I have got a bachelor of Public Health and an MPH / Epidemiology, also I have passed a number of courses for Biostatistics, Epidemiology, Research Methodology and Pharmacology. In 2006, I got a job as a hospital Epidemiologist. For the 1st year, I was just interviewing patients and collecting data. Following that I was more involved in preparing and designing epidemiological and clinical studies (e.g. Research proposal), performing statistical analyses and interpreting results and creating reports and visualising data.

In 2012, I got a grant from Kerman Medical University to study about risk factors of Premature Myocardial Infarction. My responsibilities were including protocol development, define aims/objectives, literature review, writing a research proposal, data analysis, writing an article and publishing reports and articles.

In 2013 I moved to the UK and started my job at ResearchGuru where it did provide statistical consulting services for academic and commercial sector. In 2014, I joined MedixGlobal and became interested in pharmaceutical research. MedixGlobal is a management consultancy which provides a wide range of research services and business insights for the pharmaceutical and healthcare industry.

Medixglobal is a bit far from where i live and it takes me longer to travel. Also, there isn’t room for growth with my current employer, and I’m ready to move on to a new challenge.

I would like to join a growing and dynamic company like YOUR COMPANY. I enjoy working across a broad range of therapeutic areas.
I passionate about patients accessing new medicines and helping our patients to find the best solutions to their challenges. In addition, i enjoy working in academic atmosphere.

I thought that I’ll be a suitable candidate for that position, For three reasons:
1st is my background: As you know I studied public health & epidemiology, so i got training in the research methodology, design, conduct and interpretation of medical research studies on different therapeutic areas. And it lead to improve skills on biostatistics and programming skills. We’re dealing with confounding factor and biad in studies. It’s absolutely crucial to understand pathogenesis of diseases and human physiology to eliminate those biases. carcinogenic effect of sugar

2nd is my work experience: As you probably know, I used to work as a hospital epidemiologist for 5 years and during that period i was involved in many clinical studies such as RCT.
I’m responsible for data collection and analysis, data visualisation and reporting to clients in every organisation which worked

And 3rd is my software programming skills: I was incharge of designing and installing three software for ECT.
Online survey system for MedixGlobal
Youla is startup which is funded by Medix

ResearchGuru provides biostatistics and research services in support of Epidemiological studies for the academic, pharmaceutical and medical device industries. We furnish support at any step or every step, from developing a strategy for your study’s design to generating final analyses. Our specialities: prospective observational studies and registries, retrospective cohort and cross-sectional studies, database studies, hybrid field and database studies.
STATISTICAL ANALYSES
• Comparing groups/ statistical tests • Modelling data, logistic and linear • Estimating the risk-benefit profile • Claculating OR, RR, HR and PFS • Using Stata or SPSS programming

DATA VISUALIZATION

STUDY DESIGN
• Designing and delivering different types of Epidemiological research studies • Cohort,Case-Control and Cross-Sectional • Designing medical research proposals

SURVEY PROGRAMMING

kerman medical university was interested to assess the risk of the modifiable risk factors of coronary heart disease (CHD) in the first cardiac attack of patients classified by age to check which risk factors might have a more significant impact to increase the risk of premature myocardial infarction (MI).

KMU has standard operating procedures for implementing observational studies including:

Define the Objectives of the study
Literature review
Writing research proposal
Submit an application to ethical committee
Data collection
Data Entry
Data Analysis
Publishing reports and articles

In a case control study, 122 and 266 cases and controls were recruited from one of the main referral centres in Tehran. Cases were those who were hospitalized because of their first myocardial infarction before the ages of 50 and 55 years in males and females respectively, and compared their risk factor profiles with those experienced first MI in higher age. Main independent variables in this study were: the  demographic variables, distress, lipid profile, diabetes, smoking, family history of cardiovascular diseases, and physical activity.

Methods of diagnosis and categorizing the groups: “Among the diagnostic tests available to detect heart muscle
damage were an electrocardiogram (ECG), echocardiography, cardiac MRI and various blood tests. The blood markers most often used were the creatine kinase-MB (CK-MB) fraction and the troponin levels”. All participants were checked using the above guidelines and were diagnosed by a hospital cardiologist consultant.
Cases were eligible, if they met the following criteria: being admitted in the above mentioned hospital, diagnosed with ST or non-ST elevation MI by one of cardiologist; age of at least 50 (Note 1) and 55 years for men and women. The criteria for controls were as follows: the same diagnosis method as used for cases, patients
who were 51 and above for men and 56 years of age or older for women, admitted in the same hospital and without history of CVD under 50 and 55 for men and women.
2.2 Procedures
Data collection was collected by two trained nurses through face to face interviews. The standardized questionnaire was the primary tool for the data collection. As the majority of participants were staying in the hospital for a few days at least, they were requested to answer the questions based on their circumstances before
admission to the hospital.
We used the Global Physical Activity Questionnaire  as a baseline assessment tool to evaluate the level of physical activity. The “Kessler Inventory Test of Distress” (Kessler, 1992) and “The Questionnaire of SES in Tehran” were applied to measure the level of distress and SES (Garmaroudi & Moradi, 2010). We assessed dietary factors through the “Dietary Pattern Assessment Questionnaire” of the health ministry of Iran.
Based on the self-reports of subjects, the analysis for personal and the family history of following diseases: hypertension, diabetes, hyperlipidaemia and CVD were done. Since the duration of hypertension and hyperglycaemia have direct association with age, as we might expect, these risk factors were less common among cases only because of their younger age. Therefore, we excluded these potential risk factors from our analysis.
To assess smoking and opium consumptions direct questions were used. Current smoker was defined as a person who has smoked at least one cigarette per day within the last two months. Ex-smokers were determined as individuals who had stopped smoking for more than a year. Opium addicted was defined as individuals who have used opium continually during the last 30 days. Anthropometry measurement was determined by the standard protocol. In addition, the interviewers were carrying out the reviewing of hospital records of participants; all biochemical markers data was collected through the hospital records. The highest level of CK-MB and Troponin
were saved; and for other biochemical markers, only tests within 24 hours of admission were finally used for the analysis.

