Comparative venous thromboembolic safety of oral and transdermal postmenopausal hormone therapies among women Veterans.

OBJECTIVE: Hormone therapy (HT) is used by menopausal women to treat vasomotor symptoms. Venous thromboembolism (VTE) is an important risk of HT use, and more knowledge on the comparative safety of different estrogenic compounds is useful for women who use HT for these symptoms. The objective was to compare the risk of VTE among users of oral conjugated equine estrogen (CEE), oral estradiol (E2), and transdermal E2, in a cohort of women veterans. METHODS: This retrospective cohort study included all women veterans aged 40 to 89 years, using CEE or E2, without prior VTE, between 2003 and 2011. All incident VTE events were adjudicated. Time-to-event analyses using a time-varying HT exposure evaluated the relative VTE risk between estrogen subtypes, with adjustment for age, race, and body mass index, with stratification for prevalent versus incident use of HT. RESULTS: Among 51,571 users of HT (74.5% CEE, 12.6% oral, and 12.9% transdermal E2 at cohort entry), with a mean age of 54.0 years, the incidence of VTE was 1.9/1,000 person-years. Compared with CEE use, in the multivariable regression model, there was no difference in the risk of incident VTE associated with oral E2 use (hazard ratio 0.96, 95% CI 0.64-1.46) or with transdermal E2 use (hazard ratio 0.95, 95% CI 0.60-1.49). Results were unchanged when restricting to incident users of HT. CONCLUSIONS: Among women veterans, the risk of VTE was similar in users of oral CEE, oral E2, and transdermal E2. These findings do not confirm the previously observed greater safety of transdermal and oral E2 over CEE.

High amplification levels of MDM2 and CDK4 correlate with poor outcome in patients with dedifferentiated liposarcoma: A cytogenomic microarray analysis of 47 cases.

Dedifferentiated liposarcoma (DDLS) is characterized at the molecular level by amplification of genes within 12q13-15 including MDM2 and CDK4. However, other than FNCLCC grade, prognostic markers are limited. We aim to identify molecular prognostic markers for DDLS to help risk stratify patients. To this end, we studied 49 cases of DDLS in our institutional archives and performed cytogenomic microarray analysis on 47 cases. Gene copy numbers for 12 loci were evaluated and correlated with outcome data retrieved from our institutional electronic medical records. Using cut point analysis and comparison of Kaplan-Meier survival curves by log rank tests, high amplification levels of MDM2 (>38 copies) and CDK4 (>30 copies) correlated with decreased disease free survival (DFS) (P = .0168 and 0.0169 respectively) and disease specific survival (DSS) (P = .0082 and 0.0140 respectively). Additionally, MDM2 and CDK4 showed evidence of a synergistic effect so that each additional copy of one enhances the effect on prognosis of each additional copy of the other for decreased DFS (P = .0227, 0.1% hazard). High amplification of JUN (>16 copies) also correlated with decreased DFS (P = .0217), but not DSS. The presence of copy number alteration at 3q29 correlated with decreased DSS (P = .0192). The presence of >10 mitoses per 10 high power fields and FNCLCC grade 3 also correlated with decreased DFS (P = .0310 and 0.0254 respectively). MDM2 and CDK4 gene amplification levels, along with JUN amplification and copy alterations at 3q29, can be utilized for predicting outcome in patients with DDLS.

Estimating uncertainty in respondent-driven sampling using a tree bootstrap method.

Respondent-driven sampling (RDS) is a network-based form of chain-referral sampling used to estimate attributes of populations that are difficult to access using standard survey tools. Although it has grown quickly in popularity since its introduction, the statistical properties of RDS estimates remain elusive. In particular, the sampling variability of these estimates has been shown to be much higher than previously acknowledged, and even methods designed to account for RDS result in misleadingly narrow confidence intervals. In this paper, we introduce a tree bootstrap method for estimating uncertainty in RDS estimates based on resampling recruitment trees. We use simulations from known social networks to show that the tree bootstrap method not only outperforms existing methods but also captures the high variability of RDS, even in extreme cases with high design effects. We also apply the method to data from injecting drug users in Ukraine. Unlike other methods, the tree bootstrap depends only on the structure of the sampled recruitment trees, not on the attributes being measured on the respondents, so correlations between attributes can be estimated as well as variability. Our results suggest that it is possible to accurately assess the high level of uncertainty inherent in RDS.

Estimating Population Size Using the Network Scale Up Method.

We develop methods for estimating the size of hard-to-reach populations from data collected using network-based questions on standard surveys. Such data arise by asking respondents how many people they know in a specific group (e.g. people named Michael, intravenous drug users). The Network Scale up Method (NSUM) is a tool for producing population size estimates using these indirect measures of respondents' networks. Killworth et al. (1998a,b) proposed maximum likelihood estimators of population size for a fixed effects model in which respondents' degrees or personal network sizes are treated as fixed. We extend this by treating personal network sizes as random effects, yielding principled statements of uncertainty. This allows us to generalize the model to account for variation in people's propensity to know people in particular subgroups (barrier effects), such as their tendency to know people like themselves, as well as their lack of awareness of or reluctance to acknowledge their contacts' group memberships (transmission bias). NSUM estimates also suffer from recall bias, in which respondents tend to underestimate the number of members of larger groups that they know, and conversely for smaller groups. We propose a data-driven adjustment method to deal with this. Our methods perform well in simulation studies, generating improved estimates and calibrated uncertainty intervals, as well as in back estimates of real sample data. We apply them to data from a study of HIV/AIDS prevalence in Curitiba, Brazil. Our results show that when transmission bias is present, external information about its likely extent can greatly improve the estimates. The methods are implemented in the NSUM R package.