Rethinking Residual Confounding Bias Reduction: Why Vanilla hdPS Alone is No Longer Enough

6.1 Genrate recurrence covariates

(Schneeweiss et al. 2009)

In this step, we generate 3 binary recurrence covariates for each of the candidate empirical covariates identified in the previous step:

occurred at least once
occurred sporadically (at least more than the median)
occurred frequently (at least more than the 75th percentile)

step2 <- get_recurrence_covariates(df = out1, 
                                   patientIdVarname = "idx",
                                   eventCodeVarname = "icd10", 
                                   patientIdVector = patientIds)

6.2 Example of recurrence covariates

ICD-10-CM code (dimension 1)	code appeared at least once	code appeared at least more than the median	code appeared at least more than the 75th percentile
D64.9 Anemia	rec_dx_D64_once	rec_dx_D64_sporadic	rec_dx_D64_frequent
D75.9P Blood clots	rec_dx_D75_once	rec_dx_D75_sporadic	rec_dx_D75_frequent
D89.9 Immune disorder	rec_dx_D89_once	rec_dx_D89_sporadic	rec_dx_D89_frequent
\(\ldots\)	\(\ldots\)	\(\ldots\)	\(\ldots\)
E07.9 Disorder of thyroid	rec_dx_E07_once	rec_dx_E07_sporadic	rec_dx_E07_frequent

Example of 3 binary covariates created based on the candidate empirical covariate

6.3 Recurrence covariates in the data

Here we show binary recurrence covariates for only 2 columns

6.4 Refined recurrence covariates

Below you can click to see a list of all recurrence covariates obtained in our data.

ICD-10 Recurrence Data
1	rec_dx_A49_once	rec_dx_B00_once	rec_dx_B20_once
2	rec_dx_B20_frequent	rec_dx_B35_once	rec_dx_B37_once
3	rec_dx_B96_once	rec_dx_C50_once	rec_dx_D75_once
4	rec_dx_E03_once	rec_dx_E04_once	rec_dx_E05_once
5	rec_dx_E07_once	rec_dx_E28_once	rec_dx_E28_frequent
6	rec_dx_E29_once	rec_dx_E78_once	rec_dx_E79_once
7	rec_dx_E87_once	rec_dx_F17_once	rec_dx_F20_once
8	rec_dx_F20_frequent	rec_dx_F29_once	rec_dx_F31_once
9	rec_dx_F31_frequent	rec_dx_F32_once	rec_dx_F39_once
10	rec_dx_F41_once	rec_dx_F43_once	rec_dx_F90_once
11	rec_dx_G20_once	rec_dx_G20_frequent	rec_dx_G25_once
12	rec_dx_G30_once	rec_dx_G30_frequent	rec_dx_G31_once
13	rec_dx_G40_once	rec_dx_G43_once	rec_dx_G47_once
14	rec_dx_G89_once	rec_dx_H04_once	rec_dx_H10_once
15	rec_dx_H40_once	rec_dx_H40_frequent	rec_dx_H57_once
16	rec_dx_H57_frequent	rec_dx_H66_once	rec_dx_I10_once
17	rec_dx_I10_frequent	rec_dx_I20_once	rec_dx_I21_once
18	rec_dx_I48_once	rec_dx_I48_frequent	rec_dx_I49_once
19	rec_dx_I50_once	rec_dx_I50_frequent	rec_dx_I51_once
20	rec_dx_I63_once	rec_dx_I70_once	rec_dx_I80_once
21	rec_dx_I99_once	rec_dx_J01_once	rec_dx_J02_once
22	rec_dx_J20_once	rec_dx_J30_once	rec_dx_J40_once
23	rec_dx_J42_once	rec_dx_J43_once	rec_dx_J43_frequent
24	rec_dx_J44_once	rec_dx_J44_frequent	rec_dx_J45_once
25	rec_dx_J45_frequent	rec_dx_J98_once	rec_dx_K04_once
26	rec_dx_K08_once	rec_dx_K21_once	rec_dx_K25_once
27	rec_dx_K27_once	rec_dx_K30_once	rec_dx_K31_once
28	rec_dx_K58_once	rec_dx_K59_once	rec_dx_K76_once
29	rec_dx_K92_once	rec_dx_L08_once	rec_dx_L20_once
30	rec_dx_L23_once	rec_dx_L29_once	rec_dx_L40_once
31	rec_dx_L70_once	rec_dx_L93_once	rec_dx_L93_frequent
32	rec_dx_M06_once	rec_dx_M06_frequent	rec_dx_M10_once
33	rec_dx_M13_once	rec_dx_M19_once	rec_dx_M1A_once
34	rec_dx_M25_once	rec_dx_M54_once	rec_dx_M62_once
35	rec_dx_M79_once	rec_dx_M81_once	rec_dx_N19_once
36	rec_dx_N20_once	rec_dx_N28_once	rec_dx_N30_once
37	rec_dx_N32_once	rec_dx_N39_once	rec_dx_N40_once
38	rec_dx_N42_once	rec_dx_N52_once	rec_dx_N92_once
39	rec_dx_N94_once	rec_dx_N95_once	rec_dx_R00_once
40	rec_dx_R05_once	rec_dx_R06_once	rec_dx_R07_once
41	rec_dx_R09_once	rec_dx_R10_once	rec_dx_R11_once
42	rec_dx_R12_once	rec_dx_R19_once	rec_dx_R21_once
43	rec_dx_R25_once	rec_dx_R32_once	rec_dx_R35_once
44	rec_dx_R39_once	rec_dx_R41_once	rec_dx_R42_once
45	rec_dx_R45_once	rec_dx_R51_once	rec_dx_R52_once
46	rec_dx_R60_once	rec_dx_R73_once	rec_dx_T14_once
47	rec_dx_T78_once	rec_dx_T88_once	rec_dx_Z79_once
48	rec_dx_Z95_once

Tip

Given that we had one dimension of proxy data, \(p=1\), at most \(n=200\) most prevalent codes (with the restriction that minimum number of patients in each code = 20), and \(3\) intensity, we could theoretically have at most \(p \times n \times 3 = 1 \times 200 \times \ 3 = 600\) recurrence covariates.

Based on all of the restrictions, we created 143 distinct recurrence covariates.
The merged data (analytic and proxies) size is now 7,585.

If 2 or all 3 recurrence covariates are identical, only one distinct recurrence covariate is returned. This is why you do not see any sporadic recurrence covariate here.