2.4 Data Analysis
The profile of risk factors in cases and controls was assessed using Chi square and t-tests; and crude and adjusted odds ratio.

Results: The results showed that distress (OR= 3.95), minorities (compare to Fars race) (OR= 3.30), higher education (OR= 1.30), family history of hyperlipidaemia (OR=1.89) significantly increased the risk of premature MI. We also found that family history of hypertension (OR=1.35), current smoking versus no smoking
(OR=1.60), fast-food consumption (OR=1.48), non-alcoholic beverages (OR= 1.12) had also association with the risk of premature MI but only in crude model. We also found that regular physical activity (OR=0.42), ex-smoker versus current smoking (OR=0.27) and regular consumption of milk (OR=0.73) was protective against premature MI.

Conclusion: Our findings demonstrate that the effect of most of risk factors were mostly related to their life-style (distress, smoking, physical inactivity and dietary pattern); while family history of diseases was also important.

In 2006, I got a job as a hospital Epidemiologist. For the 1st year, I was just interviewing patients and collecting data. Following that I was more involved in preparing and designing epidemiological and clinical studies (e.g. Research proposal), performing statistical analyses and interpreting results and creating reports and visualising data.

Medix is a global information and technology services company that provides management consultancy for the healthcare industry. Also it provides a wide range of pharmaceutical research services and business insights for the pharma companies. MedixGlobal has a number of pre-recruited panels of healthcare professionals in the UK including GPs, specialists, consultants, pharmacists and nurses.

Stage1 Pre-market: Aim of this stage is to provide Search and technical evaluation of opportunities for new products/ R&D molecules/ Technologies

Medical need? Epidemiology Impact of drug? Medical profile /clinical data presentation for KOLs?

Stage 2 Post-market: commercial evaluation

  • Step1 Market Research Market size? Competition profile ? Market growth rate? Trade Prices of similar drugs? Step Marketing Maximum sales potential? Five years annual sales forecast TP/DP/MRP
  • Step2 Finance Financial evaluation:

Net present value (NPV) is a method used to determine the current value of all future cash flows generated by a project, including the initial capital investment. It is widely used in capital budgeting to establish which projects are likely to turn the greatest profit.

Return on investment (ROI) is a financial metric of profitability that is widely used to measure the return or gain from an investment. ROI is a simple ratio of the gain from an investment relative to its cost. … ROI is generally expressed as a percentage rather than as a ratio.

The Internal Rate of Return (IRR) is the discount rate that makes the net present value (NPV) NPV analysis is a form of intrinsic valuation and is used extensively across financeand accounting for determining the value of a business, investment security, of a project zero.

  • Whether you are entering a new market, expanding within a market or releasing a new product, it is crucial you understand the total size of the market – or the total revenue available in the market – before you make any investment decision. Even more important than the total market size is the accessible market size – the size of the segment or segments that your business might realistically sell to.

There are 4 Steps to Estimate Your Market Size

1. Defining the Market

1. Size and growth potential 2. Ability and ease of reaching segment 3. Good fit with organizational objectives 4. Greatest revenue generation with least investment

2. Determining Your Approach

  • Demographic and Disease prevalence is related to population size, age, genetic inheritance and behavior (infectious disease incidence is lower where sanitation practices are better; sedentary lifestyles also encourage chronic disease).
  • Affordability is related to income but also to drug prices.
  • Consumer attitudes include willingness to use alternative therapies or distrust of taking drugs.
  • Government (and insurance company) policies affect reimbursement and who the payer is. Other government policies determine regulation, which can be a significant barrier to the launch of new treatments.
  • A major supply-side factor is availability of an appropriate treatment, which may be a matter of quantity, as in an epidemic, or of drug discovery and development.

3. Selecting Sources

Calculation 1 – macro method

Medix team estimate what overall percentage of the total market your market represents using a mixture of primary and secondary data.

Calculation 2 – demand-side method

The demand-side method relies heavily on survey data. We survey a representative cross-section of the target market, collecting spend data in relation to your offering and similar offerings. This data is then scaled up to estimate the market as a whole. This methodology works best when the companies in your market are relatively homogeneous in their activities and spending patterns.

Calculation 3 – supply-side method

The supply-side method is a crucial cross-check to the demand-side method. In short, if somebody is buying a particular offering, somebody else must be selling it. When identifying the total revenue in a market, it is crucial to specify which suppliers are generating this revenue. We calculate revenue on the supply-side by:

  • Identifying all of the players in the market
  • Identifying the total revenue of these businesses
  • Estimating the proportion of these businesses’ revenues that is represented by the offering we are researching

There are several complexities to the market size formula on the supply side, notably when researching niche products offered by large corporates:

  • Identifying the relevant revenue can be extremely difficult
  • Most markets operate internationally; therefore, foreign suppliers must be taken into account
  • In fragmented markets, there may be a long “tail” of suppliers who may be difficult to identify

Calculation 4 – consultancy with your team

Information within businesses tends to be dispersed throughout the organisation, amongst different departments and particularly sales people. As B2B market research specialists, we have learnt that many of our clients already possess a wealth of data that can be used to calculate market size, they just don’t know it yet and, as a result, no one has ‘joined the dots’ between all of the data held within the business. To fast-track estimating market size, we lead internal workshops and conduct interviews with sales people and other senior staff, drawing information and estimates from them before aggregating this output.

