This library implements basic tools to conduct unsupervised learning or clustering of instances - such as patients for instance - are described by multiple censored time-to-event endpoints. It has been developed to be adapted to situations where events are not associated with a change in state such as in the field of the social sciences where events such as or mark a change in status from one state to another, but have an additive impact, such as multiple long-term conditions on patients. This short vignette (on progress) will describe shortly how to conduct a full analysis using a toy dataset.
Unsupervised analyses workflow are conducted through the following steps when based on distances / dissimilarity between analysed instances:
The main purpose of the proposed tools is to be able to compute the Jaccard distance between patients on multiple censored time-to-event indicators. As a results patients having similar trajectories are expected to get clustered together, whereas patients with divergent health trajectories are likely to be assigned to different clusters.
In the fist section we will show how to construct censored state matrices from time stamped records (electonic health records) using simulated electronic health records. In section 2, we will show how to compute patients dissimilarity and derive a simple typology. In section 3 will will illustrate the use of the CLARA procedure in this setting when having to analyse larger set of patient (> 15000).
library(MSCA)
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
data(EHR)
head(EHR)
#> # A tibble: 6 × 3
#> link_id reg aos
#> <chr> <chr> <dbl>
#> 1 K_610836 ltc_30 83.0
#> 2 K_739086 ltc_22 73.0
#> 3 K_661210 ltc_15 79.9
#> 4 K_866970 ltc_31 30.6
#> 5 K_270151 ltc_31 39.6
#> 6 K_243582 ltc_16 63.5
EHR %>%
nrow()
#> [1] 4856Our toy dataset is composed of 4856 records 35 long term conditions and two absorbing states (death of censoring).
EHR %>%
group_by( reg ) %>%
tally
#> # A tibble: 37 × 2
#> reg n
#> <chr> <int>
#> 1 cens 1225
#> 2 death 927
#> 3 ltc_1 88
#> 4 ltc_10 171
#> 5 ltc_11 21
#> 6 ltc_12 83
#> 7 ltc_13 122
#> 8 ltc_14 11
#> 9 ltc_15 91
#> 10 ltc_16 259
#> # ℹ 27 more rowsThe function ( make_state_matrix ) is needed to obtain the individual patients state matrices:
The use of allow to speed the computation of Jaccard distance between patients.
library( cluster )
library( fastcluster )
#>
#> Attaching package: 'fastcluster'
#> The following object is masked from 'package:stats':
#>
#> hclust
# Compute the jaccard distance
d_mat <- fast_jaccard_dist( s_mat , as.dist = TRUE )
# Get a hierachical clustering using the built in hclust function
h_mat <- hclust(d = d_mat , method = 'ward.D2' )
h_mat
#>
#> Call:
#> hclust(d = d_mat, method = "ward.D2")
#>
#> Cluster method : ward.D2
#> Number of objects: 2152
# Get a typology
ct_mat_8 <- cutree( h_mat , k = 8 )
table( ct_mat_8 )
#> ct_mat_8
#> 1 2 3 4 5 6 7 8
#> 1172 116 242 129 157 91 136 109Once a typology has been defined it become interesting to obtain basic sequence statistics by clusters. To do so few data manipulation is needed:
# Get a data frame with patient id and cluster assignation
df1 <- data.frame( link_id = names(ct_mat_8) , cl = paste0('cl_',ct_mat_8))
head(df1)
#> link_id cl
#> 1 K_10030 cl_1
#> 2 K_101275 cl_1
#> 3 K_10227 cl_1
#> 4 K_102385 cl_1
#> 5 K_102612 cl_1
#> 6 K_103518 cl_1
# Merge with primary data
EHR_cl <- EHR %>%
left_join( df1 )
#> Joining with `by = join_by(link_id)`
# Get cluster sequences by cluster
dt_seq <- get_cluster_sequences(
dt = EHR_cl ,
cl_col = "cl",
id_col = "link_id",
event_col = "reg",
k = 2
)
# Get basic stats by cluster
sequence_stats(
seq_list = dt_seq$sequences ,
min_seq_freq = 0.03,
min_conditional_prob = 0,
min_relative_risk = 0
)
#> $cl_7
#> # A tibble: 2 × 6
#> from to seq_count seq_freq conditional_prob relative_risk
#> <chr> <chr> <int> <dbl> <dbl> <dbl>
#> 1 ltc_6 cens 82 0.383 0.536 1.15
#> 2 ltc_6 death 54 0.252 0.353 1.03
#>
#> $cl_3
#> # A tibble: 2 × 6
#> from to seq_count seq_freq conditional_prob relative_risk
#> <chr> <chr> <int> <dbl> <dbl> <dbl>
#> 1 ltc_22 cens 133 0.382 0.498 1.10
#> 2 ltc_22 death 109 0.313 0.408 1.06
#>
#> $cl_4
#> # A tibble: 2 × 6
#> from to seq_count seq_freq conditional_prob relative_risk
#> <chr> <chr> <int> <dbl> <dbl> <dbl>
#> 1 ltc_31 cens 90 0.370 0.584 1.12
#> 2 ltc_31 death 39 0.160 0.253 1.12
#>
#> $cl_2
#> # A tibble: 2 × 6
#> from to seq_count seq_freq conditional_prob relative_risk
#> <chr> <chr> <int> <dbl> <dbl> <dbl>
#> 1 ltc_16 death 64 0.358 0.504 1.07
#> 2 ltc_16 cens 52 0.291 0.409 1.18
#>
#> $cl_1
#> # A tibble: 5 × 6
#> from to seq_count seq_freq conditional_prob relative_risk
#> <chr> <chr> <int> <dbl> <dbl> <dbl>
#> 1 ltc_10 cens 76 0.0433 0.422 0.968
#> 2 ltc_10 death 76 0.0433 0.422 1.10
#> 3 ltc_13 cens 69 0.0393 0.535 1.23
#> 4 ltc_23 cens 65 0.0371 0.546 1.25
#> 5 ltc_12 cens 53 0.0302 0.582 1.34
#>
#> $cl_8
#> # A tibble: 2 × 6
#> from to seq_count seq_freq conditional_prob relative_risk
#> <chr> <chr> <int> <dbl> <dbl> <dbl>
#> 1 ltc_16 cens 83 0.464 0.654 1.08
#> 2 ltc_16 death 26 0.145 0.205 1.11
#>
#> $cl_5
#> # A tibble: 2 × 6
#> from to seq_count seq_freq conditional_prob relative_risk
#> <chr> <chr> <int> <dbl> <dbl> <dbl>
#> 1 ltc_7 cens 93 0.355 0.505 1.08
#> 2 ltc_7 death 64 0.244 0.348 1.13
#>
#> $cl_6
#> # A tibble: 3 × 6
#> from to seq_count seq_freq conditional_prob relative_risk
#> <chr> <chr> <int> <dbl> <dbl> <dbl>
#> 1 ltc_18 cens 63 0.36 0.573 1.17
#> 2 ltc_18 death 28 0.16 0.255 1.09
#> 3 ltc_22 cens 7 0.04 0.467 0.950