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Cost-Effectiveness Analysis - Science topic

Cost-effectiveness analysis (CEA) is a form of economic analysis that compares the relative costs and outcomes (effects) of two or more courses of action.
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Major operations of M&A's involving global companies are taking place. It is natural that arise concerns about possible undesirable byproducts, such as future price increases of products sold by entities resulting from these M&A's. Regulatory competition agencies are charged to decide in short time. Faced with this overview, these control agencies could count on new and more efficient rules to prevent abuse detrimental to competition? Which would be these rules?
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Yes, large mergers and acquisitions can reduce competition. Therefore, antimonopoly offices should function efficiently and their task is to limit the scale of mergers and acquisitions of companies reducing the level of competition on specific markets. The scale of the consolidation process of economic entities operating in specific sectors and sectors of the economy depends on the level of efficiency, impartiality and reliability of antimonopoly offices' operation.
Best regards,
Dariusz Prokopowicz
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I am comparing the different platforms for a paper. The approximate cost of constructs and vectors are dependent on the cell line and experiments, varying from hundreds to several thousands but I'm more interested in the cost of setting up the entire platform and the price of the equipment/cost per run.
I am guessing that CRISPR being the newest would be more expensive than its predecessors but I cannot be sure. It isn't available on the website of large biotech companies and I would have to send a query for it to each one for comparison. I was hoping someone here would have an idea. Any input would be greatly appreciated.
Also, if you have any disadvantages for CRISPR that would be helpful too. So far, there seems to be a lot of researchers opting for CRISPR over the other systems. I only have PAM-dependency and off-target edits as major disadvantages so I am looking for financial burdens or other limitations.
Thanks so much.
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When edit genes it is important to have the appropriate tonal atmosphere. Our lab highly recommend the following for best result.
Several decades of research has yielded the product above. We use this in our lab now whenever we attempt dna edits and we do not miss.
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Hi All,
I'm currently working on a research and a part of it is to make an economic analysis. The aim is to make a cost-effectiveness analysis but before that the PI suggested to make a cost analysis (only the cost without considering the outcome) for each of the two medications. So, I have the following parameters collected from real patients follow up (N=229), and want you please to guide me on how to make cost analysis for each of the two medications:
1- Duration of receiving the treatment till the end of follow up period
2- Number of specific lab tests during the overall treatment duration
3- Number of specific procedure during the overall treatment duration
4- Duration of hospitalization during the overall treatment duration
5- Number of boxes of medications completed during the study follow up period or before the discontinuation of the medication
I have the unit cost for each of these parameters. HOWEVER, cannot just take the mean and multiply it by the unit cost as my data are NOT normally distributed. The median seems to be misleading to use (I'm not sure). What do you think I should do in this case to make this cost analysis? I need it to be cost per patient per year. Thank youuu )))
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Dear Taleen,
With N=229 you have an easy case. You don't need to use the mean or median quantity or duration to calculate cost. Just calculate the cost for each item for every patient by multiplying the unit cost (which you have) by the quantity used or duration (as the case may be) of stay, annualized . After you have calculated the annual cost for each patient then you sum the individual cost to get the total cost of care (I'm assuming that all cost items are on an annual basis). It is from this total cost that you then calculate the cost per patient per year by dividing by 229. Hope this helps.
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Hello,
I am new to fmm. If I run a two component mixture model I will have two sets of predicted values (one for each component). I can also generate the posterior prob and the most likely latent class membership. How do I combine these two sets of values to produce single predicted values which correspond  to my dependent variable?
Thanks
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I am currently working a cost effective design a hybrid solar-pv gen-set for a rural community. I need help on finding the cost function of the system and a code sample could also be of help.
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Are Big Data database technologies already used to improve fundamental and technical analysis for capital companies whose securities issued by these companies are traded on the stock exchange market?
Fundamental and technical analysis are basic research methods of verification of available economic and financial data, market data and developments on the stock exchange market, whose primary function is to provide the necessary information for the purpose of making decisions on investing in securities.
For many years, discussions and considerations inspired by such questions have been conducted:
- What kind of analysis of economic and other data provides better knowledge for investing in securities listed on the stock exchange?
- Which analysis, ie fundamental or technical analysis, provides better knowledge for investing in securities listed on the stock exchange?
- Thanks to which analysis, ie fundamental and technical analysis, investors achieve the best results in investing, the highest returns on investment in securities listed on the stock exchange?
- Which investment strategies are the most effective? Are the most effective investment strategies based on conducting fundamental or technical analysis and maybe on a specific combination of both types of analysis?
However, due to the development of new computerized technologies of advanced processing of large collections of information, new questions have recently appeared in the field of fundamental and technical analysis in the context of investment decisions made for financial and investment transactions carried out on capital markets.
The current technological revolution, known as Industry 4.0, is determined by the development of the following technologies of advanced information processing: Big Data database technologies, cloud computing, machine learning, Internet of Things, artificial intelligence, Business Intelligence and other advanced data mining technologies.
Some of the advanced technology information technologies typical of the current technological revolution are already used for the improvement of analytical processes conducted in various fields of knowledge. One of these areas is the issue of economic and financial analyzes, the aim of which is to diagnose the situation of a particular enterprise, financial institution, issuer of securities or another economic entity.
In connection with the above, I am asking you with the following query:
Are Big Data database technologies already used to improve fundamental and technical analysis for capital companies whose securities issued by these companies are traded on the stock exchange market?
Please reply
I invite you to the discussion
Thank you very much
Best wishes
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yes
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Dear All.
I trying to build a model and conduct cost effectiveness analysis of diagnostic test. I have sensitivity data of different diagnostic test like ultrasonography, MRI, Tomosynthesis. What is the most appropriate way to calculate probabilities from sensitivity data.
Thanks
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How would you like to define 'most cost-effective' for this? Do you have a willingness to pay in mind?
I would like to warn that if you are working with a group of women with a positive screening mammogram, then incidence and prevalence numbers for the general population are not relevant.
Are you data on sensitivity and specificity based on a population of women with positive screening mammogram? Might the different tests even be performed at the same time on the same women?
At this stage, it may be best to read up on cost-effectiveness in general.
There are books on the subject, e.g.:
Methods for the Economic Evaluation of Health Care Programmes (Oxford Medical Publications)
by Michael F. Drummond , Mark J. Sculpher , et al.
Cost-Effectiveness in Health and Medicine 2nd Edition
by Peter J. Neumann, Gillian D. Sanders, et el.
