PharmIdeas has over 25 years of experience that can be utilized for making knowledgeable decisions about your products. Our expertise in Health Economics and Outcomes Research (HEOR) can provide informative evidence to decision makers regarding the value of your product in the marketplace. You can rely on us for any of your needs for Health Technology Assessment (HTA) for gathering Real World Evidence (RWE) based data, and generating Health Economics and Outcomes Research (HEOR) analyses results.

Our proficiency in research design, data management and statistical analyses of early and late phase clinical trials, as well as observational studies, can be tailored to any scientific or corporate goal. Our knowledge of the marketplace can be utilized for single site or single country trials and/or developed for international multi-site studies.

We have a solid grasp of the design and application of statistical methods ranging from drug development to the use of drugs in society. We have honed our expertise in the following fields:
• Outcomes Research
Health Policy and Reimbursement Support
Evidence-based Reviews and Meta-Analysis
Market Valuation
• Find out about the training sessions and materials we provide.



Our pharmacoeconomic expertise can be used for a variety of economic evaluations. These economic evaluations provide a means for comparing different treatment options with regard to their costs and their outcomes.

At PharmIdeas, we have designed and conducted all types of pharmacoeconomic analyses. We have developed and applied methodological and analytic approaches uniquely appropriate for each study.

PharmIdeas has worked with many software packages in the past such as Data TreeAge, Model Maker, Crystal Ball, MS-Excel, Vanguard and Studio, etc.

We can use retrospective analyses, design and implement prospective pharmacoeconomic evaluations piggy-backed to RCTs, and perform predictive analyses based on decision analytic modelling, including Markov models, for generating evidence of cost-effectiveness for any product, whether it is a pharmaceutical agent, a biotechnology, a medical device or other. 

The types of pharmacoeconomic analyses that we can perform include the following:


Cost Effectiveness Analysis (CEA)

In applied health economics, a cost effectiveness analysis (CEA) is used to simultaneously compare the costs and outcomes of different interventions. In a CEA, a single clinical outcome is used to measure effectiveness, such as cure or remission, or avoidance of an event, for example, hospitalization.

The incremental cost effectiveness ratio (ICER) is the pharmacoeconomic endpoint when additional costs are incurred to achieve greater clinical benefit. Although the name implies the limited application of CEAs to real-world settings, CEAs are frequently superimposed on randomized clinical trials. The use of clinical outcomes in CEAs renders results in a relevant form to clinical end-users.

However, the inability to compare interventions measured using different outcomes or across therapeutic classes may limit their usefulness to policy makers. To overcome this limitation, CEAs are often performed to yield intermediate outcomes in cost-utility analyses (CUAs) where the results are reported in cost per quality-adjusted life-year (QALY).

Example: Iskedjian M, Walker JH and Hemels M. Economic evaluation of an extended acellular pertussis vaccine programme for adolescents in Ontario, Canada. Vaccine 2004; 22 (31-32): 4215-4227 


Cost Utility Analysis (CUA)

The cost utility analysis (CUA) is similar to the cost effectiveness analysis (CEA) to the extent that both simultaneously compare the costs and outcomes of two or more interventions. In contrast to CEAs, CUAs present the outcomes in humanistic terms. Health utilities are measured and are converted to quality-adjusted life-years (QALYs) as the outcome in CUAs. This cost/QALY approach would then enable decision makers to carry out comparisons across various conditions and disease areas. As with CEAs, CUAs may be piggy-backed on randomized clinical trials or may be based on a predictive modelling approach.

Challenges to the use of CUA include validation of utility measures in certain clinical settings or through data collection form the general public, and clinical interpretation of the results. For more details on utilities, you may visit the Outcomes Research section.

Example: Iskedjian M, Walker JH, Gray T, Vicente C, Einarson TR, Gehshan A. Economic evaluation of Avonex® (Interferon beta-1a) in patients following a single demyelinating event. Multiple Sclerosis 2005; 11: 542-555 


Cost Benefit Analysis (CBA)

In economic evaluation of healthcare interventions, an alternative to cost effectiveness and cost utility analyses is a cost benefit analysis (CBA). The main characteristic of CBA consists of expressing both benefits and costs in monetary units. Thus, it allows to compute the net benefit (benefits minus costs) for each intervention in monetary units and to determine whether the benefits of an intervention exceed the costs.

