In brief

• Health data contains important information that can help save lives and cro biostatistics services provide information to support public health initiatives.
• Biostatisticians and biostatistical programming services are highly sought after to make sense of the data available from studies on public health.

 Epidemiology

Epidemiology is a branch of medicine that deals with the study of disease in population, the incidence, distribution, and ways to control the same.Centres of disease control and prevention (CDC) explains epidemiology as a scientific and systematic data-driven study of the distribution and determinants of health-related states and events in specific populations and application of the same information for control of health problems. So, we see that epidemiology consists of three aspects: –

1. Identification of disease patterns in a population,
2. Identify causes or associated conditions and
3. Provide data for prevention, control, and treatment of the health condition.

Biostatistics

            Biostatistical analysis is the science and art of dealing with variation in data to get reliable results and conclusions. Application of statistics to any field of medicine or health is biostatistics.

Biostatistics in epidemiological studies

Epidemiological studies are heavily dependent on biostatistics. Epidemiological statistical services convert data and information got from epidemiological studies, analyse it, and convert it into forms that help solve issues related to public health. Biostatistical services use quantitative methods to combine the two disciplines of epidemiology and biostatistics.

How do we know the risk factors associated with heart disease? How did we conclude that there is no one cause for this but a combination of genetic and lifestyle factors that increase the risk to have a coronary event? This understanding is thanks to the famous Framingham Heart Study which is still underway. Here, over 5000 volunteers agreed to be followed up for several decades to help public health workers understand the risk factors associated with heart disease.

As the world is struggling today to understand and curtail the pandemic of Covid-19, the role of epidemiology in public health has never been clearer. Biostatisticians are using statistics to predict how the pandemic will behave, spread, and the mortality rate.

      Thus, epidemiology and biostatistics are the basic sciences of public health.

Steps in epidemiological biostatistics

Steps involved include: –

1. Address a public health question – generate a hypothesis

A hypothesis in epidemiology is an assumption based on

• Scientific rationale
• Observation or anecdotal evidence
• Results of prior studies

2. Conduct a study –
   • Survey – studies extend of health condition in defined population
   • Surveillance – Monitors or detects health condition. It can be done actively or passively.
   • Observational – study the association between an exposure and a health condition. Here, there is a natural distribution of the control and study group.
   • Experimental – study involving exposure, treatment, and disease outcome.
3. Collect data – numerical facts and figures, observations that are obtained from the investigation. Data must be reliable and accurate for reliable and significant outcomes.
4. Describe the observations / data – by descriptive statistical methods, the data is assessed descriptively and organised in graphs, tables, and other summary methods.
5. Assess strength of evidence for or against the hypothesis – Inferential statistics does a confirmatory data analysis. The strength of evidence is assessed, comparisons are made with previous studies and predictions are made. It also provides future questions and areas of future research.
6. Make recommendations based on inferences – The study proves or disproves or is inconclusive on the hypothesis. The study may be published in peer-review publication or may be spread by any other suitable medium of communication. The outcome may be a regulatory or policy change in handling a health condition or a change in behaviour of the people.

Epidemiology and biostatistics services together provide valuable inputs on study design, disease modelling, sampling, surveillance, analysis, risk stratification, identification of vulnerable populations and prevention, control, and treatment of the health condition in the population. Biostatistics helps in designing study methodology, data models that are epidemiologically relevant to make information and data more informative by adequate analysis.

Biostatistical tools

Most studies use observational and descriptive statistics in the form of tables and graphs are most used. There are several biostatistical computerised tools that are available today that make the process automated. Of these, SAS and STATA are the most used packages. Other packages used are SUDAAN, SPSS, R, ARCGIS, HLM, IVEWARE, BUGS etc. Logistic regression is the most common classical statistical technique used.

Biostatisticians

Biostatisticians are the ones running the show for Statistical Programming Services. They usually have a master’s degree or a doctorate in statistics, some also have a degree in public health. They often have a background in mathematics or computer science. They are the ones who help make sense of the complex data that comes out of the study. They help link the cause and effect and solve the mystery of causation vis-à-vis association between the exposure and disease condition.

The road ahead

            Biostatistics is the backbone to study epidemiology public health, and it needs to be included adequately in medical curriculum. Medical professionals need to understand biostatistical tools better to be able to interpret and use public health statistics in their practice. Several courses in statistical methods are available online for working medical professionals seeking additional training.

And so

Epidemiology and biostatistics are two sides of the same coin and together they form the backbone of the public health information system. Strong biostatistical support turns scientific data into information that help epidemiologists and governments plan strong public health policies.