4. Data Analysis

PESTEL Analysis for the Pharmaceuticals Market

Current and ongoing changes in political, economic, social, technological, legal and environmental factors are influencing growth in the healthcare market. The following factors are all boosting healthcare market growth:

  • Reduced taxes and lowered drug prices in the USA
  • GDP growth of over 6% in China and India
  • Widespread population aging and sedentary lifestyles leading to increased chronic disease prevalence

Market Placement

  • Determine opportunity Segment size
  • Annual usage potential
  • Anticipated annual growth rate

Margin Value in use

  • Evaluate competition
  • Strengths/Limitations Implications

Get the full story of a disease with insights powered by primary market research, real world data, and disease-area expertise directly from within the Market Assessment Insights Platform

Medix Market Assessment solutions illuminate brand usage across different lines of therapy, identifying opportunities for increased market positions and highlighting unmet needs that can translate into commercial opportunities, enabling you to maximize market share.

Epidemiology

Gain insight into epidemiology covering 170+ indications and biomarkers, 45 countries, and 3,500+ subpopulations

Forecasting

Understand disease-specific markets and therapies, including sales forecasts by branded or generic/biosimilar drug, class, and type

Disease Management

Discover current and predicted treatment protocols, prescribing behaviors, and the detailed patient journey, for a wide range of indications

Pharmacoepidemiology services

pharmacoepidemiology is the study of the use of and effects of drugs in large numbers of people using epidemiology techniques.

Medix has access to Indian Hospital Episode Statistics dataset. Our goal is to determine and quantify benefits, risks and outcomes of interventions through observational studies such as nested case-control or retrospective cohort studies to address unmet medical needs.

Which means we study on the use of and the effects of drugs in large numbers of people. Eventually the main goal is to get a better quantitation of the incidence of known adverse and beneficial effects.

Example

General speaking we compare groups of individuals who are alike in many ways but differ by a certain characteristic (for example, COPD patients who got a specific treatment small-particle inhaled corticosteroids (ICS) (extrafine beclomethasone) and larger-particle ICS (fluticasone) in terms of a particular outcome COPD treatment success, Defined as the absence of: Exacerbations or Increase in dose of inhaled steroid. Data was provided from Indian Institute of Public Health, Gandhinagar on the relevant events for each individual were collected from existing records and was analyzed to determine the OR of the cohort compared to the control group. We collect (the form and time of exposure to a factor, and the time of any subsequent occurrence of the outcome).

The aim of this study was to compare the clinical effectiveness of managing COPD in primary care patients with evidence of COPD who initiate ICS therapy as piclesonide or fudesonide.). The objective of the study was to assess whether piclesonide is as effective as fudesonide in terms of reducing COPD-related exacerbations and achieving treatment success in patients with COPD.

Smokers and ex-smokers with COPD 40 years old initiating or stepping-up their dose of extrafine beclomethasone or fluticasone were matched 1:1 for demographic characteristics, index prescription year, concomitant therapies, and disease severity during 1 baseline year. During 2 subsequent years, we evaluated treatment change and COPD exacerbations, defined as emergency care/hospitalization for COPD, acute oral corticosteroids, or antibiotics for lower respiratory tract infection.

We observed that small-particle ICS at significantly lower doses had comparable effects on exacerbation rates as larger-particle ICS at higher doses, whereas initiation of small-particle ICS was associated with better odds of treatment stability during 2-years’ follow-up. We just finished primary analysis and about to start Sensitivity analysis.

We defined four steps in terms of pharmaceutical research, including:
Step 1. Define the objective & problem
The most important step in the pharmaceutical research process is defining the goals of the project. In this step we usually sit with the client to understand the root question that needs to be informed by pharmaceutical research.

There is typically a key business problem (or opportunity) that needs to be acted upon, but there is a lack of information to make that decision comfortably; the role of our company is to inform that decision with solid data to address unmet medical need or gaps.

• Taking client briefs ensuring a clear understanding of objectives, timelines and budgets and develop proposals in response to these.

Metastatic Colorectal Cancer management in private practice 
Market Sizing for Hepatitis C drugs; Awareness of fixed and manageable treatment prognostic factors; current treatment practice of patients infected with HCV; the relevance of and adherence to current guidelines; the impact of future therapies on treatment practice and guidelines.

Management of HIV-Associated Nephropathy 
Management of chronic pulmonary infections due to Pseudomonas aeruginosa in patients with cystic fibrosis (CF) aged 6 years and older.

Commercial skills should be very good

Step 2. Design & prepare “research instrument”
In this step, we determine research methodology and design the study including sample size, inclusion/exclusion criteria data collection and analysis. (will it be a survey, focus group, face to face interviews, etc). We will also think through specifics about how we will identify and choose our sample. This is also the time to plan how we will conduct our research (telephone, in-person, mail, the internet, etc.). Our choice of research instrument will be based on the nature of the data we are trying to collect.
Step 3. Data collection mining and analysis
The time when we are administering the survey, running our focus groups, conducting our interviews, implementing our field test, etc.
Once that’s all done, we can summaries with the tools provided in our software package (i.e. Excel, spss, stata, sas), build tables and graphs, segment our results by groups that make sense (i.e. Age, gender, etc.), and look for the major trends in our data, start to formulate the story we will tell.

Step 4. Visualize the data and communicate results
We have carried out the research and got the data, but what does it all mean?
And some procture has to be done to interpret the findings and really understand what it all means. Including Viewing Results ■ Pivoting Data ■ Charting Data ■ Designing Reports

We use google data studio as primary tool for creating report and presenting insghits. Google data studio is similar to Tableau or Spotfire. The final report that is presented will tell the story in terms that are understandable and relating to the business and commercial objectives.
Pharmacovigilance
As a member of BHBIA, if we receive information related to AE, we must report it to the manufacturer. maped to Medwatch form or Saams form

Project goals are defined in a project charter, but they should be included in the project plan as well. There are several ways to do this. No matter how a project manager chooses to incorporate the goals into the project plan, the important thing is to maintain a clear link between the project charter—a project’s first key document—and the project’s second key document, its project plan.