CBA enables policy makers to select interventions that maximise societal well-being in a constrained resource setting. CBA not only assists decision making in the financing of healthcare interventions but also non-healthcare interventions. This is particularity helpful for decision makers on improvements of allocative efficiency across different sectors of the economy.

One of the limitation of CBA is that it requires the monetary valuation of health outcomes. Based on welfare economic theory, Willingness-To-Pay (WTP) is the most widely agreed upon method to measure the benefits in monetary terms.

In summary, CBA based on the WTP approach provides strong evidence to stakeholders on the opportunity costs of health interventions and their attractiveness compared to other healthcare as well as non-healthcare interventions.

Example: Bazarbashi S, De Vol EB, Maraiki F, Al-Jedai A, Ali AA, Aljuffali IA, Iskedjian M. Empirical Monetary Valuation of a Quality-Adjusted Life-Year in the Kingdom of Saudi Arabia: A Willingness-to-Pay Analysis. PharmacoEconomics (Open) 2020 April, Online open publication.

Example: Iskedjian M, Desjardins O, Piwko C, Bereza B, Jaszewski B, Einarson TR. Willingness to pay for a treatment for pain in multiple sclerosis. PharmacoEconomics 2009; 27 (2): 149-158 


Cost Minimization Analysis (CMA)

A cost minimization analysis (CMA) is a type of health economic analysis used to compare the cost of technologies with equivalent efficacy and safety. The technology costs are measured and compared. The fundamental assumption of the CMA is the clinical equivalence of the two interventions, and identification of differences in other aspects that may impact outcomes.

Given the burden of testing and proving the assumption of equivalence, CMAs usually have limited application. Some applications include comparisons of drugs within the same therapeutic class and the delivery of the same medication in different settings.

Example: Narine L, Hague LK, Walker JH, Vicente C, Schilz R, Desjardins O, Einarson TR, Iskedjian M. Cost-minimization analysis of treprostinil vs. epoprostenol as an alternate to oral therapy non-responders to the treatment of pulmonary arterial hypertension. Current Medical Research and Opinion 2005; 21 (12): 2007-2016 


 Analytical Designs 

The analytic designs can vary as follows:
• Retrospective analyses based on actual studies, such as a pivotal trial.

Example: Iskedjian M and Einarson TR, Cost analysis of ropinirole versus levodopa in the treatment of Parkinson’s disease. PharmacoEconomics 2003; 21 (2): 115-127
• Prospective analyses based on economic arms piggy-backed onto randomized controlled trials.
• Predictive analyses, based on decision analytic models.

Example: Iskedjian M, Walker JH, Bereza B, Einarson TR. Cost effectiveness of escitalopram in generalized anxiety disorder: a Canadian analysis. Current Medical Research and Opinion 2008; 24 (5): 1539-1548
A variety of other analyses can also be carried out to support formulary submissions for drug plan reimbursement, drug pricing structures, as well as for drug development (go/no go decision support).


Burden or Cost of Illness studies

The burden of a disease can have a psychological, humanistic, clinical or economic impact. In healthcare research, most burden of illness studies determine the clinical and/or economic impact. It is sometimes also reported as Cost of Illness analysis.

The cost of illness studies can be prevalence or incidence-based. The prevalence-based method is based on the tabulation of the costs (direct and indirect) of the disease for all prevalent cases for a specific reference-year, regardless of the time of onset of the particular disease. On the other hand, the incidence-based approach provides all the future costs associated with all cases that show disease onset in a specific base case year. It is interesting to note that mortality costs (indirect costs) are usually calculated based on the discounted value of current and future costs of premature deaths occurring in a reference base case year, which in itself is an incidence-based human capital approach.

