dataesr/scientific-paragraphs-categorization huggingface dataset:
A review of mirrorimage studies suggested that LAI antipsychotics were superior to oral antipsychotics in preventing hospitalization  [13]. Similarly, a recent mirror-image study demonstrated that the use of LAI aripiprazole (prospective data) compared with prior oral antipsychotics (retrospective data) was associated with lower 3-month rates of inpatient psychiatric hospitalizations (2. 7 vs 27. 1%, respectively; p<0. 0001) in a community setting  [14]. In an analysis of a large Medicaid database, initiation of LAI therapy resulted in significant reductions in all-cause and mental health-related hospitalizations and significantly lower direct inpatient costs  [15]. In randomized, double-blind, controlled trials, the LAI aripiprazole once-monthly 400 mg (AOM 400) was shown to improve symptoms and delay time to relapse compared with placebo  [16].
Study design A pharmacoeconomic evaluation was performed alongside a 28-week, randomized, open-label, rater-blinded, multinational, head-to-head comparative study of AOM 400 and PP in patients with schizophrenia (NCT01795547). The study procedures have been presented in detail elsewhere  [19]. Briefly, eligible patients were men and women 18 to 60 years old with stable schizophrenia (diagnosed using Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision [DSM-IV-TR] criteria  [20] ) who needed to change existing oral antipsychotic therapy because of inadequate efficacy, poor tolerability, or lack of adherence, and who, based on the investigator's judgment, may benefit from LAI treatment. The patients were required to have ≥3 months of oral antipsychotic treatment and mild to marked illness as assessed by Clinical Global Impression-Severity (CGI-S) score of 3-5.
Assessments and statistical analyses A study-specific form, the health economic assessment (HEA) questionnaire, was used to collect health-care resource utilization data across the 28-week study, including oral conversion and LAI initiation periods; the questionnaire was administered at the final study visit. This standardized form captured consultation visits with primary care physicians and psychiatrists as well as contact with other health-care ISSN: 1740-4398 ORIGINAL RESEARCH -US cost analysis-aripiprazole once-monthly compared with paliperidone palmitate drugsincontext. com providers, such as psychologists and nurses. Data from other outpatient services, such as those provided by day-care centers or group therapy, and inpatient services associated with hospitalizations were also collected.
The prespecified pharmacoeconomic analyses were conducted using data from patients who, besides QLS, had at least one valid postbaseline assessment of the HEA questionnaire. Baseline patient characteristics were presented as frequencies and percentages for categorical variables and as means and standard deviations for continuous parameters. As was done for the primary analysis of the QUALIFY study, effectiveness outcomes (mean QLS and CGI-S score change from baseline) were analyzed using a mixed model for repeated measures with an unstructured covariance matrix including baseline score-by-visit interaction, geographic region (Europe vs North America), visit, and treatment-by-visit interactions as fixed effects. All statistical analyses were performed using SAS version 9. 4 (SAS Institute, Cary, NC, USA).
com the difference in treatment costs between AOM 400 and PP drove the difference in total direct costs. Treatment costs were estimated based on actual dosing during the study. QUALIFY was designed to reflect real-world conditions, and dosing of both LAIs could be adjusted based on tolerability and effectiveness during the study. Most patients in the AOM 400 group were treated with the 400-mg dose, and those in the PP group were treated with 156-or 234-mg doses. These dosing scenarios align closely with published dosing scenarios seen in clinical trials and real-world treatment  [16, 31, 32]. Effectiveness per QLS and CGI-S measures in the current analysis was consistent with data reported for the full QUALIFY study patient population  [19]. In the QUALIFY primary analysis, the least squares mean treatment difference in QLS total score was 4.
Conclusion Based on head-to-head results from QUALIFY, AOM 400 treatment provided greater improvement in functional outcomes at a lower cost compared with PP therapy. Considering the economic burden of schizophrenia for individuals and society, cost-effectiveness is a key component of the treatment decision process. AOM 400 is an economically dominant strategy over PP for the treatment of patients with stable schizophrenia in the United States. In many cost-effectiveness analyses, data for different treatment strategies have been collected from separate sources, with efforts made to reduce disparities between data sources  [35]. The analysis presented here used data from a randomized, head-to-head trial that controlled for variables such as patient characteristics and the measurement of effectiveness, thus allowing for direct comparison of therapeutic interventions.
Acknowledgments: Editorial assistance for preparation of the manuscript was provided by  Vandana Sharma  at  C4 MedSolutions, LLC (Yardley, PA ), a  CHC Group company , and funded by  Otsuka Pharmaceutical Development & Commercialization, Inc. and H. Lundbeck A/S. ISSN: 1740-4398 ORIGINAL RESEARCH -US cost analysis-aripiprazole once-monthly compared with paliperidone palmitate drugsincontext. com.