References

• Park K. Textbook of preventive and social medicine. 21st ed. BanarasidasBhanot; 2011.
• Mahajan BK. Methods in biostatistics. 7th ed. New Delhi: Jaypee Brothers Medical Publishers; 2010.
• Bhuyan, Dhrubajyoti&Dua, Neha & Kothari, Tejal. (2015). Epidemiology and biostatistics: Fundamentals of research methodology. Open Journal of Psychiatry & Allied Sciences. 7. 10.5958/2394-2061.2015.00022.1.
• Sundaram KR, Dwivedi SN, Sreenivas V. Medical statistics principles and methods. New Delhi: BI Publications; 2010.
• Villeneuve, P.J., Paradis, G. &Muhajarine, N. Always better together: the Canadian Journal of Public Healthand the Canadian Society for Epidemiology and Biostatistics. Can J Public Health 111, 305–307 (2020). https://doi.org/10.17269/s41997-020-00362-x
• Hayat, M. J., Powell, A., Johnson, T., &Cadwell, B. L. (2017). Statistical methods used in the public health literature and implications for training of public health professionals. PloS one, 12(6), e0179032. https://doi.org/10.1371/journal.pone.0179032

The post ROLE OF BIOSTATISTICIAN AND BIOSTATISTICAL PROGRAMMING IN EPIDEMIOLOGICAL STUDIES appeared first on pepgra.

In brief

• Clinical research studies the effect of an intervention on a specific population or the causation-association between a predicting variable and a health condition.
• The results of these studies are analysed by scientific statistical methods and statistical significance is determined.
• Starting with an adequate sample size is an important prerequisite to obtain accurate results and outcomes as determined statistically.
• Power analysis is widely recommended as a method for estimation of adequate sample size.

Clinical research and sample size

Clinical research services involves studying the effect of an intervention on a specific group of people with the condition under study. The study is conducted on a sample group of people and the results are extrapolated to the entire population. Hence, selection of inadequate sample size influences on the final outcomes and is an important determinant of the appropriateness of the methodology and Clinical study design.

If the sample size is too small, it may fail to detect significant associations and outcome of the research study, if any, becomes questionable for application to the entire population. And if the sample size is too large, the time, energy, and money required for the study is unnecessarily increased. Also, if the sample size is too large, it may also influence the accurateness of the outcome of the study. Thus, to achieve the desired outcome of any research study, i.e. to be able to get statistically significant outcomes that can be extrapolated to the entire population, an optimum sample size is a must. Statistical analysis planning for clinical trial involves use of power analysis for estimating desired sample size in a research study. Scientific medical writing services provide sample size calculations and power analysis support in the process of study designing.

Statistical analysis

Every research study begins with a hypothesis or an assumption called the null hypothesis or the research question. This is done to be able to apply statistical tests. Power analysis is done to estimate the required sample size to prove or disprove the null hypothesis or research question. During statistically analysis, two types of errors are possible. Type I error occurs when the null hypothesis is rejected incorrectly while type II error occurs when the null hypothesis is accepted incorrectly. Sample size estimation by power analysis is done to avoid both type I and type II errors.

This is done by determining the p value, which is the probability of obtaining a test statistic as large or larger than obtained in the study by chance if the null hypothesis is true. α is the predetermined level of statistically significance. The null hypothesis is rejected if they obtained p value is less than α and the null hypothesis is accepted if the p value is greater than α.

Power analysis and sample size estimation

Sample size is determined by: –

1. Hypothesis – The research question is formulated in the form of the null hypothesis and alternate hypothesis. Alternate hypothesis can be one-sided or two-sided. A two-sided hypothesis requires a greater sample size as compared to one sided hypothesis.
2. Effect size – If the association between the variable under study and the

outcome is strong, then a smaller sample size can pick up the association. However, if the association between the variable under study and the outcome is weak, then a larger sample size is required to pick up the association. To know this, one requires to study prior research done on the subject. If adequate material is not available, then one should consider doing a pilot study prior to taking up the research project.

3. Level of statistics significance – The researcher along with the statistician should establish the acceptable probability of making type I error (α)and type II error (β). Type I error is serious and the aim of statistically analysis is to avoid type I error. So, the level of significance is usually set at α = 0.05. As the significance criterion becomes more precise, the sample size required to pick up the association increases.
4. Power – Power refers to the sample size required to avoid type II error and probability to avoid this error is the statistical power of the study.

Sample size calculation  

The above four factors are used to calculate the sample sizealong with the null and alternate hypothesis and the statistical test chosen.Statistically analysis is then carried out by the statistician using standard textbooks or computer software programs. Medical writing companies provide power analysis and sample size estimation support.

Maximize the power with minimum sample size

• The sample size calculated should be a practical number and feasible for the Clinical research study. Continuous variables can be used for smaller sample sizes.
• In cohort studies, taking two measurements, one at the beginning and the other at the end of the study can be used for sample size calculation.
• Studying few precise variables with same or similar outcome helps to derive conclusions with a smaller sample size.