The direct costs are defined as a resource or a service associated with the treatment and rehabilitation of a disease. The indirect costs are the associated with the economic value of absenteeism, lost productivity cost, or additional training time needed for new employees, and those related to premature death or mortality costs.

In order to determine the costs, a first step is required for measuring the extent of significant morbidity outcomes and their related impact on healthcare resource utilisation, e.g, acute myocardial infarction and related hospitalizations.

Another type of burden that is observed in numerous public health issues like cancer, multiple sclerosis, and depression is that impact on health-related quality of life. They can be measured in global terms as disability-adjusted life years (DALYs) or quality-adjusted life years (QALYs).

Example: Iskedjian et al. Pain due to multiple sclerosis: Analysis of the prevalence and economic burden in Canada. Pain Research & Management 2007;12 (4):259-65. 


 Cost Analyses – Macro and Micro 

Cost analyses can be performed in support of full pharmacoeconomic evaluations and cost dictionaries can be created for the US, Canada, Australia, the United Kingdom, France, Germany, Italy, Spain or any other major European or South American country.

Example: Iskedjian M, Walker J, Vicente C, Trope G, Buys Y, Einarson TR, Covert D. Cost of glaucoma in Canada: Analyses based on visual field and physician’s assessment. Journal of Glaucoma 2003; 12 (6): 456-462


Willingness-to-Pay Analysis 

A willingness-to-pay (WTP) study tool is generally used for valuing public goods and interventions. This tool is also used for valuing the benefits from healthcare interventions.

WTP approach has its theoretical ground in welfare economics theory. The welfare economics theory assumes that the benefit to an individual of an intervention is defined as that individual’s WTP for the intervention. WTP is defined as the maximum amount of money the subject would hypothetically pay to have access to a specific treatment, if needed. In other words, WTP relates to the subject’s preference for a specified intervention.

The WTP approach usually has two steps: A first step presenting two choices to the survey participant to choose from and a second step to measure the participant’s WTP on the selected choice. The first component can either be a simple question presented to the subject but can also be developed into a much more elaborate decision board. In the second step, in order to measure WTP for healthcare intervention, specific questionnaires using a bidding game approach (based on specific algorithms) are usually developed.

WTP allows the valuation of benefits in the same unit as costs, which is required for advising decision makers and developing cost benefit analyses (CBAs). These analyses, based on a WTP approach, provide strong pharmacoeconomic evidence for the adoption or not of a treatment.

Example: Iskedjian M, Desjardins O, Piwko C, Bereza B, Jaszewski B, Einarson TR. Willingness to pay for a treatment for pain in multiple sclerosis. PharmacoEconomics 2009; 27 (2): 149-158



Health economic modelling consists of a simulation and prediction used to extrapolate clinical research results in “real world” health outcomes and simultaneously determined costs associated with the treatment paradigm following the administration of a given product. Decision-makers use these models to evaluate if a product or a technology will be beneficial to society and cost-effective for various health care budgets.

Any health economic model is a representation of an acute event or of a chronic disease, comparing the product studied to the gold standard or often, the standard of care. Frequently, expert clinicians are approached to determine how the model will be built in terms of representation, and to determine the utilisation of which health resources should be valued. The model is developed portraying all the different probabilities of treatment and outcomes for each branch (choice of treatment). These probabilities may be pulled from many sources such as literature, epidemiological data, clinical trial data (efficacy and safety data) or meta-analyses (based on real-life data, if available, or performed for the purpose of the model itself); and inserted into the model.

Through the years at PharmIdeas, we have developed a wide variety of models including retrospective analyses based on previously performed trials, prospective analyses piggybacked onto clinical trials, and predictive models, mainly decision tree models and Markov models, and are very knowledgeable in performing probabilistic simulations such as Monte Carlo.

A decision tree model replicates various treatment algorithms for the different alternatives analyzed. It is usually built using 3 types of nodes:

• Decision nodes
• Chance nodes
• End nodes 


Markov Model

A Markov Model uses different health states of a specific disease to represent the evolution of a cohort of patients affected by the disease. Chronic diseases are often represented by Markov Model, since they have clear health stages of the progression of the disease or the disease has different degrees of severity. For example, in oncology, a Markov model can represent the progression, remission, relapse, etc. up to death. The model calculates probabilities and costs until the complete cohort has gone through the model or after a certain time horizon has been reached.

