dataesr/scientific-paragraphs-categorization huggingface dataset:
gov/show/NCT01644188) has a double-blind treatment period of 104 weeks, comparing alirocumab with ezetimibe in 660 planned patients receiving statin therapy (but no other LLTs). The primary efficacy endpoint is the difference between treatment arms in percent change in low-density lipoprotein cholesterol (LDL-C) from baseline to week 24. Both studies utilized a starting dose of alirocumab 75 mg every 2 weeks (Q2W; administered as 1 mL solution via auto-injector). Patients with LDL-C levels ≥70 mg/dL after 8 weeks of treatment were up-titrated in a blinded manner at week 12 to alirocumab 150 mg Q2W (also 1 mL auto-injector). Discussion: In conclusion, the COMBO studies will provide information on the long-term efficacy and safety of alirocumab in high-risk patients when administered in addition to maximally tolerated statin therapy, with a flexible dosing strategy which allows for individualized therapy based on the degree of LDL-C lowering needed to achieve the desired treatment response.
[20] ). Alirocumab is a fully human mAb to PCSK9 that has shown considerable promise in Phase 2 trials  [21] [22] [23]  and is currently in Phase 3 trials for the treatment of hypercholesterolemia and reduction of cardiovascular events. The COMBO I (http://clinicaltrials. gov/show/NCT0164 4175) and II (http://clinicaltrials. gov/show/NCT01644188) trials form part of the alirocumab Phase 3 ODYSSEY clinical trial program, which currently comprises 14 studies across a range of patient groups and clinical settings involving more than 23,500 planned patients, to further assess the efficacy and safety of alirocumab. The COMBO studies have been specifically designed to evaluate the long-term efficacy and safety of alirocumab as add-on therapy to stable, maximally tolerated, daily statin therapy in patients with hypercholesterolemia at high cardiovascular risk.
Study design COMBO I is a Phase 3, randomized, double-blind, placebocontrolled, parallel-group, multicenter, 52-week study being conducted at 76 sites in the USA. This study evaluates the efficacy and safety of alirocumab as add-on therapy to stable, maximally tolerated doses of daily statin, with or without other stable LLT, in a planned population of 306 high-risk patients with poorly controlled hypercholesterolemia (Figure  1A ). COMBO I began screening patients in July 2012 and completed collection of data in April 2014 (http://clinicaltrials. gov/show/NCT01644175); data analyses are ongoing. COMBO II is a Phase 3, randomized, double-blind, active-controlled, parallel-group, multinational 104-week study being conducted at 126 sites in Europe, Israel, North America, South Africa, and South Korea.
The planned population size is 660 high-risk patients with poorly controlled hypercholesterolemia on stable, maximally tolerated daily statin therapy. This study began screening patients in August 2012 and is anticipated to complete in July 2015 (http://clinicaltrials. gov/show/ NCT01644188). Unlike COMBO I, with a placebo arm, COMBO II incorporates an active-treatment arm (ezetimibe) with double-dummy design (Figure  1b ) and patients are not allowed to receive any other LLTs besides statin and their randomized treatment. The studies are being conducted in compliance with the principles laid down by the 18th World Medical Assembly (Helsinki, 1964) and all applicable amendments laid down by the World Medical Assemblies and according to the International Conference on Harmonization Guidelines for Good Clinical Practice.
Statistical analyses Sample size determination In COMBO I, a sample size of 45 patients (30 in alirocumab, 15 in placebo) was determined to have 95% power to detect a difference in mean percentage change in LDL-C of 30% with a 0. 05 two-sided significance level, assuming a common standard deviation of 25% and all 45 patients having an evaluable primary endpoint. Meanwhile, in COMBO II, a sample size of 96 patients (64 in alirocumab, 32 in ezetimibe) was determined to have 95% power to detect a difference in mean percentage change in LDL-C of 20% with a 0. 05 two-sided significance level, assuming common standard deviation of 25% and all 96 patients having an evaluable primary endpoint. However, to meet regulatory requirements across the overall ODYSSEY Program, sample sizes were increased in most of the alirocumab Phase 3 studies to assess the safety of alirocumab appropriately in the overall integrated safety database.