Decision Modelling for Health Economic Evaluation (Handbooks in Health Economic Evaluation) 1st Edition
by Andrew Briggs, Karl Claxton, Mark Sculpher
Decision Making in Health and Medicine: Integrating Evidence and Values
by M. G. Myriam Hunink , Milton C. Weinstein, et al.
ISPOR focuses on drugs, which works a bit differently than evaluation of tests, but they do make some good background readily available:
While old, I think that I should still recommend chapter 10 from my dissertation concerning specific aspects of breast cancer screening evaluation:
I also uploaded it to the ResearchGate web site:
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Hi everyone,
I'm trying to do a partitioned survival analysis (PartSA) to model a cohort of patients directly from survival data in the trial and evaluate cost-effectiveness of two different treatments. There are Kaplan-Meier OS and PFS curves published for one of these trials, so I have fitted both curves separately with Weibull distribution and extrapolated them to the time horizon of interest (15 years). However, the extrapolated PFS curve turned out to be higher than extrapolated OS curve, which obviously does not make sense for the analysis.
I'm sure this is happening quite a lot because the fact that correlation between time-to-event outcomes is not considered is one of the major limitations of this model. I was wondering if there any practical solutions on how to troubleshoot this. Is it possible to transform the data in any way to overcome this problem?
Many thanks,
Eugenia
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Hi Eugenia,
From visual inspection, it seems that the extrapolated survival curve has not fitted very well. It would be worth checking other parametric survival functions and evaluate their goodness of fit by AIC-BIC relative to each other and then select the best fit. Have you read NICE DSU guidance ( http://nicedsu.org.uk/wp-content/uploads/2016/03/NICE-DSU-TSD-Survival-analysis.updated-March-2013.v2.pdf )? Maybe you might not have this problem (PFS > OS) later on.
All the best,
Masnoon.
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I want to submit a paper exploring a cost-effectiveness analysis for a novel technology. The model we developed is currently being published as a subsection in a paper (with all details in the supplementary) with the manuscript's primary aim focused on the clinical aspects of the technology and there are many aspects of the cost-effectiveness analysis which are not included in that paper.
I am looking for advise on the best way I can reference the model structure to the initial paper and develop a manuscript which can aptly explain the scope and type of findings such an evaluation can report, specially when the initial paper in itself is under review and not published. Also, I feel that a health economics related journal would be better able to review and validate the model structure and methods as the model methods is an addition on the existing methodology being used and would be of interest for the readers of health economics and HTA focussed journals.
I look forward to some guidance on this matter, thank you so much in advance!
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I may have misunderstood your questions, but about: "the best way I can reference the model structure". I see only one way, therefore that is automatically the best way: refer to the submitted manuscript as 'submitted'.
However, as reviewer I would find it unacceptable if you only refer to a manuscript that I cannot read (yet) for your model structure.
BTW: You can't give a specific reference to a submitted paper, therefore I don't see how it matters how well-respected journal is. As it happens, just recently I saw a paper in an OK journal refer to a preceding paper with their model that was published in a journal on Beall's List of predatory open-access publishers. They practically repeated their model description, though.
You could include a copy of the submitted manuscript with your submission of your CEA paper. However, I would not like getting referred to another paper, find a sub-section on CEA, and get referred from there to a technical appendix.
I would suggest to include a description of your model in your CEA paper, and include a new technical appendix with full detail concerning the model version of this CEA paper. You can keep the description of your model in the main text relatively brief because you can refer to a submitted manuscript and to the appendix. I also advise to write an original description in the main text because papers do not like copying other papers, i.e. they generally require original material. I don't expect that the appendix needs to be so original but make sure that it does describe the model version of your new paper and not of the clinical paper.
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Hi everyone,
I am going to conduct a cost-effectiveness analysis, but currently I only have data regarding mean scores of SF-36. How can I estimate QALY from this data?
Thank you so much
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I am comparing the existing methods of government support for creative industries. Could you tell me, in your view, what methods of quantitative analysis are reasonable to use to estimate the effectiveness of government support measures for the development of creative industries planned for implementation? Is it appropriate to use cost-effectiveness analysis method for this purpose? Is it possible to use any other methods and what kind of literature would you recommend for study practical examples of their use?
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Noemi, thank you for your questions! They helped me to refine my evaluation plan!
I would like to compare several different measures of state support aimed at increasing the contribution of the creative industries to the city's economy: subsidizing the creation of creative clusters, providing benefits for renting housing to specialists in creative professions, grant support for private cultural and creative initiatives, subsidizing additional education for adults and children. I put emphasis on forecasting for adoption decision making for the development of the government support program. Ideally, I would like to estimate the results in terms of monetary benefits. But it is also interesting to use "growth in the number of specialists per unit of money spent" as an indicator of effectiveness.
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What we know:
So, what percentage of physicians actually order KRAS genetic testing for their colon cancer patients? To be determined.
Is it cost effective? Turns out it's average.
"Results
Screening for both KRAS and BRAF mutations compared with the base strategy (of no anti-EGFR therapy) increases expected overall survival by 0.034 years at a cost of $22 033, yielding an incremental cost-effectiveness ratio of approximately $650 000 per additional year of life. Compared with anti-EGFR therapy without screening, adding KRAS testing saves approximately $7500 per patient; adding BRAF testing saves another $1023, with little reduction in expected survival.
Conclusions
Screening for KRAS and BFAF mutation improves the cost-effectiveness of anti-EGFR therapy, but the incremental cost effectiveness ratio remains above the generally accepted threshold for acceptable cost effectiveness ratio of $100 000/quality adjusted life year."
To Consider:
(1) What drugs might be more effective against colon cancer cells bearing the KRAS mutation?
(2) What drugs might be more cost effective against colon cancer cells bearing the KRAS mutation?
(3) Relating to cost effective treatment, how often do we prescribe drugs or assign treatment plans that are expensive $$$, decrease the length of the patient's life, and decrease the patient's quality of life? How can this be prevented? (E.g., recommending surgery procedures for aged colon cancer patients). How do we incentivize treatment that is most cost effective?
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Health Economics, Cost-effectiveness analysis, Uncertainty analysis and Oncology modelling
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Thanks Rob. Your response was very helpful. Considering alternate survival distribution seems very logical here. Thank you again for sharing your knowledge with us and I hope more people will get benefit by reading this. Best Regards, Masnoon
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Autism is a disorder which effects the behavioral skills of a child for the rest of his/ her life. Trying to estimate the cost of different variables to asses the cost benefit effectiveness of a Communication centered Parent mediated intervention for Autism in South Asia. Maximum literature is from the developed parts of the world which makes it in appropriate to adopt similar tools in Low and Middle Income Countries in South Asia.
For further details kindly refer to this link for the project description.