When the model is completed, internal validation takes place to ascertain the absence of errors, and that the data populating the model and the mathematical and statistical formulas are correct.

Example: Iskedjian M, Walker JH, Gray T, Vicente C, Einarson TR, Gehshan A. Economic evaluation of Avonex® (Interferon beta-1a) in patients following a single demyelinating event. Multiple Sclerosis 2005; 11: 542-555


Outcomes Research

Health outcomes such as morbidity, success rates, adverse events, mortality, costs, quality of life, patient preference, satisfaction, and patient adherence are all extensively studied, as are the effects of drugs on those outcomes.

We have experience in developing and applying: survey instruments, interviews, focus groups, analytical models, Delphi panels, chart reviews, database analyses, and combinations of approaches to examine a wide variety of patient outcomes, such as quality of life, health status, and development of questionnaires to collect preference based data.

Data can examine general health status or disease-specific symptom-based outcomes and quality of life. Outcomes can be elicited directly from the patient, from the clinician or from the general public. The following instruments have been administered, and approaches applied:
• Preference-based direct quality of life measures, such as Time Trade-Off (TTO), Standard Gamble (SG) or Visual Analogue Scale (VAS);
• Preference-based indirect quality of life measures, such as HUI-III or EQ-5D;
• Health status or symptom based measures, such as SF-36 or BS-11;
• In-person or telephonic support for data collection.
• Electronic applications such as web based or laptop based tools including our proprietary electronic quality of life tool, eQTool™.

A specific, quantitative measure of quality of life used is the health utility. Health utilities may be collected directly using timetrade off, standard gamble and, to a lesser extent, visual analog scales. Indirect measurement of utilities are obtained using instruments such as the health utility index (HUI), EQ-5D, and are sometimes extrapolated from health status outcomes questionnaires such as the study 36-item short form (SF-36). For more details on our capabilities to electronically capture utilities and other quality of life data, please visit our page dedicated to the eQTool™ .

Below are examples of publications in Outcomes Research:
Patient Reported Outcomes:

Example: Iskedjian M, De Vol E, Elshanawy M, Bazarbashi S. Elicitation of Health Utilities in Oncology in the Kingdom of Saudi Arabia. Journal of Global Oncology 2020, In Press.

Example: Iskedjian M, Bereza B, Desjardins O, Jaszewski B, Piwko C, Einarson T. Converting the scores of a clinical instrument for measuring pain to a preference based one. Value in Health 2006; 9(3): A6

Patient Reported Preferences:

Example: Khattak S, Carter G, Maturi B, Walker JH, Einarson TR, Iskedjian M. Patient preferences for nonstimulant therapy over stimulants in attention deficit hyperactivity disorder: a pharmaco-epidemiologic pilot study. Canadian Journal of Clinical Pharmacology 2003; 10 (3): 158

Clinician Reported Patient Outcomes:

Example: Walker JH, Buys Y, Trope GE, Vicente C, Einarson TR, Covert D, Iskedjian M. Association Between Corneal Thickness, Mean Intraocular Pressure, Disease Stability and Severity, and Cost of Treatment in Glaucoma. Current Medical Research and Opinion 2005; 21 (4): 489-494

Preferences from the General Public:

Example: Iskedjian M, Tinmouth A, Arnold DM, Deuson R, Isitt J, Mikhael J. Elicitation of utility scores in Canada for immune thrombocytopenia treated with romiplostim or watch and rescue. Journal of Medical Economics 2012, 15 (2): 313-331

Example: Iskedjian M, Desjardins O, Einarson TR. Development and use of Decision Boards for determination of the general public’s in Willingness to Pay Analysis. Value in Health 2008; 11(3): A298


Example: Iskedjian M, Einarson TR, MacKeigan LD, Shear N, Addis A, Mittmann N, Ilersich AL. Relationship between daily dose frequency and adherence to antihypertensive pharmacotherapy: Evidence from a meta-analysis. Clinical Therapeutics 2002; 24 (2): 302-316


Health Policy and Reimbursement Support

We provide factual data in the form of “best available evidence” to support healthcare decisions made at the policy level by government policymakers and private health plan decision-makers. The evidence can be used to develop sets of guidelines or to identify areas in need of research/attention.