Sample size calculation should be done before during or after the study

Ideally sample size should be estimated prior to the study. Occasionally it is done during or sometimes even after the study. To make any research study worthwhile, efficient, and cost effective, it is best to design the study properly with respect to sample type and sample size. This is often based on assumptions made at the beginning of the study.

These may need to be revisited during the study however in an unbiased fashion and the research study should be completed unless interim results show extremely harmful effects on the subjects.

In case the research study yields negative results, one should revisit the sample size ensure that the study was not underpowered resulting in a type II error.

And so,

            Researchers should use power analysis and statistically analysis support to predetermine an effective sample size to make the study worthwhile.

References

• Zodpey SP. Sample size and power analysis in medical research. Indian J Dermatol Venereol Leprol. 2004;70(2):123-128.
• Osborn JF. Minimising errors in clinical studies by proper selection of sample size. Ann Acad Med Singapore. 1991;20(1):95-100.
• Carneiro AV. Estimating sample size in clinical studies: basic methodological principles. Rev Port Cardiol. 2003;22(12):1513-1521.
• Jones SR, Carley S, Harrison M. An introduction to power and sample size estimation. Emergency Medicine Journal 2003;20:453-458.
• Rao UK. Concepts in sample size determination. Indian J Dent Res [serial online] 2012 [cited 2020 Jul 22];23:660-4. Available from: http://www.ijdr.in/text.asp?2012/23/5/660/107385
• Browner WS, Newman TB, Hulley SB. Getting Ready to Estimate Sample Size: Hypotheses and Underlying Principles. In: Cummings SR, Browner WS, Grady D, Hearst N, Newman TB, editors. Designing Clinical Research: An Epidemiologic Approach. 2nd ed. Philadelphia: Lippincott Williams and Wilkins; 2001. p. 76

The post ESTIMATION OF SAMPLE SIZE AND POWER ANALYSIS FOR CLINICAL RESEARCH STUDIES appeared first on pepgra.

• Does the need for protection of patients and public health increasing? Unquestionably yes. The science of regulating and understanding this process is known as pharmacovigilance for all clinical research organization.
• Medical-related problems for the Clinical trial Monitoring Services have a considerable impact on the pharmacovigilance process as it detects, monitors and analyze the medical activities says the world health organization(WHO).
• Pepgra explains the general guidelines that apply to the EU legislation to promote the regulations in health care sectors.

Introduction

Pharmacovigilance system is an organization that fulfils all the legal process and regulations that are related to medicinal products and detecting its adverse effects. This system is responsible for the safety authorization for all medical products. For every specific process pharmacovigilance, there are several guidelines and safety measures and a committed module. Pepgra lists the basic ad general guidelines for these practices included in GVP with the help of Clinical Biostatistics services.

Quality cycle

The quality system must be having the following activities a pharmaceutical regulatory consulting services should look after,

• Planning of quality- creating new structures and integral planning for consistent processes
• Adherence of quality- Perform the tasks and duties according to the available quality requirements for the processes.
• Quality assurance and control- investigating and evaluating the structure of the processes should be practical and useful in medical care sectors.
• Improvements in quality- improving and correcting the procedure wherever it is necessary for healthcare data analytics services.

Quality objectives of pharmacovigilance (GVP)

• Documenting the legal requirements that are required to perform pharmacovigilance tasks and duties
• Protecting humans from the adverse effects causing from authorized medical products inside or outside the marketing exposure
• Building safe and practical applications of medical products to the patients and the public using pharmacovigilance literature search services.

Principles of pharmacovigilance guidelines

The principles listed are a few basic principles that should be processed during the design of structures while conducting all tasks in pharmacovigilance processes.

• To meet the needs of patients, medical professionals and the common public to analyze the safety regulations of medicines in the clinical study design process
• Provide guidelines for GVP ( Good Pharmacovigilance Practices)
• The hierarchies should give leadership for implanting quality system and motivate all the employers to the quality objectives for pharmacovigilance processes
• All persons in the organization must be involved in and encourage the pharmacovigilance processes based on task ownership and responsibility in a degree to their tasks and assigned duties accordingly.
• All staff members involved with the entire company should apply in continuous quality improvement according to their quality system cycle
• Resources and tasks must be organized as a structure and proceed in a manner that supports the risk-proportionate, proactive, continuous and integrated process of the pharmacovigilance Literature review search.
• The available evidence for the risk-benefit balance of medical products must be sought and  relates to the aspects, that could impact on the risk-benefit ratio, and product users must be considered for any decision-making processes
• A good organization must be foster to all the healthcare professionals, marketing authorization holders, public health organizations, patients, competent authorities, learned societies and other relevant bodies following the applicable legal provisions.

