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Efficiency of two-phase methods with focus on a planned population-based case-control study on air pollution and stroke

Oudin, Anna LU ; Björk, Jonas LU and Strömberg, Ulf LU (2007) In Environmental Health 6(34).
Abstract
ABSTRACT: We plan to conduct a case-control study to investigate whether exposure to nitrogen dioxide (NO2) increases the risk of stroke. In case-control studies, selective participation can lead to bias and loss of efficiency. A two-phase design can reduce bias and improve efficiency by combining information on the non-participating subjects with information from the participating subjects. In our planned study, we will have access to individual disease status and data on NO2 exposure on group (area) level for a large population sample of Scania, southern Sweden. A smaller sub-sample will be selected to the second phase for individual-level assessment on exposure and covariables. In this paper, we simulate a case-control study based on... (More)
ABSTRACT: We plan to conduct a case-control study to investigate whether exposure to nitrogen dioxide (NO2) increases the risk of stroke. In case-control studies, selective participation can lead to bias and loss of efficiency. A two-phase design can reduce bias and improve efficiency by combining information on the non-participating subjects with information from the participating subjects. In our planned study, we will have access to individual disease status and data on NO2 exposure on group (area) level for a large population sample of Scania, southern Sweden. A smaller sub-sample will be selected to the second phase for individual-level assessment on exposure and covariables. In this paper, we simulate a case-control study based on our planned study. We develop a two-phase method for this study and compare the performance of our method with the performance of other two-phase methods. METHODS: A two-phase case-control study was simulated with a varying number of first- and second-phase subjects. Estimation methods: Method 1: Effect estimation with second-phase data only. Method 2: Effect estimation by adjusting the first-phase estimate with the difference between the adjusted and unadjusted second-phase estimate. The first-phase estimate is based on individual disease status and residential address for all study subjects that are linked to register data on NO2-exposure for each geographical area. Method 3: Effect estimation by using the expectation-maximization (EM) algorithm without taking area-level register data on exposure into account. Method 4: Effect estimation by using the EM algorithm and incorporating group-level register data on NO2-exposure. RESULTS: The simulated scenarios were such that, unbiased or marginally biased (< 7 %) odds ratio (OR) estimates were obtained with all methods. The efficiencies of method 4, are generally higher than those of methods 1 and 2. The standard errors in method 4 decreased further when the case/control ratio is above one in the second phase. For all methods, the standard errors do not become substantially reduced when the number of first-phase controls is increased. CONCLUSION: In the setting described here, method 4 had the best performance in order to improve efficiency, while adjusting for varying participation rates across areas. (Less)
Please use this url to cite or link to this publication:
author
; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Environmental Health
volume
6
issue
34
article number
(8 s)
publisher
BioMed Central (BMC)
external identifiers
  • pmid:17988388
  • wos:000252362600001
  • scopus:37849004595
ISSN
1476-069X
DOI
10.1186/1476-069X-6-34
language
English
LU publication?
yes
id
d285208e-2625-4df1-8ac7-da5a55b78da7 (old id 1139873)
date added to LUP
2016-04-01 16:20:57
date last changed
2022-01-28 19:06:46
@article{d285208e-2625-4df1-8ac7-da5a55b78da7,
  abstract     = {{ABSTRACT: We plan to conduct a case-control study to investigate whether exposure to nitrogen dioxide (NO2) increases the risk of stroke. In case-control studies, selective participation can lead to bias and loss of efficiency. A two-phase design can reduce bias and improve efficiency by combining information on the non-participating subjects with information from the participating subjects. In our planned study, we will have access to individual disease status and data on NO2 exposure on group (area) level for a large population sample of Scania, southern Sweden. A smaller sub-sample will be selected to the second phase for individual-level assessment on exposure and covariables. In this paper, we simulate a case-control study based on our planned study. We develop a two-phase method for this study and compare the performance of our method with the performance of other two-phase methods. METHODS: A two-phase case-control study was simulated with a varying number of first- and second-phase subjects. Estimation methods: Method 1: Effect estimation with second-phase data only. Method 2: Effect estimation by adjusting the first-phase estimate with the difference between the adjusted and unadjusted second-phase estimate. The first-phase estimate is based on individual disease status and residential address for all study subjects that are linked to register data on NO2-exposure for each geographical area. Method 3: Effect estimation by using the expectation-maximization (EM) algorithm without taking area-level register data on exposure into account. Method 4: Effect estimation by using the EM algorithm and incorporating group-level register data on NO2-exposure. RESULTS: The simulated scenarios were such that, unbiased or marginally biased (&lt; 7 %) odds ratio (OR) estimates were obtained with all methods. The efficiencies of method 4, are generally higher than those of methods 1 and 2. The standard errors in method 4 decreased further when the case/control ratio is above one in the second phase. For all methods, the standard errors do not become substantially reduced when the number of first-phase controls is increased. CONCLUSION: In the setting described here, method 4 had the best performance in order to improve efficiency, while adjusting for varying participation rates across areas.}},
  author       = {{Oudin, Anna and Björk, Jonas and Strömberg, Ulf}},
  issn         = {{1476-069X}},
  language     = {{eng}},
  number       = {{34}},
  publisher    = {{BioMed Central (BMC)}},
  series       = {{Environmental Health}},
  title        = {{Efficiency of two-phase methods with focus on a planned population-based case-control study on air pollution and stroke}},
  url          = {{http://dx.doi.org/10.1186/1476-069X-6-34}},
  doi          = {{10.1186/1476-069X-6-34}},
  volume       = {{6}},
  year         = {{2007}},
}