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Thank you for the text. It was very helpful in understanding the broader context of costs involved and research practices.
I am presently developing a cost of Illness Inventory which will collect information on the monetary and time costs related with the child from the parents. I am having difficulties in capturing opportunity cost of the parent who are willing to do a job but cannot do so.
Given scarcity of data and literature on autism in India it difficult to assume such costs. It seems very speculative.
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I want to do a cost-benefit analysis for the removal of heavy metals by biosorption . Can someone give me some basic materials? Thank you very much!
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Cost benefit Analysis can be explained as a procedure for estimating all cost involved and possible profits to be derived from a business opportunity . There 6 steps: * Understand the cost of status quo. You need this to measure the relative merit of an investment against the do nothing option.
* identify cost
*identify benefits
*determine the cost savings
*create a timeline for expected costs and revenue
*evaluate non - quantifiable benefits and costs
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Any ideas and suggestions would be helpful.
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Community land trusts are one option.
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Can anyone please explain me how to determine effectiveness of any treatment over the other in cost effective analysis.
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Dear Ankit,
If you are conducting CEA using intermediate or short-term outcome(s), the ICER that you obtain may only be reflective for the short-duration of period. Although it may not be cost-effective for short term duration, if you project the outcomes for longer duration, it may be cost-effective, provided that you have set the CE threshold or WTP threshold. It would be great if you can compare it to your country's threshold or suggested threshold by WHO.
Good luck!
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I'm working on a project; a pharmacoeconomics study, I want to know the exact no of patients to take into my study, given that:
My study is a cost effectiveness analysis that, I'll do on patients.
So, I used G Power, but, the main problem is how to know the effect size?.
The previous studies were done on a large samples and only mentioning confidence interval and RR, OR.
Can anyone guide me for an easy way to calculate the effect size?
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You mentioned you found a meta-analysis study that provided the result as mean difference. That study should also have provided the pooled variance. Divide the mean difference by the square root of the variance (aka standard error). That should give you the effect size. Hope this helps.
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Cost Effectiveness Analysis using modelling and Prevalence based methods.
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I assume your question is about a health-related intervention. The CEA equation is (cost of intervention - change in medical costs - other monetized benefits)/change in non-monetized outcome. Typically the nonmonetized outcome is expressed as deaths averted, disease cases prevented or cured, DALYs prevented, or QALYs preserved. Powering with a prevalence-based method requires that you have data on the change in medical costs and in DALYs/QALYs over a time period. Often, however, (a) unless you've conducted a randomized control trial, those data are not available or the change in medical costs/DALYs between intervention and comparator is quite uncertain. (b) even with a RCT, because the standard error of mean monthly medical costs typically exceeds the mean, the trial is likely to be powered to detect changes in endpoints with much lower variance (e.g., DALYs, depression days, mean survival time) and be unable to accuately assess the change in medical costs using the prevalnce-based data on the study cohort, and (c) more importantly, medical costs from a health condition often extend over a person's lifetime. If the intervention prevents cases or reduces their lifetime severity, the medical costs and DALYs should be assessed over the person's remaining lifespan. They should be incidence-based, not prevalence-based. But no one could or would want to track a cohort of patients until they die before conducting a CEA. The alternative is to model the costs and outcomes based on the prevalence-based and incidence-based data available. For example, to build a model, with a prevalence-based survey, you might estimate the average treatment costs and DALY levels for Stage 2 breast cancer by year after first detection. Combining that information with a life table tailored to breast cancer would let you model the mean and standard error of lifetime DALY gains and medical cost changes absent the intervention that you are evaluating. The obvious downsides of modelling include: (a) it requires skill to build a good model of the changes resulting from the intervention, (b) the data needed for the ideal model often are extensive, (c) lack of needed data of high quality often forces the modeler to turn to less objective sources - either assumptions or flawed data, but at least these assumptions are explicit and the sensitivity of the results to alternate choices is readily calculatable, (d) interventionists like to see cost savings data specific to their cohort, have to be educated about why they lack the power to use that approach. Hope this helps.
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I am working on an economic evaluation of a drug to promote wound healing on diabetic foot ulcers based on retrospective data (cost-consequences). I need to make a sensitivity analysis of efficacy and cost of the intervention. To an univariate analysis how to select the range of these variables? Which other alternatives can I use for the analysis?
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If the sensitivity analysis is intended to show robustness, then I think you should use plausible ranges and not and arbitrary percentage up and down.
I would also say that a plausible range is generally wider than a standard deviation up and down.
I would start with trying to find or estimate 95% confidence intervals.
Is that what you mean by "per cent intervals"?
If so, then I assume that the critique you got about that is because there may be more uncertainty than described by the confidence interval. That only describes uncertainty due to random error in a specific analysis. They should have given you some clue why they think that there is more uncertainty and preferably also suggest a way to determine actual uncertainty. It is hard for me to tell without detailed knowledge about your input parameters and their function in your evaluation.
(I don't know what you mean by "media analysis".)
I wish you the best of success with getting your degree and I hope that you will still want to continue with this type of work after you're done!
By the way: I would also love to know a bit more about how health economics is applied in Cuba.
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I would like to get a software to plot the acceptability curve.
Thank you
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or can do it in Excel (not free as such, but most people have it)
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I intend to do a cost-effectiveness analysis of hep A in the Middle East.
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I am also interested in a free tool to perform cost-effectiveness analysis. Specifically, I would need a software to plot the acceptability curve. Does anyone know a spreadsheet software or program that I could use?
Thank you
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I had conducted study on cost effectiveness analysis of oral hypoglycemic agents in Type II DM, i calculated ACER and ICER, now i need to apply modeling techniques to select best cost effectiveness option in treatment DM. I am interested in Markov-modelling  tool in decision making. Any literature or source is required to make completion of this study. suggest me?
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Dear Narayana,
I would suggest you read a bit more on modelling in general to make sure that indeed a Markov model is appropriate for your study. The Briggs textbook on modelling is an excellent read and has some guided examples for you to follow. 
Sonnenberg's paper may also be helpful. All the best.
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Presently i am  working on cost effective analysis by using decision tree model. in this process i conducted met analysis on safety of  an intervention and results were presented as polled relative risk (RR,1.67,95%CI,1.2-1.8) when compared to placebo.
I want to convert this relative risk into probability.
Please help me how to calculate this probability from odds ratio OR relative risk
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Hi. I am almost certain that the first answer you received is incorrect.
First off, the answer is different for OR versus RR because the two are defined differently. Here is a link to an excellent ResearchGate description of the difference by Rafik Karaman that shows both formulas.
