Lasofoxifene in postmenopausal women with osteoporosis.
N Engl J Med. 2010 Feb 25;362(8):686-96
Authors: Cummings SR, Ensrud K, Delmas PD, LaCroix AZ, Vukicevic S, Reid DM, Goldstein S, Sriram U, Lee A, Thompson J, Armstrong RA, Thompson DD, Powles T, Zanchetta J, Kendler D, Neven P, Eastell R,
BACKGROUND: The effects of lasofoxifene on the risk of fractures, breast cancer, and cardiovascular disease are uncertain. METHODS: In this randomized trial, we assigned 8556 women who were between the ages of 59 and 80 years and had a bone mineral density T score of -2.5 or less at the femoral neck or spine to receive once-daily lasofoxifene (at a dose of either 0.25 mg or 0.5 mg) or placebo for 5 years. Primary end points were vertebral fractures, estrogen receptor (ER)-positive breast cancer, and nonvertebral fractures; secondary end points included major coronary heart disease events and stroke. RESULTS: Lasofoxifene at a dose of 0.5 mg per day, as compared with placebo, was associated with reduced risks of vertebral fracture (13.1 cases vs. 22.4 cases per 1000 person-years; hazard ratio, 0.58; 95% confidence interval [CI], 0.47 to 0.70), nonvertebral fracture (18.7 vs. 24.5 cases per 1000 person-years; hazard ratio, 0.76; 95% CI, 0.64 to 0.91), ER-positive breast cancer (0.3 vs. 1.7 cases per 1000 person-years; hazard ratio, 0.19; 95% CI, 0.07 to 0.56), coronary heart disease events (5.1 vs. 7.5 cases per 1000 person-years; hazard ratio, 0.68; 95% CI, 0.50 to 0.93), and stroke (2.5 vs. 3.9 cases per 1000 person-years; hazard ratio, 0.64; 95% CI, 0.41 to 0.99). Lasofoxifene at a dose of 0.25 mg per day, as compared with placebo, was associated with reduced risks of vertebral fracture (16.0 vs. 22.4 cases per 1000 person-years; hazard ratio, 0.69; 95% CI, 0.57 to 0.83) and stroke (2.4 vs. 3.9 cases per 1000 person-years; hazard ratio, 0.61; 95% CI, 0.39 to 0.96) Both the lower and higher doses, as compared with placebo, were associated with an increase in venous thromboembolic events (3.8 and 2.9 cases vs. 1.4 cases per 1000 person-years; hazard ratios, 2.67 [95% CI, 1.55 to 4.58] and 2.06 [95% CI, 1.17 to 3.60], respectively). Endometrial cancer occurred in three women in the placebo group, two women in the lower-dose lasofoxifene group, and two women in the higher-dose lasofoxifene group. Rates of death per 1000 person-years were 5.1 in the placebo group, 7.0 in the lower-dose lasofoxifene group, and 5.7 in the higher-dose lasofoxifene group. CONCLUSIONS: In postmenopausal women with osteoporosis, lasofoxifene at a dose of 0.5 mg per day was associated with reduced risks of nonvertebral and vertebral fractures, ER-positive breast cancer, coronary heart disease, and stroke but an increased risk of venous thromboembolic events. (ClinicalTrials.gov number, NCT00141323.)
PMID: 20181970 [PubMed - indexed for MEDLINE]
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Lasofoxifene in postmenopausal women with osteoporosis.
Bayesian methods for meta-analysis of causal relationships estimated using genetic instrumental variables.
Stat Med. 2010 Mar 8;
Authors: Burgess S, Thompson SG,
Genetic markers can be used as instrumental variables, in an analogous way to randomization in a clinical trial, to estimate the causal relationship between a phenotype and an outcome variable. Our purpose is to extend the existing methods for such Mendelian randomization studies to the context of multiple genetic markers measured in multiple studies, based on the analysis of individual participant data. First, for a single genetic marker in one study, we show that the usual ratio of coefficients approach can be reformulated as a regression with heterogeneous error in the explanatory variable. This can be implemented using a Bayesian approach, which is next extended to include multiple genetic markers. We then propose a hierarchical model for undertaking a meta-analysis of multiple studies, in which it is not necessary that the same genetic markers are measured in each study. This provides an overall estimate of the causal relationship between the phenotype and the outcome, and an assessment of its heterogeneity across studies. As an example, we estimate the causal relationship of blood concentrations of C-reactive protein on fibrinogen levels using data from 11 studies. These methods provide a flexible framework for efficient estimation of causal relationships derived from multiple studies. Issues discussed include weak instrument bias, analysis of binary outcome data such as disease risk, missing genetic data, and the use of haplotypes. Copyright (c) 2010 John Wiley & Sons, Ltd.
PMID: 20209660 [PubMed - as supplied by publisher]
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Bayesian methods for meta-analysis of causal relationships estimated using genetic instrumental variables.
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Bayesian methods for meta-analysis of causal relationships estimated using genetic instrumental variables.
Stat Med. 2010 Mar 8;
Authors: Burgess S, Thompson SG,
Genetic markers can be used as instrumental variables, in an analogous way to randomization in a clinical trial, to estimate the causal relationship between a phenotype and an outcome variable. Our purpose is to extend the existing methods for such Mendelian randomization studies to the context of multiple genetic markers measured in multiple studies, based on the analysis of individual participant data. First, for a single genetic marker in one study, we show that the usual ratio of coefficients approach can be reformulated as a regression with heterogeneous error in the explanatory variable. This can be implemented using a Bayesian approach, which is next extended to include multiple genetic markers. We then propose a hierarchical model for undertaking a meta-analysis of multiple studies, in which it is not necessary that the same genetic markers are measured in each study. This provides an overall estimate of the causal relationship between the phenotype and the outcome, and an assessment of its heterogeneity across studies. As an example, we estimate the causal relationship of blood concentrations of C-reactive protein on fibrinogen levels using data from 11 studies. These methods provide a flexible framework for efficient estimation of causal relationships derived from multiple studies. Issues discussed include weak instrument bias, analysis of binary outcome data such as disease risk, missing genetic data, and the use of haplotypes. Copyright (c) 2010 John Wiley & Sons, Ltd.
PMID: 20209660 [PubMed - as supplied by publisher]
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Bayesian methods for meta-analysis of causal relationships estimated using genetic instrumental variables.
