Neurosurgery and coronavirus: impact and challenges-lessons learnt from the first wave of a global pandemic.

INTRODUCTION AND OBJECTIVES: The novel severe acute respiratory syndrome coronavirus 2 (COVID-19) pandemic has had drastic effects on global healthcare with the UK amongst the countries most severely impacted. The aim of this study was to examine how COVID-19 challenged the neurosurgical delivery of care in a busy tertiary unit serving a socio-economically diverse population. METHODS: A prospective single-centre cohort study including all patients referred to the acute neurosurgical service or the subspecialty multidisciplinary teams (MDT) as well as all emergency and elective admissions during COVID-19 (18th March 2020-15th May 2020) compared to pre-COVID-19 (18th of January 2020-17th March 2020). Data on demographics, diagnosis, operation, and treatment recommendation/outcome were collected and analysed. RESULTS: Overall, there was a reduction in neurosurgical emergency referrals by 33.6% and operations by 55.6% during the course of COVID-19. There was a significant increase in the proportion of emergency operations performed during COVID-19 (75.2% of total, n=155) when compared to pre-COVID-19 (n = 198, 43.7% of total, p < 0.00001). In contrast to other published series, the 30-day perioperative mortality remained low (2.0%) with the majority of post-operative COVID-19-infected patients (n = 13) having underlying medical co-morbidities and/or suffering from post-operative complications. CONCLUSION: The capacity to safely treat patients requiring urgent or emergency neurosurgical care was maintained at all times. Strategies adopted to enable this included proactively approaching the referrers to maintain lines of communications, incorporating modern technology to run clinics and MDTs, restructuring patient pathways/facilities, and initiating the delivery of NHS care within private sector hospitals. Through this multi-modal approach we were able to minimize service disruptions, the complications, and mortality.

Modeling the impact of preplanned dose titration on delayed response.

Dose titration becomes more and more common in improving drug tolerability as well as determining individualized treatment doses, thereby maximizing the benefit to patients. Dose titration starting from a lower dose and gradually increasing to a higher dose enables improved tolerability in patients as the human body may gradually adapt to adverse gastrointestinal effects. Current statistical analyses mostly focus on the outcome at the end-of-study follow-up without considering the longitudinal impact of dose titration on the outcome. Better understanding of the dynamic effect of dose titration over time is important in early-phase clinical development as it could allow to model the longitudinal trend and predict the longer term outcome more accurately. We propose a parametric model with two empirical methods of modeling the error terms for a continuous outcome with dose titrations. Simulations show that both approaches of modeling the error terms work well. We applied this method to analyze data from a few clinical studies and achieved satisfactory results.

Extending the classification approach for comparing two active treatment arms to binary and time-to-event outcomes.

For regulatory purposes, in a trial comparing two active treatments, a hypothesis such as noninferiority or superiority must be prespecified even when there is little known about how they compare against each other or when the objective is simply to identify the best. In this article, we extend an alternative classification methodology, the classification approach of Qu et al. (Statistics in Medicine, 30:3488-3495), to compare two active treatments when outcomes are binary and time-to-event variables. This method based on estimation approach instead of hypothesis testing can be useful when little prior information is available on which treatment has better efficacy. The entire decision space is divided into eight distinct possible outcomes based on predefined lower and upper non-inferiority margins, and the conclusion will be drawn according to the location of the confidence interval for relative risk or hazard ratio (or its logarithm transformation). We demonstrate theoretically that this method controls the misclassification rate at the specified level. We also illustrate the method by simulations and using data from a Phase 3 first-line nonsmall cell lung cancer study.

Nonparametric covariate adjustment in estimating hazard ratios.

