Top 5 Biostatistical Methods Every Researcher Should Know

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Are you diving into a research project and feeling overwhelmed by the world of biostatistics? You’re not alone! Biostatistics is the backbone of clinical trials, epidemiology studies, and healthcare research, turning raw data into meaningful insights. Whether you’re a researcher, a biotech startup, or a public health professional, mastering key biostatistical methods can make or break your study’s success.

At BioepiNet, we’ve helped countless clients navigate complex data with our biostatistical consulting services. In this post, we’ll break down the top 5 biostatistical methods every researcher should know, complete with practical tips and real-world applications. Let’s simplify the stats and empower your research!

Why Biostatistical Methods Matter

Biostatistical methods help researchers analyze data, test hypotheses, and draw reliable conclusions. From designing clinical trials to evaluating public health interventions, these methods ensure your findings are robust and credible. According to a 2023 study by the National Institutes of Health (NIH), over 70% of clinical trial failures stem from poor statistical design or analysis. That’s where expertise, like our healthcare statistical consulting, comes in handy.

Ready to level up your research? Here are the five must-know biostatistical methods, explained in plain English.

1. Regression Analysis: Uncovering Relationships in Data

What Is It?

Regression analysis explores how one variable (e.g., a patient’s blood pressure) is influenced by others (e.g., age, diet, or medication). It’s a go-to method for identifying trends and making predictions.

Why It’s Important

  • Versatility: Used in clinical trials, epidemiology, and healthcare analytics.

  • Types: Linear regression (for continuous outcomes), logistic regression (for yes/no outcomes), and Cox regression (for survival data).

  • Example: A study in The Lancet used logistic regression to predict COVID-19 hospitalization risks based on patient demographics.

How to Use It

  • Step 1: Identify your dependent (outcome) and independent (predictor) variables.

  • Step 2: Use software like R or SAS (tools we love at BioepiNet) to run the analysis.

  • Step 3: Interpret the results—look at p-values and coefficients to understand relationships.

Pro Tip: Avoid overfitting by selecting only relevant variables. Need help? Our biostatistical consulting team can guide you through model selection.

Engagement Question: Have you used regression analysis in your research? Share your experience in the comments!

2. Hypothesis Testing: Proving (or Disproving) Your Ideas

What Is It?

Hypothesis testing checks if your research findings are statistically significant. It compares a null hypothesis (no effect) against an alternative (there’s an effect).

Why It’s Important

  • Core of Research: Used to validate results in clinical trials and observational studies.

  • Common Tests: t-tests (comparing two groups), ANOVA (multiple groups), and chi-square tests (categorical data).

  • Example: A 2024 JAMA study used t-tests to compare drug efficacy between treatment groups.

How to Use It

  • Step 1: Define your null and alternative hypotheses (e.g., “Drug A has no effect” vs. “Drug A reduces symptoms”).

  • Step 2: Choose the right test based on your data type.

  • Step 3: Calculate the p-value. If it’s <0.05, you can reject the null hypothesis.

Pro Tip: Watch out for multiple testing issues, which can inflate false positives. Our clinical trial statistical analysis services ensure accurate p-value adjustments.

Fun Fact: Did you know p-values don’t measure the size of an effect? They only tell you if it’s statistically significant!

3. Survival Analysis: Tracking Time-to-Event Data

What Is It?

Survival analysis studies the time until an event occurs, like patient recovery or disease recurrence. It’s a staple in oncology and epidemiology.

Why It’s Important

  • Handles Censored Data: Accounts for patients who drop out or haven’t experienced the event by study’s end.

  • Key Tools: Kaplan-Meier curves (visualizing survival) and Cox proportional hazards models (predicting risks).

  • Example: A New England Journal of Medicine study used survival analysis to evaluate cancer treatment outcomes.

How to Use It

  • Step 1: Collect time-to-event data (e.g., months until remission).

  • Step 2: Plot a Kaplan-Meier curve to visualize survival probabilities.

  • Step 3: Use a Cox model to assess factors affecting survival, like age or treatment type.

Pro Tip: Ensure your data meets the proportional hazards assumption. Our epidemiology consulting services can help validate your models.

Engagement Question: What’s the trickiest part of survival analysis for you? Let us know below!

4. Bayesian Statistics: A Modern Approach to Uncertainty

What Is It?

Bayesian statistics uses prior knowledge (prior probabilities) combined with new data to update beliefs about an outcome. It’s gaining traction in personalized medicine and AI.

Why It’s Important

  • Flexibility: Ideal for small sample sizes or complex models, like those in machine learning consulting for healthcare.

  • Real-World Use: Used in drug development to assess treatment efficacy with limited data.