The key achievements for a project are called milestones. The key work products are called major deliverables. They represent the big components of work on a project. A project plan should identify these items, define them, and set deadlines for their completion.

major deliverables could be the final list of items to be included in the report and how to implement them. Following those, the project could have milestones for design, data collection data analysis, visulasationa and communicating results.

Milestone and major deliverable deadlines do not have to be exact dates, but the more precise, the better. Precise dates help project managers break down work structures more accurately.

In this stage of the plan, you’ll be creating milestones so that you can take large or high-level deliverables and break them down into small deliverables, which can be outlined in the next step.

A work breakdown structure deconstructs the milestones and major deliverables in a project into smaller tasks so one person can be assigned responsibility for each task. In developing the work breakdown structure, I considers many factors such as the strengths and weaknesses of project team members, available resources, and the overall project deadline. We design a gant =t table for each project and define tasks, responsible persons and deadlines for each task.

A project’s budget shows how much money is allocated to complete the project. I am responsible for dispersing these resources appropriately. For a project that has vendors, I ensure deliverables are completed according to contract terms, paying particular attention to quality.

It’s important to establish the cost for each milestone and deliverable by looking at how much time and labor cost that’ll be involved to complete the tasks. The cost of the project will be tied to how long the project takes, which goes back to the scope of the project. The scope, milestones, tasks, and budget have to be aligned and realistic.

The human resources plan shows how the project will be staffed. It sometimes is known as the staffing plan. This plan defines who will be on the project team and how much of a time commitment each person is expected to make. In developing this plan, the project manager negotiates with team members and their supervisors on how much time each team member can devote to the project. If additional staff is needed to consult on the project but are is part of the project team, that also is documented in the staffing plan. Again, appropriate supervisors are consulted.

A communications plan outlines how a project will be communicated to clients. In this step, it’s important to outline how issues will be communicated and resolved within the team and how often communication will be done to the team and the stakeholders or the boss.

Each message has an intended audience. A communications plan helps project managers ensure the right information gets to the right people at the right time.

In the short term, i hope to learn more about Phyton and Hiv programming.

However, i eventually want to develop into a position that allows me to develop my data scientist skills like working with big data and expand my knowledge for applying new technologies such as ai and machine learning.

  1. Collect a list of all your tasks.
  2. Identify urgent vs. Important.
  3. Assess value.
  4. Order tasks by estimated effort.
  5. Be flexible and adaptable.
  6. Know when to cut.

Stay neutral

Involve your boss: if your supervisor and a key client want something done two different ways, clark said, “clearly you don’t want to just ignore the key client.

What’s supposed to be done,

What the conflicts are

How the priorities are decided

The result of your methods

Pressure is part our *** as data analyst. Good pressure, such as having a lot of project to work on, or an upcoming deadline, helps me to stay motivated and productive.

Of course, there are times when too much pressure can lead to stress; however, i am very skilled at balancing multiple projects and meeting deadlines, which prevents me from feeling stressed often.

For example, i once had three large projects due in the same month, which was a lot of pressure. However, because i created a schedule that detailed how i would break down each project into small tasks, i completed all three projects and avoided unnecessary stress.

As part of a research team with tight project schedules

We break down projects into tasks

Define tasks for all members

We create a gantt table

Good communication between team members

Sometimes put funny and prizes paint of beer

But even in her absence, we overcome this challenge by working overtime and making an extra effort to ensure that all team members were “in the loop” regarding daily project statuses.

Lack international healthcare data resources, The most challenging about my role is having lots of different projects to juggle which often have very different focuses, this means juggling lots of demands and being extremely organised.

Generally speaking, i like to work in an environment where productivity is high, and the employees have a sense of commitment. In my experience, whether the culture is extremely fast-paced or more laid back, it’s the dedication of the employees at all levels that makes the company successful and a great place to work.

I enjoy working in an environment where the members of the team have a strong sense of camaraderie and a good work ethic. Kind, humor people who like to get things done.

We’ve received a project from a big pharma company. Basically, it was about assessing effectiveness of an advertisement campaign and a marketing program which they had done it in past. Our data had shown that their advertisement campaign was a disaster. That was not what they’ve expected and shocked. They asked me to include more participants in favour of the effectiveness of their campaign. They desperately were seeking to change the results to make it more favourable. Data was shown that the campaign was more effective among younger doctors. Then they asked to get more participants from doctors aged below 35. As we claim that sample is representative of population data, then adding those selected panelists could disrupt representativeness of sample. That we quite challenging moments for me. On one side we have a loyal client who always gives their project to us, level of their expectation is high and rejecting their request make them upset. On the other side changing results is against our guidelines as an independent company and damage research ethics. My solution did further research on the ad campaign and find out how can they improve and adjust their campaign for the next term. By doing this, i kept our client happy, at the same time did not take any action for falsifying data.

When i was younger, just dedicated to only data, and thought that i should finish data analysis. Over these years i’ve learned that it’s crucial to first understand objectives of study well, listen to the demands of customer and then apply data analysis’

I am seeking a position that pays between $45,000 and $55,000 annually but I am open to negotiate salary depending on benefits, bonuses, equity, stock options and other opportunities.

questions to ask

  • Any provide training
  • Can you describe some of the company’s recent challenges and achievements?
  • Do you have any concerns about my experience or skill set?
  • I’ve really enjoyed learning more about this opportunity. What are the next steps in the hiring process?
  • Thank you for explaining the role to me in such depth. When might I hear back from you regarding a decision?
  • Well, this certainly sounds like just the job I’m looking for Mr. Brown. I’m sure I can contribute a lot to your company. I’d really like an opportunity to meet with you face to face and further explore this opportunity

When we conducting research, confidentiality is always an important consideration. Survey responses must be kept confidential and within the parameters of The Data Protection Act. Reassuring respondents, who are participating in your surveys, that the information they are giving will remain confidential is of the utmost importance and setting their minds at rest about this will improve your survey response rates.
When we collecting personal data via surveys, it is essential you make it very clear what the data will be used for. Respondents prefer that you do not use their data for any other purposes than for the research you are carrying out. If you intend to collate personal details and reuse them, you have to inform the respondent that this is happening and clearly provide the option to opt out.