Primary analysis The primary efficacy analysis population will be the ITT population, comprising all randomized patients with at least one baseline calculated LDL-C value available and at least one calculated LDL-C value available at one of the planned time points from weeks 4 to 24 (regardless of treatment adherence). The percentage change in calculated LDL-C from baseline to week 24 will be analyzed using a mixed effect model with repeated measures (MMRM) approach to account for missing data  [24, 25]. All available postbaseline data at planned time points from week 4 to 52 regardless of status on-or off-treatment will be used in the MMRM for the ITT analysis, with the model used to provide least-squares means estimates and comparison between treatment arms of LDL-C reductions at week 24.
Safety analysis AEs (including adjudicated cardiovascular events), laboratory parameters, and vital signs will be reported descriptively, based on the safety population (all randomized patients who received at least one dose or partial dose of study treatment). The safety analysis will focus on the treatment-emergent AE period, defined as the time from the first double-blind dose to the last double-blind dose of the investigational product + 70 days (10 weeks). The studies are not powered to assess the impact on cardiovascular outcomes, which will be assessed in a separate, large outcomes study (http://clinicaltrials. gov/show/NCT01663402)  [26].
These studies follow a robust approach to investigate a new class of drugs with a novel mechanism of action, with efficacy and safety studies ranging from 24-104 weeks duration (rather than 12-52 weeks) to provide a greater amount of double-blind safety data for building confidence in alirocumab as a potential therapeutic option. Of note, the ODYSSEY program also includes a large cardiovascular outcomes study (http:// clinicaltrials. gov/show/NCT01663402) [26]  which will determine the long-term impact of alirocumab and lower levels of LDL-C on the occurrence of cardiovascular events in 18,000 patients after a recent (<52 weeks) acute coronary syndrome event, with a randomized treatment period of 64 months. In summary, the COMBO studies are the longest duration placebo/ezetimibe-controlled trials a PCSK9 inhibitor in high-risk patients with poorly controlled LDL-C on maximum tolerated standard of care.
Data The estimation of the model of Eq. (  8 ) is subject to two major data constraints. On the one hand, the macroeconomic variables included to measure economic fundamentals have a low frequency (quarterly or monthly) and some are available with a lag of two quarters. On the other hand, the sovereign crisis started in 2009, representing three years of crisis at the time of this analysis. Therefore, to obtain a critical number of observations, our estimation is based on an unbalanced panel of the five peripheral European countries in which the sovereign yield has been most under pressure, Greece, Ireland, Italy, Spain and Portugal, between January 2006 and September 2011. The definitions, frequency and sources of the data are presented in the appendix. Our dependent variable is an estimate of the default probability, in percentage, measured as the sovereign bond spread, which prices the default risk of a country.
It is defined as the difference between the sovereign bond yield and the risk-free rate of the same maturity. For each country in the sample, we use the long-term German yield, which is the benchmark riskfree rate for the Euro area, and the government yield of this country at the same maturity. We rely on monthly observations of Maastricht criterion bond yields provided by the Eurostat database. A key choice is the set of explanatory variables included in X t in Eq (8). We test the following variables: debt-to-GDP ratio, unemployment, unit labor cost, risk, liquidity. First, the country's credit risk is traditionally related to fiscal sustainability. We therefore include the debt-to-GDP ratio from Eurostat 8. The fiscal data are revised data. Other variables relevant in forming default expectations are those variables that may appear in the authorities' objective function.
The economic activity and the country's competitiveness are potential candidates because the deterioration of these fundamentals increases the social cost of austerity efforts and thus the benefit from defaulting. We proxy the economic activity using the unemployment rate rather than GDP to avoid colinearity issues with the debt-to-GDP ratio. The unit labor cost is included to proxy the country's competitiveness. These data are taken from Eurostat. The trade balance (a proxy for competitiveness) is excluded from the vector of determinants because of the specific behavior of Ireland, which ran a trade surplus (the variable is positive), contrary to the other countries in the sample. This variable was found to be not significant in other studies (De Grauwe, P. , Y. Ji, 2012). An issue with our macroeconomic data is that they are available only at a quarterly frequency (debt, unemployment and unit labor cost).