  • Example: A 2025 Nature article highlighted Bayesian methods in optimizing clinical trial designs.

How to Use It

  • Step 1: Define a prior probability based on existing research or expert opinion.

  • Step 2: Update it with new data using Bayes’ theorem.

  • Step 3: Interpret the posterior probability to make decisions.

Pro Tip: Bayesian methods require careful prior selection to avoid bias. Our AI model validation services ensure robust Bayesian analyses.

Did You Know? Bayesian stats powered early COVID-19 vaccine trials, allowing faster results with smaller datasets.

5. Mixed Models: Handling Complex, Hierarchical Data

What Is It?

Mixed models (or mixed-effects models) analyze data with both fixed effects (e.g., treatment type) and random effects (e.g., patient variability). They’re perfect for longitudinal studies.

Why It’s Important

  • Versatility: Handles repeated measures, like patient data collected over time.

  • Applications: Common in clinical trials, epidemiology, and real-world evidence consulting.

  • Example: A BMJ study used mixed models to analyze patient outcomes across multiple hospitals.

How to Use It

  • Step 1: Identify fixed and random effects in your data.

  • Step 2: Use software like R’s lme4 package to fit the model.

  • Step 3: Interpret results, focusing on effect sizes and variance components.

Pro Tip: Mixed models can be complex to interpret. Partner with our healthcare statistical consulting team for clear, actionable insights.

Engagement Question: Have mixed models helped your longitudinal studies? Share your tips below!

How BioepiNet Can Help

Mastering these biostatistical methods can transform your research, but it’s not always easy to apply them correctly. That’s where BioepiNet comes in. Our PhD-led team specializes in biostatistical consulting, offering end-to-end support for clinical trials, epidemiology studies, and advanced analytics. From study design to data interpretation, we’ve got you covered.

Why Choose BioepiNet?

  • Expertise: Over a decade of experience in biostatistics and epidemiology.

  • Tailored Solutions: Customized analyses for biotech, pharma, and public health.

  • Advanced Tools: Expertise in R, SAS, Python, and AI-driven analytics.

Ready to take your research to the next level? Contact us today for a free consultation!

Bonus Tips for Researchers

  • Stay Updated: Follow journals like Biostatistics for the latest methods.

  • Use Visuals: Graphs (e.g., Kaplan-Meier curves) make results easier to communicate.

  • Double-Check Assumptions: Every method has assumptions (e.g., normality for t-tests). Violating them can skew results.

  • Collaborate: Work with a biostatistical consultant to avoid costly errors.

Conclusion

Biostatistical methods like regression analysis, hypothesis testing, survival analysis, Bayesian statistics, and mixed models are essential tools for any researcher. By understanding and applying these techniques, you can unlock powerful insights from your data and drive impactful discoveries.

At BioepiNet, we’re passionate about helping researchers succeed. Whether you need help with study design, data analysis, or clinical trial support services, our team is here to simplify the process and deliver results.

Epidemiology Consulting for Digital Health & AI: RWE, Validation Studies, and FDA-Ready Evidence

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In the race to revolutionize healthcare, Digital Health and AI Health companies are redefining how we detect, monitor, and manage disease. From predictive algorithms to virtual care platforms and AI-powered diagnostics, these innovations promise to transform clinical care. But one challenge continues to separate visionary tech from clinical credibility: the need for rigorous evidence.

Why Digital Health Startups Need Epidemiology Consulting

Investors, regulators, and payers want proof that your solution works—not just promising demos. Epidemiology helps you:

  • Design FDA-aligned real-world studies
  • Validate AI model performance in clinical populations
  • Demonstrate generalizability, fairness, and outcomes impact
  • Support publications, grant funding, and payer value discussions

Real-World Evidence (RWE) Design for Digital Health

We design retrospective cohort studies, case-control analyses, and pragmatic trials that generate regulatory-grade RWE—using EHRs, claims data, or platform-based analytics.

AI Model Validation and Subgroup Analysis in Health Tech

We evaluate your AI’s generalizability and performance across populations with subgroup analysis, calibration metrics, and transparent reporting frameworks.

Protocol Design and FDA Strategy

We help you prepare robust, credible protocols that meet FDA, payer, and partner expectations—streamlining validation and regulatory alignment.

Publication and Evidence Communications

We support manuscript drafting, journal submission, and conference preparation—turning your data into publishable clinical impact stories.

Partner with BioEpiNet for Epidemiology Consulting

Your product may be brilliant—but without evidence, it won’t scale. Let’s validate your work with strategy, science, and speed. Learn more about our epidemiology consulting services.