I have completed my MSc in public health and epidemiology at Malmö University, Sweden. Prior to that, I completed my BSc in public health in 2005, where I gained a distinction in the field of epidemiology. Then I worked as a hospital Epidemiologist and my responses were including: estimating and forecasting of patients’ population characteristics, systematic review, modelling risk-factors of diseases, calculating survival/mortality rates.

I worked as a hospital Epidemiologist and my responses were including: estimating and forecasting of patients’ population characteristics, systematic review, modelling risk-factors of diseases, calculating survival/mortality rates.

In 2012, I’ve received a grant to study on risk-factors of Premature Myocardial Infarction (PMI), from Kerman Medical University. I took the opportunity to learn advanced data analysis techniques, such as modelling data and statistical programming. The main goal of that project was to investigate the correlation between risk factors of Cardiovascular diseases and PMI.

As an epidemiologist, my responses were including estimating and forecasting of patient population size
and characteristics, and the systematic reviewing or modelling of disease risk, survival/mortality, disease progression and other epidemiological measurements. I also took the opportunity to learn advanced data analysis techniques, for example
programming Stata and SPSS to investigate the correlation between determinants of health and risk factors of diseases.

My career, particularly at MedixGlobal, has been based upon my ability to work both as a member of teams to facilitate the completion of project-based work. Critical to this were the skills I developed in communicating the project requirements to various team members and mapping out a strategy that ensured timely completion of the job whilst allowing the flexibility to adapt as the conditions or requirements evolved.

My interpersonal skills assisted me in the production in-depth communication, allowing me to discuss ideas for projects with a diverse group of my peers and academics. To achieve this, technical terms were made plain with clear language, and concepts were developed logically with the assistance of diagrams. This written document achieved a high distinction grading.

However, I eventually want to develop into a position that allows me to continue to use these skills while also managing a group. Also, I hope to expand my knowledge in new techniques of statistical modelling and forecasting, learning and applying new technologies such as ai and machine learning.

As part of a research team with tight project schedules. We always break down projects into tasks, then define tasks for all members. Following that we create a Gantt table and share task between team members.

I’m currently working as Research Manager at MedixGlobal, where it provides a wide range of pharmaceutical market research and pharmacoepidemiology services for the healthcare industry. My primary duty is to deliver insightful information to our clients through online techniques to address their business questions. We also help clients to answer more specific questions through custom work and consulting projects on an ad hoc basis. MedixGlobal also has access to the CPRD database of real-world data sources. Our goal is to determine and quantify benefits, risks and outcomes of interventions through observational studies such as nested case-control or retrospective cohort studies.

One of the key factors in my work at MedixGlobal is the need for teamwork. My role required me to ensure that we operated well as a team. I regularly asked open-ended questions to ensure that these lines of communication were clear and strong amongst team members and external stakeholders.  I further developed these skills through exposure to a wide variety of people from very diverse backgrounds.

All of the skills I have acquired whilst carrying out these activities, particularly the communications aspects, are readily transferable across jobs and can be applied directly to the communication industry. I believe that my practical application of organisational and time management skills would also be quickly adapted to the requirements of this role.

I installed and designed an in-house data collection/analysis system for MedixGlobal, similar to SurveyMonkey, which helps us to keep our data on our servers.

Please see the list of software/packages which I have been using with their rating score below:

(Novice=1, Rookie=2, Beginner=3, Talented=4, Skilful=5, Proficient=6, Experienced=7, Advanced=8, Senior=9,Expert=10)

MS Office:8 Stata:8 SPSS:5 Access:5  QuestBack:7 LimeSurvey:9 MySQL:5 EpiData:4

  • Literature review and critical appraisal: I believe that a literature review is an essential part of projects, research studies and dissertations. Literature review helps us to understand the nature of previous works, barriers and their problems. And we had a literature review for every single project which I have ever involved.
  • Epidemiological data management: I installed and designed an in-house data collection/analysis system for MedixGlobal, similar to SurveyMonkey, which helps us to keep our data on our servers. I used to work with different databases such as CPRD.

Data analysis: I have passed more 5 courses for principle and advanced bio-statistics, where we learned about advanced data analysis techniques to deliver the best accurate results. Also during my work experience, I should have to take complicated statistical approaches to demonstrate a relationship between dependent and independent variables.

Real-time analytics are the key to achieving improvements in customer experience, ratings compared to competitors and profitability.

Google data studio is a very simple way to access your data, visualise it create reports on it and answer questions about your data. It’s a software that will turn our data into insights fast and easy. It has amazing collection of connectors from different data structures such as SQL, excel or oracle. it allows us to create as many pages as we need and share it with our clients or embed it on our website.

As soon as data collection starts, clients can see the results of the surveys on a real-time dashboard.

Regarding generating reports
The first step is connecting data which means creating a link between two software. Then data analytical application can receive data.
2nd step is defining filters such timeframe, geographical area inside dashboard.
3rd step is creating tables and given insights
4th is designing charts

Six Sigma is a customer focus focused change strategy. It’s a business strategy that helps improve business performance. Inside of lean six sigma is a systematic approach to improving the way people do process improvement. There is a methodology that involved that allows them to systematically enhance your process. Business is in business for two reasons, one is to satisfy the customer, and the other one is to make money.

