Analyzing left-truncated right-censored data with uncertain onset time with parametric models

Prevalent sampling is an efficient and focused approach to the study of the natural history of disease. Right-censored time-to-event data observed from prospective prevalent cohort studies are often subject to left-truncated sampling. Left-truncated samples are not randomly selected from the population of interest and have a selection bias. Extensive studies have focused on estimating the unbiased distribution given left-truncated samples. However, in many applications, the exact date of disease onset was not observed. For example, in an HIV infection study, the exact HIV infection time is not observable. However, it is known that the HIV infection date occurred between two observable dates. Meeting these challenges motivated our study. We propose parametric models to estimate the unbiased distribution of left-truncated, right-censored time-to-event data with uncertain onset times. We first consider data from a length-biased sampling, a specific case in left-truncated samplings. Then we extend the proposed method to general left-truncated sampling. With a parametric model, we construct the full likelihood, given a biased sample with unobservable onset of disease. The parameters are estimated through the maximization of the constructed likelihood by adjusting the selection bias and unobservable exact onset. Simulations are conducted to evaluate the finite sample performance of the proposed methods. We apply the proposed method to an HIV infection study, estimating the unbiased survival function and covariance coefficients. ^

Cost-effectiveness of intensified interventions to control healthcare-associated MRSA infections -- A systematic literature review

Methicillin Resistant Staphylococcus aureus healthcare-associated infections (MRSA HAIs) are a major cause of morbidity in hospitalized patients. They pose great economic burden to hospitals caring for these patients. Intensified Interventions aim to control MRSA HAIs. Cost-effectiveness of Intensified Interventions is largely unclear. We performed a review of cost-effectiveness literature on Intensified Interventions , and provide a summary of study findings, the status of economic research in the area, and information that will help decision-makers at regional level and guide future research.^ We conducted literature search using electronic database PubMed, EBSCO, and The Cochrane Library. We limited our search to English articles published after 1999. We reviewed a total of 1,356 titles, and after applying our inclusion and exclusion criteria selected seven articles for our final review. We modified the Economic Evaluation Abstraction Form provided by CDC, and used this form to abstract data from studies.^ Of the seven selected articles two were cohort studies and the remaining five were modeling studies. They were done in various countries, in different study settings, and with different variations of the Intensified Intervention . Overall, six of the seven studies reported that Intensified Interventions were dominant or at least cost-effective in their study setting. This effect persisted on sensitivity testing.^ We identified many gaps in research in this field. The cost-effectiveness research in the field is mostly composed of modeling studies. The studies do not always clearly describe the intervention. The intervention and infection costs and the sources for these costs are not always explicit or are missing. In modeling studies, there is uncertainty associated with some key model inputs, but these inputs are not always identified. The models utilized in the modeling studies are not always tested for internal consistency or validity. Studies usually test the short term cost-effectiveness of Intensified Interventions but not the long results.^ Our study limitation was the inability to adjust for differences in study settings, intervention costs, disease costs, or effectiveness measures. Our study strength is the presentation of a focused literature review of Intensified Interventions in hospital settings. Through this study we provide information that will help decision makers at regional level, help guide future research, and might change clinical care and policies. ^