Contact BioEpiNet:
📞 (312) 709‑7336
🌐 www.bioepinet.com
📧 [email protected]
👉 Schedule a consultation

Epidemiology Consulting Services for Pharma & Biotech: Real-World Evidence, Study Design, and Publication Support

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In a world where data drives approvals, partnerships, and payer access, mid-size pharmaceutical and biotech companies are under increasing pressure to generate robust, defensible, and publication-ready evidence—without the resources of Big Pharma.

You may be advancing a first-in-class therapeutic, leading a promising post-marketing program, or preparing to submit to regulators. Yet you face real constraints: lean internal teams, tight timelines, and complex demands from regulators, payers, and investors alike.

Why Mid-size Pharmaceutical Companies Need Epidemiology Consulting

Mid-size firms are the engine of innovation in life sciences. But they are being asked to deliver more, with less:

How BioEpiNet Adds Strategic Value

We plug into your existing Medical Affairs, RWE, or Clinical teams as your expert epidemiology partner:

1. Real-World Evidence Generation

We design and analyze studies that meet FDA/EMA standards for post-marketing surveillance, label expansion, and comparative effectiveness.

2. Systematic Literature Reviews (SLRs)

We deliver rapid, PRISMA-compliant literature reviews and evidence gap analyses to support market access and regulatory submissions.

3. Protocol & Statistical Analysis Plan (SAP) Development

We draft and refine protocols and SAPs to support registries, Phase IV studies, and investigator-led RWE programs.

4. Post-marketing Study Design & Safety Analytics

We support PSURs, signal detection, and RMPs with statistically sound observational methods.

5. Manuscript & Scientific Communication Support

From journal submissions to conference abstracts, we help transform your evidence into compelling, publication-ready outputs.

Real-World Examples of Our Impact

  • RWE study for label expansion in cardio-renal drug class
  • Meta-analysis of real-world safety data for CNS pipeline
  • Protocol & SAP development for oncology registry
  • SLR and value narrative for rare disease market access

Contact BioEpiNet for Epidemiology Consulting

If you’re preparing for your next submission, payer conversation, or evidence milestone, partner with BioEpiNet—your trusted epidemiology and biostatistics resource for pharma and biotech.

BioEpiNet
Based in Chicago — Serving clients nationwide
📞 (312) 709‑7336
🌐 www.bioepinet.com
📧 [email protected]
👉 Schedule a consultation

Machine Learning Consulting for Healthcare & Research: Model Design, Validation & Deployment

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As healthcare embraces AI and machine learning, effective and trusted model development becomes essential. At BioEpiNet, our ML consulting goes beyond algorithms—it integrates clinical insight, epidemiologic rigor, and deployment strategy to build models that perform reliably, ethically, and in real-world settings.

Why Machine Learning in Healthcare Needs Expert Guidance

  • Clinical validity: Models must work across diverse patient populations and settings
  • Regulatory scrutiny: FDA expectations for algorithm transparency and validation are increasing
  • Publication readiness: Journals now expect clear methodology and reproducibility (e.g., in bioinformatics)
  • Deployment complexity: Integration into workflows demands robust testing and monitoring

How BioEpiNet Supports Your ML Journey

Our team bridges domain expertise and advanced methods. We help with:

1. Model Selection & Development

Choose the right algorithm—whether random forests, neural networks, or support vector machines—based on data structure, clinical context, and predictive goals. We follow best practices in reproducible data pipelines and feature engineering.

2. Validation, Bias, and Generalizability

We perform:

  • Cross-validation and holdout testing
  • Subgroup performance and fairness assessments
  • Calibration analysis using clinical benchmarks

3. Regulatory & Clinical Alignment

Our protocols align with FDA guidance for digital health tools and AI, ensuring transparency and readiness for deployment. We help you prepare Pre-Sub meeting materials or technical validation documents.

4. Deployment Strategy & Monitoring

We guide real-world implementation with:

  • Human-in-the-loop pilot studies
  • Monitoring pipelines for drift and performance decay
  • Clinical partnerships and stakeholder feedback loops

5. Publication & Funding Support

From drafting Methods sections to preparing figures and writing grant components, we help translate your ML insights into high-impact manuscripts, funding proposals, or white papers.

Who Should Work with Us?

  • Digital Health & AI startups building diagnostic tools
  • Health systems & EHR platforms seeking clinical insights
  • Pharma & biotech using ML for RWE, biomarker development, or predictive safety models
  • Academics & CROs developing translational ML research or pilot actionable algorithms

ML Use Cases We’ve Supported

  • Cardiovascular risk model validation on EHR datasets
  • Clinical outcome classifier for ICU patient triage
  • Genomic subtype prediction using neural nets and ML interpretability
  • Workflow-integrated AI tool for remote patient monitoring

At BioEpiNet, our Machine Learning consulting is powered by a multidisciplinary team of PhD data scientists, biostatisticians, epidemiologists, and MD-clinicians. This unique blend of technical and clinical expertise ensures that every model we help build is not only algorithmically sound, but also scientifically valid, clinically meaningful, and aligned with regulatory and ethical standards.