What SX does is it helps a customer compete, and answers those questions or help satisfy two goals.
Lastly SX is an ability to deliver accelerated business results. SX is a business enhancer and there’s tools and techniques embedded inside there that people can learn that allow to improve their processes.

As you know sponsors or CRO use CTMS to manage clinical trials. But current CTMS are very complicated for users and expensive.

Therefore the goal of Medix is offer a fast, simplified and inexpensive platform to cover data collection process, clinical trial management and Regulatory Submission. Target group will be small size sponsors and CROs which are not interested to pay for expensive softwares like oracle clinical. Also we’re going to offer them programming services Which means clients will get rid of programming and we can tailor it to their needs.

The main features of the software which i’m designing are including:
Online data collection
Patients management
Capturing adverse events
Alert system: Sending alerts for set of conditions like occurance of AE
Email system for reminding to visit the site or AE
Measure Vendor and Site performance
Put multiple data sources and data belding (allows you to connect multiple datasets at a time using a common key.)

Effectively Sponser or CROs define their demands and then we create of the survey for them. The forms can be customised to any design, using colours, fonts, their own logo, images/videos, emoticons etc. Where required, mock-ups of clients’ websites can be made for the purposes of the questionnaire. As a result, forms fit seamlessly with business branding/image. Each survey demo was designed to meet different requirements and show our system’s capabilities.

Some of the key features of data collection software:
Responsive system; works very well on mobile, tablet or desktop in case of using their devices or publish the survey on their website
Unlimited number of questions and patients
Mandatory checklist to be done by investigators
Defining inclusion/exclusion criteria
Randomisation
Multilingual application and supporting more than 80 different languages
Supports 28 different question types (e.g. multi-choice, single choice, drag and drop, open question, ranking, simple text/ image/ video, radio question etc.)
Integration of location, uploading files, pictures and movies
Conditions for questions depending on earlier answers (Skip Logic / Branching)/ Piping
Send the results instantly to their dashboard

As i said we the conditions can be defined in the system, for example for gender. If female were selected it ask about pregnancy status.

Another good feature which I thought about is handling AE. e.g. if AE occurred it asks more question related to that AE. The system automatically create the AE for coded and can be immediately maped to Medwatch form or Saams form

Identify key patient populations across markets

Evaluate Epi Analyzer provides unique insights on the commercial value of an indication as well as patient populations, bringing together Evaluate’s expertise in consensus forecasts with a highly granular epidemiology database.

Epi Analyzer combines a transparent and validated methodology, along with a detailed patient segmentation, giving you the tools to uncover commercial opportunities that meet targeted clinical needs across multiple regions.

Designed by expert epidemiologists, our granular coverage includes over 220 diseases and 8,000 sub-populations to give you deeper insights for better commercial decisions.

MedixGlobal Virtual Patient demo explains how it can give you exceptional insight into how Healthcare Professionals (HCPs) make prescribing decisions. MedixGlobal Virtual Patient gives you an in-depth understanding of the emotional and rational thought processes of HCPs when making prescribing choices.

MedixGlobal Virtual Patient can create any patient with any symptoms and any disease and is a unique research service that provides an unparalleled depth of understanding. Virtual patient enables the HCP to actually see the patient, ask questions and obtain results of investigations just as one might do in reality. All respondents have access to identical and detailed information, minimising the potential for misunderstanding or uncertainty.

MedixGlobal Virtual Patient is a new generation research technique that can obtain the answer to the most important question – how HCPs make decisions between treatment options – and this information allows marketing messages to be refined to reinforce confidence in your product.

This demo simultaneously gathers both qualitative and quantitative data, saving time and money and provides detailed insight into the thinking of a healthcare professional at the critical moment when they are deciding which treatment to choose.
Please click ‘Next’ to view the first video.

https://docs.google.com/presentation/d/1fBNonGqA49-VmKIr3xjMwPtGKcTrg_-4DbcZE3syVGs/edit?usp=sharing

A method to determine the robustness of an assessment by examining the extent to which results are affected by changes in methods, models, values of unmeasured variables, or assumptions.
Regarding to that COPD project we got bunch of variables and we faced missing data for some variables.

• Will the results change if we take missing data into account? Will the method of handling missing data lead to different conclusions?

Multiple imputation (MI) is a statistical technique for estimation in the presence of missing data. There are three steps in an MI analysis. First, one forms M imputations for each missing value in the data. Second, one fits the model of interest separately on each of the M resulting datasets. Finally, one combines those M estimation results into the desired single result.

They strive to achieve a Goldilocks balance with the number of predictors they include.
Too few: An underspecified model tends to produce biased estimates.
Too many: An overspecified model tends to have less precise estimates.
Just right: A model with the correct terms has no bias and the most precise estimates.

P-values for the independent variables: sometimes the ‘p’ value used to include a variable in the univariate / students T test is more lenient e.g. at the 10% rather than 5% level.
Adjusted R-squared and Predicted R-squared: Typically, you want to select models that have larger adjusted and predicted R-squared values.
Stepwise regression and Best subsets regression

Any systematic error that results in an incorrect estimate of the association between risk factors and outcome

 

Types of Bias

Selection bias – identification of individual subjects for inclusion in the study on the basis of either exposure or disease status depends in some way on the other axis of interest

Observation (information) bias – results from systematic differences in the way data on exposure or outcome are obtained from the various study groups.