Through predictive models, estimation of overall costs of healthcare and based on evidence, PharmIdeas provides the biotechnology, device and pharmaceutical industries with the best information possible to support eventual reimbursement policies related to the targeted product. PharmIdeas also produces critique reports and submissions to governmental agencies and review boards prior to submission.

PharmIdeas has, through years of experience, developed several reimbursement strategies and submissions for various health authorities. Our team applies and follows CADTH guidelines in Canada as well as other specific guidelines in countries such as Australia, the United States and the “Euro-5” countries, as well as other European countries such as Switzerland and Belgium.

We are particularly experienced in CDR submissions, but have also developed Budget Impact Analysis Models in numerous therapeutic areas and for various settings. Furthermore, we create Product Value Dossiers, including core dossiers as well as dossiers applied to specific settings and countries.

We have extensive knowledge and experience in performing Market Access Strategies, including defending submissions, either in writing reports and responses to reviewers’ comments, or performing face-to-face presentations for presenting the evidence in support of submissions.

We also keep abreast of specific pricing guidelines, such as those of the PMPRB, as well as more general reimbursement and pricing guidelines, such as the ISPOR recommendations.

Example of policy related publication: Bazarbashi S, De Vol EB, Maraiki F, Al-Jedai A, Ali AA, Aljuffali IA, Iskedjian M. Empirical Monetary Valuation of a Quality-Adjusted Life-Year in the Kingdom of Saudi Arabia: A Willingness-to-Pay Analysis. PharmacoEconomics (Open) 2020 April, Online open publication.

Budget Impact Analysis (BIA)

A BIA is a tool used to evaluate and understand the potential budgetary impact of introducing a new drug or technology on formularies of reimbursed products as benefits or partial benefits, or as products subjected to a Special Authorization Process of specific drug plans from any governmental jurisdiction, third party payer or Managed Care Organization (MCO).
This analysis consists in comparing the market sales projections of a new drug or technology, usually over 2 to 4 years, to the market sales without the new product. The difference in costs or savings constitutes the budget impact of the drug.
Depending on the perspective taken, different types of costs should be considered, based on the specific guidelines of the targeted payer.

This information is crucial to decision-makers in order for them to adopt any policy attached to a health plan which eventually will benefit the insured population.
The following key elements need to be undertaken to perform the BIA:
1) Review of the requirements and guidelines in accordance with the chosen perspective;
2) Verification of epidemiological data in order to determine the population benefiting from the new drug;
3) Selection of proper comparators and adjunct therapies;
4) Development of assumptions, with supporting evidence;
5) Forecast for each scenario, i.e., with and without the new drug;
6) Model validation and verification of the uncertainty through sensitivity analyses.
Simplicity and transparency are of the essence. Assumptions need to be accurate and supported with the strongest available evidence and the choice of comparators should be justified.
We have developed a powerful interactive tool for displaying the results of BIAs, the Budget Impact Model, using the same platform as our Pharmacoeconomic Display Model.

Example of publication in BIA: Benjamin L, Buthion V, Iskedjian M, Farah B, Rioufol C, Vidal-Trécan G. Budget impact analysis of the use of oral and intravenous anti-cancer drugs for the treatment of HER2-positive metastatic breast cancer. Journal of Medical Economics 2013, 16 (1): 96-207

Product Value Dossier

A Product Value Dossier is a document that summarizes and organizes all valuable information about a product and provides key messages on its value. This is done through a 3-step approach.