In randomized clinical trials with time-to-event outcomes, the hazard ratio is commonly used to quantify the treatment effect relative to a control. The Cox regression model is commonly used to adjust for relevant covariates to obtain more accurate estimates of the hazard ratio between treatment groups. However, it is well known that the treatment hazard ratio based on a covariate-adjusted Cox regression model is conditional on the specific covariates and differs from the unconditional hazard ratio that is an average across the population. Therefore, covariate-adjusted Cox models cannot be used when the unconditional inference is desired. In addition, the covariate-adjusted Cox model requires the relatively strong assumption of proportional hazards for each covariate. To overcome these challenges, a nonparametric randomization-based analysis of covariance method was proposed to estimate the covariate-adjusted hazard ratios for multivariate time-to-event outcomes. However, empirical evaluations of the performance (power and type I error rate) of the method have not been studied. Although the method is derived for multivariate situations, for most registration trials, the primary endpoint is a univariate outcome. Therefore, this approach is applied to univariate outcomes, and performance is evaluated through a simulation study in this paper. Stratified analysis is also investigated. As an illustration of the method, we also apply the covariate-adjusted and unadjusted analyses to an oncology trial.

To adjust or not to adjust for baseline when analyzing repeated binary responses? The case of complete data when treatment comparison at study end is of interest.

The benefits of adjusting for baseline covariates are not as straightforward with repeated binary responses as with continuous response variables. Therefore, in this study, we compared different methods for analyzing repeated binary data through simulations when the outcome at the study endpoint is of interest. Methods compared included chi-square, Fisher's exact test, covariate adjusted/unadjusted logistic regression (Adj.logit/Unadj.logit), covariate adjusted/unadjusted generalized estimating equations (Adj.GEE/Unadj.GEE), covariate adjusted/unadjusted generalized linear mixed model (Adj.GLMM/Unadj.GLMM). All these methods preserved the type I error close to the nominal level. Covariate adjusted methods improved power compared with the unadjusted methods because of the increased treatment effect estimates, especially when the correlation between the baseline and outcome was strong, even though there was an apparent increase in standard errors. Results of the Chi-squared test were identical to those for the unadjusted logistic regression. Fisher's exact test was the most conservative test regarding the type I error rate and also with the lowest power. Without missing data, there was no gain in using a repeated measures approach over a simple logistic regression at the final time point. Analysis of results from five phase III diabetes trials of the same compound was consistent with the simulation findings. Therefore, covariate adjusted analysis is recommended for repeated binary data when the study endpoint is of interest.

Non-monotone Exponential Time (NEXT) Model for the Longitudinal Trend of a Continuous Outcome in Clinical Trials.

The dose-response curve has been studied extensively for decades. However, most of these methods ignore intermediate measurements of the response variable and only use the measurement at the endpoint. In early phase trials, it is crucial to utilize all available data due to the smaller sample size. Simulation studies have shown that the longitudinal dose-response surface model provides a more precise parameter estimation compared to the traditional dose response using only data from the primary time point. However, the current longitudinal models with parametric assumptions assume the treatment effect increases monotonically over time, which may be controversial to reality. We propose a parametric non-monotone exponential time (NEXT) model, an enhanced longitudinal dose-response model with greater flexibility, capable of accommodating non-monotonic treatment effects and making predictions for longer-term efficacy. In addition, the estimator for the time to maximum treatment effect and its asymptotic distribution have been derived from NEXT. Extensive simulation studies using known data-generating models and using real clinical data showed the NEXT model outperformed the existing monotone longitudinal models.

Sensitivity in statistical evaluation of biomarkers.

In clinical research and practice, biomarkers help in understanding disease progression and are useful when monitoring patients and making treatment decisions. Correct quantification of biomarkers in predicting the clinical outcome is important for better treatment of individual patients. Despite the rich literature in statistical validation, application of these validation methods in real data is not well studied. Specifically, the question is whether the change in a biomarker or the actual assessed value of the biomarker should be used. Contrary to most published papers in which the actual value of the biomarker is used, we showed through theory, simulation, and an example that it is more appropriate to use the change or the actual value in the marker with adjustment for the baseline value in evaluating a marker.

Joint modeling of progression-free survival and overall survival by a Bayesian normal induced copula estimation model.