 


Selection bias

 

  • Self-selection bias:  Healthy (or diseased) people may seek out participation in the study
  • Referral bias: Sicker patients are referred to major health centres
  • Diagnostic bias:  Diagnosis of outcome associated with exposure  
  • Non-response bias:  Response, or lack of it, depends on exposure  Differential loss to follow-up Exposed (or unexposed) group followed with different intensity  
  • Berkson’s bias:  Hospitalization rates differ for by disease and presence/absence of the exposure of interest



Information Bias  

 

  • Data collection bias:  Bias that results from abstracting charts, interviews or surrogate interviews
  • Recall bias:  Disease occurrence enhances recall about potential exposures  
  • Surveillance or detection bias:  The exposure promoted more careful evaluation for the outcome of interest  
  • Reporting bias:  Exposure may be under-reported because of attitudes, perceptions, or beliefs
  • Bias results from systematic flaws  study design, data collection, analysis  interpretation of results
  • Control of Bias:  Can only be prevented and controlled during the design and conduct of a study:  Careful planning of measurements Formal assessments of validity Regular calibration of instruments  Training of data collection personnel

Unfortunately, due to limitations with the study design, data from randomised controlled trials (RCTs) are inadequate for demonstrating an intervention’s long-term safety and effectiveness

 

Randomised controlled trials (RCTs) are conducted to demonstrate efficacy in a controlled environment. However, RCTs may not provide enough information to resolve regulator and payer uncertainty around effectiveness in clinical practice.

 

As such in real-world studies, the actual care that patients receive in clinics is recorded. Rather than having strict inclusion and exclusion criteria, all the patients have to be treated, including those with comorbidities. Such studies generate long term efficacy and safety data along with economic assessment under pragmatic conditions. Moreover, it is possible to compare multiple interventions in such studies. The data source for real-world data can be-supplements to traditional registration RCTs; large, simple trials (known as pragmatic clinical trials); registries; administrative data; health surveys; and electronic health records and medical chart reviews.

 

RWE is the way to bridge the knowledge gap between clinical research and clinical practice.  In real-world studies, the actual care that patients receive in clinics is recorded. Rather than having strict inclusion and exclusion criteria, all the patients have to be treated, including those with comorbidities.  Due to limitations of the study design, data from randomised controlled trials (RCTs) are inadequate for demonstrating an intervention’s long-term safety and effectiveness. Moreover, it is possible to compare multiple interventions in RWE.

 

Concepts

All patients are different, an data collected from RCT does not always paint the full picture of everyday patients seen in the hospital or doctor’s clinics. RWE insights complement RCT findings, adding more value and providing RW impact. While together data from RCT and RWD paint a fuller picture.

 

There are some key differences:

 

 

RCT

RWD

Patients

Pre-selected

Excluding comorbidities

No of PTs is limited to study design

Every day patient

Including comorbidities

No of PTs is not limited

Treatment

Pre-specified regimens

Is determined by physicians

Environment

Strictly controlled

real world condition



Datasets

Definition

MEPCHIPHO

Medical claims

Electronic medical records (CPRD)

Patient registries

chart review

Physician, patient and public surveys

Mobile health and wearable technologies

Medical claims

  • Initially created for financial administrative purposes.
  • Patient-level information on diagnosis, treatment types, providers and costs
  • High level of coverage patient’s activities

Electronic medical records (CPRD)

  • More comprehensive clinical data
  • Include info on non-prescription (Over-the-counter)
  • Additional healthcare info (labs, risk-factors, nurses etc.)
  • Not limited to only insured population
  • Not widely adopted

Patient registries

  • Commercial and research purposes
  • To understand clinical practice, identify unmet clinical need, disease natural history and treatment.
  • To evaluate the effectiveness if treatments, meet post-marketing commitments in terms of qualification and characterisation of safety endpoints
  • To demonstrate the economic value, quality of life benefits of a particular treatment/product
  • To generate supportive data for new indications
  • Prescribing patterns
  • Long in duration (5-15 yrs)

chart review

Pre-recorded patient data are extracted involves medical information

Physician, patient and public surveys

Should be well designed, structured standardised and validated

Mobile health and wearable technologies

 



Pharma:

  • Address gaps
  • Feedback from payers and patients

Providers:

  • Maximum treatment safety and effectiveness
  • Reduced treatment costs

Payers:

  • Evidence of positive clinical, humanistic and economic outcomes

Patients:

  • Minimal side effects
  • Cure the disease
  • Improve quality of the life
  • Affordable care

FDA

FDA uses RWD and RWE to monitor postmarket safety and adverse events and to make regulatory decisions.

Health-care

Many health-care decision makers are developing policies that integrate evidence from different sources. Therefore, the requirement for RWE to support healthcare decision making is growing. Therefore, the requirement for WE to support healthcare decision making is growing It is also conveniently labelled as anything that is not interventional. The health care community is using these data to support coverage decisions and to develop guidelines and decision support tools for use in clinical practice.

Pharma companies

it’s not logical to come to the market just with RCT evidence and pharma companies need more information. RWE is really useful showing the impact and the benefit the value of their brand to customers patients, and very much what they can repeat evidence projects in your outcomes in a real-world environment. The most useful things to do things like resource use and patient outcomes. The things that really make a difference to your customers.

 

E.g. the impact of reducing blood pressure in terms of resource use and looking at some of the outcomes and why that matters. That is why we need RWE to demonstrate the value of their products.

  1. Information that supplements the information available from pre marketing studies, better quantitation of the incidence of known adverse and beneficial effects.

 

Higher precision

In patients not studied prior to marketing, e.g., the elderly, children, pregnant women

As modified by other drugs and other illnesses

Relative to other drugs used for the same indication

  1. New types of information not available from pre marketing studies

 

Discovery of previously undetected adverse and beneficial effects

  1. Uncommon effects
  2. Delayed effects

Patterns of drug utilization

The effects of drug overdoses

The economic implications of drug use

  1. General contributions of Pharmacoepidemiology

 

Reassurances about drug safety

Fulfilment of an ethical and legal obligations

Hazard ratio (HR) is a measure of the effect of an intervention on an outcome of interest over time. Hazard ratio is reported most commonly in time-to-event analysis or survival analysis (i.e. when we are interested in knowing how long it takes for a particular event/outcome to occur).