Responsibilities for the quality system of a pharmacovigilance organization

An adequate number of well-qualified professionals should be available for performing the pharmacovigilance tasks. They must possess certain qualities and fulfil specific duties for a systematic approach in the qualitative medical practices accordingly to quality cycle. They are

• To ensure that the documents providing quality check are relevant to the papers controls the approval and implementation of subjects.
• To check that sufficient resources are available for training is provided.
• To ensure that the premises, facilities and equipment are readily available for the processes.
• To verify that the given compliance management is sufficient.
• To provide adequate management of records.
• To review the pharmacovigilance is verifying the risk and adverse effects of the medicinal products and increasing quality system.
• To introduce the control measures wherever necessary.
• To guarantee that the given mechanisms and clinical study protocol exist timely.
• To escalate the safety concerns of the healthcare products.
• To identify the medical errors and non-adherence documents of the quality systems
• To investigate the requirements and action plan regularly
• To check the audits are cleared
• Motivating and creating interests to all the staff members based on shared values
• To appoint a good leader
• To provide freedom and right to speak to all the staff members
• To fulfil the staff’s requirements by investigating their contributions to the organization assign roles accordingly
• To check their potential abilities and give promotions for the development of the organization
• To check the functioning of the quality system and to make sure that the organization implements quality in the pharmacovigilance practices with a systematic approach

Conclusion

These are a few essential guidelines essential for in therapeutics clinical research acceptable pharmacovigilance practices. Patient’s health and safety is the most critical and challenging task for all the medical sectors. These can be achieved with the systematic approach of the pharmacovigilance services with the help of this Pepgra blog.

References:

1. Edwards, I. R. (2012). Good pharmacovigilance practice and the curate’s egg.
2. Xie, Y. M., & Tian, F. (2013). Interpretation of guidelines on good pharmacovigilance practices for European Union. Zhongguo Zhong Yao za zhi= Zhongguo Zhongyao Zazhi= China Journal of Chinese Materia Medica, 38(18), 2963-2968.
3. Naik, P., Umrath, T., van Stekelenborg, J., Ruben, R., Abdul-Karim, N., Boland, R., … & Stergiopoulos, S. (2015). Regulatory definitions and good pharmacovigilance practices in social media: challenges and recommendations. Therapeutic Innovation & Regulatory Science, 49(6), 840-851.

The post Guidelines on Virtuous Pharmacovigilance Practices appeared first on pepgra.

In-Brief:

• The patient registry is a specific study or a detailed survey of research question or hypothesis generally.
• Clinical research statistical service is a collection of data from a particular population; the following details explain the patient registry and their methodological and operational aspects.
• Pepgra strengthens your knowledge about the patient registries in the medical field to develop future clinical aspects and studies in the clinical biostatistics and programming.

INTRODUCTION

Patient registry is a coordinated system takes place using observational methods to acquire unwavering data on patient population based on the specific disease, exposure and conditions. It follows the time. The patient disease registries are created by public organizations, including educational institutions, medical research association and clinical study design. The overall objectives may vary such as to explain the natural history of a disorder, to analyze the impacts of disease on patients health and routine life, to find patients with an affinity for new treatments. The registry also used for the evaluation of the safety of medicines and in the Clinical Biostatistics service.

PATIENT DISEASE REGISTRIES

Our biostatistics consulting services consider these essential factors to create and maintain a patient registry they are.

• Patient population
• Time elements
• Core data elements
• Terminologies
• Quality management
• Safety analysis
• Governance

PATIENT POPULATION

The patient population should be with more excellent care, and various factors influence the selection of community. It is vital to ensure comprehensive enrolment of patients and bias selection with the Clinical Biostatistics & Statistical Programming Service during the therapeutics of clinical research.

The four essential steps to be carried out in our biostatistics programming services are.

• To create a logical definition on selected population.
• Convert logical description into the operational definition
• Make a process in which enrolled patients should represent the working definition
• Complete follow up on each patient to gather a detailed data

TIME ELEMENTS

The accurate follow up on date and time is essential in the Biomedical research as it is the primary factor for the computation of period critical to the valid analysis of data includes time difference between registry entry and exposure to medicines, different treatment time and the onset of adverse events on a particular interest, recovery from an adverse effect, effectiveness time, clinical improvement time etc.

Clinical Biostatistics Consulting Services have the time elements that varies based on disease and various factors but there are some major time elements should be present in any registry are:

• Patient dates include the date of birth, date of death, date of pregnancy, registry entry date, registry exit date, informed consent date.
• Disease dates include the date of the first symptom, date of early diagnosis, date of a definitive diagnosis, date of cure or improvement, date of relapse, date of comorbid events occurrence, date of the resolution of comorbid events
• Investigation dates include the date of test 1, test2, test 3
• Treatment dates include start date, stop date
• AESI dates include the date of any AESI occurrence, date of any AESI resolution
• Other event dates
• Observation dates include the date of follow up

CORE DATA ELEMENTS

Core data elements are the harmonized data form on all patients the information of the same disease in all registries should be identical to maintain standard data quality system, data exchange, formal data analysis for many patients and to compare the results of different registries in the Statistical programming CRO services.