Learn more about our ML services: Machine Learning Consulting

Ready to Build Trusted ML in Healthcare?

Contact BioEpiNet and let’s discuss your vision—whether it’s a pilot validation study, FDA readiness plan, or publication strategy.

Contact Us:
📞 (312) 709‑7736
🌐 www.bioepinet.com
📧 [email protected]
👉 Schedule a free consultation

Biostatistical Consulting: Why Expert Statistical Support is Critical to Research Success

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In today’s evidence-driven world, biostatistics is not just a technical skill—it’s the backbone of credible science. Whether you’re a pharma company preparing a regulatory submission, a digital health firm validating a clinical algorithm, or a researcher designing a trial, the quality of your statistics can make or break your project.

That’s why organizations across life sciences and healthcare turn to biostatistical consulting firms like BioEpiNet—to get expert guidance, minimize errors, and produce defensible, publication-ready results.

What is Biostatistical Consulting?

Biostatistical consulting applies advanced statistical methods to solve complex problems in health research. It ensures:

Who Needs Biostatistics Consulting?

  • Pharma & Biotech – for clinical trial design, SAPs, interim analyses, and regulatory submissions
  • Digital Health & AI Companies – to validate models and support FDA readiness
  • Academic Researchers – for grant proposals and publication support
  • HEOR & Medical Affairs – to support real-world evidence and value demonstration
  • Startups & CROs – needing expert statistics without full-time staff

What BioEpiNet Offers

1. Study Design & Sample Size Estimation

We support protocols, endpoint strategy, and power calculations for clinical and observational studies. Visit our services page for more.

2. Statistical Analysis Plan (SAP) Development

We write regulatory-grade SAPs aligned with CDISC SDTM and FDA guidance.

3. Advanced Statistical Modeling

We apply time-to-event, GLM, GEE, mixed-effects, Bayesian models, and causal inference techniques tailored to your study. For methods like GEE, we ensure proper model assumptions are met.

4. Data Analysis & Interpretation

We deliver clean, reproducible code in R, SAS, or Python with clear narrative reporting for clinical and regulatory audiences.

5. Manuscript & Submission Support

We help you publish your results or prepare regulatory briefing books with concise statistical methods and clear figures/tables. For manuscript transparency, we follow CONSORT reporting standards.

Use Cases We’ve Supported

  • Power and SAP development for a Phase II oncology trial
  • Longitudinal PRO modeling in a DTx study
  • Validation of a cardiovascular risk prediction model
  • RWE safety data analysis for rare disease registry
  • Co-authoring statistical methods for peer-reviewed publications

Why Clients Choose BioEpiNet

  • PhD-level biostatisticians and MD epidemiologists
  • Fast turnaround and high responsiveness
  • Confidential, compliant, and publication-ready
  • Flexible pricing and scoped engagements
  • Clear statistical narratives for diverse audiences

Contact BioEpiNet for Biostatistical Consulting

Whether you’re preparing a study, regulatory package, or publication, BioEpiNet delivers expert statistical consulting with strategic insight. Learn more about our biostatistics support services.

Contact Us:
📞 (312) 709‑7336
🌐 www.bioepinet.com
📧 [email protected]
👉 Request a free consultation

Data Challenges in Businesses, Hospitals, and Healthcare

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Data analysis consulting services today not only play a huge role in business development but have the potential to revolutionize healthcare and hospitals in several ways. In the long run, businesses should be able to forecast the outbreak of a future pandemic and how it would impact business efficiency.

A data analytic software, for example, may help scrutinize patients’ prescribed medications to alert physicians and patients of wrong prescriptions. This minimizes human errors and spending trends in the healthcare sector.

However, recent events suggest that hospitals, businesses, and healthcare have dropped behind other sectors in data utilization. One study showed that data analytics consulting services are within reach of 95% of hospital and healthcare executives, but most have never thought of utilizing them.

A survey also showed that almost 60% of businesses surveyed did not know how much bad data can reduce business efficiency. This is because they don’t recognize the importance of data in the first place.

The indifferences of some health and business leaders to data are one of the biggest challenges that are peculiar to businesses, hospitals, and healthcare. To this end, we’ve compiled a list of challenges in these sectors. So let’s look at these challenges and how to solve them.

1. Conventional Practices

Conventional practices in the delivery of data services often force most major sectors to discountenance the potential of the data analytics consulting firms. In any case, businesses, hospitals, and healthcare are no exception. One straightforward explanation for this challenge is that while data can enhance the decision-making process, it’s often assumed to be far from being a solid practical support system.

