No abstract

Cohort studies can be biased by unmeasured confounding. We propose a hybrid ecologic–epidemiologic design called the trend-in-trend design, which requires a strong time-trend in exposure, but is unbiased unless there are unmeasured factors affecting outcome for which there are time-trends in prevalence that are correlated with time-trends in exposure across strata with different exposure trends. Thus, the conditions under which the trend-in-trend study is biased are a subset of those under which a cohort study is biased. The trend-in-trend design first divides the study population into strata based on the cumulative probability of exposure given covariates, which effectively stratifies on time-trend in exposure, provided there is a trend. Next, a covariates-free maximum likelihood model estimates the odds ratio (OR) using data on exposure prevalence and outcome frequency within cumulative probability of exposure strata, across multiple periods. In simulations, the trend-in-trend design produced ORs with negligible bias in the presence of unmeasured confounding. In empiric applications, trend-in-trend reproduced the known positive association between rofecoxib and myocardial infarction (observed OR: 1.2, 95% confidence interval: 1.1, 1.4), and known null associations between rofecoxib and severe hypoglycemia [OR = 1.1 (0.92, 1.3)] and non-vertebral fracture [OR = 0.84 (0.64, 1.1)]. The trend-in-trend method may be useful in settings where there is a strong time-trend in exposure, such as a newly approved drug or other medical intervention.

No abstract

OBJECTIVES:To review the changes in HIV in terms of course of the disease over the past five years and its consequences, to assess whether existing economic models in HIV would need to be updated to account for these changes in case these were used for the economic evaluation of new treatments in HIV. METHODS: A systematic literature review was carried out in PubMed, using both MeSH and key words, focusing on the following areas: opportunistic infections (OIs), health consequences, costs, quality of life, adherence and compliance and efficacy of treatments. In addition, current guidelines were identified and reviewed. For treatments we focussed on maraviroc, etravirine and raltegravir. RESULTS: At total of 1787 hits were obtained from the above-mentioned strategy. Finally data on 341 articles extracted. For treatments, data from six trials on efficacy and adverse event data were extracted and used in a meta-analysis (not reported here). In terms of OIs it was clear that fewer patients suffer from these infections compared to the early 2000s. Data on costs indicated that new costs were available by CD4 cell count. There are substantial new data available on quality of life in HIV, however, with several publications providing data by CD4-cell strata. Data on health consequences showed that patients increasingly live long enough to suffer from LT health consequences such as cardiovascular disease and cancer. Guidelines indicated that treatment algorithms had changed markedly in the last five years and that comparison to OBT is no longer an acceptable comparator strategy in economic modelling. CONCLUSIONS: The management of HIV has changed substantially since 2006. Patients live longer, are healthier and suffer from "common" health consequences such a cardiovascular disease and cancer. Any health economic model in this disease area should take these aspects into consideration.