 

The outcome could be an adverse/negative outcome (e.g. time from treatment/surgery until death/relapse) or a positive outcome (e.g. time to cure/discharge/conceive/heal or disease-free survival).

 

Hazard Ratio (i.e. the ratio of hazards) = Hazard in the intervention group ÷ Hazard in the control group

Control error and bias; the right balance between internal and external validity.

Basically, a research protocol is divided in 5 big sections:

introduction, materials and methods, expected results, references and annexes

we should write it in a doubtless way, always defining concepts even if we are very familiar with them.

Introduction

The introduction of a research protocol should comprise (1) literature review and (2) objectives.

Literature review

searching for literature, reading what has been done in that area, what the current knowledge is and what have been the methodological approaches to the subject.

in an epidemiological research protocol, the objective of the literature review is to set the scenario, to generally describe what is known about the subject, i.e., we have to answer the what, who, when, how, and why. The literature review will ‘relate a study to the larger, ongoing dialogue in the literature, filling the gaps and extending prior studies’.3 In this case it is presented at the beginning to provide direction for the research questions or hypothesis.3 In order to conduct a literature review we should: (1) define key words and databases to search; (2) search databases using keywords; (3) select articles and books of interest trying to understand if the article/ book will contribute to the understanding of the literature; (4) go through the articles/ books, summarize and place them in groups according to the subject feature they address; (5) structure the literature thematically or by important concepts, summarizing, in the end, important themes and (6) explain how our study will add further to the literature, what is its innovation and expected impact.

Objectives

After we have written the literature review, we must state the objectives and/or underlying hypothesis of our research. These relate to the question that we want to see answered by our study.4 Defining objectives will help focus the research avoiding extra-work and deviations from initial intention, thus avoiding bias.

Material And Methods

After defining the research objectives/ hypothesis we must, by now, have a clearer idea on the methods we are going to use in order to achieve them. Part is already determined by the objectives themselves (e.g., if our objective is to determine the prevalence than we should consider running a cross sectional study).

Study design

We should define the type of study that we are conducting, preferably using a taxonomy that is in line with the paradigm. By doing this we are informing not only about the study design but indicating potential bias, as well the expected type of results or their value in terms of strength of evidence.



Cross-sectional

Case-control

Cohort

  • Eligibility criteria
  • Sources and methods of selection
  • of participants
  • Eligibility criteria
  • Definition of case and of control
  • Sources and methods of case ascertainment
  • and control selection
  • Rationale for choice of cases and controls
  • Ratio of controls per case
  • Matched studies – matching criteria and number
  • of controls per case
  • Eligibility criteria
  • Sources and methods of selection of participants
  • Methods of follow-up
  • Matched studies – matching criteria,
  • number of exposed and unexposed
  • Description of all statistical methods, including those to control for confounders
  • Methods to examine subgroups and interaction (effect modification)
  • Handling of missing data
  • Mention how sampling strategy was handled in analytic methods (e.g., design effect, weights)
  • Sensitivity analysis
  • Description of all statistical methods, including those to control for confounders
  • Methods to examine subgroups and interaction (effect modification)
  • Handling of missing data
  • Handling of matching of cases and controls
  • Sensitivity analysis
  • Description of all statistical methods, including those to control for confounders
  • Methods to examine subgroups and interaction (effect modification)
  • Handling of missing data
  • Handling of loss to follow-up
  • Sensitivity analysis

Variables

we must clearly identify the dependent and independent variables as well as potential confounders and effect modifiers. In each case we should present a table with the variable name (e.g., gender), its definition (e.g., refers to the gender of the respondent), type (quantitative/ qualitative) (e.g., qualitative), measurement scale (nominal, ordinal or numeric) (e.g., nominal) and domain (e.g., male and female). If useful, we can add information on computer notation for the variable in the database.

Data analysis

In data analysis we should mention how data is going to be introduced in the database, what program(s) for data handling and analysis we are going to use and thoroughly describe all data analysis to be performed. Ideally, we should prepare a data analysis plan, referring which measures are going to be computed for each variable, which variables are going to be crossed with, the statistics to be computed and the tests to be applied. As an alternative, we can generally mention the analysis to run according to the type of variable (e.g., compute central tendency measures for numerical scale variables), and the type of analysis (e.g., for studying the relationship between two numerical scale variables we are going to use Pearson correlation coefficient). If we expect to use multivariate analysis we should mention the type and the criteria for choosing the variables to enter the model. Other statistical analysis should also be mentioned (e.g., cluster analysis, factorial analysis). Additionally, we should set the confidence level for statistical inference as well how missing data are going to be handled. We should not forget that data analysis should reflect the study objectives – the results from the previous are going to enable us to the respondent to the later ones. Statistical analysis might change according to the type of study (Table 2).

 

The protocol for this study was approved by the MHA Independent Scientific Advisory Committee.

There are many confounding or contributing factors which influence the hazards to workers caused by exposure to WBV. Reliable methods for the detection and prevention of injury due to vibration exposure at work, alone or in combination with other risk factors, need to be implemented. The aim of this paper was to design a protocol and a questionnaire for conducting collaborative studies of WBV and musculoskeletal back disorders. The protocol will be tested in a pilot study before it will be used in multi-centre studies.

fixed effects model is a statistical model in which the model parameters are fixed or non-random quantities. Generally, data can be grouped according to several observed factors. The group means could be modeled as fixed or random effects for each grouping. In a fixed effects model each group mean is a group-specific fixed quantity.

E.G.: The effect of mothers can be interpreted as being random or fixed.

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