To transform them to monthly frequencies, we used a local quadratic with the average matched to the source data 9 In line with the literature, we include a variable of liquidity risk and a measure of international risk aversion. Our proxy for liquidity is the size of the government's bond markets. For each country in the sample, liquidity is measured as the share of total outstanding Euro-denominated long-term government securities issued in the Euro zone. Data are available on a monthly basis from the European Central Bank. Following  Borgy et al. (2010) , our measure for international risk aversion is computed as the spread between US AAA corporate bonds and US 10-year sovereign bonds. Data are available on a daily basis from Bloomberg. We compute the average of daily data to obtain monthly frequencies.
Background: Efficacy and safety of alirocumab were compared with ezetimibe in hypercholesterolemic patients at moderate cardiovascular risk not receiving statins or other lipid-lowering therapy. Methods: In a Phase 3, randomized, double-blind, double-dummy study (NCT01644474), patients (low-density lipoprotein cholesterol [LDL-C] 100-190 mg/dL, 10-year risk of fatal cardiovascular events ≥1%-b5% [systemic coronary risk estimation]) were randomized to ezetimibe 10 mg/day (n = 51) or alirocumab 75 mg subcutaneously (via 1mL autoinjector) every 2 weeks (Q2W) (n = 52), with dose up-titrated to 150 mg Q2W (also 1 mL) at week 12 if week 8 LDL-C was ≥ 70 mg/dL. Primary endpoint was mean LDL-C % change from baseline to 24 weeks, analyzed using all available data (intent-to-treat approach, ITT). Analyses using on-treatment LDL-C values were also conducted.
Methods This was a Phase 3, randomized, double-blind, active-controlled, double-dummy study (NCT01644474) conducted in eight centers in the USA, Belgium, Finland, and the Netherlands, from July 2012 to July 2013. The study was performed in accordance with the ethical principles in the Declaration of Helsinki, the International Conference on Harmonization/Good Clinical Practice and appropriate regulatory requirements. The study protocol was approved by the appropriate local independent ethics committees. Written, informed consent was received from all patients before enrollment.
a Back pain was also counted as a TEAE of special interest (musculoskeletal and connective tissue disorders NEC). b The six alirocumab patients and the one ezetimibe patient with blood glucose ≥126 mg/dL (7 mmol/L) had abnormal fasting glucose at screening or baseline (and three of the six alirocumab patients were identified as having diabetes mellitus at screening); in these patients, no pattern was observed in changes of blood glucose over time (See Supplementary Table  6 ). to complement the range of data expected to emerge from the ODYSSEY Phase 3 clinical trial program, which has been designed to further assess the efficacy and safety of alirocumab, primarily when combined with statins. The program, comprising 14 studies of more than 23,500 patients and over 2000 study centers worldwide, will also evaluate alirocumab as monotherapy in a larger statin intolerant population (ODYSSEY ALTERNATIVE; NCT01709513), as well as assessing the effects of alirocumab in addition to statin therapy in a large CV outcomes trial (ODYSSEY OUTCOMES; NCT01663402).
MATERIALS AND METHODS The initial analysis of the relationship between plasma pazopanib concentrations and increased blood pressure was conducted with data from patients with advanced solid tumours enroled in a firstin-human Phase I study  (Hurwitz et al, 2009). That dose-escalation study provided individual hypertension data over a wide range of pazopanib concentrations; patients received pazopanib doses of 50 and 100 mg three times weekly, 50-2000 mg once daily, and 300 and 400 mg twice daily. Blood pressure and pharmacokinetic data were available for 54 of the 63 enroled patients. Serial blood samples (4 ml) were collected for determination of plasma pazopanib concentrations on days 1 and 22 in the Phase I study as described previously  (Hurwitz et al, 2009). Plasma pazopanib area under the curve (AUC), maximum concentration (C max ), and trough concentrations (Ct) observed on study day 22 were included to investigate the pharmacokinetic and pharmacodynamic relationships between systemic exposure and increased blood pressure.
