Establishing a standard method for analysing case detection delay in leprosy using a Bayesian modelling approach

Table 3 Summary of model comparison using leave-one-out (LOO) cross-validation

PEP4LEP dataset
Model	LOOIC	ELPD	ELPD difference	Standard error (ELPD difference)
Gamma	1532.4	−766.2	−6.5	8.1
Weibull	1543.9	−771.9	−12.2	9.8
Log-normal	1519.5	−759.8	0	0

Global dataset
Model	LOOIC	ELPD	ELPD difference	Standard error (ELPD difference)
Gamma	738.1	−369.0	−6.3	5.5
Weibull	732.9	−366.4	−3.7	4.1
Log-normal	725.5	−362.7	0	0

Combined datasets
Model	LOOIC	ELPD	ELPD difference	Standard error (ELPD difference)
Gamma	2255.4	−1127.7	−3.8	8.9
Weibull	2261.7	−1130.8	−7.0	9.5
Log-normal	2247.7	−1123.9	0	0

LOOIC is LOO information criterion; ELPD is expected log predictive density. LOOIC = −2 × ELPD and ELPD difference is the ELPD value relative to that of the best performing model, i.e., the log-normal model, which has the lowest LOOIC (or highest ELPD). The standard error is a measure of uncertainty for the ELPD difference relative to the log-normal model
ELPD Expected log predictive density, LOOIC LOO information criterion, PEP4LEP Post exposure prophylaxis for leprosy

ISSN: 2049-9957