Funding declaration: The study was supported by  Otsuka Pharmaceutical Development & Commercialization, Inc. and H. Lundbeck A/S. Trial registration: ClinicalTrials. gov,NCT01795547 Copyright: Copyright © 2016  Sapin C, Hartry A, Kamat SA ,  Beillat M, Baker RA ,  Eramo A. Distributed  under the terms of the Creative Commons License Deed  CC BY NC ND 3. 0  which allows anyone to copy, distribute, and transmit the article provided it is properly attributed in the manner specified below. No commercial use without permission.
The reader is invited to agree with the hardly controversial proposition that 'the folks we know are not like that', and encouraged to infer that this must be why ordinary folks' choices have the apparently irrational patterns that behavioural economics and psychologists have discovered  (TS,. But this inference is not as obviously valid as it may seem. We will return to this issue later, but to lay a foundation for later arguments, we invite the reader to think about the following question. Imagine a beinglet us call him SuperReasonerwho has the intelligence of Einstein, the memory of Big Blue and the self-control of Gandhi. Imagine in addition (since this is also a part of Sunstein and Thaler's characterisation of perfect reasoning) that SuperReasoner's capacious memory contains every item of information that can be extracted from any existing publication or database.
We searched PubMed, The Cochrane Methodology Register and Google Scholar for publications from the start of 2008 to the end of 2014. No language restriction was applied, and Google Translate was used for nonfamiliar languages. The search strategy was developed iteratively (see Additional file 2). One author (LJ) decided on inclusion of publications and categorised them as "major comments" and "minor comments" (and "peripheral remarks"). A second author (AS) checked the categorisation. Two authors (LJ and AS) extracted data independently. Any disagreements were solved by discussion and by consulting a third author (DL or AH). The following information was extracted: publication year, publication type, tool version considered (i. e. 2008 or 2011) and the exact wording of the comment. Comments from the included publications were categorised according to whether they expressed "strengths," "challenges" or "suggestions" and summarised into broader themes (each addressing a similar type of topic).
Observational study of how the tool is used in systematic reviews One author (DL) identified 100 Cochrane reviews (or Cochrane review updates) from PubMed in reverse chronological order from 31. 12. 2014 until 20. 11. 2014 (see Additional file 2). The same author manually identified 100 non-Cochrane reviews from PubMed in reverse chronological order from 31. 12. 2014 until 22. 12. 2014. A second author (AS) checked the inclusion. We defined a non-Cochrane review as a self-declared systematic review with at least one included randomized clinical trial. We excluded any non-Cochrane review that was also published as a Cochrane review. Three authors (AS, DL and LJ) extracted data independently: intervention type (pharmacological or nonpharmacological); inclusion of meta-analyses; number of trials and how many trials were categorised as "high," "unclear" and "low" risk of bias; the method used for judging risk of bias (or quality) and how it was implemented; the type and frequency of both standard and non-standard domain use; the use of merging or splitting of standard domains (e.
Analysis of user patterns in systematic reviews All Cochrane reviews assessed risk of bias using the Cochrane risk of bias tool (100/100, 100 %) (Tables  3  and 4 ). Most of the non-Cochrane reviews assessed risk of bias (80/100, 80 %), with the Cochrane tool being the most frequently used (31/80, 39 %). Other tools and scales used to assess risk of bias included the Jadad Quality Assessment Scale (19/80, 24 %)  [13]  and the Physiotherapy Evidence Database (short PEDro) scale (5/80, 6 %)  [14]  (Table  4 ). The majority of Cochrane reviews included one or more meta-analyses (85/100, 85 %). According to the information reported in their methods section, most of the Cochrane reviews had planned to perform sensitivity analyses based on risk of bias (70/100, 70 %). One fifth of the Cochrane reviews reported to have performed sensitivity analyses (19/100, 19 %).
Strengths and weaknesses We are not aware of other reviews of published comments on the Cochrane risk of bias tool. Our study complements previous studies of user experience  [8]  and inter-observer variance  [9] [10] [11]. It is challenging to search for published comments as not all are indexed in standard databases. However, we focused on "major comments," which are more reliably identified. It is reasonable to assume that the threshold for publishing a comment pointing out a problem with the tool (and maybe suggesting an improvement) is lower than for publishing a comment praising the tool. Thus, we consider the qualitative summary of the expressed themes as more interesting than the quantitative distribution of the themes. The analyses of how the tool was used were based on samples of representative and contemporary Cochrane and non-Cochrane reviews, enabling both a description and comparison between the two types of reviews.