Ct was defined as the plasma pazopanib concentration at 24 h after the first dose on day 22 in the oncedaily and twice-daily cohorts and as the predose concentration on day 22 in the three-times-weekly cohorts. AUC from 0 to 24 h after dosing was measured in the three-times-weekly and once-daily cohorts. AUC from 0 to 12 h after dosing was measured in the twice-daily dosing cohorts. Blood pressure was measured in the Phase I study during clinic visits at baseline, on study days 8, 15, and 22, and every 3 weeks thereafter until the patient was withdrawn from the study. Patients also recorded blood pressure with a digital monitor at least twice daily at home between clinic visits, and all blood pressure data were included in the analysis. A significant increase in blood pressure was defined as a 15 mm Hg or greater increase from baseline in the mean arterial blood pressure (MAP; calculated as (2 Â diastolic pressure þ systolic pressure)/3) on at least three separate occasions, initiation or escalation of antihypertensive medications, or both.
The correlation between AUC and Ct was also investigated. Pharmacokinetics/pharmacodynamics in patients with RCC. The analysis of the relationship between plasma pazopanib concentrations and sVEGFR2, clinical efficacy, and safety in patients with RCC used data from 225 patients with locally advanced or metastatic RCC treated with pazopanib monotherapy in a single-arm Phase II trial  (Hutson et al, 2010). The primary efficacy end point was overall response rate, and PFS was a secondary end point. Response was assessed by RECIST version 1. 0  (Therasse et al, 2000)  at week 12 and every 8 weeks thereafter. Serial blood samples (2 ml) were collected at week 4 at the following times: predose (within 60 min before study drug administration; Ct) and 1-2 h, 3-4 h, and 6-8 h post dose. sVEGFR2. Plasma concentrations of sVEGFR2 were measured by enzyme-linked immunosorbent assay before initiation of treatment with pazopanib (baseline) and at 4, 8, and 12 weeks of pazopanib treatment.
Models were fit to the data with R version 2. 14. 0 using the linear models or nonlinear least squares' packages. Model fits were compared by the Akaike information criteria. Safety. Adverse events (AEs) from the Phase II study that occurred during the first 12 weeks of pazopanib treatment were used to explore concentration-effect relationships to allow sufficient time for the AEs to emerge and to minimise the confounding effects of factors that tend to increase over time on study, such as dose modifications, addition of concomitant medications, and disease progression. AEs were graded according to National Cancer Institute Common Terminology Criteria for Adverse Events (CTCAE) version 3. 0. The incidence of all AEs was summarised according to the week 4 pazopanib Ct quartiles to allow for a sufficient number of events within each group to reveal concentration-related trends in the frequency.
Statistical analyses of clinical efficacy data were conducted using SAS version 9. 2 (SAS Institute, Inc. , Cary, NC, USA). Efficacy. Disease assessments were performed at week 12 of pazopanib treatment and every 8 weeks thereafter. The response rate was defined as the percentage of patients who achieved either a confirmed complete or partial response. A best response of stable disease required stable disease for at least 12 weeks after the first dose of pazopanib. Progression-free survival was defined as the interval between the first day of treatment and the earliest date of disease progression or death due to any cause, whichever occurred first. The Phase II study originally was designed using randomized discontinuation design. All patients initially received pazopanib 800 mg once daily for 12 weeks.
RESULTS Blood pressure in patients with solid tumours. Plots of the occurrences of a significant increase in blood pressure vs AUC, C max , and Ct are displayed in Figure  1A-C , respectively, for patients with solid tumours enroled in the Phase I study. The relationship between the occurrence of a significant increase in blood pressure and Ct yielded the largest r 2 value (0. 91) and the lowest P-value (0. 0075) of the pharmacokinetic parameters. A significant relationship between AUC and Ct was observed (r 2 ¼ 0. 63, Po0. 001; Figure  1D ). Pharmacokinetics/pharmacodynamics in patients with advanced/ metastatic RCC. Predose plasma pazopanib concentrations at 4 weeks and AE data were available for 205 of the 225 patients (91%) in the Phase II study. Longitudinal tumour size measurements sufficient to assess tumour shrinkage, and response rates were available from 197 patients (88%).