The most critical data elements for patient registries are

• Patient data
• Disease
• Co-morbidities
• Disease-related treatments
• Other therapies
• Adverse effects
• Pregnancy

TERMINOLOGIES

The data harmonization, it is essential to change native languages to international terminologies, the guidelines for languages are followed based on the suggestion from EMA as per the clinical trial statistics service.

QUALITY MANAGEMENT

Clinical research biostatistics services quality management involves four main activities they are.

• Quality planning
• Quality assurance
• Quality control
• Quality improvement

There are four main requirements of data quality

• Consistency
• Completeness
• Accuracy
• Timeliness

SAFETY ANALYSIS

Statistical programming CRO’s Safety analysis involves

• Reporting of safety information
• Monitoring of adverse effects of special interests
• Aggregate analysis of adverse effects

GOVERNANCE

Most registries have a governance model relying on principles and constraints based on their mandate operating procedure, legal environment or funding sources. Registry coordinators, MAA/MAH and regulators, strengthen the use of registry data

CONCLUSION

Patient registry is an essential aspect of medicinal regulation as valuable data sources on disease and treatment. In the pilot phase and specific disease-related workshop, it is necessary to consider some factors such as a recommendation from the regulators on the type of data, an acceptable level of quality and requirement regards safety reporting guided by Pepgra.

References:

1. McGettigan, P., Olmo, C. A., Plueschke, K., Castillon, M., Zondag, D. N., Bahri, P., … & Mol, P. G. (2019). Patient Registries: An Underused Resource for Medicines Evaluation. Drug safety, 42(11), 1343-1351.
2. Culver, D. A., Behr, J., Belperio, J. A., Corte, T. J., de Andrade, J. A., Flaherty, K. R., … & Ryerson, C. J. (2019). Patient registries in idiopathic pulmonary fibrosis. American journal of respiratory and critical care medicine, 200(2), 160-167.
3. Rice, H. E., Englum, B. R., Gulack, B. C., Adibe, O. O., Tracy, E. T., Kreissman, S. G., & Routh, J. C. (2015). Use of patient registries and administrative datasets for the study of pediatric cancer. Pediatric blood & cancer, 62(9), 1495-1500.

The post Uses of Patient Registries for Clinical Studies to Acquire Unwavering Data appeared first on pepgra.

In-Brief:

• Healthcare data analytics solutions provide a powerful approach to understand the spectrum of disease with applications in observational and analytic epidemiology, screening and diagnosis randomized clinical trials, and prognosis in the clinical research organization.
• Causes of measurement errors of biomarkers differ with the individual to the laboratory. Discussing problems that affect the biomarker analysis along with recommendations on how to concern with bias and confounding
• This Pepgra blog review describes the concerns before using biomarkers in clinical investigation.

Introducing Biomarkers

Biological markers are “cellular, biochemical alterations that are accountable in biological media such as human tissues, or fluids.” the definition broadens as an indicator of normal biological processes, or pharmacological responses, pathogenic processes to a therapeutic intervention.  Biomarkers include tools that can aid in understanding the prediction, diagnosis, progression, cause, regression, or outcome of therapeutics of disease. In the nervous system, there is a wide range of techniques that gain information about the brain in a healthy and diseased state. Healthcare Analytics Services may involve measurements directly on biological media or sizes such as brain imaging which do not include a direct sampling of physical media using Clinical trial Monitoring Services.

Generations of physicians, epidemiologists, have used biomarkers, and scientists to study and treat human disease. The benefits of biomarkers in the diagnosis and management of cardiovascular disease, immunological and genetic disorders, infections, and cancer are well known. The neuroscientists have relied on biomarkers to assist in the diagnosis and therapeutics of nervous system disorders and to investigate their cause. The rapid growth of cellular biology and laboratory has expanded using the application of technically advance biomarkers.  Molecular biomarkers will be in clinical investigators, provide a dynamic and powerful approach to understanding the spectrum of neurological disease with obvious applications in analytic epidemiology, diagnosis, and disease management, clinical trials and disease prevention.

Practical considerations before using biomarkers:

• Errors in Measurement

Improper measurement of the biomarker would naturally lead to low validity of the relation to the disease. There are many types of errors other than those errors that occur in the cellular laboratory. Problems for transporting the specimens to the laboratory can alter the measurement of the biomarker. Storing samples improperly or changes in storage surroundings can also disturb the size of biomarkers. Technicians handle most of the specimens, and so appropriate training of personnel is there. Finally, receipt and control errors such as in the transcription of identification numbers if done by hand can always be a source of error. A well-organized procedures manual outlining the details for documentation, storage, monitoring of specimens and maintaining records, can alleviate many of these issues. The laboratories in an institute, the quality assurance and quality-control program conduction to reduce measurement errors with Clinical Biostatistics services.

