Estimating the odds ratio when exposure has a limit of detection
نویسندگان
چکیده
منابع مشابه
Estimating the odds ratio when exposure has a limit of detection.
BACKGROUND In epidemiologic research, little emphasis has been placed on methods to account for left-hand censoring of 'exposures' due to a limit of detection (LOD). METHODS We calculate the odds of anti-HIV therapy naiveté in 45 HIV-infected men as a function of measured log(10) plasma HIV RNA viral load using five approaches including ad hoc methods as well as a maximum likelihood estimate ...
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BACKGROUND Misclassification of exposure variables in epidemiologic studies may lead to biased estimation of parameters and loss of power in statistical inferences. In this paper, the inverse matrix method, as an efficient method of the correction of odds ratio for the misclassification of a binary exposure, was generalized to nondifferential misclassification and 2 × 2 × J tables. METHODS Si...
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Epidemiologic studies often aim to estimate the odds ratio for the association between a binary exposure and a binary disease outcome. Because confounding bias is of serious concern in observational studies, investigators typically estimate the adjusted odds ratio in a multivariate logistic regression which conditions on a large number of potential confounders. It is well known that modeling er...
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In recent years odds ratios have become widely used in medical reports—almost certainly some will appear in today’s BMJ. There are three reasons for this. Firstly, they provide an estimate (with confidence interval) for the relationship between two binary (“yes or no”) variables. Secondly, they enable us to examine the effects of other variables on that relationship, using logistic regression. ...
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Let Xi be non-negative, independent random variables with finite expectation, and X∗ n = max{X1, . . . , Xn}. The value EX∗ n is what can be obtained by a “prophet”. A “mortal” on the other hand, may use k ≥ 1 stopping rules t1, . . . , tk, yielding a return of E[maxi=1,...,k Xti ]. For n ≥ k the optimal return is V n k (X1, . . . , Xn) = supE[maxi=1,...,k Xti ] where the supremum is over all s...
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ژورنال
عنوان ژورنال: International Journal of Epidemiology
سال: 2009
ISSN: 1464-3685,0300-5771
DOI: 10.1093/ije/dyp269