For instance, most challenging clinical, healthcare, and business decisions need to involve many players and stakeholders. They’re bound to follow established rules designed as a result of teamwork. Data systems that disrupt their existing groundwork without providing a refined approach are less effective. Consequently, many efforts to ensure that most of these sectors employ data analysis consulting services have proved abortive.

For data analytics to create transformational change, the software designers must be well-informed about the context of use in the sectors mentioned earlier.

Positive revolution is unattainable without an open mind, and those who are not ready to learn new things cannot revolutionize anything. The industry leaders must also open their minds to provide an enabling environment for data-driven practices.

Know that we provide rich data analytics consulting services to get you started. By using our services, we can help educate and empower every key player within your industry.

2. Data Security

Data security also applies to businesses, healthcare, and hospitals. Once these industries figure out the significance of data, they focus on understanding and leveraging it without looking back. However, they tend to underestimate the potential hazards of collecting enormous data. This often includes privacy issues, data loss, and data breaches.

The security of your organization or hospital’s data is critical. Data security in these spaces is also one of the trending challenges in data analytics. Unsecured data sources can become an easy target point for questionable characters.

In any case, pay attention to security problems once you decide to utilize data analysis. Here are some tips that can help you to work around it:

  • Hire data experts from data analytics consulting firms to guard against potential risks.
  • Hire cybersecurity experts to protect the data when the need arises.
  • Mobilize certified professionals from data analytics consulting companies to conduct training events on data for your patients, physicians, and business stakeholders.
  • Have a go with data analytics software.

3. Gathering useful data

There’s data in every healthcare organization and business. This often becomes burdensome for doctors, healthcare practitioners, and business owners. The surplus data from different platforms make it difficult for stakeholders to narrow down and ascertain valuable insights. So they settle for unhelpful data, not the one that moves their enterprises forward.

You can hire experienced data analytics consulting companies that would carve out the data that can improve your enterprise. This will go a long way.

4. Data Visualization

It’s one thing to enter data into a database but a different matter to evaluate and visualize the data appropriately in order to make the most of it. After all, the efforts you expend on gathering data help you make sound decisions. Hence, data visualization is very crucial in data analytics and also demanding.

With stimulating data visualization, it’s easier for healthcare professionals and business owners to take up pieces of information. In this way, they can utilize it appropriately.

Color-coding, for example, is a well-known technique that typically produces quick results. Red typically means ‘risky.’ On the other hand, the green signal means “safe.”

Every healthcare organization and business should also consider excellent data presentation techniques. This includes charts that utilize the right proportions to explain contrasting figures.

Excellent instances of data visualizations include pie charts, heat maps, and many others. All these tools have unique ways to illustrate the information.

5. Integration

It can be challenging to manage data from different sources and channels. In this case, it becomes even more difficult if employees work from different spaces and utilize different rules. Hiring data analytics consulting firms would go a long way in making data integration easy.

Why Work With Us

Data analysis requires a great deal of know-how and practical knowledge. However,  it can be challenging to work around it without competent hands to manage the structure and tactics.

Of course, you need a team of experienced data experts to help overcome several data challenges plaguing your business or healthcare organization. The good news is that we have all these professionals at our fingertips.

Our team has professionals with extensive knowledge and experience in several data analytical practices, such as predictive analysis and data consolidation. You can be confident that we have all it takes to provide you with one of the best data analytics consulting services.

Don’t sleep on this. Hurry up and eliminate data challenges standing against your organization’s undertakings and growth today. Our service professionals will be glad to provide customized solutions to help you.

Contributions of Biostatistics and Epidemiology in the Management of Covid-19 Crisis

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Coronavirus (COVID-19) is a disease caused by a new strain of the coronaviridae family known as SARS-CoV-2. This microorganism was prior called 2019-nCoV and originally identified amid a respiratory illness epidemic in a Chinese city. The first official report of the disease to the World Health Organization (WHO) was on 31 December 2019. Shortly after the report, WHO declared the outbreak a global pandemic on March 11, 2020, the first designation since the body ruled H1N1 influenza pandemic in 2009.

Since December 2019, the novel virus has spread rapidly from Wuhan to 185 countries, causing over 179 000,000 infection cases. Also, more than three million deaths have been associated with the disease, and these numbers kept growing. The alarming morbidity and mortality rate accompanying the disease has also led to several economic meltdowns and recession. In subsequent sections of this resource, you’ll be exposed to the role of biostatistics and epidemiology in managing this pandemic crisis.

