Benchmark dose modeling for epidemiological dose-response assessment using prospective cohort studies
© 2023 The Authors. Risk Analysis published by Wiley Periodicals LLC on behalf of Society for Risk Analysis..
Benchmark dose (BMD) methodology has been employed as a default dose-response modeling approach to determine the toxicity value of chemicals to support regulatory chemical risk assessment. Especially, a relatively standardized BMD analysis framework has been established for modeling toxicological data regarding the formats of input data, dose-response models, definitions of benchmark response, and model uncertainty consideration. However, the BMD approach has not been well developed for epidemiological data mainly because of the diverse designs of epidemiological studies and various formats of data reported in the literature. Although most of the epidemiological BMD analyses were developed to solve a particular question, the methods proposed in two recent studies are able to handle cohort and case-control studies using summary data with consideration of adjustments for confounders. Therefore, the purpose of the present study is to investigate and compare the "effective count"-based BMD modeling approach and adjusted relative risk (RR)-based BMD analysis approach to identify an appropriate BMD modeling framework that can be generalized for analyzing published data of prospective cohort studies for BMD analysis. The two methods were applied to the same set of studies that investigated the association between bladder and lung cancer and inorganic arsenic exposure for BMD estimation. The results suggest that estimated BMDs and BMDLs are relatively consistent; however, with the consideration of established common practice in BMD analysis, modeling adjusted RR values as continuous data for BMD estimation is a more generalizable approach harmonized with the BMD approach using toxicological data.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2024 |
|---|---|
| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:44 |
|---|---|
| Enthalten in: |
Risk analysis : an official publication of the Society for Risk Analysis - 44(2024), 4 vom: 18. Apr., Seite 743-756 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
De Pretis, Francesco [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Arsenic exposure |
|---|
| Anmerkungen: |
Date Completed 08.04.2024 Date Revised 08.04.2024 published: Print-Electronic Citation Status MEDLINE |
|---|
| doi: |
10.1111/risa.14196 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM359964621 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM359964621 | ||
| 003 | DE-627 | ||
| 005 | 20240408232037.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2024 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1111/risa.14196 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1369.xml |
| 035 | |a (DE-627)NLM359964621 | ||
| 035 | |a (NLM)37496455 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a De Pretis, Francesco |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Benchmark dose modeling for epidemiological dose-response assessment using prospective cohort studies |
| 264 | 1 | |c 2024 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 08.04.2024 | ||
| 500 | |a Date Revised 08.04.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2023 The Authors. Risk Analysis published by Wiley Periodicals LLC on behalf of Society for Risk Analysis. | ||
| 520 | |a Benchmark dose (BMD) methodology has been employed as a default dose-response modeling approach to determine the toxicity value of chemicals to support regulatory chemical risk assessment. Especially, a relatively standardized BMD analysis framework has been established for modeling toxicological data regarding the formats of input data, dose-response models, definitions of benchmark response, and model uncertainty consideration. However, the BMD approach has not been well developed for epidemiological data mainly because of the diverse designs of epidemiological studies and various formats of data reported in the literature. Although most of the epidemiological BMD analyses were developed to solve a particular question, the methods proposed in two recent studies are able to handle cohort and case-control studies using summary data with consideration of adjustments for confounders. Therefore, the purpose of the present study is to investigate and compare the "effective count"-based BMD modeling approach and adjusted relative risk (RR)-based BMD analysis approach to identify an appropriate BMD modeling framework that can be generalized for analyzing published data of prospective cohort studies for BMD analysis. The two methods were applied to the same set of studies that investigated the association between bladder and lung cancer and inorganic arsenic exposure for BMD estimation. The results suggest that estimated BMDs and BMDLs are relatively consistent; however, with the consideration of established common practice in BMD analysis, modeling adjusted RR values as continuous data for BMD estimation is a more generalizable approach harmonized with the BMD approach using toxicological data | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Bayesian analysis | |
| 650 | 4 | |a arsenic exposure | |
| 650 | 4 | |a benchmark dose | |
| 650 | 4 | |a bladder cancer | |
| 650 | 4 | |a epidemiological risk assessment | |
| 650 | 4 | |a lung cancer | |
| 700 | 1 | |a Zhou, Yun |e verfasserin |4 aut | |
| 700 | 1 | |a Xun, Pengcheng |e verfasserin |4 aut | |
| 700 | 1 | |a Shao, Kan |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Risk analysis : an official publication of the Society for Risk Analysis |d 1990 |g 44(2024), 4 vom: 18. Apr., Seite 743-756 |w (DE-627)NLM012965340 |x 1539-6924 |7 nnns |
| 773 | 1 | 8 | |g volume:44 |g year:2024 |g number:4 |g day:18 |g month:04 |g pages:743-756 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1111/risa.14196 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 44 |j 2024 |e 4 |b 18 |c 04 |h 743-756 | ||