ACKNOWLEDGEMENTS We thank  Jerome F Sah, PhD ,  ProEd Communications, Inc. , for his medical editorial assistance with this manuscript. Financial support for this study (Study VEG105192; clinicaltrials. govidentifier  NCT00334282 ) and medical editorial assistance was provided by  GlaxoSmithKline Pharmaceuticals, Philadelphia, Pennsylvania. This work is published under the standard license to publish agreement. After 12 months the work will become freely available and the license terms will switch to a Creative Commons Attribution-NonCommercial-Share Alike 3. 0 Unported License.
Properties of AR Confidence Sets In this section, we use simulation experiments to study various properties of AR confidence sets, including their conditional coverage. We generate artificial data from the DGP specified by (  9 ) and (10). Because this DGP uses weak instrument asymptotics, the sample size does not matter much once it exceeds a threshold size. In  Davidson and MacKinnon (2010) , we found that the performance of various test statistics for β changed very little once n exceeded 400. We therefore set n = 400 in all our experiments. For each DGP, we generated 500,000 simulated datasets. The key parameters in our experiments are a, ρ, and lk. To save space, we report results only for lk = 7, which means that the model is moderately overidentified. Results for substantially smaller or larger values of lk might look quite different, but that would primarily be because a needs to increase with lk in order to keep the strength of the instruments constant.
Setting and Data The study took place in the abdominal surgery department of the Montpellier University Hospital, a French 2 150-bed tertiary centre. All consecutive inpatient surgical procedures performed during the year 2010 in this department were included. Total hospital expenses incurred from the entry into the operating room until the exit from the post-anaesthesia care unit were considered, including those related to the preparation, cleaning and management of the operating theatres. Three categories of expenses were considered and provided by the hospital accounting department: (i) medical, nurse and administrative staff, (ii) drugs and medical equipment (including depreciation and maintenance) and (iii) overhead costs (supplies, taxes, insurance, utilities and loan interest). Prostheses and other single-use materials were not considered, as our purpose was to focus on expenses that are potentially subjected to allocation errors.
The ASA score ranges from 1 (full health) to 6 (brain-dead patient). Age, ASA score, RCI, staffing level and intervention duration were analysed as continuous variables. Then, a multivariate analysis of the cost difference was implemented using a multiple linear regression model. Only variables significantly associated with the cost difference in bivariate analysis were included in the multivariate model. All statistical tests were performed using a 5% type-one risk. Statistical analyses were performed with the statistical software SAS version 9. 2 (SAS Institute, Cary, N. C. ).
We used an original database created by the Mutualité Sociale Agricole (MSA -Agricultural Social Mutual Fund) which is an inventory of all contracts signed in the agricultural sector between 2002 and 2009. We selected a sample of individuals who signed at least one temporary agricultural contract between 2002 and 2009. We then reconstructed their career path by defining the different states occupied successively during the study period. To find the periods of employment outside the agricultural sector, we complemented the MSA data with the information from the Echantillon Inter-régimes des Cotisants (EIC -Inter-regime Sample of Contributors). To describe the transitions between different employment states, we used a discrete-time competing risks duration model. This empirical specification allows controlling the effect both of the individual characteristics and of the time dependence.
DATA We used information from various sources, with the main database being supplied by the MSA (Agricultural Social Mutual Fund). This is a social protection scheme providing cover for the entire population of salaried and non-salaried agricultural workers in France. Work contracts are counted by the means of the employer declaration forms. When someone is hired, the farm managers make a declaration to the MSA giving the type of the work contract signed, the wage and the number of hours worked for each employee. Unlike the retrospective surveys, which can suffer from the effects of the memory recall error, the database we used provides an exhaustive coverage of the scope of application. It groups together all employment contracts between 2002 and 2010 for workers contributing to the agricultural social security scheme.
As each individual is identified by a personal number that is unique in time, it was easy to create a longitudinal data file. The database also provided a fairly wide range of information on the job held when the person first became a member of the agricultural scheme (sector of activity, wage, start and end dates of activity, type of contract, etc. ). To complete the workers' trajectories by including the periods of work outside agriculture, which were thus not covered by the MSA, we used the EIC (Inter-regime Sample of Contributors). This follows a sample of contributors to different French pension schemes throughout their career. We used the last available sample, from 2009, which includes almost 240 000 individuals. 9  The EIC is updated every 4 years and at each deadline, in this case 31st December 2009; it collates all contributors aged from 23 to 75.