TY - JOUR
T1 - Approximate likelihood-based estimation method of multiple-type pathogen interactions
T2 - An application to longitudinal pneumococcal carriage data
AU - Man, Irene
AU - Bogaards, Johannes A.
AU - Makwana, Kishan
AU - Trzciński, Krzysztof
AU - Auranen, Kari
N1 - Funding Information: This work was supported by grant S/113005/01/PT (Prometheus project) through the Strategic Programme from the National Institute for Public Health and the Environment (RIVM) of the Netherlands. Publisher Copyright: © 2022 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.
PY - 2022/3/15
Y1 - 2022/3/15
N2 - While the serotypes of Streptococcus pneumoniae are known to compete during colonization in human hosts, our knowledge of how competition occurs is still incomplete. New insights of pneumococcal between-type competition could be generated from carriage data obtained by molecular-based detection methods, which record more complete sets of serotypes involved in co-carriage than when detection is done by culture. Here, we develop a Bayesian estimation method for inferring between-type interactions from longitudinal data recording the presence/absence of the types at discrete observation times. It allows inference from data containing co-carriage of two or more serotypes, which is often the case when pneumococcal presence is determined by molecular-based methods. The computational burden posed by the increased number of types detected in co-carriage is addressed by approximating the likelihood under a multi-state model with the likelihood of only those trajectories with minimum number of acquisition and clearance events between observation times. The proposed method's performance was validated on simulated data. The estimates of the interaction parameters of acquisition and clearance were unbiased in settings with short sampling intervals between observation times. With less frequent sampling, the estimates of the interaction parameters became more biased, but their ratio, which summarizes the total interaction, remained unbiased. Confounding due to unobserved heterogeneity in exposure could be corrected by including individual-level random effects. In an application to empirical data about pneumococcal carriage in infants, we found new evidence for between-serotype competition in clearance, although the effect size was small.
AB - While the serotypes of Streptococcus pneumoniae are known to compete during colonization in human hosts, our knowledge of how competition occurs is still incomplete. New insights of pneumococcal between-type competition could be generated from carriage data obtained by molecular-based detection methods, which record more complete sets of serotypes involved in co-carriage than when detection is done by culture. Here, we develop a Bayesian estimation method for inferring between-type interactions from longitudinal data recording the presence/absence of the types at discrete observation times. It allows inference from data containing co-carriage of two or more serotypes, which is often the case when pneumococcal presence is determined by molecular-based methods. The computational burden posed by the increased number of types detected in co-carriage is addressed by approximating the likelihood under a multi-state model with the likelihood of only those trajectories with minimum number of acquisition and clearance events between observation times. The proposed method's performance was validated on simulated data. The estimates of the interaction parameters of acquisition and clearance were unbiased in settings with short sampling intervals between observation times. With less frequent sampling, the estimates of the interaction parameters became more biased, but their ratio, which summarizes the total interaction, remained unbiased. Confounding due to unobserved heterogeneity in exposure could be corrected by including individual-level random effects. In an application to empirical data about pneumococcal carriage in infants, we found new evidence for between-serotype competition in clearance, although the effect size was small.
KW - Bayesian inference
KW - Streptococcus pneumoniae
KW - approximate likelihood
KW - co-carriage
KW - longitudinal data
KW - multiple-type interactions
UR - http://www.scopus.com/inward/record.url?scp=85123628889&partnerID=8YFLogxK
U2 - https://doi.org/10.1002/sim.9305
DO - https://doi.org/10.1002/sim.9305
M3 - Article
C2 - 35083763
SN - 0277-6715
VL - 41
SP - 981
EP - 993
JO - Statistics in medicine
JF - Statistics in medicine
IS - 6
ER -