• Bigotry

Healthcare data analytics services evade the bias that occurs in any study, with the help of biomarkers. When biases arise without regard to the outcome, so-called nondifferential bias, the effects on the survey are less severe but favour the null hypothesis of no association. Problems occur when the availability of the biomarker is differentially related to either the disease or exposure of the specimen acquisition, storage, measurement, or specific procedures differ with the disease compared to those without the disease. Differential biases tend to favour an association in either direction; the real relationship has a high response rate from all cases and controls have maintenance. The investigators should have an objective review board review and monitor the conduct of the study, observing possible biases in subject participation or specimen ascertainment to reduce such biases for pharmaceutical regulatory consulting services.

• Astounding

Astounding is the failure to identify factors that may change the measurement of the biomarker. It can be internal, the weight of the subject, or external, a batch of laboratory kits used. Individual properties of biomarkers should influence the choice and interpretation for its inclusion in any investigation. The effects of confounders like age, gender, diet, and other metabolic factors must have examinations before initiating the study. Biologic stability is critical mainly if the biomarker storage for any length of time. Banked serum or plasma is of great value in any review unless it affects the pharmacologic properties of the biomarker in the healthcare data analytics services.

For example, some nutrients such as vitamins do not store well because they are light-sensitive. Storage of all tissues, including lymphocytes and extracted DNA, can be expensive, and the evaluating stability of the biomarker if storage needed for extended intervals. These are often overlooked in the analyses and can seriously affect the outcome. One should use data on potential confounders when designing the study and collect relevant internal and external information that might affect the measurement of clinical study design, including the analysis of the relation between the biomarker and the outcome of interest.

• Price

The choice of the biomarker for research should be guided by the scientific question and by the financial resources. Cost is always a concern. In a small clinical trial, this may be important; if an epidemiologic study includes thousands of subjects, the price can be quite high unless the laboratory procedure is automated and relatively simple. In fact, for some investigations, larger sample sizes can bring down the cost per subject. It generally implies that the biomarker is readily available, and its inclusion in the study is feasible. For example, automated procedures have made the inclusion of lipid profiles in clinical studies of stroke quite possible. Methods have improved to the point that a “finger-stick” can provide the necessary amount of blood. Healthcare data analytics works depends on the type of investigation, researchers should have an idea of the false-positive or false-negative profile of the biomarker.

As might be expected “false positives” create extra work regardless of whether it is a biomarker of exposure, susceptibility, or disease. “False negatives” increase the overall cost of the study. Tolerance for this problem depends on the funding available even in therapeutics of clinical research.

• Acceptability

Healthcare data analysis service provides data from biomarkers of human tissues or body fluids, the choice of biomarkers are not trivial. Biomarkers possess some degree of risk. In Healthcare Analytics Solutions the clinical trials, that is less a concern because the patient will possibly benefit from the “new treatment.” In quasi-experimental studies, the source of the biomarker may be critical. Body fluids, such as blood and urine, are usually well tolerated.

However, biopsy and collection of cerebrospinal fluid is more complicated and associated with slight risks. Risk-benefit will be an issue for the investigator to resolve. Pilot studies are always beneficial for convincing institutional review boards that your research is safe and that the risk-benefit ratio favours a benefit for biomedical research.

Conclusion:

Many studies using biomarkers never achieve their full potential because of the failure to adhere to the same rules that would apply for the use of variables that are not biological. The development of any biomarker should precede or go in parallel with the standard design of any epidemiological project or healthcare analytics protocol and the formal investigations listed in this Pepgra blog.

References:

1. Dobbin, K. K., Cesano, A., Alvarez, J., Hawtin, R., Janetzki, S., Kirsch, I., … & Zhang, J. (2016). Validation of biomarkers to predict response to immunotherapy in cancer: Volume II—clinical validation and regulatory considerations. Journal for immunotherapy of cancer, 4(1), 77.
2. Gazerani, P. (2019). Current evidence on potential uses of MicroRNA biomarkers for migraine: from diagnosis to treatment. Molecular Diagnosis & Therapy, 23(6), 681-694.
3. Mayeux, R. (2004). Biomarkers: potential uses and limitations. NeuroRx, 1(2), 182-188.

The post Practical Guidance for Biomarkers into Early Phase Clinical Research Purposes appeared first on pepgra.