Other similar studies Based on feedback from focus groups and an online survey, Savović and colleagues concluded that users of the Cochrane tool identified positive experiences and perceptions of the tool and that revisions and associated guidance as well as improved provision of training may improve implementation  [8]. Several studies have analysed the assessment of risk of bias in systematic reviews  [10] [11] [12] [13] [14] [15]. Hartling and colleagues and Armijo-Olivo and colleagues concluded unsatisfactory agreement rates by users of the tool and suggested the need for more detailed guidance in assessing the risk of bias  [9, 15]. Comments made by the authors of all three studies are included in our study. Hopewell and colleagues  [16]  studied assessment of risk of bias in Cochrane and non-Cochrane reviews indexed in The Database of Abstracts of Reviews of Effects (DARE)  [17]  and published in 2012.
Acknowledgements LJ would like to thank  Allison E. Crank  for her assistance in editing the manuscript. The study received no funding or grant other than standard salary to the data collectors (LJ, AS and DL) provided by  The Nordic Cochrane Centre (Rigshospitalet, Copenhagen). The  National Institute supports JS for Health Research Collaboration for Leadership in Applied Health Research and Care West (NIHR CLAHRC West ). The views expressed are those of the authors and not necessarily those of the  NHS , the  NIHR  or the Department of Health.
This budget is a lump sum calculated on an annual basis from historical trends and estimated future needs. An IBP approach would require a complete transformation of the funding mechanisms and information system for hospital management and more specifically hospital pharmacies. Some regions have begun using mandatory registration of patients for certain drug indications, or centralized authorization of several high cost drugs, thus creating an environment facilitating future implementation of IBP. At present, the drugs included in such schemes are mostly cancer therapeutics and orphan drugs, but the inclusion does not depend on clear prespecified criteria. Only consistent evidence of the benefits of maintaining and analysing such huge databases despite the increased transaction costs will generate political support and trigger a broad change in policies.
The reimbursement system within the NHS in England is complex. Once an individual drug receives market authorization and, if required, a positive NICE assessment, it can be reimbursed from multiple budgets, including the primary care prescribing budget, hospital-based tariff (payment by results Á PbR), PbR-excluded drug budgets (also known as high-cost drugs, such as drugs for HIV or antifungals)  (21) , and for some oncology medicines the Cancer Drugs Fund (22). It is unlikely that a drug would have indications across all budgets, but there are many examples of drugs where reimbursement is drawn from multiple budgets depending upon the indication, treatment setting, or some other national or local parameter. This situation creates a significant barrier for the implementation of IBP. However, in oncology the Systemic Anti-Cancer Therapy (SACT) data set covers chemotherapy treatment for all solid and liquid tumours (23).
Background: Missing data are a common problem in prospective studies with a long follow-up, and the volume, pattern and reasons for missing data may be relevant when estimating the cost of illness. We aimed to evaluate the effects of different methods for dealing with missing longitudinal cost data and for costing caregiver time on total societal costs in Alzheimer's disease (AD). Methods: GERAS is an 18-month observational study of costs associated with AD. Total societal costs included patient health and social care costs, and caregiver health and informal care costs. Missing data were classified as missing completely at random (MCAR), missing at random (MAR) or missing not at random (MNAR). Simulation datasets were generated from baseline data with 10-40 % missing total cost data for each missing data mechanism.
Datasets were also simulated to reflect the missing cost data pattern at 18 months using MAR and MNAR assumptions. Naïve and multiple imputation (MI) methods were applied to each dataset and results compared with complete GERAS 18-month cost data. Opportunity and replacement cost approaches were used for caregiver time, which was costed with and without supervision included and with time for working caregivers only being costed. Results: Total costs were available for 99. 4 % of 1497 patients at baseline. For MCAR datasets, naïve methods performed as well as MI methods. For MAR, MI methods performed better than naïve methods. All imputation approaches were poor for MNAR data. For all approaches, percentage bias increased with missing data volume. For datasets reflecting 18-month patterns, a combination of imputation methods provided more accurate cost estimates (e.
Background Many studies of people with Alzheimer's disease (AD) determine disease-related costs through cross-sectional analysis or from retrospective databases. Such studies do not account for missing cost data, which can lead to bias and reduce the statistical power to detect effects  [1, 2]. Also, they often assume that costs are missing completely at random (MCAR) or missing at random (MAR) rather than missing not at random (MNAR); see Table  1  for a description of these missing data patterns  [3]. Although data can be missing for many reasons, dropout of patients with AD dementia from longitudinal studies is a known issue and may not occur completely at random. About 20 % of patients drop out of a longitudinal AD study each year  [2]  and, in the GERAS observational study  [4] , 32. 4 and 50.