In cancer clinical trials, in addition to time to death (i.e., overall survival), progression-related measurements such as progression-free survival and time to progression are also commonly used to evaluate treatment efficacy. It is of scientific interest and importance to understand the correlations among these measurements. In this paper, we propose a Bayesian semi-competing risks approach to jointly model progression-related measurements and overall survival. This new model is referred to as the NICE model, which stands for the normal induced copula estimation model. Correlation among these variables can be directly derived from the joint model. In addition, when correlation exists, simulation shows that the joint model is able to borrow strength from correlated measurements, and as a consequence the NICE model improves inference on both variables. The proposed model is in a Bayesian framework that enables us to use it in various Bayesian contexts, such as Bayesian adaptive design and using posterior predictive samples to simulate future trials. We conducted simulation studies to demonstrate properties of the NICE model and applied this method to a data set from chemotherapy-naive patients with extensive-stage small-cell lung cancer.

Evaluating and utilizing probability of study success in clinical development.

BACKGROUND: Drug development has become increasingly costly, lengthy, and risky. The call for better decision making in research and development has never been stronger. Analytic tools that utilize available data can inform decision makers of the risks and benefits of various decisions, which could lead to better and more informed decisions. PURPOSE: Through some real oncology examples, we will demonstrate how using available data to analytically evaluate probability of study success (PrSS) can lead to better decisions in clinical development. METHODS: The predictive power, or average conditional power, is used to quantify the PrSS. To calculate the probability, we follow a general two-step process: (1) use Bayesian modeling and appropriate assumptions to synthesize relevant data to derive the distribution of treatment effect and (2) evaluate the PrSS analytically or via trial simulation. RESULTS: We applied the procedure to several compounds in our oncology pipeline. The analysis informed decision making where PrSS was an important factor to consider. LIMITATIONS: When modeling the treatment effect, we made certain assumptions, including how two drugs work together and exchangeable treatment effects across studies. Those assumptions are reasonable for our specific situations but may not generalize well. CONCLUSIONS: From our experience, PrSS based on available data can help decision making in drug development, particularly the Go/No-Go decision after the proof of concept trial is completed. When applicable, we recommend this evaluation be regularly done in addition to the routine data analysis for clinical trials.

Multiscale modeling with smoothed dissipative particle dynamics.

In this work, we consider two issues related to the use of Smoothed Dissipative Particle Dynamics (SDPD) as an intermediate mesoscale model in a multiscale scheme for solution of flow problems when there are local parts of a macroscopic domain that require molecular resolution. The first is to demonstrate that SDPD with different levels of resolution can accurately represent the fluid properties from the continuum scale all the way to the molecular scale. Specifically, while the thermodynamic quantities such as temperature, pressure, and average density remain scale-invariant, we demonstrate that the dynamic properties are quantitatively consistent with an all-atom Lennard-Jones reference system when the SDPD resolution approaches the atomistic scale. This supports the idea that SDPD can serve as a natural bridge between molecular and continuum descriptions. In the second part, a simple multiscale methodology is proposed within the SDPD framework that allows several levels of resolution within a single domain. Each particle is characterized by a unique physical length scale called the smoothing length, which is inversely related to the local number density and can change on-the-fly. This multiscale methodology is shown to accurately reproduce fluid properties for the simple problem of steady and transient shear flow.

A test of systematic coarse-graining of molecular dynamics simulations: Transport properties.

To what extent can a "bottom-up" mesoscale fluid model developed through systematic coarse-graining techniques recover the physical properties of a molecular scale system? In a previous paper [C.-C. Fu, P. M. Kulkarni, M. S. Shell, and L. G. Leal, J. Chem. Phys. 137, 164106 (2012)], we addressed this question for thermodynamic properties through the development of coarse-grained (CG) fluid models using modified iterative Boltzmann inversion methods that reproduce correct pair structure and pressure. In the present work we focus on the dynamic behavior. Unlike the radial distribution function and the pressure, dynamical properties such as the self-diffusion coefficient and viscosity in a CG model cannot be matched during coarse-graining by modifying the pair interaction. Instead, removed degrees of freedom require a modification of the equations of motion to simulate their implicit effects on dynamics. A simple but approximate approach is to introduce a friction coefficient, γ, and random forces for the remaining degrees of freedom, in which case γ becomes an additional parameter in the coarse-grained model that can be tuned. We consider the non-Galilean-invariant Langevin and the Galilean-invariant dissipative particle dynamics (DPD) thermostats with CG systems in which we can systematically tune the fraction φ of removed degrees of freedom. Between these two choices, only DPD allows both the viscosity and diffusivity to match a reference Lennard-Jones liquid with a single value of γ for each degree of coarse-graining φ. This friction constant is robust to the pressure correction imposed on the effective CG potential, increases approximately linearly with φ, and also depends on the interaction cutoff length, rcut, of the pair interaction potential. Importantly, we show that the diffusion constant and viscosity are constrained by a simple scaling law that leads to a specific choice of DPD friction coefficient for a given degree of coarse-graining. Moreover, we find that the pair interaction distance cutoffs used for DPD random and dissipative forces should be considered separately from that of the conservative interaction potential.