I’d add that by the method of Zhang & Yu, If P is the probability of the outcome in the unexposed group, RR = OR /((1-P) + (P*OR))
Zhang J, Yu KF. What’s the relative risk? A method of correcting the odds ratio in cohort studies of common outcomes. JAMA. 1998; 280:1690–1.
Second, the first response assumed you have log odds (the logit coefficient) rather than the OR or RR. Most statistical packages let you choose your outputs and offer OR output that has already been exponentiated. Don’t do that twice.
Third, if you look at the definitions, you will realize that both RR and OR are ratios. You cannot compute the probabilities from the OR or the RR without supplying some of the probabilities from another source. Since RR is the ratio of the probabilities of a positive outcome, treatment/control, if you know the probability absent intervention, simply multiply by the RR to get the probability with intervention. So the (presumably good) outcome in your case would be 1.67 times as likely in the treatment group as it is in the control group.
I am a bit surprised that your meta-analysis gives you a RR rather than an effect size differential. If indeed, you have an effect size there, the answer is entirely different. BESD gives a quick approximation. But BESD can be way off. For a better method, see my article:
Exact Method for Computing Absolute Percentage Change in a Dichotomous Outcome from Meta-Analytic Effect Size: Improving Impact and Cost-Outcome Estimates, T Miller, J Derzon, D Hendrie, Value in Health, 14:1, 144-151, 2011.
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Namibia is one of the countries in sub-Sahara Africa with high HIV/AIDS prevalance, HIV/AIDS consumes most of the health expenditure which is currently mainly funded by donors, and the donors are withdrawing for the government to take full financing. I am thinking of an intervention on the youths in schools, if effective to be extended to communities. The intervention is continuous screening and testing of HIV in youths, by knowing their status can empower them to take full responsibility on protection against HIV and also those diagnosed can be supported and properly managed early. A main limitation to this can be fear of stigmatization but yet creating awareness with this intervention can also help fight stigma in HIV.
i would like your ideas in how i can do a cost analysis for this intervention and its feasibility as a cost-effective study/intervention.
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The first thing you must do (if you haven't done so already) is find a recent systematic review of of the efficacy of HIV testing and counseling (HTC)  in preventing HIV and STIs in youth in sub-Saharan Africa. Ideally, the review should also assess evidence quality. If there is no quite recent review, you may need to conduct your own. After you know the relative risk of incident infection in youth receiving HTC, compared to those who don't, you will have a basis for your cost-effectiveness analysis. Without knowing this, you cannot do a cost-effectiveness analysis.
Our review (conducted in 2011 to inform WHO's 2013 adolescent HTC guidelines) found mostly very low quality, very indirect evidence that such interventions are efficacious. However, several years have passed and new, more directly-applicable research may have been done since that time.
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Where could I find quality sources, databases for evidence-based cost effectiveness analysis, or cost benefit research, for online work training programs?
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I suggest you rethink what you are searching for a little. If the effectiveness of the program is published and so is the cost, then cost effective is simply (cost per participant/effectiveness per participant). So for example, Royer Cook's Healthy Workplace program (aka Prime Life, based on a social-cognitive model of health behavior change) is embedded in a wellness website that the employer urges employees to explore. The program costs $13 per employee. It results in 2.2 percent of employees no longer binge drinking. Dividing $13 by 2.2 percent, the cost-effectiveness ratio is $591 per employee who stops binge drinking. If I used published estimates of the costs of binge drinking to society, the benefit-cost ratio is 22.4, with medical cost savings alone exceeding the program costs. The cost per QALY is less than zero. One place where you probably will find BCAs for some online training programs is the Washington State Institute for Public Policy's trove of BCAs. Some of the substance abuse prevention programs they feature probably use online training approaches. The National Registry of Evidence-based Programs and Policies (NREPP) also lists the costs and the outcomes for some online prevention programs, so you could readily compute the CE ratio. If you do a division like mine, be sure to use the absolute percentage reduction, not the percentage of users who quit (e.g., in my example that 2.2 percent of employees could represent a 30% reduction in employees who binge-drink). 
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Please give suggestion which simulator can be useful to find out area and number of solar panels, if we have initial parameters volume and temperature. We need to heat a district with population 5000 sitizens
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I think you can use TRNSYS
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I'm developing a cost-effectiveness analysis where I need to calculate time-dependent reintervention rates derived from published sources. I have multiple studies with different follow-up times and different presentations of the data (Kaplan-Meier risk estimates, cumulative probabilities...) 
How would I go about calculating a yearly probability of recurrence that could be applied consistently through out my model, and reducing the probability by a constant factor every year?
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Your best bet is to covert everything to cumulative density functions. Easy if you know the distribution (Poisson, weibull, or whatever and its parameters). The Kaplan-Meier plot is essentially a non-parametric CDF. See. e.g., this link
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I am conducting a cost-efffectiveness analysis of uterine fibroid treatments and will be using quality of life values from published literature. However, many articles only publish the raw data obtained from SF-36 questionnaires or present them in SSS mean scores. How can I change these scores into utility values that I can incorporate in my economic model?
Thanks!
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A wide range of utility scorings have been derived from SF36. If you have the raw data or means for the data items it requires, probably the best is the Sengupta SF12 scoring based on HUI2. (SF12 is a subset of SF36.) For a listing and citations to 25 scorings, see my article Quality of Life Loss of People Admitted to Burn Centers, United States, T Miller, S Bhattacharya, W Zamula, D Lezotte, K Kowalske,D Herndon, J Fauerbach, L Engrav, Quality of Life Research, 22:9, 2293-2305, 2013.  If you have SF12 or SF36 domain scores but not the raw responses, use the scoring in Lawrence et al. (2004). Another reasonable choice is the Franks et al. (2003) component scores based on MEPS. If you do not have the raw data and are working from published means, do NOT use a non-linear regression scoring that, e.g.,includes squared terms or cross-products. (I did not look back to see if  Lawrence or Franks included nonlinear terms.)
Franks P, Lubetkin E, Gold M, et al. Mapping the SF-12 to preference-based instruments convergent validity in a low-income, minority population. Med Care 2003;41:1277-1283.
Lawrence W, Fleishman J. Predicting EuroQol EQ-5D preference scores from the SF-12 health survey in a nationally representive sample. Med Decis Making 2004;24:160-169.
Sengupta N, Nichol MB, Wu J, et al. Mapping the SF-12 to the HU13 and VAS in a managed care population. Med Care 2004;42:927-937.
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regarding optimum solution, we are implementing algorithm in workflowsim,
steps to find processing cost
random allocation start;
cost+=cloudlet.getProcessingCost(); // is this way right to find processing cost.
after getting processing cost, how we allocate solution from among solutions,
which has minimum cost.
