Multi-Site Analysis for Independent Data Sources

The multi-site analyses included in this suite are intended to be executed against data that are all stored in the same place. However, there may be some instances where the data associated with each site is stored in independent locations. This vignette outlines how the multi-site analysis can be executed in these instances.

After following the instructions to reproduce the analysis, you will also need to change the output_function column to tell the uc_output function which check you executed. Reference the table below for the labels that are associated with each check:

Check Type	output_function
Multi Site, Exploratory, Cross-Sectional	uc_ms_exp_cs
Multi Site, Exploratory, Longitudinal	uc_ms_exp_la
Multi Site, Anomaly Detection, Cross-Sectional	uc_ms_anom_cs
Multi Site, Anomaly Detection, Longitudinal	uc_ms_anom_la

Multi-Site Exploratory Analysis

The process for the exploratory analysis is the same for both the cross-sectional and longitudinal configurations.

First, execute either of the Single Site, Exploratory analyses, configured appropriately for your study, against each data source.

library(unmappedconcepts)

my_table <- uc_process(cohort = my_cohort,
                       multi_or_single_site = 'single',
                       anomaly_or_exploratory = 'exploratory',
                       time = T / F,
                       patient_level_tbl = T / F
                       ...)

The median_all_* columns will not reflect the overall median using this method. This is typically used as a baseline comparison metric in uc_output. If you do not mind the lack of an overall comparison metric and would just like to compare site-level medians, all you need to do is union each table together and adjust the output_function accordingly.

my_final_results <- my_table1 %>% dplyr::union(my_table2) ... %>%
  dplyr::union(my_table_n) %>%
  dplyr::mutate(output_function = 'uc_ms_exp_(cs/la)')

If you would like to have the overall median available, you will need to output patient-level results in addition to the standard summary output (patient_level_tbl = T). Once patient-level output is returned from each data source, you will need to union these tables together and recompute the overall median.

my_combined_results <- my_table1 %>% dplyr::union(my_table2) ... %>%
  dplyr::union(my_table_n) %>%
  dplyr::mutate(output_function = 'uc_ms_exp_(cs/la)')

## Cross-Sectional
my_final_results <- my_combined_results %>%
  group_by(variable) %>%
  summarise(median_all_with0s = as.numeric(median(unmapped)),
            median_all_without0s = as.numeric(median(unmapped[unmapped!=0]))) %>%
  mutate(median_all_without0s = ifelse(is.na(median_all_without0s), 0L, median_all_without0s))

## Longitudinal
my_final_results <- my_combined_results %>%
  group_by(variable, time_start, time_end) %>%
  summarise(median_all_with0s = as.numeric(median(unmapped)),
            median_all_without0s = as.numeric(median(unmapped[unmapped!=0]))) %>%
  mutate(median_all_without0s = ifelse(is.na(median_all_without0s), 0L, median_all_without0s))

Multi-Site Anomaly Detection Analysis

Multi-site anomaly detection can only be executed against the proportional results produced by this module. As such, you may follow the instructions for Multi-Site, Exploratory output without producing patient-level results, then proceed to the appropriate anomaly detection method below.

Cross-Sectional

After producing a combined result set, execute the code below (using functions from the squba.gen package) to run the anomaly detection analysis. The p_value can be selected by the user.

df_start <- compute_dist_anomalies(df_tbl = my_combined_results,
                                   grp_vars = c('variable'),
                                   var_col = {`unmapped_row_prop`, `unmapped_pt_prop`},
                                   denom_cols = c('variable', 'total_pt', 
                                                  'total_rows'))

df_final <- detect_outliers(df_tbl = df_start,
                            tail_input = 'both',
                            p_input = p_value,
                            column_analysis = {`unmapped_row_prop`, `unmapped_pt_prop`},
                            column_variable = 'variable') %>%
  mutate(output_function = 'uc_ms_anom_cs')

Longitudinal

After producing a combined result set, execute the code below (using functions from the squba.gen package) to run the anomaly detection analysis.

df_final <- ms_anom_euclidean(fot_input_tbl = my_combined_results,
                              grp_vars = c('site', 'variable'),
                              var_col = {`unmapped_row_prop`, `unmapped_pt_prop`})