Essential team science skills for biostatisticians on collaborative research teams.

Introduction: Despite the critical role that quantitative scientists play in biomedical research, graduate programs in quantitative fields often focus on technical and methodological skills, not on collaborative and leadership skills. In this study, we evaluate the importance of team science skills among collaborative biostatisticians for the purpose of identifying training opportunities to build a skilled workforce of quantitative team scientists. Methods: Our workgroup described 16 essential skills for collaborative biostatisticians. Collaborative biostatisticians were surveyed to assess the relative importance of these skills in their current work. The importance of each skill is summarized overall and compared across career stages, highest degrees earned, and job sectors. Results: Survey respondents were 343 collaborative biostatisticians spanning career stages (early: 24.2%, mid: 33.8%, late: 42.0%) and job sectors (academia: 69.4%, industry: 22.2%, government: 4.4%, self-employed: 4.1%). All 16 skills were rated as at least somewhat important by > 89.0% of respondents. Significant heterogeneity in importance by career stage and by highest degree earned was identified for several skills. Two skills ("regulatory requirements" and "databases, data sources, and data collection tools") were more likely to be rated as absolutely essential by those working in industry (36.5%, 65.8%, respectively) than by those in academia (19.6%, 51.3%, respectively). Three additional skills were identified as important by survey respondents, for a total of 19 collaborative skills. Conclusions: We identified 19 team science skills that are important to the work of collaborative biostatisticians, laying the groundwork for enhancing graduate programs and establishing effective on-the-job training initiatives to meet workforce needs.

A test of systematic coarse-graining of molecular dynamics simulations: thermodynamic properties.

Coarse-graining (CG) techniques have recently attracted great interest for providing descriptions at a mesoscopic level of resolution that preserve fluid thermodynamic and transport behaviors with a reduced number of degrees of freedom and hence less computational effort. One fundamental question arises: how well and to what extent can a "bottom-up" developed mesoscale model recover the physical properties of a molecular scale system? To answer this question, we explore systematically the properties of a CG model that is developed to represent an intermediate mesoscale model between the atomistic and continuum scales. This CG model aims to reduce the computational cost relative to a full atomistic simulation, and we assess to what extent it is possible to preserve both the thermodynamic and transport properties of an underlying reference all-atom Lennard-Jones (LJ) system. In this paper, only the thermodynamic properties are considered in detail. The transport properties will be examined in subsequent work. To coarse-grain, we first use the iterative Boltzmann inversion (IBI) to determine a CG potential for a (1-φ)N mesoscale particle system, where φ is the degree of coarse-graining, so as to reproduce the radial distribution function (RDF) of an N atomic particle system. Even though the uniqueness theorem guarantees a one to one relationship between the RDF and an effective pairwise potential, we find that RDFs are insensitive to the long-range part of the IBI-determined potentials, which provides some significant flexibility in further matching other properties. We then propose a reformulation of IBI as a robust minimization procedure that enables simultaneous matching of the RDF and the fluid pressure. We find that this new method mainly changes the attractive tail region of the CG potentials, and it improves the isothermal compressibility relative to pure IBI. We also find that there are optimal interaction cutoff lengths for the CG system, as a function of φ, that are required to attain an adequate potential while maintaining computational speedup. To demonstrate the universality of the method, we test a range of state points for the LJ liquid as well as several LJ chain fluids.

Methods for training collaborative biostatisticians.

