Expert Phd Biostatistics Service
Get biostatistical analysis support for Your PhD Research
Professional Clinical Biostatistics and Biometrics support
Biostatistics: Expert Support for PhD Scholars, Researchers, and Clinicians
Designing a scientifically sound study and conducting accurate biostatistical analysis is one of the most critical—and often most challenging—tasks faced by PhD scholars, academic authors, and clinical researchers. Many people have found biostatistics to be very complex, complicated, abstract and numerically frustrating. This apprehension is intensified by the need to master a wide array of statistical modules and methodologies essential to making valid, data-driven inferences.
Understanding statistical concepts such as hypothesis testing, regression analysis, Bayesian modelling, longitudinal data analysis, and survival analysis is crucial for any scholar working in biomedical science, public health, or epidemiology. Moreover, modern research demands familiarity with specialized techniques like genetic hierarchical models, spatial modelling, stochastic modelling, and phylogenetic analysis, as well as proficiency in statistical software such as SAS, SPSS, STATA, and R. Many scholars struggle to connect theoretical knowledge to real-world data, particularly when dealing with bioinformatics, genetics, or neuroimaging datasets, where biological variability and data complexity add further layers of difficulty.
At PhD Assistance Research Lab, we understand these academic and technical hurdles. Our Biostatistics team is comprised of PhD experts and domain experts in biostatistics, epidemiology, and clinical research methodology. We provide customized peer-to-peer statistical support to support the research goals. We help you clearly define your research objectives, select appropriate analytical techniques, and interpret the results in a manner that does justice to the findings. Our consultants help you every step of the way, by providing you with the methodological rigor that you need, especially when you're comparing the treatment effect from a clinical trial; modelling disease progression; and/or high-dimensional genomic data analysis.
We apply advanced statistical techniques such as longitudinal and hierarchical modelling, Bayesian methods, and spatial-temporal analysis to ensure your data evidencing strong scientific conclusions. With a thorough understanding of variability in biological systems, we help you distinguish between causation and correlation, evaluate the validity of inferences, and meet the highest standards required by peer-reviewed journals.
So, whether you're in the early planning stages or approaching publication, you can relax—your biostatistical analysis is in expert hands. Let PhD Assistance enable you to get past the complexity and share robust results, with confidence.
Our statistical analysis and reports are fully human generated, consistent with original research designs, and in accordance with the project’s stated aim. We do not use AI-generated writing. All our submissions are checked using Turnitin, WriteCheck and leading AI-detection software to check reports for originality, authorship and adherence to institutional submission procedures
We provide biostatistical support for any relevant biomedical or public health disciplines, including:
- Infectious Disease Modelling
- Epidemiology
- Genetics and Genomics
- Bioinformatics
- Neuroimaging
- Clinical Trials
- Health Economics
- Public Health Research
All analysis is conducted by individuals with subject-matter expertise, including university researchers, licensed clinicians, and experienced data analysts with a broad understanding of biostatistical methodologies.
We provide all statistical outputs, tables, graphs, results, and interpretations according to feel university or journal specific guidelines, including:
- Consistent statistical reporting format
- APA/AMA/Vancouver format for tables and citations
- Research ethics compliance and data transparency
We support scholars from institutions such as Oxford, Harvard, IITs, IIMs, Cambridge, NUS, and from other global upper tier and accredited UGC universities.
All reported interpretations are sourced from peer-reviewed data sets and validated secondary sources from trusted academic data bases, including:
- PubMed, Scopus, Web of Science
- ClinicalTrials.gov, CDC, WHO data bases
- ICPSR, NIH Founded repositories
This assures that the statistical models are built upon current, peer-reviewed, and corresponding scientific evidence
Our statisticians are proficient in a wide range of analysis tools, including:
- SPSS
- R
- STATA
- SAS
- MATLAB
- AMOS
- NVivo (for qualitative data linkage)
We handle everything from data cleaning, sample size determination, and hypothesis testing to survival analysis, Bayesian modelling, spatial analysis, and multi-level regression.
We provide unlimited revisions for statistical sections based on supervisor or reviewer feedback—whether it’s model adjustment, reanalysis, or result interpretation. Your satisfaction and adherence to institutional standards are our top priority.
We follow the most recent version of a statistical style document (if a journal is not specified) following the style documents of APA, Vancouver,, Harvard Statistical Style documents or WordPress tools. We utilize Mendeley, Zotero and EndNote that allow us to ensure that you are able to cite data sources, datasets, and statics outputs correctly.