 

Role of epidemiologists and biostatisticians in gathering data and understanding the disease

The complexity and novel nature of the Covid-19 virus came with several challenges. On top of the list is how to gather reliable data about cases and provide a trend in formulating appropriate interventions. To bridge this gap, epidemiologists started with creating a case definition, a common way to define the disease across every continent. As soon as the condition became a global pandemic and relevant authorities reported cases to designated surveillance centers, experts began data collection, analysis, and interpretation. These public health surveys revealed more about the disease than the mere incidence rate. These data also served as a predictive tool in assessing the trend and possible hot zones for disease spread.

The collected data included basic demographic details such as name, age, address, and gender. It also contained details on symptoms, remedies, and reinfection, giving scientists an idea about the course and cause of the disease. After adequate data collection from ministries, hospitals, and other allied agencies, biostatisticians stepped in to analyze and make tangible inferences from the various information. During analysis, biostatisticians employ charts and graphs to visualize the data.

From these data and other strategies employed by epidemiologists to stop the disease’s mortality and morbidity rate, scientists concluded and understood the disease’s transmission routes. An example of such a strategy is the inference from contact tracing. Epidemiologists talk to patients to learn how they spread the disease to different contacts and relatives. Then they used this information to trace the chain of transmission and inferred that the virus is airborne and can be passed on through close personal contact touching a contaminated object or surface.

 

Community health actions on reduction of disease spread

As part of epidemiologic studies and surveys, community health experts have made some recommendations in limiting infection spread and morbidity. A team of epidemiologists and public health scientists carried out a contact tracing study in 2020, which supports some recommendations like hand washing, social distancing, and wearing face masks in public. Since the scientific community agrees largely that the virus is transmitted mainly by contact with contaminated surfaces, handwashing remains arguably the best defense against the spread of the infection. This practice must be done regularly and after touching secretions from a suspected or confirmed patient. More so, hand washing is an effective preventive practice in the transmission of many airborne diseases.

The center for disease control also recommends wearing a face mask in public for those above two years old. The mask should fit over the nose and aid proper breathing. These masks, which are recommended for travelers on buses or planes and even in passenger cars, come in various sizes and types.

And with more understanding of the disease, there have been significant innovations in producing effective masks. Another public health recommendation is to maintain six feet between people with suspected cases or non-household members. Other advice includes avoiding crowded areas, avoiding portly ventilating spaces, thorough washing of food items before consumption, and cleaning all surfaces regularly.

Evaluation of the efficacy of these practices is highly needed as there’s generally a lack of evidence that supports these protective measures against SARS-CoV-2 infection in many places. However, the contact tracing mentioned earlier, which is one of the few ones to date, proved a 77% lower risk of getting the SARS-CoV-2 infection if one wears a face mask all the time, especially during contact. Furthermore, the researchers noted that the study population was also likely to obey other public health recommendations, giving rise to an above-average result. Overall, even though this is one study from a possible many in the nearest future, the experts concluded that these preventive practices could serve as a way of reducing disease spread.

 

Designing clinical trials to test vaccine efficacy and the role of epidemiologists and biostatisticians

With the increasing understanding of the pathogenesis and etiology of the Covid 19 disease, clinical researchers embarked on the production of vaccines. Vaccine development is multifaceted and involves many disciplines like epidemiology, biostatistics, biochemistry, and biology. The journey to producing an effective vaccine like the Oxford University-AstraZeneca vaccine has raised many eyebrows on their safety. However, vaccines go through various testing stages before they are deemed fit for human use.

Human testing, which usually comes after animal trials, is one way of determining the safety of a vaccine. Clinical experts subjected various people to safe doses of this vaccine in three different phases in human trials. The main discerning factor in each stage is the increase in the study population and changes in selection criteria. Finally, epidemiologists collected the results of these trials, and the vaccines were rolled out completely after post-approval surveillance.

However, pharmaceutical companies consider the duration and efficacy of a trial based on statistical decisions. These decisions will help them foresee the gains and outcomes of any clinical study. Biostatisticians play a crucial role in this stage by helping these companies with appropriate trial designs, interim analysis, immune response, subgroup analysis, toxicity monitoring, and group sequential design.

 

Conclusion

Though alarming and difficult to control, most global health crises offer a clear insight into the role of various scientific disciplines. For COVID 19, the role of epidemiologists and biostatisticians has been apparent.

From evaluating incidence rates to the groundbreaking discovery of the determinants of the disease, these professionals helped bridge several gaps. In addition, they have also provided the scientific community and the world with insight into preventive actions, disease course, sequelae, and appropriate management. Thankfully, we also have a vaccine that resulted from various clinical trials with these experts at the forefront of all these discoveries. Truly, it would not have been possible to control the spread of COVID-19 without them.

