TY - JOUR
T1 - Avoiding bias from aggregate measures of exposure
AU - Duffy, Stephen W
AU - Jonsson, Håkan
AU - Agbaje, Olorunsola F
AU - Pashayan, Nora
AU - Gabe, Rhian
PY - 2007
Y1 - 2007
N2 - BACKGROUND: Sometimes in descriptive epidemiology or in the evaluation of a health intervention policy change, proportions exposed to a risk factor or to an intervention are used as explanatory variables in log-linear regressions for disease incidence or mortality. AIM: To demonstrate how estimates from such models can be substantially inaccurate as estimates of the effect of the risk factor or intervention at individual level. To show how the individual level effect can be correctly estimated by excess relative risk models. METHODS: The problem and solution are demonstrated using data on prostate-specific antigen testing and prostate cancer incidence.
AB - BACKGROUND: Sometimes in descriptive epidemiology or in the evaluation of a health intervention policy change, proportions exposed to a risk factor or to an intervention are used as explanatory variables in log-linear regressions for disease incidence or mortality. AIM: To demonstrate how estimates from such models can be substantially inaccurate as estimates of the effect of the risk factor or intervention at individual level. To show how the individual level effect can be correctly estimated by excess relative risk models. METHODS: The problem and solution are demonstrated using data on prostate-specific antigen testing and prostate cancer incidence.
U2 - 10.1136/jech.2006.050203
DO - 10.1136/jech.2006.050203
M3 - Article
C2 - 17435216
SN - 0143-005X
VL - 61
SP - 461
EP - 463
JO - Journal of epidemiology and community health
JF - Journal of epidemiology and community health
IS - 5
ER -