The first step consists in providing the scientific information on the target disease. This is to respond to regulatory needs for the new product indication approval, to shed light on the unmet needs of treating specific conditions, and determine the medical importance of the product for the targeted audience. Highlighting what the product will bring to respond to an unmet need would widen the listening channels of decision makers with third party payers and health technology assessment authorities.

The second step of the Product Value Dossier relates to product leverage associated with pricing and reimbursement. This will position the product in the disease treatment algorithm, and its clinical relevance will be clearly displayed. The relative effectiveness of the product can then be compared to the treatment choices. The product’s impact on morbidity, mortality, quality of life, cost-effectiveness, and its value for money are all determined during this second step. In addition, the cost impact of listing the product for this condition on a health care budget is also obtained (i.e., BIA). All relevant, additional economic information is also gathered. The obtained results will provide key messages for the real value of your drug: the product story.

Finally, the third step of the approach is the message of the product manufacturer or distributor.


Evidence-Based Reviews and Meta-Analysis 

A systematic review is a clinical research tool used to identify, evaluate and summarize the best evidence to address an epidemiological or clinical question. The shift in healthcare from experience- to evidence-based medicine has produced a vast amount of research that is difficult to leverage by clinical decision makers. The breadth of evidence in the literature includes individual studies with disparate conclusions, varying grades of methodological quality, bias, or even paucity that obscure accurate interpretation. Combining the results of comparable studies provides a more accurate and precise outcome estimate than is available from individual studies. Systematic reviews use explicit and extensive methods to search for, select, critically appraise, extract data from, and summarize the available evidence to produce a reliable and thorough summary of the evidence that may be readily adopted by clinical decision makers.

A systematic review may be quantitative or qualitative. A meta-analysis is an approach for quantitative systematic review that uses statistical techniques to combine the data across individual studies to produce an overall net effect size across all studies. Meta-analyses of randomized controlled trials are considered the pinnacle of the hierarchy of evidence.

Where insufficient data are available to make quantitative comparisons, qualitative systematic reviews are undertaken, whereby selected individual studies are critically appraised and summarized. An overall assessment of the evidence is made based on the number of studies, the magnitude of reported effect sizes, and methodological quality of the evidence. In practical terms, meta-analyses can also be performed in order to clarify any uncertainty between conflicting reports, answer new questions not posed at the start of individual trials, establish reliably what is already known prior to initiating a new study, identify specific areas that need to be further investigated and assist in the identification of relevant, measurable outcomes to then be used for further analysis such as pharmacoeconomic modelling.

Evidence-based reviews adopt from systematic reviews the extensive and explicit literature search strategy to summarize the state of the art for the clinical topic of interest. Evidence-based reviews may apply a quantitative or qualitative approach, depending on the depth and breadth of available data. Evidence-based reviews may also be used to derive global epidemiology or burden of illness data.

At PharmIdeas, we have been performing and publishing evidence-based reviews and meta-analyses since the company’s inception in 1994. Please visit our Publication section for more details.

Below are examples of publications pertaining to Evidence-based Reviews and Meta-analysis:

Example: Iskedjian M, Bereza B, Gordon A, Piwko C, Einarson TR. Meta-Analysis of Cannabis Based Treatments for Neuropathic and Multiple Sclerosis Related Pain. Current Medical Research and Opinion, 2007; 23 (1):17-24

Example: Einarson TR, Kulin NA, Tingey D, Iskedjian M. Meta-analysis of the effect of latanoprost and brimonidine on intraocular pressure in the treatment of glaucoma. Clinical Therapeutics 2000; 22 (12): 1502-1515

Example: Iskedjian M, Piwko C, Shear NH, Langley RGB, Einarson TR. Evidence Based Review of Topical Calcineurin Inhibitors in the Treatment of Atopic Dermatitis. American Journal of Clinical Dermatology 2004; 5 (4): 267-279

Example: Einarson TR, Vicente C, Machado M, Covert D, Trope G, Iskedjian M. Screening for Glaucoma in Canada: A systematic review. Canadian Journal of Ophthalmology 2006; 41 (6):709-21



We develop and apply the methods of pharmacoepidemiology and health epidemiology, both as primary research tools and as data inputs into other forms of analysis for use in pharmacoeconomics and healthcare decision making. Incidence and prevalence studies assist in understanding patterns of disease, while drug utilization studies form the foundation of market analysis, which determines the potential of a drug and its impact on the market.