Healthcare data analytics

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Bioepinet helps clinics, hospitals, pharmaceutical companies, and other medical-based institutions generate, collect, consolidate, and analyze data in the healthcare industry. We can help your institution or organization come up with strategic ways to analyze even a massive amount of data, whether collected about your patients or in-house processes.

Even if you already have an in-house team of medical or health data analysts, your business can benefit from the experience of our highly trained and experienced biostatisticians and clinicians. To schedule a consultation, contact us. Or you may continue reading to learn more about our healthcare data analytics solutions.

 

What is Healthcare Analytics?

Healthcare analytics means the analysis of current and past healthcare data coming from sources such as hospital records and results of medical examinations. The analysis helps health institutions predict trends, improve patient care, and make good management decisions.

Healthcare data as a form of big data comes from various sources, including devices, hospital records, patients’ medical records, and medical examination results.

Healthcare data is complex. This is not only because it comes from many channels but also due to the data having different formats. This is why healthcare big data requires sophisticated technology to analyze. Besides, the collection and use of this type of data have to comply with government regulations.

Whether you need help collecting and analyzing clinical healthcare data or are looking to put in place a healthcare data analytics suite that is right for your business, know that our biostatisticians and scientists are always available for help. Contact us for more information about our biostatistics consulting solutions.

 

Why Healthcare Data Analytics?

Without data analysis and analytics in the healthcare industry, it could be difficult or even impossible for hospitals and other medical-based institutions to improve their business, healthcare, and management needs.

How many patients are more likely to come into your health institution at certain hours of the day or days of the week can be determined using insights from data from a healthcare analytics suite. With this type of information, it becomes easy for shift managers to determine the number of workers to be on duty at any given period. Data-driven decisions like this can help organizations reduce or even eliminate unnecessary labor costs.

Healthcare data analytics is important to improving patient care. By analyzing industry data alongside the digital record of every patient, it becomes easy for medical-based institutions to easily identify potential health risks for patients. Also, healthcare analytics can help healthcare managers schedule optimal medical appointments. With the analytics, they can match physician records with patient histories. This can assist the managers in scheduling the right doctors or professionals for individual clients.

On the management side, data from a healthcare analytic suite can help any business’ health care management team do its day-to-day activities effectively. These service professionals, for example, can make better budget decisions, plan ways their facility can meet established goals, make decisions about performance evaluations, to mention a few.

Other areas where healthcare analytics are important includes:

  • Electronic health records.
  • Real-time health alert.
  • Enhanced patient engagement.
  • Predictive healthcare analytics.

Because there are several ways in which healthcare data can help your institution’s needs, your clinic, hospital, or health institution needs to use the right health data analysts. Our experts will not only help you analyze your data but also see to it that you’re collecting the right data the right way.

 

How we Can Help You With Healthcare Analytics

At Bioepinet, we have highly educated, well-trained, and experienced medical data analysts providing innovative solutions. Our service experts are familiar with today’s always-improving technologies. This helps them to not only analyze data and convert them into relevant critical insights, but also assist them in carrying out research studies and clinical trials that help organizations draw conclusions or make predictions.

We also have experienced scientists who come to work every day to advance medical science through comprehensive clinical research solutions. Remember that it is important to collect data the right way. Wrong data collection approaches can lead to inaccurate data analysis. When you work with our consulting experts here at Bioepinet, whether for observational studies or clinical trials, be assured of proper data collection and analysis.

We can come into your organization for clinical trials, determining whether a surgical, medical, or behavioral intervention is working for intended patients. Our biostatisticians and medical data analysts always work together to see if a new drug, diet, or medical advice is safe for your patients. Whether for our biostatistics consulting or medical healthcare analytics, we’re always available to assist.

And if you would be needing our help for observational studies, our experts can help you collect the right to data through medical tests, exams, or questionnaires about lifestyles and other factors.

 

Why Choose Bioepinet

You probably might have come across different healthcare analytics companies on Google or the internet. Finding us, however, is never a coincidence. We have years of experience helping healthcare institutions with medical and clinical data collection and analysis, doing so at the best possible standards. Thanks to our biostatisticians and scientists who are hard at work and use strategic approaches to data analytics.

So don’t keep searching online or looking for recommendations about clinical, biostatistics, or medical analytics solutions, Bioepinet is the right consulting company to call. Our professionals work as a team to ensure the requirements of every client are met. We understand that the needs and requests of every organization vary. So, we carefully listen to and take note of all the details of your medical or clinical data analytics project before getting started.

If during the project your needs change, you can count on our knowledgeable experts to make necessary adjustments so the output or result could be exactly what your business needs.

To enjoy the professional services that always make our clients use our services over and over again, contact us today. Our experts are always available and ready to speak with you about your needs.

