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A simple approach to the estimation of incidence rate difference.

ABSTRACT: The incidence rate difference (IRD) is a parameter of interest in many medical studies. For example, in vaccine studies, it is interpreted as the vaccine-attributable reduction in disease incidence. This is an important parameter, because it shows the public health impact of an intervention. The IRD is difficult to estimate for various reasons, especially when there are quantitative covariates or the duration of follow-up is variable. In this paper, the authors propose an approach based on weighted least-squares regression for estimating the IRD. It is very easy to implement because it boils down to performing ordinary least-squares regression analysis of transformed variables. Furthermore, if the outcome events are repeatable, the authors propose that data on all events be analyzed instead of first events only. Four versions of the Huber-White robust standard error are considered for statistical inference. Simulation studies are used to examine the performance of the proposed method. In a variety of scenarios simulated, the method provides an unbiased estimate for the IRD, and the empirical coverage proportion of the 95% confidence interval is very close to the nominal level. The method is illustrated with data from a vaccine trial carried out in the Gambia in 2001-2004.

SUBMITTER: Xu Y

PROVIDER: S-EPMC2917058 | biostudies-literature | 2010 Aug

REPOSITORIES: biostudies-literature

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A simple approach to the estimation of incidence rate difference.

Xu Ying Y Cheung Y B YB Lam K F KF Tan S H SH Milligan Paul P

American journal of epidemiology 20100706 3

The incidence rate difference (IRD) is a parameter of interest in many medical studies. For example, in vaccine studies, it is interpreted as the vaccine-attributable reduction in disease incidence. This is an important parameter, because it shows the public health impact of an intervention. The IRD is difficult to estimate for various reasons, especially when there are quantitative covariates or the duration of follow-up is variable. In this paper, the authors propose an approach based on weigh ...[more]

PMID: 20606039

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Project description:HIV/AIDS is an ongoing global pandemic, with an estimated 39 million infected worldwide. Early detection is anticipated to help improve outcomes and prevent further infections. Point-of-care diagnostics make HIV/AIDS diagnoses available both earlier and to a broader population. Wide-spread and automated HIV risk estimation can offer objective guidance. This supports providers in making an informed decision when considering patients with high HIV risk for HIV testing or pre-exposure prophylaxis (PrEP). We propose a novel machine learning method that allows providers to use the data from a patient's previous stays at the clinic to estimate their HIV risk. All features available in the clinical data are considered, making the set of features objective and independent of expert opinions. The proposed method builds on association rules that are derived from the data. The incidence rate ratio (IRR) is determined for each rule. Given a new patient, the mean IRR of all applicable rules is used to estimate their HIV risk. The method was tested and validated on the publicly available clinical database MIMIC-IV, which consists of around 525,000 hospital stays that included a stay at the intensive care unit or emergency department. We evaluated the method using the area under the receiver operating characteristic curve (AUC). The best performance with an AUC of 0.88 was achieved with a model consisting of 53 rules. A threshold value of 0.66 leads to a sensitivity of 98% and a specificity of 53%. The rules were grouped into drug abuse, psychological illnesses (e.g., PTSD), previously known associations (e.g., pulmonary diseases), and new associations (e.g., certain diagnostic procedures). In conclusion, we propose a novel HIV risk estimation method that builds on existing clinical data. It incorporates a wide range of features, leading to a model that is independent of expert opinions. It supports providers in making informed decisions in the point-of-care diagnostics process by estimating a patient's HIV risk.

Dataset Information

A simple approach to the estimation of incidence rate difference.

Publications

A simple approach to the estimation of incidence rate difference.

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