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I definitely recommend You usage of transportation problem including additional criteria like time lapse between operations in workflow or level of units queues. You can also combine transportation algorithm with any other, for example taken from queuing theory.
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I need to explore the economics of various countries in context with health related issues. I mean the money being spent for treating/preventing any diseases/disorders. If any one came across such kind of database or websites or institutes, Please let me know.
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You could try with OECD Health at a glance. here the link.
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In this case we are considering 3 goods. And we are considering only one country as a trade partner. In addition w*a is used as the determinant of the comparative advantage, being 'w' nominal wage and 'a' the unit labour requirement. The lower w*a is the better. Could someone explain me how is this possible and how could I justify? 
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I think that it is possible to have an absolute advantage in everything. However, it is not possible to have a comparative advantage in everything.
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Hello,
One of the outcomes of my cost-effectiveness analysis is admission to a nursing home prevented (at patient level 0=no, 1=yes, prevented), calculated as a proportion per group. Differences between groups are quite small, for example 0.2 with +€250 differences in cost, resulting in an ICER of €1250 for 1....?? It should be one nursing home admission prevented...but what, 1 proportion, 1 person?? 1 proportion is odd, as that equals 100%,right?  
Kind regards, Ronald
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Hello Susanne
For now I settled with an additional percent, as I am still not sure how this works for dichotomous outcomes. This means I multiplied the proportions (0.95 became 95%) etc.)
What I found on wiki regarding efficacy:
When talking in terms of efficacy vs. effectiveness, effectiveness relates to how well a treatment works in the practice of medicine, as opposed to efficacy, which measures how well treatment works in clinical trials or laboratory studies  
I am not sure if and how an equivalence trial and cost-effectiveness relate, but my guess is that the cost-effectiveness analysis are actually the same, but should be framed/interpreted differently? 
KR Ronald
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I am conducting cost -effectiveness analysis comparing different treatments. I am using the life-year gained, i.e. survival time as the primary effectiveness outcomes. Although the survival times follow a normal distribution, it is inappropriate to fit a normal distribution because negative values may randomly be generated from this distribution. I am not sure what is the best distribution for fitting this data to make it probabilistic?
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A priori there is no best distribution but let's say that there is a number of candidate distributions (exponential, Weibull, etc...) among which you have to select the one giving the best fit. The Normal distribution is certainly not among these. You can have a look at NICE guidelines for further explanation:
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what parameters can be considered that helps in designing cost function in VANETs for finding always best connected network ?
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The parameters are based on your idea and scenario such as urban or highway. Secondly, for urban environment the parameters are related to traffic such as density, street maps, vehicle direction, velocity, junctions, radio range, link quality. 
I think, you will discuss your idea first, then on the basis of your idea I will suggest parameters to you. Hope this answer help you for further process. 
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I want to compare the two technologies to see which one is more sustainable. 
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A 1MW wind turbine will have a different power output as a 1MW PV array.  A direct comparison of environmental impact of the two therefore does not give much useful information.  It would make more sense to compare the environmental impact per kWh produced of typically sized units.  There is already quite some literature about the subject that can be easily found using Google Scholar.  I would recommend to look into this literature to see how it is done.
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In any academic fora and in most institutions in India, NICE guidelines (UK) are given the status of "gold standard" especially when considering incorportation of newer medications /procedures that are expensive. NICE guidelines are considered conservative compared to many US guidelines and hence when a drug is approved by NICE, many institutes are happy to adopt it in India. I wonder if we have got it even remotely right.
Notwithstanding the various criticisms of the basis of identifying the NICE Cost-Effectiveness Threshold and how they calculated QALY (http://www.echoutcome.eu/images/Echoutcome__Leaflet_Guidelines___final.pdf) the value of 1 QALY in NICE was taken at between GBP 20000-30000 (or USD 30000-USD 45000). Though nowhere is it mentioned how this figure was arrived at (See the article "NICE’s cost effectiveness threshold How high should it be? BMJ 2007") but is is suspiciously close to their per capita income and I suspect that was the deciding factor.
So, In UK , where the per capita income is about GBP 30000-35000, the NICE says it is fine to spend GBP 30000 for a procedure to get 1 QALY. In India, where the per capita is just USD 1600, can NICE guidelines make instinctive sense. 
It is all the more relevant when newer drugs like the biological agents (eg anti TNF etc costing USD 6000-8000 per year) or various anticancer drugs or cardiac stents flood Indian markets at  astronomical prices. Many of us get taken in by various cost effectiveness studies and NICE guidelines that possibly are not relevant all in Indian context.
I guess it is time we took the Indian reality into account and made our own cost analysis yardsticks.
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I think that there are two questions at stake:
1. Does the maximum acceptable value of cost-effectiveness ratio that is applied by NICE also apply in a setting in India?
This maximum is also called 'willingness to pay' which represents the relative value of increasing health compared to other economic values. That is not necessarily close to the per capita income. Health economists can investigate what this willingness to pay appears to be but it is not up to us to determine it. I think that in a health system as in the UK, the willingness to pay is a political decision. With a more dispersed system of decision making it is less clear how to set the right willingness to pay.
2. Does the cost-effectiveness ratio that is estimated for England and Wales apply to a setting in India?
In general, costs are lower in India than in the UK. If all costs would be the same %age lower and the expected effectiveness is the same, then the cost-effectiveness ratio for India would be lower with that same %age. However, epidemiology of many diseases is quite different, expected effects of an intervention may or may not be substantially different, therefore the effectiveness of an intervention may be quite different. Furthermore, costs are not all equally different from the UK, e.g. the cost of new technology that is imported from the same manufacturer in India and the UK is probably much more similar than the cost of a local nurse or doctor.
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This applies to the analytical processes and any challenges faced in a human services program setting.
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in my area of expertise, that is quite remote from yours, cost-effectiveness has a main issue: it fits well when your target has a single dimension (like saving patients in clinical context). Its relevance however becomes more discussed when your target is multidimensional. For instance if you want to estimate the cost benefit balance of a transport project you may find that a project will rank badly on CB criteria altough it may rank well for a given effect/cost ratio (but poorly on other ratios). Cost effectivness narrows your view on a single dimensions, in many contexts this is a problem. 
