority debug redcap_instrument passing_test
<chr> <chr> <int> <lgl> <chr> <chr>
1 baseline_prealbu~ Serum pre-al~ 1 FALSE baseline_data "function (~
2 missing_serum_ma~ Relevant nut~ 1 FALSE baseline_data "function (~
3 serum_prealbumin~ Baseline pre~ 2 FALSE baseline_data, v~ "function (~
4 serum_prealbumin~ Baseline pre~ 2 FALSE baseline_data, v~ "function (~
5 serum_prealbumin~ Baseline pre~ 2 FALSE baseline_data, v~ "function (~
6 serum_prealbumin~ serum prealb~ 3 FALSE baseline_data, v~ "function (~
7 baseline_first_v~ Serum prealb~ 3 FALSE baseline_data, v~ "function (~
8 daily_first_visi~ In-addition ~ 3 FALSE baseline_data, p~ "function (~
9 daily_protein_in~ npcr levels ~ 3 FALSE baseline_data, p~ "function (~
10 hospitalization_~ Patient was ~ 1 FALSE completion_proje~ "function (~
11 optimal_daily_pr~ Daily protei~ 2 FALSE completion_proje~ "function (~
12 recommended_npcr~ NPCR values ~ 2 FALSE completion_data "function (~
13 npcr NPCR at comp~ 2 FALSE completion_data "function (~
14 npcr_comparison NPCR at comp~ 3 FALSE completion_data "function (~
Code
as.data.frame(checks$rules)
Output
check_name
1 baseline_prealbumin_levels
2 missing_serum_marker_levels
3 serum_prealbumin_levels_1
4 serum_prealbumin_levels_2
5 serum_prealbumin_levels_completion_data
6 serum_prealbumin_levels_expectations
7 baseline_first_visit_lab_parameters
8 daily_first_visit_lab_and_workup_parameters
9 daily_protein_intake
10 hospitalization_reason
11 optimal_daily_protein_intake
12 recommended_npcr_range
13 npcr
14 npcr_comparison
error_message
1 Serum pre-albumin level of all enrolled patients do not meet the study criterion
2 Relevant nutritional serum markers are missing
3 Baseline prealbumin levels are not missing however levels were not carefully monitored in the subsequent visit
4 Baseline prealbumin levels and pre-albumin levels during the 1st visit are not missing however levels in the next reading are missing
5 Baseline prealbumin levels are not missing however readings are not carefully monitored at completion
6 serum prealbumin levels are not missing however subsequent readings did not come as expected
7 Serum prealbumin levels are low and protein intake at baseline is less than optimal
8 In-addition to baseline & visit lab protein parameters, blood work-up npcr levels are also low
9 npcr levels in study have not improved as intended
10 Patient was hospitalized but reason and the date of hospitalization is missing
11 Daily protein intake is optimal but one of the nutritional marker is not within the normal range
12 NPCR values are not within the recommended range at completion
13 NPCR at completion is missing
14 NPCR at completion is not greater than npcr at baseline
priority debug
1 1 FALSE
2 1 FALSE
3 2 FALSE
4 2 FALSE
5 2 FALSE
6 3 FALSE
7 3 FALSE
8 3 FALSE
9 3 FALSE
10 1 FALSE
11 2 FALSE
12 2 FALSE
13 2 FALSE
14 3 FALSE
redcap_instrument
1 baseline_data
2 baseline_data
3 baseline_data, visit_lab_date
4 baseline_data, visit_lab_date, visit_blood_workup
5 baseline_data, visit_lab_date, visit_blood_workup, completion_date
6 baseline_data, visit_lab_date, visit_blood_workup, completion_date
7 baseline_data, visit_lab_data
8 baseline_data, patient_morale_questionnaire
9 baseline_data, patient_morale_questionnaire
10 completion_project_questionnaire
11 completion_project_questionnaire, completion_data
12 completion_data
13 completion_data
14 completion_data
passing_test
1 function (d) {\n dplyr::if_else(\n !is.na(d$date_enrolled),\n dplyr::between(d$baseline_prealbumin_level, 30, 40),\n TRUE\n )\n}\n
2 function (d) {\n dplyr::if_else(\n !is.na(d$date_enrolled),\n (\n !is.na(d$baseline_prealbumin_level) &\n !is.na(d$baseline_creatinine_level) &\n !is.na(d$baseline_transferrin_level)\n ),\n TRUE\n )\n}\n