6 % of patients discontinued over 18 and 36 months, respectively (data on file). Such dropout can be associated with various patient factors, including poor cognitive performance or impaired functional ability due to worsening disease status, comorbid medical illness, as well as hospitalisation, institutionalisation or death  [2, 5]. Loss of the caregiver from the study due to death, illness, increased burden or a change in caregiver will also result in cost and resource use data not being collected. The assumptions made when analysing cost data could have a considerable influence on the conclusions being made. It is, therefore, important to understand the limitations of using a single imputation method for imputing missing cost data (as explored by Oostenbrink & Maiwenn  [6] ) compared with using different imputation methods depending on the nature of the missingness.
Caregiver informal care time was recorded as time spent on basic activities of daily living (ADL, e. g. bathing, feeding), instrumental ADL (e. g. shopping, cooking) and supervision. Cost data were calculated by country from the resource use information collected for the month before the baseline visit. Monthly costs in Euros (2010 values) were estimated by applying country-specific unit costs for services and products used, with UK costs in pounds sterling being converted to Euros as reported previously  [4]. Total societal costs were calculated by combining patient health care costs (including hospitalisations, outpatient visits and medication), patient social care costs (including home care and day centre sessions), caregiver health care costs, and caregiver informal care costs (from time spent giving informal care), each of which were calculated as reported previously  [4].
Design The present analysis evaluated the performance of different imputation methods for dealing with missing cost data for patients with AD dementia through simulations using actual cost data taken from the GERAS study. As 99. 4 % of patients reported complete cost data at the baseline assessment, there was a very low level of missing data. The simulations were constructed to look at different patterns and volumes of missing total societal cost data. We have not explored imputation at the cost item level. The simulated datasets were generated using baseline GERAS data where the 'true' (actual) cost estimates were known, thus allowing the performance of each imputation method in terms of mean costs and standard errors (SEs) to be compared with known GERAS cost data. As the purpose of this analysis was to test the effectiveness of different methods for dealing with missing cost data, there is no interpretation of the cost data and we do not make any statements about the relationship between missing data and the factors used to generate the simulated datasets.
Simulation datasets A brief description of the theoretical generation of missing cost data for the simulations is given below; full details of how the simulation datasets were generated are provided in Additional file 1. The simulation datasets were derived from the baseline GERAS cost data; 1488 (99. 4 %) of the 1497 patients analysed had sufficient resource use information to calculate costs  [4]. Total societal costs were used for the simulations and patient baseline characteristics from the GERAS study were used in the creation of the simulation datasets. Algorithms were created to generate 12 different simulation datasets with three different mechanisms of missing cost data (MCAR, MAR and MNAR), each at different volumes of missing total cost data (10, 20, 30 and 40 %), the potential range of missing data from longitudinal studies of people with AD.
In addition, two further simulation datasets were generated (GERAS-1, GERAS-2), where the pattern and volume of missing data reflected the observed patient dropout rate during 18 months of followup in the GERAS study. MCAR datasets were created using a random number generator to select patients to be missing and using values that gave the required volume of missing data  (10-40 %). MAR scenarios assume that some known factors predict which data are MAR. Our simulations were based on a theoretical example of missing cost data being associated with two baseline characteristics: patient functional and cognitive status at baseline (i. e. Alzheimer's Disease Cooperative Study of Activities of Daily Living Inventory, ADCS-ADL  [26]  and MMSE scores). Thus, MAR datasets were generated by assigning missing cost data based on a specific score for these two combined variables, with the score being set to achieve the required volume of missing data.
MNAR scenarios assume that some unknown factor(s) cause the missing data. However, a factor must be used to generate the theoretical MNAR dataset for the simulations; we used baseline GERAS total cost data above a specific value (which was set based on the volume of missing data required for each dataset) to assign patients as having missing costs. In the real world, missing data comprise a mixture of different patterns of missingness, with some patient data being MCAR, and other data being MAR or MNAR. Because this was likely to be the case with the GERAS cost data, we generated two simulation datasets, GERAS-1 and GERAS-2, to more closely represent the structure of real missing data. First, we used the rate of patient discontinuation at the GERAS 18-month assessment and the reasons for study discontinuation to give both a level of missing cost data and different patterns of missing cost data for specific groups of patients.
Three different reasons for missing data were defined: (1) missing due to patient institutionalisation; (2) missing due to patient death; and (3) missing because the patient had left the study (i. e. lost to follow-up). The 18-month data from GERAS showed that 15 % of patients were institutionalised, 6 % had died, and 12 % were lost to follow-up, which gave 33 % of missing cost data. Two simulation datasets were then generated based on this missing data pattern. In dataset GERAS-1, patients assigned as being institutionalised were generated as MAR based on observed data (e. g. MMSE and ADCS-ADL scores and caregiver time), while in dataset GERAS-2, institutionalised patients were assumed to be MNAR due to unobserved data; these data were generated using total caregiver time at baseline as the unknown factor (which was not used as part of the imputation methods).