• There are two main parts in medical writing, medical communication and regulatory writing.
• Pepgra blog focuses on regulatory writing, which involves preparing the clinical study and regulatory submission documentation and its importance.
• Regulatory writing is one of the critical factors in the clinical research field and global regulatory service

Introduction:

Regulatory medical writing produces a wide variety of clinical documents during the life‑cycle of treatment, opening with describing and writing data from clinical trials and preparing regulatory suggestion documents. If a regulatory authority accepts the therapy, regulatory writers also produce post-approval reports on patients’ treatment. Examples of clinical documents produced by Medical writing services most commonly include Investigator Brochures (IBs), the Common Technical Document (CTD) Clinical Study Protocols, Clinical Study Reports (CSRs) using regulatory writing services.

Functions of the regulatory writer:

• The clinical writer’s critical capacity is to change the vast amounts of clinical information delivered during clinical preliminaries into accurate, reasonable and agreeable records for accommodation to the pertinent administrative specialists as a feature of the advertising application.
• Data should be introduced plainly and compactly, yet with the reasonable degree of detail to help the accommodation cycle.
• If conceivable, the clinical author ought to use standard layouts to guarantee the harmonization of phrasing and archives across the segment portions of the accommodation.
• Clinical Study Reports are the establishment of most of the administrative entries. The clinical writer may likewise be needed to plan incorporated synopses of wellbeing and viability. Of note, the ISS/ISE are coordinated investigations of the information from the clinical examinations and are for the most part a lot of superior archives than the viability and wellbeing rundowns introduced in Modules 273 and 274.
• Allowing adequate time for the clinical author, related to different individuals from the undertaking group, to design, compose and survey records is at last savvy.
• Documents that are hurried will contain mistakes and exclusions, bringing about inquiries and solicitations for explanation sometime later. It can prompt expensive postponements.
• Compliance with nearby administrative prerequisites ought to be tended toward the beginning of the creative cycle to guarantee that any country’s precise necessities are sufficiently met. For instance, close down, and index prerequisites can fluctuate between nations.
• Writing ought to be a community-oriented cycle. An outside advisor can viably oversee the majority of the work is essential. Contribution from the support as far as the crucial comprehension of the product and the therapeutic zone under assessment is vital to generally speaking achievement.
• The report audit cycle can be hard to control since time is scant and archives complex. Audit courses of events ought to be reachable and concurred already. Report survey gatherings are an effective method to arbitrate remarks to guarantee that all issues raised are talked about, and enough tended to.
• Further down the line, reacting to questions raised by the administrative assessors requires a comparably realistic methodology. Reactions ought to be all around developed, compact and spotlight on the central issues. Additional data should be given where significant, yet the arrangement of colossal measures of additional information ought to stay away from as this will serve to cloud the issues.

Importance of regulatory writing in clinical research

• The importance of preparing high-quality regulatory papers is often assessed. A CSR is the final product of many months and at times years of hard work by education teams, and has to review the conduct and outcomes of a clinical study transparently and impartially.
• A well-written CSR will support the subsequent production of high-quality proposal papers using medical devices writing services. Equally, poorly written or presented controlling documents may lead to interruptions in regulatory support and price the sponsor stage and money.
• In the ever-changing regulatory atmosphere, experienced regulatory writers can add importance to clinical study documentation production.
• As an essential piece of the clinical exploration measure, the administrative writer can go about as a group of information at each progression, from convention improvement to the medication accommodation measure.
• Regulatory scholars are gifted at meeting worldwide administrative prerequisites and holding fast to direction records.
• They comprehend, decipher, and sum up regularly complex logical and measurable information while giving clear direction to clinical examination groups, which for the most part incorporate specialists from different fields, for example, clinical/clinical, measurements, administrative issues, pharmacovigilance, and pharmacology.
• Some administrative writer becomes specialists in a specific therapeutic zone or illness and can give priceless experiences to the backers and study groups.
• The regulatory writer can likewise perform quality control (QC), peer survey and offer other article help on archives delivered by supports and additionally study groups, whenever required.

Conclusion:

Pepgra offers a wide range of Medical Writing services, including clinical and regulatory writing, supporting any or all pharmaceutical development life-cycle stages. Our Pepgra Writers deliver accurate, timely and cost-effective documents to the highest ethical and clinical writing service.

References:

1. Gordon, B. G., Kessinger, A., Mann, S. L., & Prentice, E. D. (2003). The impact of escalating regulatory requirements on the conduct of clinical research. Cytotherapy, 5(4), 309-313.
2. Griggs, R. C., Batshaw, M., Dunkle, M., Gopal-Srivastava, R., Kaye, E., Krischer, J., … & Merkel, P. A. (2009). Clinical research for rare disease: opportunities, challenges, and solutions. Molecular genetics and metabolism, 96(1), 20-26.
3. Jacob, M. A. (2017). The strikethrough: an approach to regulatory writing and professional discipline. Legal Studies, 37(1), 137-161.
4. Wood, L. F., & Foote, M. (Eds.). (2009). Targeted regulatory writing techniques: clinical documents for drugs and biologics. Springer Science & Business Media.

