Table 3.
Positive predictive values of computer-coded verbal autopsy methods versus physician-coded verbal autopsy reference standards
| |
Test cases |
Open-source random forest |
Open-source tariff method |
InterVA-4 |
Average for top cause (%) |
|||
|---|---|---|---|---|---|---|---|---|
| Dataset | Top (%) | Top 3 (%) | Top (%) | Top 3 (%) | Top (%) | Top 3 (%) | ||
|
China |
400 |
35 |
57 |
36 |
70 |
N/A |
N/A |
36 |
|
Institute for Health Metrics and Evaluation |
400 |
33 |
55 |
34 |
53 |
N/A |
N/A |
34 |
|
Million Death Study |
6100 |
58 |
82 |
52 |
76 |
42a |
63a |
51 |
|
Agincourt |
2900 |
45 |
77 |
42 |
69 |
42 |
58 |
43 |
|
Matlab |
1600 |
49 |
74 |
52 |
74 |
48 |
64 |
50 |
| Average | 44 | 69 | 43 | 68 | 44 | 62 | ||
Top cause represents accuracy of the CCVA method’s most probable cause matching the cause assigned by PCVA; Top 3 represents whether CCVA’s three most probable causes contain the cause assigned by PCVA. Averages calculated across CCVA methods only use results for the top cause. aThe Million Death Study dataset used for InterVA-4 contained a sample of 552 cases, in which we extracted additional InterVA-4 indicators from the narratives.