The emphasis on team science in clinical and translational research increases the importance of collaborative biostatisticians (CBs) in healthcare. Adequate training and development of CBs ensure appropriate conduct of robust and meaningful research and, therefore, should be considered as a high-priority focus for biostatistics groups. Comprehensive training enhances clinical and translational research by facilitating more productive and efficient collaborations. While many graduate programs in Biostatistics and Epidemiology include training in research collaboration, it is often limited in scope and duration. Therefore, additional training is often required once a CB is hired into a full-time position. This article presents a comprehensive CB training strategy that can be adapted to any collaborative biostatistics group. This strategy follows a roadmap of the biostatistics collaboration process, which is also presented. A TIE approach (Teach the necessary skills, monitor the Implementation of these skills, and Evaluate the proficiency of these skills) was developed to support the adoption of key principles. The training strategy also incorporates a "train the trainer" approach to enable CBs who have successfully completed training to train new staff or faculty.

Efficacy and safety of pemetrexed in elderly cancer patients: results of an integrated analysis.

PURPOSE: To analyze pemetrexed in elderly patients (>or=65 years) based on data collected in three randomized, phase III registration trials. METHODS: Patients who received pemetrexed as monotherapy or in combination with another drug were included in this analysis (N=764). In all studies, pemetrexed 500 mg/m(2) was administered every 21 days. Data from patients receiving pemetrexed were stratified by age +/-65 years. RESULTS: Out of the 764 patients randomized, 271 were >or=65 years of age (35.4%). Of these, 28% had non-small cell lung cancer, 41% pancreatic cancer, and 31% had malignant pleural mesothelioma that was either locally advanced or metastatic. The overall response rate of the integrated database of elderly patients was 21.4%, with complete response in three patients (1.11% in >or=65 years vs. 1.01% in <65 years), partial response in 55 (20.30% vs. 19.68%), and stable disease in 116 (42.80% vs. 43.00%). Median survival time was 8.34 months in both groups, and median time to progressive disease was 4.80 months versus 4.60. Toxicity observed in the elderly group included 70 patients (25.8% vs. 17.0%; p=0.005) with grade 4 toxicity; myelosuppression was the major toxicity, with grade 3/4 neutropenia in 33% versus 22% (p<0.05), and thrombocytopenia in 13% versus 6% (p<0.05). Febrile neutropenia occurred in 4.8% versus 4.7% of patients. Non-hematological grade 3/4 events were fatigue (10.3% vs. 9.5%) and nausea (6.3% vs. 6.5%). CONCLUSIONS: Pemetrexed produced similar treatment effects in older and younger patients, and appeared to be well tolerated in the elderly population. This analysis was limited by the pooling of different disease types and the lack of uniform treatment regimens.

Comparative effects of raloxifene and alendronate on fracture outcomes in postmenopausal women with low bone mass.

UNLABELLED: The double-blind, randomized raloxifene alendronate comparison trial was the first study designed to compare two osteoporosis therapies head-to-head for fracture risk reduction. The original protocol planned to treat 3000 postmenopausal women with alendronate 10 mg/day (ALN) or raloxifene 60 mg/day (RLX) for 5 years, and to recruit women (50-80 years old) with a femoral neck bone mineral density (BMD) T-score between -2.5 and -4.0, inclusive, no prevalent vertebral fractures, and no prior bone-active agent use. The trial was stopped early, due to difficulty in finding treatment-naïve women to meet enrollment goals within the planned timeline, resulting in insufficient power to show non-inferiority between therapies in the primary endpoint (number of women with >or=1 new osteoporotic vertebral or nonvertebral fracture). Except for vertebral fractures, fracture analyses were based upon 1412 of the 1423 women randomized (mean age of 66 years). After 312+/-254 days (mean+/-SD), 22 women in the ALN group and 20 in the RLX group had new vertebral or nonvertebral fractures. Four women in the ALN group and none in the RLX group had moderate/severe vertebral fractures, a pre-specified endpoint (P=0.04). Lumbar spine, femoral neck, and total hip BMD were increased from baseline at 2 years in each group (P<0.001), with greater increases in the ALN group (each P<0.05). Similar numbers of women in each group had >or=1 adverse event and discontinued due to an adverse event. The only adverse events with an incidence that differed between groups were colonoscopy, diarrhea, and nausea; each was more common with ALN treatment (each P<0.05). One woman in each group had a venous thromboembolic event. One case of breast cancer occurred in each group. In summary, as this trial was terminated early, there was insufficient power to compare the fracture risks between alendronate and raloxifene. Safety profiles were as expected from clinical trial and post-marketing reports. TRIAL REGISTRATION: ClinicalTrials.gov Identifier NCT00035971.