Time is critical to the research process. We promise:
- Minor edits on text within 24 hours
- Major edits within 48 hours
- Respond to you within 30 minutes during working hours
Our turnaround times are designed around your thesis submission dates, deadlines for publications, or conference presentations.
We believe in transparency and share all of our clean data, statistical syntax, and the references we accessed:
- Clean data files
- R/STATA/SPSS code or output files
- Graphs, tables, and test statistic outputs
- Published journal references
- Source PDFs, repots, and supporting files
You’ll have everything you need to easily approach your viva voce, peer review, and future publications.
We won’t beat around the bush as we are all about succinct, well- considered analysis that doesn’t waste space. Word counts are in accordance with a thesis or journal standards, less appendices, tables, or figures unless otherwise specified.
PhD Assistance’s biostatistics operations not only give you the data analysis you need but also assign you a partner in your research. Let our statistical knowledge allow you to turn your complicated data into insights suitable for publication with confidence and clarity.
Our Biostatistics Service by Subject Area
Biostatistical Services that we offer
Study Design & Planning
- Research question formulation and hypothesis development
- Selection of appropriate study design (cross-sectional, cohort, case-control, RCTs)
- Sampling techniques and power analysis
- Sample size determination
- Randomization and blinding strategies
- Protocol and CRF (Case Report Form) design
- Ethical considerations and statistical justification for IRB proposals
Descriptive statistics and Explorative analyses
- The issue is sufficiently focused to be solved in the scope and duration of the PhD.
- Doesn’t delve too far into its scope or ambition in lack of adequate resources.
Example:
“In spite of policy encouragement, digital financial instruments are used by only 30% of rural SMEs in South India. The internal behavioural causes for such a gap remain under-researched.”
Analysis Of Variance (ANOVA)
Focus:
ANOVA tests for an overall difference in means of three or more independent groups.
Purpose:
It limits the Type I error rate when testing more than one group, estimates variation both among groups and within groups.
Example:
A researcher compares mean haemoglobin levels in patients taking three types of iron supplements with one-way ANOVA. Linear Regression
Chi-Square test
Focus:
The Chi-square test assesses the association between two categorical variables by comparing the observed frequencies with the expected frequencies under the assumption of independence.
Purpose:
To establish whether a substantial association by chance exists between two categorical variables or multiple categorical variables.
Example:
To statistically test to association between gender (Male / Female) with smoking status (smoker/not smoker).
Survival analysis
Focus:
Survival analysis involves “time-to-event” data, and in most applications, censored observations since the event (e.g., death, relapse) has not occurred within the study period.
Purpose:
Estimation of survival probabilities and hypothesis testing of the determinants of the time to an event to occur.
Example:
Estimation of the median survival time in cancer patients on a new drug from Kaplan–Meier curves.
Discriminant analysis
Focus:
Discriminant analysis is a classification technique that identifies the variables that distinguish between two or more naturally occurring groups.
Purpose:
Its objective is to learn how groups vary and to make predictions of group membership as linear combinations of the independent variables.
Example:
In an epidemiological study, discriminant analysis assists in selecting the most suitable lifestyle and clinical to discriminate between diabetic and non-diabetic patients
Data Management & Preparation
- Data cleaning, transformation, and preprocessing
- Missing data handling (e.g., imputation techniques)
- Codebook and variable dictionary development
- Data entry validation and dataset formatting
- Merging and managing large, complex datasets (e.g., longitudinal, multi-center)
Probability distribution
Focus:
Probability distributions (Normal, Binomial, Poisson) are mathematical formulations that provide the probabilities for a given experiment/observation for each outcome that is possible.
Purpose:
Inferential statistical analyses are based on these distributions to make predictions, estimate probabilities and identify the expected outcomes in a statistical inference.
Example:
Estimation of the occurrence of rare diseases, like, Alzheimer’s diseases, cancer in a population of subjects, by using a Poisson distribution to estimate how often these occur, over the specified time frame.
Linear Regression
Focus:
Linear regression studies the relationship between a continuous dependent variable and one or more independent variables including estimating how the dependent variable changes when you change any one of the independent variables.
Purpose:
To predict outcomes and measure how strong, directional, and significant that predictor is.
Example:
A health economist uses linear regression to predict hospital cost as a function of age of patient, length of stay, and diagnosis type.
Non-Parametric statistical test
Focus:
Used when data violates the assumptions of parametric tests (e.g., non-normal data etc.) or when rank-order is more useful than the true values of the data.
