PMID- 33966518
OWN - NLM
STAT- MEDLINE
DCOM- 20211125
LR  - 20220720
IS  - 1552-681X (Electronic)
IS  - 0272-989X (Print)
IS  - 0272-989X (Linking)
VI  - 41
IP  - 6
DP  - 2021 Aug
TI  - Bayesian versus Empirical Calibration of Microsimulation Models: A Comparative 
      Analysis.
PG  - 714-726
LID - 10.1177/0272989X211009161 [doi]
AB  - Calibration of a microsimulation model (MSM) is a challenging but crucial step 
      for the development of a valid model. Numerous calibration methods for MSMs have 
      been suggested in the literature, most of which are usually adjusted to the 
      specific needs of the model and based on subjective criteria for the selection of 
      optimal parameter values. This article compares 2 general approaches for 
      calibrating MSMs used in medical decision making, a Bayesian and an empirical 
      approach. We use as a tool the MIcrosimulation Lung Cancer (MILC) model, a 
      streamlined, continuous-time, dynamic MSM that describes the natural history of 
      lung cancer and predicts individual trajectories accounting for age, sex, and 
      smoking habits. We apply both methods to calibrate MILC to observed lung cancer 
      incidence rates from the Surveillance, Epidemiology and End Results (SEER) 
      database. We compare the results from the 2 methods in terms of the resulting 
      parameter distributions, model predictions, and efficiency. Although the 
      empirical method proves more practical, producing similar results with smaller 
      computational effort, the Bayesian method resulted in a calibrated model that 
      produced more accurate outputs for rare events and is based on a well-defined 
      theoretical framework for the evaluation and interpretation of the calibration 
      outcomes. A combination of the 2 approaches is an alternative worth considering 
      for calibrating complex predictive models, such as microsimulation models.
FAU - Chrysanthopoulou, Stavroula A
AU  - Chrysanthopoulou SA
AUID- ORCID: 0000-0001-5580-4397
AD  - Brown University, Providence, RI, USA.
FAU - Rutter, Carolyn M
AU  - Rutter CM
AUID- ORCID: 0000-0002-4396-8594
AD  - RAND Corporation, Santa Monica, CA, USA.
FAU - Gatsonis, Constantine A
AU  - Gatsonis CA
AD  - Brown University, Providence, RI, USA.
LA  - eng
GR  - U01 CA079778/CA/NCI NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
DEP - 20210508
PL  - United States
TA  - Med Decis Making
JT  - Medical decision making : an international journal of the Society for Medical 
      Decision Making
JID - 8109073
SB  - IM
MH  - *Bayes Theorem
MH  - Calibration
MH  - Humans
PMC - PMC9294658
MID - NIHMS1821774
OTO - NOTNLM
OT  - Bayesian calibration
OT  - comparative analysis
OT  - empirical calibration
OT  - microsimulation model
EDAT- 2021/05/11 06:00
MHDA- 2021/11/26 06:00
PMCR- 2022/07/19
CRDT- 2021/05/10 05:31
PHST- 2021/05/11 06:00 [pubmed]
PHST- 2021/11/26 06:00 [medline]
PHST- 2021/05/10 05:31 [entrez]
PHST- 2022/07/19 00:00 [pmc-release]
AID - 10.1177/0272989X211009161 [doi]
PST - ppublish
SO  - Med Decis Making. 2021 Aug;41(6):714-726. doi: 10.1177/0272989X211009161. Epub 
      2021 May 8.