Skeletal effects of raloxifene after 8 years: results from the continuing outcomes relevant to Evista (CORE) study.

UNLABELLED: In the CORE breast cancer trial of 4011 women continuing from MORE, the incidence of nonvertebral fractures at 8 years was similar between placebo and raloxifene 60 mg/day. CORE had limitations for assessing fracture risk. In a subset of 386 women, 7 years of raloxifene treatment significantly increased lumbar spine and femoral neck BMD compared from the baseline of MORE. INTRODUCTION: The multicenter, double-blind Continuing Outcomes Relevant to Evista (CORE) trial assessed the effects of raloxifene on breast cancer for 4 additional years beyond the 4-year Multiple Outcomes of Raloxifene Evaluation (MORE) osteoporosis treatment trial. MATERIALS AND METHODS: In CORE, placebo-treated women from MORE continued with placebo (n = 1286), whereas those previously given raloxifene (60 or 120 mg/day) received raloxifene 60 mg/day (n = 2725). As a secondary endpoint, new nonvertebral fractures were analyzed as time-to-first event in 4011 postmenopausal women at 8 years. A substudy assessed lumbar spine and femoral neck BMD at 7 years, with the primary analysis based on 386 women (127 placebo, 259 raloxifene) who did not take other bone-active agents from the fourth year of MORE and who were > or =80% compliant with study medication in CORE. RESULTS: The risk of at least one new nonvertebral fracture was similar in the placebo (22.9%) and raloxifene (22.8%) groups (hazard ratio [HR], 1.00; Bonferroni-adjusted CI, 0.82, 1.21). The incidence of at least one new nonvertebral fracture at six major sites (clavicle, humerus, wrist, pelvis, hip, lower leg) was 17.5% in both groups. Posthoc Poisson analyses, which account for multiple events, showed no overall effect on nonvertebral fracture risk, and a decreased risk at six major nonvertebral sites in women with prevalent vertebral fractures (HR, 0.78; 95% CI, 0.63, 0.96). At 7 years after MORE randomization, the differences in mean lumbar spine and femoral neck BMD with raloxifene were 1.7% (p = 0.30) and 2.4% (p = 0.045), respectively, from placebo. Compared with MORE baseline, after 7 years, raloxifene treatment significantly increased lumbar spine (4.3% from baseline, 2.2% from placebo) and femoral neck BMD (1.9% from baseline, 3.0% from placebo). BMDs were significantly increased from MORE baseline at all time-points at both sites with raloxifene. CONCLUSION: Raloxifene therapy had no effect on nonvertebral fracture risk after 8 years, although CORE had limitations for fracture risk assessment. BMD increases were maintained after 7 years of raloxifene.

Vascular events in the Multiple Outcomes of Raloxifene Evaluation (MORE) trial: incidence, patient characteristics, and effect of raloxifene.

OBJECTIVE: To determine the incidence of arterial and venous thromboembolic (VTE) events, to determine the effect of raloxifene on the incidence of combined vascular (arterial and VTE) events, and to identify patient characteristics associated with these vascular events, in women participating in the MORE trial. DESIGN: In a post hoc analysis using MORE data, arterial, VTE, and combined vascular event rates were compared between participants receiving placebo (n = 2,576) and those receiving 60 mg/d of raloxifene (n = 2,557). Baseline characteristics were compared between those who did and did not experience an arterial event. The same analysis was performed for VTE events. RESULTS: Overall, during a mean follow-up time of 41 months, 178 women experienced an arterial event and 40 experienced a VTE event. In the placebo group, the incidence of arterial events exceeded VTE events by at least sevenfold. Raloxifene had no significant effect on the incidence of combined vascular events in the overall cohort (hazard ratio 0.95, 95% CI, 0.73-1.24). In a subset of women retrospectively determined to be at increased cardiovascular risk, raloxifene was associated with a lower incidence of combined vascular events (hazard ratio 0.63, 95% CI, 0.40-0.97). Baseline characteristics differed between those who did and those who did not experience an arterial event, but this was generally not the case for VTE events. CONCLUSIONS: Arterial events were more common than VTE events. The characteristics of women experiencing an arterial event differed from those experiencing a VTE event. Raloxifene had a neutral effect on the risk of combined vascular events in the overall population, and was associated with a reduced combined vascular event rate in women at increased cardiovascular risk. Additional studies are needed to confirm the effect of raloxifene on overall vascular outcomes.