3 function (d) {\n events_to_check <- c("enrollment_arm_1", "visit_1_arm_1")\n dplyr::if_else(\n (\n d$redcap_event_name %in% events_to_check &\n !is.na(d$baseline_prealbumin_level)\n ),\n !is.na(d$visit_lab_prealbumin_level),\n TRUE\n )\n}\n
4 function (d) {\n events_to_check <- c("visit_1_arm_1", "visit_2_arm_1")\n dplyr::if_else(\n (\n d$redcap_event_name %in% events_to_check &\n !is.na(d$baseline_prealbumin_level) &\n !is.na(d$visit_lab_prealbumin_level)\n ),\n !is.na(d$visit_blood_workup_prealbumin_level),\n TRUE\n )\n}\n
5 function (d) {\n events_to_check <- c("visit_1_arm_1", "visit_2_arm_1", "final_visit_arm_1")\n dplyr::if_else(\n (\n d$redcap_event_name %in% events_to_check &\n !is.na(d$baseline_prealbumin_level) &\n !is.na(d$visit_lab_prealbumin_level) &\n !is.na(d$visit_blood_workup_prealbumin_level)\n ),\n !is.na(d$completion_data_prealbumin_level),\n TRUE\n )\n}\n
6 function (d) {\n events_to_check <- c("visit_1_arm_1", "visit_2_arm_1", "final_visit_arm_1")\n dplyr::if_else(\n (\n d$redcap_event_name %in% events_to_check &\n !is.na(d$baseline_prealbumin_level) &\n !is.na(d$visit_lab_prealbumin_level) &\n !is.na(d$visit_blood_workup_prealbumin_level) &\n !is.na(d$completion_data_prealbumin_level)\n ),\n (d$visit_lab_prealbumin_level < d$completion_data_prealbumin_level),\n TRUE\n )\n}\n
7 function (d) {\n events_to_check <- c("enrollment_arm_1", "visit_1_arm_1")\n dplyr::if_else(\n (\n d$redcap_event_name %in% events_to_check &\n (d$baseline_prealbumin_level < 30) &\n (d$baseline_normalized_protein_catabolic_rate < 1.2)\n ),\n (d$baseline_normalized_protein_catabolic_rate <= d$visit_lab_npcr),\n TRUE\n )\n}\n
8 function (d) {\n events_to_check <- c("enrollment_arm_1", "visit_1_arm_1")\n dplyr::if_else(\n (\n (d$redcap_event_name %in% events_to_check) &\n (d$baseline_prealbumin_level < 30) &\n (d$baseline_normalized_protein_catabolic_rate < 1.2) &\n (d$baseline_normalized_protein_catabolic_rate <= d$visit_lab_npcr)\n ),\n d$visit_lab_npcr <= d$visit_blood_workup_npcr,\n TRUE\n )\n}\n
9 function (d) {\n events_to_check <- c("enrollment_arm_1", "visit_1_arm_1", "visit_2_arm_1", "final_visit_arm_1")\n dplyr::if_else(\n (\n (d$redcap_event_name %in% events_to_check) &\n (d$baseline_prealbumin_level < 30) &\n (d$baseline_normalized_protein_catabolic_rate < 1.2) &\n (d$baseline_normalized_protein_catabolic_rate <= d$visit_lab_npcr) &\n (d$visit_lab_npcr <= d$visit_blood_workup_npcr)\n ),\n d$visit_blood_workup_npcr < d$completion_data_npcr,\n TRUE\n )\n}\n
10 function (d) {\n dplyr::if_else(\n d$completion_project_questionnaire_hospitalization == 1L,\n !is.na(d$completion_project_questionnaire_hospitalization_cause) & !is.na(d$completion_project_questionnaire_hospitalization_date),\n TRUE\n )\n}\n
11 function (d) {\n dplyr::if_else(\n d$completion_data_npcr >= 1.2,\n dplyr::between(d$completion_data_prealbumin_level, 30, 40),\n TRUE\n )\n}\n
12 function (d) {\n dplyr::between(d$completion_data_npcr, 1.2, 1.4)\n}\n
13 function (d) {\n events_to_check <- c("final_visit_arm_1")\n dplyr::if_else(\n d$redcap_event_name %in% events_to_check,\n !is.na(d$completion_data_npcr), # If this row exists in the desired event, then check for nonmissingness.\n TRUE # Otherwise, the test passes for rows associated with all other events.\n )\n}\n
14 function (d) {\n dplyr::if_else(\n !is.na(d$completion_data_npcr),\n (d$npcr_at_baseline < d$completion_data_npcr),\n TRUE\n )\n}
Code
checks$rules_inactive
Output
# A tibble: 1 x 6
check_name error_message priority debug redcap_instrument passing_test
<chr> <chr> <int> <lgl> <chr> <chr>
1 pre_albumin_levels pre_albumin ~ 3 FALSE visit_lab_date "function (~
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