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We conducted a study to estimate the economic burden of ADHD in United States using the Medical Expenditure Panel Survey (national survey by Agency of Healthcare Research and Quality). Our primary objective was to estimate the incremental cost for ADHD compared to the non-ADHD population. We used a two-part model to estimate the incremental cost for ADHD. The variable total cost is the sum of direct and indirect cost categories mentioned in the table attached here. We ran separate models to estimate incremental costs for each category. However, when we add the incremental estimates of each cost category, it does not equal to the incremental estimate of the variable "total cost". We looked for literature that might explain this anomaly but could not find any explanation. Can total cost ever be lower than the sum of individual incremental cost estimates? Did anyone come across a similar situation before. Please share your thoughts on it. I have attached the results table (Title: Cost) for your reference.
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Hi Rakesh,
I have a couple  comments based on your post:
I don't think you used the appropriate model for your estimations. A two-part model starts with estimating the probability of having any costs (>0), and the second part is modeling the costs that are >0. In your data, you are comparing those with and without ADHD, and I am assuming that those in the first part are non-ADHD, and then those in the second part are ADHD? If so, this is not the intent of the two-part model. Moreover, I would argue that there is inherent bias in who has zero costs vs those with >0 costs (regardless of their disease status). In this case, the more appropriate model is a Heckman selection model, which estimates the two parts separately. Moreover, there are model checks to see if indeed the two parts follow different underlying processes.
As for the issue of total costs being lower than the sum of the variable and fixed costs: If those are adjusted costs (ie., you estimated some model and these costs are the predicted values as opposed to the actual values), then yes, I can see how those separately estimated costs may be higher than the actual total costs.
Without any adjustment, does the sum of the various costs add up to the total cost? If not, then there is a problem with the data. Also, the MEPS data is weighted. Did you use that weight in your models?
Ariel   
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I am analyzing cohort data for cancer patients in order to estimate life-year gained that will subsequently be used  in decision tree analysis . The final outcomes in my decision tree are either death or alive .
For the treatment -pathway branch that end with death , will the life-year gained attached to the terminal node be zero or do I have to attach the life-year gained for patients until they died using the survival curve?. Moreover, regarding the patients who remained alive at the end of follow up. Do I have to attach the life-year gained estimated from the area under curve for the patients who only still alive or do I have to attach the ones estimated from the area under the curve for all the patients, considering death as the event of interest ?
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I hope to get real data,and the curve of survival analysis. We try ti use the DALY analysis for mortality and morbility analysis.
SZ Yu
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I am working on a cost effectiveness analysis linking with survival of patients with cancer. The survival part of the analysis is necessary to estimate the "number of life-years gained". To find this quantity I need to calculate the area under the survival curve. In this specific context of long term survival of cancer patient, i need to choose the parametric function of Gompertz which fit the survival in the best way. I'm working on stata software and I would to know if anyone has already done a Gompertz model in STATA, and how to calculate the area under the curve which is the integration of the Gompertz function. Thank you so much for you suggestions.
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A major difference between KM and Gompertz plus other such models is that KM deals with historical or current data while the others mentioned above also extrapolate. The latter can be interesting but are also more risky since you do not know a priori that the existing data trend will continue. So it all depends on the purpose of your exercise, do you want to present existing data or do you want to present educated guesses/guestimates?
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In a cost-effectiveness analysis, we need to estimate probability of an event at each node. The probability can be calculated by the formula, p = 1- e-(rt), where r = rate (n/N) and t= time. However, when researchers utilize network meta-analysis data to estimate the probability of an event, they need to convert odds ratio/risk ratio to probability of an event for treatment A versus treatment B. How can we derive such probability estimates ?
For example, estimation of probability of death with drug A and B:
Published Network-meta-analysis of indirect comparison:
A vs C : Odds ratio: 0.43 (0.34-1.38),
B vs C: Odds ratio: 1.2 (0.80-1.40)
A vs B: This estimates are usually derived from the indirect comparison: 0.3 (0.2-1.2)
If I am conducting a cost-effectiveness analysis of A versus B per death averted how should I calculate the probability of death for A and B from the given estimate with or without out any denominator on rates?
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Dear Amit,
OR/RR/RD are relative measures/effect sizes. You cannot calculate the incidence/risk/probability of an event without  additional data.
NMA provides usually OR estimates (even for prospective studies - statistical/convergence reasons). Here is a ugly solution.
If P is a probability of death then the odds is equal to P/(1-P), and so OR for treatment a vs. b is: OR=(Pa/(1-Pa))/(Pb/(1-Pb)), where: Pa is a probability of death in 'a' arm and Pb - 'b' arm. To use NMA results (or adjusted indirect comparison results) you will need OR and Pb.
OR will be provided by NMA and Pb you can calculate (e.g. meta-analysis of proportion of deaths in arm b in all studies included in NMA assuming that all of them have similar follow-up; data from one study i.e n/N).  
With OR and Pb you can calculate Pa from the equation for OR, i.e. Pa = OR Pb / (1-Pb + OR Pb).
Difference in Pa and Pb will give you what you need (the denominator of ICER, i.e. marginal cost per death averted).
Note that the solution do not consider correlation between effect sizes and assume proportional odds between treatments (OR the same in each time interval).
I hope this helps
Regards
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Kindly tell me how will we measure cost of drug..
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There is no "best" method for cost-effectiveness, because different jurisdictions consider use different methodologies acceptable. For instance, NICE (www.nice.nhs.uk) uses cost/utility analysis, ie cost per QUALY, whereas IQWIG in Germany does not. A good starting point is the ISPOR website (www.ISPOR.org), which has an overview of the pharmacoeconomic guidelines published by different countries. There is a whole body of literature devoted to this, as there is a lot of money involved.
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Is there any good literature for Cost-Effectiveness Analysis in Health (specially oncology and Rad Onc)?
Suggestion of books or articles
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Also consider that cost-effectiveness analysis methods are not very different depending on the therapeutic area (i.e. oncology) or type of medical intervention (i.e. rad onc). Therefore you may also be interested in more general advice, e.g. as generated by ISPOR groups, see the 'Economic Evaluation Methods' and 'Modeling Methods' sections at: http://www.ispor.org/workpaper/practices_index.asp
Rob
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I found that most checklists are used for  either model or trial-based economic evaluations. However, some checklists, like the one by Evers, 2005 seems to be model-based, yet classified as to be used for trial- based. Can someone please explain? Also, is it alright to use a model-based checklist for studies not model-based? 
Thanks!
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The first criteria list was published in 1974 by Alan Wiliam’s [1] and since that time there has been so much activity, you will find systematic reviews on the subject [2] and seems endless attempts to reach a consensus on the subject [3-4]. For general introduction, CRD have provided an good overview of assessing quality of economic evaluations [5]. The Drummond (BMJ) Checklist remains the most highly cited [6]
Choice of checklist depends on your specific question, the perspective you are coming from and types of economic evaluation.