Database analyses can identify the growth of sales and behavior of competing comparators. We develop and implement all types of methods including the following:
• Database analyses for utilization studies and pharmacovigilance
• Health surveys (geared to patients and clinicians as well as the general public)
• Incidence and prevalence studies
• Patient adherence studies
Below are examples of publications in Pharmacoepidemiology:

Example: Iskedjian M, Shapiro M, Wang M, Farah B, Walker J. Prevalence and burden of illness of menopause: A Canadian observational study. Value in Health 2009, 12 (7): A291

Example: Iskedjian M, Walker JH, Einarson TR, Desjardins O, Herings RMC, Sukel MPP, Covert D. Prostaglandin agonist use with and without adjunctive therapy for the treatment of glaucoma: A Netherlands population based analysis. Value in Health 2008, 11 (3): A298

Example: Desjardins O, Bereza B, Jaszewski B, Malmberg C, Iskedjian M, Einarson TR, Piwko C. The prevalence and management of pain due to multiple sclerosis in Canada. Proceedings of the Consortium of Multiple Sclerosis Centers 2006 Annual Meeting, Scottsdale, AZ, May 2006

Example: Einarson TR, Metge CJ, Iskedjian M, Mukherjee J. An examination of the effect of CYP-450 drug interactions of Hydroxymethylglutaryl-Coenzyme A Reductase Inhibitors on healthcare utilization: A Canadian population based study. Clinical Therapeutics 2002; 24(12): 2126-2136

Example: Joffe R, Iskedjian M, Einarson TR, O’Brien B, Stang MR. Examining the Saskatchewan Health drug database for antidepressant use: The case of fluoxetine. Canadian Journal of Clinical Pharmacology 2001; 8 (3): 146-151


Market Valuation 

Our expertise in research design, data management and statistical analyses of early and late phase clinical trials, as well as observational studies, can be tailored to any scientific or corporate goal. Our expertise in the marketplace can be utilized for single site or single country trial and/or developed for international multi-site studies.

Keeping abreast of PMPRB guidelines and other market regulations, we apply scientifically validated approaches and use our expertise in pharmacoeconomics, pharmacoepidemiology and modelling to determine the fair market price of products and estimate their financial value.

Early health economic modelling, information review of the epidemiologic and clinical literature and market projections based on hard demographic and epidemiologic data ascertain the go or no-go decisions to invest in a new therapeutic area, product or start-up company by comparing it to its competing parties.

Market valuation of any health technology or drug is important for decision-makers. Burden of illness analysis is used in market valuation, informing the decision-makers on health resources utilization for treating a disease. Information on the size of the market, comparators and adjunct therapies is also obtained through this analysis with a view to clarifying the product market niche.

Other techniques to estimate market financial value can be applied to drugs and devices developed by pharmaceutical firms, including biotech startups. These services help secure the best possible valuation for acquisitions or product licensing.

PharmIdeas has been offering these services to small, medium and large biotechnology and pharmaceutical companies, as well as VC firms, since 1999.

We have developed a powerful interactive tool for displaying the results of market valuations, the eValuation Model, using the same platform as our Pharmacoeconomic Display Model.



We offer a wide range of training and educational upgrading programs in pharmacoeconomics, outcomes research, pharmacoepidemiology and research methods. Seminars, workshops and hands-on in-house training sessions can be arranged and tailored to the client’s specific needs.

Our technical writing team specializes in the development of technical manuals, support materials and Standard Operating Procedures.
Please inquire at ideas@pharmideas.com


4 Macassa Circle
Kanata (Ottawa), ON
K2T 1J7


1967 Wehrle Drive, Suite 9
Williamsville, NY





© Copyright 2020 PharmIdeas Research and Consulting Inc.- All Rights Reserved