Biostatistics and statistics

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Statistics is the mathematical science that is concerned with data collection, analysis, and interpretation. Biostatistics is the application of statistical methods to a broad range of biological topics. This includes the design of biological experiments, the gathering and evaluation of data from the experiments, and the evaluation of the outcomes.

The amount of available information to inform healthcare choices and decisions and the application of data science in the healthcare industry has become important in recent years. Biostatistics services play a primary function in the public health sector, letting scientists support choices made regarding patient care and enhanced focus on medical research and comprehend all the presented data. Besides, it is crucial to understand the statistical and scientific principles behind the decision-making and the importance of biostatistics in health delivery and patient care.

What is biostatistics? This is the discipline of study that connects biology and statistics by applying traditional statistical methods used in clinical trials and public health. Biostatistical consulting involves expert professionals behind the science, establishing connections to determine, for instance, whether a recognized treatment is functional or the cause behind an identified illness. Technically, biostatistics consulting converts available clinical trial and public health data into meaningful information.

Furthermore, through the application of biostatistics, clinical researchers are capable of drawing inferences from collected data. Biostatistics includes clinical research in a vast range of ways as a collaborative work from the beginning to the end, including but not limited to the sectors below.

  • Design and development of clinical research frameworks. In an ideal setting, biostatistics services are required in a clinical research study at the start to improve the clinical creation team through study objectives, strategies of data evaluation, and general study design to enhance study results. A primary element of the study design, for instance, is the size sample, a sector of specialization for any biostatistics expert. A significantly small size sample will lead to an underpowered study that can result in no relevant conclusions. In contrast, a large sample size can be a waste of money and time.
  • Data management and monitoring. Biostatisticians support the development of data management strategies and determine areas of prospective vulnerability in data gathering. Biostatisticians also develop a high standard of validity in the collection and evaluation of data.
  • Data evaluation and reporting. Biostatisticians take data gathered as a section of a clinical research examination and apply statistical methods to summarize that information and report the presence of any strange data patterns or variables. Statistical methods and a description of the technique involved, visual representations such as tables or graphs, and data interpretation are later included in clinical study reporting, ideally as a portion of the collaborative work between biostatisticians and researchers.

The implementation of biostatistics in the healthcare sector keeps growing together with innovations in the industry. Anywhere data-based decisions can:

  • Support the general public health and other related policies
  • Improve the efficiency of healthcare programs
  • Result in enhanced healthcare effectiveness and patient outcomes

The field of biostatistics is essential, and BioepiNet offers the best clinical data science among other biostatistical services; improve your data gathering and evaluation.

What is statistics? This mathematical branch deals with gathering, compiling, evaluation, interpretation, and presentation of data. Statistics can be used solve a social, industrial, or scientific problem. The statistician starts with a statistical model or study population. Population might include a broad range of topics, such as each atom making up a crystal or everyone living in a particular country. Statistics consists of all data aspects, including the planning of data gathering based on the design of experiments and surveys. Besides, when census information cannot be gathered, statisticians collect information by creating detailed experiment surveys and design samples. Representative sampling guarantees that conclusions and inferences can be generalized to the whole population. In an experimental study, individuals are assigned to two groups: a control group and a treatment group. The treatment group is exposed to a treatment or intervention whereas the control group does not receive that specific treatment. Contrary, an observational study does not include treatment or intervention assignment; we follow individuals without assigning them a treatment or intervention.

Statistical firms typically apply two primary statistical methods in data analysis: descriptive statistics that summarize information from a sample using frequencies, means, and standard deviation; and inferential statistics that draw conclusions from data that is subject to random variation such as sampling errors, measurement errors, among others. Descriptive statistics are descriptive coefficients that summarize a particular set of data that can represent the whole or a portion of a population; they are broken down into measures of variability (spread) and measures of central tendency. Conclusions are drawn based on a probability theory framework that focuses on dealing with random phenomena analysis.

Furthermore, statistical consulting companies use a standard statistical process to test the connection between two statistical data sets and synthetic data drawn from the preferred model. A hypothesis is given for the statistical association between the two sets of data. This is compared as an option to an idealized null hypothesis of no connection between the two sets of data. Rejecting the null hypothesis is conducted by applying statistical tests that can quantify the aspect in which the theory can be proved to be false, based on the data used in the research. Besides working with the null hypothesis, two fundamental forms of errors are noted: type I errors and Type II errors. The former is a “false positive,” which is a falsely rejected null hypothesis; on the other hand, the latter is a “false negative,” which is when the null hypothesis is not rejected. The real difference between the population is missed. Many problems are connected with this type of framework, ranging from getting an adequate size sample to specifying a sufficient null hypothesis.

BioepiNet is the perfect company for you for any biostatistics and statistics services; this where accuracy meets value!