If you wish to check ‘does this constitute an economic evaluation?’, the checklists mentioned already will confirm whether basic items required have been considered. For example, Drummond/BMJ or CHEC might initially screen the economic evaluation in a funding applications. Alternatively, CHEERS might be used to assess quality of reporting of pharmaco-economic evaluations.
However, if you wish to check ‘is this a reliable economic evaluation’ (so the objective is critical appraisal models), you should also note Cochrane Handbook, Chapter 15: Incorporating economics evidence, section 15.5.2 Critical appraisal of methodological quality [7]. Specifically for critical appraisals of decision-analytic models, the handbook has cited Philips checklist [8]. For modelling, of equal (or maybe greater) importance might be assessment of the quality of data inputs used for decision models; Cooper et al (2005) provide a useful framework using a hierarchical assessment to assess the quality of the data sources [9] .
References
  1. THE COST-BENEFIT APPROACH http://www.med.mcgill.ca/epidemiology/courses/EPIB654/Summer2010/CBA/CB%20approach%20Williams.pdf
  2. Walker, Damian G., et al. "Best practices for conducting economic evaluations in health care: A systematic review of quality assessment tools." (2012). http://effectivehealthcare.ahrq.gov/ehc/products/485/1302/MethodsBestPractices_FinalReport_20121023.pdf
  3. Evers, Silvia, et al. "Criteria list for assessment of methodological quality of economic evaluations: Consensus on Health Economic Criteria." International journal of technology assessment in health care 21.02 (2005): 240-245. http://journals.cambridge.org/action/displayAbstract?fromPage=online&aid=292673&fileId=S0266462305050324
  4. Husereau, Don, et al. "Consolidated health economic evaluation reporting standards (CHEERS) statement." BMC medicine 11.1 (2013): 80. http://www.biomedcentral.com/1741-7015/11/80 
  5. CRD's guidance for undertaking reviews in health care. Centre for Reviews and Dissemination, 2009. http://www.york.ac.uk/inst/crd/SysRev/!SSL!/WebHelp/5_5_QUALITY_ASSESSMENT.htm
  6. Drummond, M. F., and T. O. Jefferson. "Guidelines for authors and peer reviewers of economic submissions to the BMJ. The BMJ Economic Evaluation Working Party." BMJ: British Medical Journal 313.7052 (1996): 275. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2351717/ 
  7. Shemilt I, Mugford M, Byford S, Drummond M, Eisenstein E, Knapp M, Mallender J, McDaid D, Vale L, Walker D. Chapter 15: Incorporating economics evidence. In: Higgins JPT, Green S (editors). Cochrane Handbook for Systematic Reviews of Interventions. Version 5.0.1 [updated September 2008]. The Cochrane Collaboration, 2008. Available at: http://hiv.cochrane.org/sites/hiv.cochrane.org/files/uploads/Ch15_Economics.pdf
  8. Philips Z, Ginnelly L, Sculpher M, Claxton K, Golder S, Riemsma R, et al.Review of guidelines for good practice in decision-analytic modelling in health technology assessment. Health Technol Assess 2004;8(36). http://www.journalslibrary.nihr.ac.uk/hta/volume-8/issue-36 , p.89-93
  9. Cooper, Nicola, et al. "Use of evidence in decision models: an appraisal of health technology assessments in the UK since 1997." Journal of health services research & policy 10.4 (2005): 245-250. http://hsr.sagepub.com/content/10/4/245.long
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The study objective was to see if intervention improved adherence in a sample of patients, using claims data. Adherence was calculated using proportion of days covered (pdc).
For N=101, pdc_pre is the baseline pdc value for each patient, range 0-1, mean=0.74 , the outcome variable is adherent or not (post intervention pdc>0.8 vs not). Additional variables in the model were age, gender, type of insurance, CMS risk score. The c-statistic of the logistic model is 0.75, and the convergence criterion was satisfied. The adjusted OR for pre_pdc is 18 (95% CI, <.0001 to >999.99). Also, every patient has a pdc_pre value which is non-missing and >0. What is the reason for such a wide CI for this variable?
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Firstly, see what CI (e.g. 95% CI) means? You have used a sample randomly chosen from the study population. This is because you are not able to include all member of the study population in you research. Therefore, all estimates obtained for the study sample needs to be extrapolated to the study population. Your point estimate was OR; and the 95% CI describes the range of values within which you can be 95% sure that the true effect (estimate of the effect in the study population) lies. In other word, if the study was repeated, then 95% of the intervals would contain the true effect. Indeed, the wider the CI, the more the uncertainty of the true effect.  
The width of the CI depends to a large extent on the sample size as well as on the variability (e.g. the standard deviation) in the outcome measurements (in your study the pre_pdc) .
Larger sample size and smaller standard deviation (SD) tend to give more precise estimates of effects (and hence have narrower confidence intervals) than smaller studies.
Therefore, you should recheck the appropriateness of your sample size and also look for possible outlier data in your results. You also need to double-check whether any mistakes were made when transferring your data into the software worksheet.
It is highly likely that you had a very wide range of values for the pre_pdc data (resulting in a very large SD) or there was a very big mistake in data entry!
Good luck!
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In a simple decision-tree, consider the phenomena of only two branches with two events 1) bleeding and 2) death. In a clinical phenomena, suppose after taking drug A or B, individuals are at risk of bleeding or death.
I am interested in making first-node of bleeding (yes/no) and second node as death (yes/no). I find a clinical trial assessing rates of bleeding and death of A and B. The calculated probability would depend on the drug A and drug B for bleeding and death are independent probability. However, in a decision-tree, when I am calculating probability, it is joint-probability,
i.e. probability of death among the individuals with bleeding given drug A
probability of death among the individuals without bleeding given drug A
However, I am using the probability of death from drug A based on literature ignoring the joint-probability of death with bleeding. Is this approach wrong? What would be ideal way to deal with it?
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I think you can ignore the joint probability of death and bleeding; since death is an independent probability; whereas the probability of bleeding is dependent on the drug.
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GRADE remains skeptical about the trustworthiness of cost effectiveness analysis in the development of recommendations, but other researchers have opposing opinions. 
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Louis Thank you
but I did not see any link?
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Some studies examine individual preferences, these type of works have specific methodology. For example the questionnaire they use might not necessarily need to be validated. The type of biases are pretty different from usual quantitative studies. I think quality appraisal tools which are designed for quantitative or cross-sectional studies, may not cover all essential aspects.
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Chris I really appreciate your answer
 
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see above
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It sounds like you have a randomized open label/not blinded trial. That would mean that you may not only have drop out but also also cross-over where patients in group A start taking drug B and vice versa.