Purpose:
To provide valid tests for skewed or small samples data without assuming any structure for the distributional shape.
Example:
Using the Mann–Whitney U test to compare patient satisfaction ratings from two hospital wards where the data was ordinal rather than normally distributed.
Cluster analysis
Focus:
Used when data violates the assumptions of parametric tests (e.g., non-normal data etc.) or when rank-order is more useful than the true values of the data.
Purpose:
To provide valid tests for skewed or small samples data without assuming any structure for the distributional shape.
Example:
Using the Mann–Whitney U test to compare patient satisfaction ratings from two hospital wards where the data was ordinal rather than normally distributed.
Cluster analysis
Focus:
Cluster analysis is an unsupervised method of finding natural clusters in a data set measuring similarity or distance between data points. There are no labels or categories that were predetermined.
Purpose:
The technique is applied for the detection of homogeneous clusters among dissimilar data. It assists researchers in grouping observations (e.g., patients, schools, regions) with comparable characteristics, thereby facilitating pattern detection, segmentation, and hypothesis generation.
Example:
Cluster analysis is used in a health care utilization study to segment the patients based on visitation rates in the hospital, prescription use, and demographic so that interventions would be directed to the high-risk segments of the patient population.
Descriptive & Exploratory Analysis
- Descriptive statistics (mean, median, SD, IQR)
- Frequency distributions, tables, and graphs
- Outlier detection and normality testing
- Exploratory data analysis (EDA) using visual analytics
Hypothesis testing
Focus:
Hypothesis testing is an important biostatic design that tests a hypothesis, usually a null and alterative hypothesis, about a subject variable through sample data using values such as confidence intervals and p-values.
Purpose:
To see if the differences or effects observed are by chance or represent true changes at the population level.
Example:
A paired t-test to compare weights before and after the program to analyse the effectiveness of a new diet program.
Logistic Regression
Focus:
Logistic regression graphically represents the relationship between one or more independent variables and a binary outcome variable (for example, disease vs not disease, cancer vs not cancer).
Purpose:
Gives an estimate of odds ratios, and provides details on the occurrence of one case, controlling for many confounders.
Example:
Gives an estimate of the risk of hypertension of a person depending on their BMI, age, and physical activity.
Correlation
Focus:
Identify and measure the strength and direction of the relationship between two continuous variables. This is useful for researchers to determine whether values of one variable change consistently with changes to values of another variable.
Purpose:
To measure the strength of relationship between two variables, and to determine linear relationships.
Example:
A sample of 300 adults shows a Pearson correlation of r = -0.55 between daily activity and BMI indicating a moderate negative relationship – as activity increased BMI decreased.
Principle component analysis
Focus:
Principal Component Analysis (PCA) is a dimension-reduction, data-reduction method that seeks to simplify the data by converting a set of correlated variables into a set of uncorrelated variables.
Purpose:
The aim overall is to reduce the dimensions of the data as simply as possible and losing as little of the overall variability as possible. PCA will also make exploring, visualizing, and finding patterns/latent structures, that would not be as easily differentiated without.
Example:
For example, if a public health researcher is investigating a study that measures multiple health indicators (e.g., alcohol use, shorts physical activity, diet, BMI, cholesterol) then PCA may be used to summarize a few key lifestyle factors.
Advanced & Specialized Modelling
- Bayesian Modelling
- Hierarchical and Multilevel Models
- Structural Equation Modelling (SEM)
- Spatial Modelling (e.g., disease mapping, spatial autocorrelation)
- Stochastic Modelling
- Time Series Analysis (ARIMA, Holt-Winters, etc.)
Software & Programming Support at PhD Assistance Research Lab
SPSS
We apply SPSS to perform well over 50 statistical tests (ex. t-tests, ANOVA, regression), descriptive statistics, and visualizations, ability to integrate Excel or SQL Database. We apply this to conduct descriptive statistics, ANOVA, taking a hypothesis-testing approach, regression in public health, psychology and medical science.
SAS
We utilize SAS models to conduct Analytical functions, clinical trial data management, survival analysis, macro programming features, compliance with FDA procedure. We apply this to conduct pharmaceutical research, epidemiology, and healthcare modeling, data analysis of large clinical datasets.
R
R model facilitate varying packages (example: survival, ggplot2, lme4), allows for great data visuals and advanced statistical modelling. We apply this to conduct advance, multivariate analyses; great for survival analyses, bioinformatics, and research studies that allow for flexible statistical programming.
