PMID- 30126431
OWN - NLM
STAT- MEDLINE
DCOM- 20181217
LR  - 20181217
IS  - 1476-069X (Electronic)
IS  - 1476-069X (Linking)
VI  - 17
IP  - 1
DP  - 2018 Aug 20
TI  - Statistical software for analyzing the health effects of multiple concurrent 
      exposures via Bayesian kernel machine regression.
PG  - 67
LID - 10.1186/s12940-018-0413-y [doi]
LID - 67
AB  - BACKGROUND: Estimating the health effects of multi-pollutant mixtures is of 
      increasing interest in environmental epidemiology. Recently, a new approach for 
      estimating the health effects of mixtures, Bayesian kernel machine regression 
      (BKMR), has been developed. This method estimates the multivariable 
      exposure-response function in a flexible and parsimonious way, conducts variable 
      selection on the (potentially high-dimensional) vector of exposures, and allows 
      for a grouped variable selection approach that can accommodate highly correlated 
      exposures. However, the application of this novel method has been limited by a 
      lack of available software, the need to derive interpretable output in a 
      computationally efficient manner, and the inability to apply the method to 
      non-continuous outcome variables. METHODS: This paper addresses these limitations 
      by (i) introducing an open-source software package in the R programming language, 
      the bkmr R package, (ii) demonstrating methods for visualizing high-dimensional 
      exposure-response functions, and for estimating scientifically relevant 
      summaries, (iii) illustrating a probit regression implementation of BKMR for 
      binary outcomes, and (iv) describing a fast version of BKMR that utilizes a 
      Gaussian predictive process approach. All of the methods are illustrated using 
      fully reproducible examples with the provided R code. RESULTS: Applying the 
      methods to a continuous outcome example illustrated the ability of the BKMR 
      implementation to estimate the health effects of multi-pollutant mixtures in the 
      context of a highly nonlinear, biologically-based dose-response function, and to 
      estimate overall, single-exposure, and interactive health effects. The Gaussian 
      predictive process method led to a substantial reduction in the runtime, without 
      a major decrease in accuracy. In the setting of a larger number of exposures and 
      a dichotomous outcome, the probit BKMR implementation was able to correctly 
      identify the variables included in the exposure-response function and yielded 
      interpretable quantities on the scale of a latent continuous outcome or on the 
      scale of the outcome probability. CONCLUSIONS: This newly developed software, 
      integrated suite of tools, and extended methodology makes BKMR accessible for use 
      across a broad range of epidemiological applications in which multiple risk 
      factors have complex effects on health.
FAU - Bobb, Jennifer F
AU  - Bobb JF
AD  - Biostatistics Unit, Kaiser Permanente Washington Health Research Institute, 1730 
      Minor Ave #1600, Seattle, WA, 98101, USA. jennifer.f.bobb@kp.org.
AD  - Department of Biostatistics, University of Washington, Seattle, WA, USA. 
      jennifer.f.bobb@kp.org.
FAU - Claus Henn, Birgit
AU  - Claus Henn B
AD  - Department of Environmental Health, Boston University School of Public Health, 
      Boston, MA, USA.
FAU - Valeri, Linda
AU  - Valeri L
AD  - Psychiatric Biostatistics Laboratory, McLean Hospital, Belmont, MA, USA.
FAU - Coull, Brent A
AU  - Coull BA
AD  - Department of Biostatistics, Harvard T H Chan School of Public Health, Boston, 
      MA, USA.
LA  - eng
GR  - P01 CA134294/CA/NCI NIH HHS/United States
GR  - P30 ES000002/ES/NIEHS NIH HHS/United States
GR  - R00 ES022986/ES/NIEHS NIH HHS/United States
GR  - R01 ES024332/ES/NIEHS NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
PT  - Research Support, Non-U.S. Gov't
PT  - Research Support, U.S. Gov't, Non-P.H.S.
DEP - 20180820
PL  - England
TA  - Environ Health
JT  - Environmental health : a global access science source
JID - 101147645
RN  - 0 (Environmental Pollutants)
SB  - IM
MH  - Bayes Theorem
MH  - Environmental Exposure/*adverse effects
MH  - Environmental Health/*methods
MH  - Environmental Monitoring/*methods
MH  - Environmental Pollutants/*adverse effects
MH  - Models, Statistical
MH  - Software
PMC - PMC6102907
OTO - NOTNLM
OT  - Exposure-response
OT  - Health risk estimation
OT  - Mixtures
OT  - Multiple exposures
OT  - Variable selection
COIS- ETHICS APPROVAL AND CONSENT TO PARTICIPATE: Not applicable CONSENT FOR 
      PUBLICATION: Not applicable COMPETING INTERESTS: The authors declare that they 
      have no competing interests. PUBLISHER'S NOTE: Springer Nature remains neutral 
      with regard to jurisdictional claims in published maps and institutional 
      affiliations.
EDAT- 2018/08/22 06:00
MHDA- 2018/12/18 06:00
PMCR- 2018/08/20
CRDT- 2018/08/22 06:00
PHST- 2018/01/18 00:00 [received]
PHST- 2018/08/10 00:00 [accepted]
PHST- 2018/08/22 06:00 [entrez]
PHST- 2018/08/22 06:00 [pubmed]
PHST- 2018/12/18 06:00 [medline]
PHST- 2018/08/20 00:00 [pmc-release]
AID - 10.1186/s12940-018-0413-y [pii]
AID - 413 [pii]
AID - 10.1186/s12940-018-0413-y [doi]
PST - epublish
SO  - Environ Health. 2018 Aug 20;17(1):67. doi: 10.1186/s12940-018-0413-y.