The post How regulatory writing plays an essential role in clinical research? appeared first on pepgra.

• Fronting ever-increasing costs of running a clinical trial, sponsors must guarantee they are correctly directing their financial plan and resolving the highest risk areas while preserving patient safety and data reliability in Patient recruitment for clinical trials.
• How can sponsors implement a vigorous process to allow earlier documentation of emerging risks during a trial?
• Pepgra blog covers five tips for significant risk levels, categorizing risk and maintaining oversight to confirm that risks and responses are correctly identified, documented, tracked, and achieved throughout the patient recruitment companies’ life cycle and offers patient recruitment clinical trials.

Introduction:

Risk management includes a series of activities or processes undertaken through a clinical trial’s life cycle to recognize, evaluate, monitor, switch, prevent, moderate, communicate, and analyze any factor that threatens the test’s quality. It pertains to participants’ risks and all other steps related to the prosecution, especially the trial data’s quality, consistency, and integrity. Risk management must start at the trial opening so that risk justification can be a part of the protocol and additional essential forms and clinical research patient recruitment process.

Top 5 tips for managing risks in clinical studies

Outlining your levels of risks

Risk is a natural incidence in any trial at the program, study level, site level and working level. Defining it is the first step to attaining control. At a high level, the full risk of a study can be assessed. For eg, a Phase II oncology study would specify a higher risk that needs a more rigorous monitoring strategy than a low-risk Phase IV observational work.

Study risks can also differ based on known, high-performing spots versus new sites with less knowledge of clinical trial recruitment companies‘ helpful area. Finally, operational risk can be projected based on real-time patient acceptance data to compare actual presentation to the forecast.

Evaluating and categorizing risk

The distinct levels of risk at the study, site and working levels, and overall risk valuation can be produced for a protocol and across a program. The Risk Assessment Categorization Tool, One module of the platform, relates an algorithm to generate an overall category score based on the chance, impact and detectability of the risks, permitting sponsors to make a data-driven decision about the most suitable intervention levels.

Concentrating on essential areas of risk

After risks are considered, they can also be riddled through Monitoring’s user interface to highlight those with the most significant impact on a study, enabling sponsors to redirect resources appropriately. With Risk and Issue Management, all study team members can create, view, and manage real-time issues from a single interface using patient recruitment services.

For instance, if the framework recognizes key risks as inordinate underreporting and patient maintenance, the support and CRO can cooperate to guarantee they are checking and controlling these regions for the examination duration. This cycle empowers early usage of preventive activities and can help limit quality disappointments.

Observing and controlling risks

While observing the risks defined and categorized, it’s essential to monitor the changes’ status throughout a study’s life. With automatic metrics, the system makes recommendations to escalation, reduction, or maintenance Monitoring at a site using essential risk indicator scoring for clinical trials patient recruitment. It helps the trial’s project team take action and allows sponsors and CROs to prioritize and target particular areas. The automated process also helps manage growth paths and fulfils regulatory guidance surrounding adapted and triggered site monitoring.

Estimating the efficiency of risk management

As risks are identified, categorized and achieved over time, sponsors and their supportive CRO can view the increasing actions taken month over month, assessing their level of success and determining if the activities accomplished helped bring a site back to a lower risk level in clinical study recruitment.

Ideally, sponsors should see that a more significant proportion of sites are moving into the standard and low-risk categories over time, with an overall decrease in the high-risk types. This transparency level helps with continuous improvement practices and demonstrates full control and compliance with regulatory agencies.

Conclusion

With today’s extensive global trials and virtual project teams using several systems acting in separation, sponsors need an effective method to quicken decision making and close the gaps in trial error. Unifying quality and risk supervision across a single study or a portfolio of studies support revealing signals before they become general issues that disrupt a trial.

References:

1. Sundar, S., & Olliaro, P. L. (2007). Miltefosine in the treatment of leishmaniasis: clinical evidence for informed clinical risk management. Therapeutics and clinical risk management, 3(5), 733.
2. Vincent, C., Taylor-Adams, S., Chapman, E. J., Hewett, D., Prior, S., Strange, P., & Tizzard, A. (2000). How to investigate and analyze clinical incidents: clinical risk unit and association of litigation and risk management protocol. Bmj, 320(7237), 777-781.
3. Hall, J. A., Salgado, R., Lively, T., Sweep, F., & Schuh, A. (2014). A risk-management approach for effective integration of biomarkers in clinical trials: perspectives of an NCI, NCRI, and EORTC working group. The Lancet Oncology, 15(4), e184-e193.

The post How to manage risks in clinical studies? Top 5 tips appeared first on pepgra.