I think that I agree with David Schmidt but I am not certain where he says: "as if they took the medication exactly as instructed"
An intent to treat analysis follows patients split by trial arm according to the originally intended treatment at time of enrollment = according to randomization.
You measure the health effects as the differences between the trial arms and you also measure the cost impacts as differences between the trial arms.
If a patients stops taking the drug to which he or she was randomized, the the trial results reflect less health effects than when the patient would have taken the drug but because the cost of taking the drug is also not incurred, nor the costs associated with the decreased health effects so that you get a proper health economic analysis that follows the statistically valid analysis of the trial results.
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cutoff......... cost ........ Walking distance
175 ......... 16.8 ....... 17606.2
200 ........ 13.2 ..... 19278.1
225.......... 10.8 ....... 21858.2
250 ....... 8.4 ...... 24698.2
275 ...... 7.2 ....... 27714.9
300 ...... 7.2 ...... 28462.1
325 ....... 6 ....... 33006.4
350 ....... 6........ 31805.0
The cutoff column represents the alternatives while the next column (Cost) is the cost associated with each alternative. The "walking distance" is the benefit of each alternative. The lower the "walking distance" the better benefit to the public. However Government tend to favor lower cost. Which alternative is the best? Or how can one arrive at an optimum solution i.e. considering tradeoff between cost and benefits
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hello,
Identify governments willingness to pay threshold (in your case, cutoff), or a range willingness to pay threshold. If there threshold, you can use the cutoffs to create a range.
Then you will have to create Net benefits at each cut off.
Net benefits = cutoff*walking distance - cost
create a net benefit variables for all the cutoffs.
Then estimate incremental net benefit using simple regression technique.
Using the p-value from each regression, you can generate the cost-effectiveness acceptability curve.
You can use this curve to determine the "probability of cost-effectiveness at each cutoff point".
This will help you determine the probability of cost-effectiveness of the intervention at a given cutoff.
You can use this as a form of sensitivity analysis, if you dont know "how much the government is willing to pay".
Good luck!
Thanks,
Ashish
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I am particularly interested to know what types of POCT have been in use at A&E, and whether there is any evidence on benefits and disadvantages.
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i believe my question has been misunderstood. By POCT, I mean conducting tests near the site of patient care. These may be simple medical blood tests which can be performed at the bedside rather than doing them in the main Lab at the hospital. Other examples include simple imaging for example using portable ultrasound device rather than using medical imaging department.
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I would like to know if there are any guidelines for this kind of studies.
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The National Institute of Health and Clinical Excellence in the UK have started to assess diagnostics using CEA. They have started putting togethers methods at http://www.nice.org.uk/media/164/3C/DAPInterimMethodsStatementProgramme.pdf
You can also see some examples have some of the assessments that have been done on the website.
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I am interested in knowing how cost-effectiveness results are used in different systems, particularly those with no explicit threshold.
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Eldon –
Think of C/E analysis as the marginal cost side of the marginal cost/marginal benefit analysis of full-blown cost-benefit analysis. Both in the abstract and in application, this concept is used in estimating the optimal (rational) level of pursuit of a particular objective – given current knowledge, technology, and values – depicted in the attached Figure 1 for “the optimal level of pollution” as an example. Then think of each C/E ratio as a “point” on that curve (or as a rectangle underneath the curve, where a large number of opportunities exist for applying that particular technology). On the marginal cost curve (marginal abatement cost curve or MACC in the attached), there is typically a point at which currently-available technologies and related resources exhaust the lower-cost options for achieving the stated objective (valued on the vertical axis in Figures 1 & 2, if you were doing full benefit-cost analysis). Beyond that point, the costs turn up sharply. The point at which it turns sharply upwards is called the “knee” of the marginal cost function by some and the “elbow” of that function by others (marked in Figure 2). Repeated applications of C/E analysis to various technologies for achieving a particular objective allows experienced analysts and scholars in that field to develop a sense of the current shape of the overall marginal cost function and, thus, the current state of the technology in those applications. I will leave it to the experts in each application to discuss the applications of that information for policy purposes in that sector, but I can tell you in the abstract that it can be extremely valuable information, particularly in early stages of policy-making in the sector.
Cheers,
Bill Ward
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Cost effectiveness analysis needs you evaluate cost first and probably more difficult, effectiveness of research? In complex situation you have to be simple, I just consider researchers’ production evaluated in number of publication in a major database. I work in life sciences domain, so my reference is PubMed.
In France, the national bureau of statistic INSEE produces an amount of spending in R&D each year at nation level and region by region. In 2008, each publication costs € 619 842 for the whole country. This figure has no sense in absolute value, namely because the gross national spending in R&D has a perimeter much larger than the PubMed domain. But the comparison of the figure from region to region should be valid.
I observe big differences between regions.
Paris and Lyon area are closed to the national mean, which is not surprising since around 60% of publications are localized here. But for Languedoc Roussillon (Montpellier, 4.7% of France ‘Publications) the cost is 799 691 €, 34 % over the mean and the other Mediterranean shore region, Provence, Alpes Cote d’Azur (Marseille and Nice area 8.4% of France ‘Publications ) is 561 550 €, -6% from the national mean.
I suspect a flaw, is someone has an idea?
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Many thanks for your stimulating interaction.
I am not statistician but just a pharmacist trains 40 years ago! I am not certain to understand all the issues in your comment. However, I understand that the natural variability of the data makes the difference non significant.
So I went back to raw data. I tabulate for the various regions the cost per publication year by year from 1999-2009, (the ten last years where the information is available).
As you can see the general trend is decreasing cost. Obviously, that is consequence of the famous “publish or perish”. With the same amount of money, they publish more since it is so important!
I send you In the attached file the data.
What your suggestion for a smart analysis?
Again, many thanks for your precious comments.
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In both experimental and quasi-experimental studies, sometimes it becomes known to the researcher that one or more participants have been compromised with regard to the study goals. This "contamination" can occur when a member of the control/comparison group is exposed to factors (eg, receives treatment) that are similar to the experimental group. It can also occur when members of the experimental group unintentionally receive a different or additional type of treatment than the original study design intends. Does removal or censoring of a limited number of participants impact the reliability of the study results?
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Removal of "protocol deviation" subjects from analysis reduces the statistical power of the research study for the control or comparison group but I don't know why this would compromise the internal validity of the study. If such "protocol deviation" events become more than simple isolated occurrences (e.g., a cluster at a single treatment center) then it does raise questions regarding the capability of the investigator(s) at that institution to participate and comply with the requirements of the research study and the potential of other (hidden) "protocol deviations" that might alter the results of the study findings.