Effects of raloxifene and low-dose simvastatin coadministration on plasma lipids in postmenopausal women with primary hypercholesterolemia.

Abstract Raloxifene and low-dose simvastatin can each reduce low-density lipoprotein (LDL) cholesterol without affecting high-density lipoprotein (HDL) cholesterol and triglycerides. The objective of this double-blind, 12-week study is to determine whether raloxifene and simvastatin coadministration gives added benefit beyond either monotherapy in affecting fasting lipoproteins and apolipoproteins. Ninety-five postmenopausal women with moderately elevated LDL cholesterol (mean, 146 mg/dL) were randomized to placebo, raloxifene 60 mg/d, simvastatin 10 mg/d, or raloxifene 60 mg/d coadministered with simvastatin 10 mg/d. Raloxifene, simvastatin, and coadministration therapy reduced mean LDL cholesterol by 10.5%, 23.3%, and 31.0% from baseline, respectively ( P < .003 vs baseline; P < .02 vs placebo), and mean apolipoprotein B by 10.4%, 24.2%, and 30.0% from baseline, respectively ( P < .003 vs baseline; P < .02 vs placebo). Each active treatment decreased non-HDL cholesterol compared with placebo ( P < .01). Coadministration treatment was more effective than either monotherapy in reducing LDL cholesterol ( P < .05). Coadministration treatment reduced mean apolipoprotein B ( P < .001) and non-HDL cholesterol ( P < .001) when compared with raloxifene, but was not significantly different when compared with simvastatin. Coadministration therapy increased HDL cholesterol and apolipoprotein A1 levels compared with placebo ( P < .02). No significant effect on triglycerides, very low density lipoprotein cholesterol, and lipoprotein (a) occurred with any active treatment. Raloxifene, simvastatin, and the coadministration therapy were generally well tolerated with clinical adverse effects similar to placebo. No woman had clinically significant elevated liver function tests requiring drug discontinuation. Further data on safety and lipid-lowering effects are needed before raloxifene and statin coadministration may be considered as therapeutic interventions for treating postmenopausal women to achieve National Cholesterol Education Program-Adult Treatment Panel III treatment guidelines.

Similar proportions of women lose bone mineral density with raloxifene or alendronate treatment.

The ISCD recommends that bone mineral density (BMD) be monitored in patients undergoing antiresorptive therapy to identify patients with a significant BMD loss. This analysis compares the proportions of postmenopausal women treated with raloxifene 60 mg/d (n=82) or alendronate 10 mg/d (n=83) who had significant BMD loss in a randomized, double-blind, placebo-controlled trial that assessed changes in lumbar spine and femoral neck BMD from baseline to 1 yr, measured using dual-energy X-ray absorptiometry. According to ISCD criteria, significant BMD loss was defined as greater than the least significant change, calculated as precision multiplied by 2.77 (95% confidence interval). Assuming a 1% precision at the lumbar spine, the proportions of women with a loss of BMD greater than the least significant change (3%) were similar (p>0.05) between the raloxifene (3%) and alendronate (2%) groups. Assuming 2% precision at the femoral neck, the proportions of women with a loss greater than the least significant change (6%) were similar (p>0.05) between the raloxifene (1%) and alendronate (2%) groups. In conclusion, similar proportions of women did not respond to raloxifene or alendronate therapy, as measured by changes in lumbar spine or femoral neck BMD, when precision error was taken into account.