Stata
Data management features, useful for analyzing time-series and panel data, user-written commands, allows for reproducible reports on research. We apply this to conduct Health & economics modelling, longitudinal (stepwise or covariance studies), biostatistical modelling.
JMP (by SAS)
Interactive and visualizations, data exploration, statistical modelling, DOE tools with drag and drop. We apply this to conduct Clinical trials, quality in healthcare evaluation, and exploratory modelling in biostatistics.
Jamovi / JASP
Used for structural equation modelling (SEM), particularly in psychometrics, health behavior studies, and validating survey-based models.
Minitab
Tools for statistical quality control, regression, ANOVA, DOE, run control charts, also capable of Six Sigma analysis. We apply this to conduct evaluation and effect of public health programs, use for data relating to manufacturing processes, or improvement studies relating to healthcare.
Python
Scripting language and open-source, libraries include stats models, pandas, scikit-learn, and seaborn for prediction modeling and analysis. We apply this to conduct machine learning opportunities for biostatistics, predictive modeling, live data analysis, and algorithm opportunities.
GraphPad Prism
Nonlinear regression, survival analysis, dose-response curves, graphing for science, easy to enter data. We apply this to conduct Biomedicine, pharmacology, and lab-based investigations using smaller sample sizes that have a reliance on visual interpretations.
Epi Info
Allows for planning surveys, epidemiological statistics, mapping (GIS), and templates for formal outbreak investigations. We apply this to conduct Field-based studies associated with epidemiology and public health research, particularly in the areas of surveillance and rapid response assessments.
NVivo
Supports mixed-methods research by linking qualitative data with quantitative coding and analysis—often paired with surveys or interviews.
AMOS
Used for structural equation modelling (SEM), particularly in psychometrics, health behavior studies, and validating survey-based models.
MATLAB
Applied in complex modelling, signal processing (EEG/fMRI), and systems biology. Useful for high-level algorithmic analysis in biomedical engineering.
Features We Offer while ordering Biostatistics Service
- Research Title or Topic Area
- Approved Proposal
- Your Research Aims or Questions
- Research Hypotheses
- Additional Data
- Raw Dataset (Excel, SPSS, STATA, etc.)
- Previous Statistical Output
- Supervisor Comments
- University Guidelines (APA, Harvard, specific formatting requirements)
- One-on-One Biostatistics assistance
- Study Design Support
- Assumption Checking & Statistical Test Selection
- Hypothesis Testing (t-tests, ANOVA, Chi-square, etc.)
- Advanced Analysis (Survival Analysis, Regression Logistic Models, Multivariate methods)
- Software-specific Guidance (R, STATA, SPSS, GraphPad Prism, SAS, etc.)
- Interpretation of biostatistical Results aligning with Research problems.
- Graphical Presentation
- Table Formatting
- Full Dissertation Writing from Beginning to End
- Topic Identifying from - nothing, (unless the service is identified - as a stand-alone service)
- Full Pilot Study - or Full Data Collection
- Interview Transcription or Translation Services
- Ethics Committee submissions or ethics approvals.
- Publication & submitting to journals
Biostatistical Analysis –Service Package
Biostatistics Service Packages – Comparison Table
| Features & Inclusions | Basic Plan | Standard Plan | Premium Plan |
|---|---|---|---|
| Data Cleaning | ✔ | ✔ | ✔ |
| Descriptive Statistics | ✔ | ✔ | ✔ |
| Appropriate Statistical Tests Applied | ✔ | ✔ | ✔ |
| Interpretation of Results | ✘ | ✔ | ✔ |
| Literature Review-Based Discussion | ✘ | ✘ | ✔ |
| Table Formatting (as per Journal/Thesis) | ✘ | ✘ | ✔ |
| Graphical Representation (Charts/Graphs) | ✘ | ✔ | ✔ |
| PowerPoint Presentation (For Viva/Defense) | ✘ | ✘ | ✔ |
| Statistical Software Used (SPSS/R/STATA etc.) | ✔ | ✔ | ✔ |
| Personal Expert Consultation | ✘ | ✘ | ✔ |
| Support with Supervisor/Reviewer Feedback | ✘ | Optional Add-on | ✔ |
| Add-on Services | No | Yes | Yes (Includes Supervisor Query Support) |
| Unlimited Revisions | ✘ | ✔ | ✔ |
| Delivery Timeline | 3–5 Days | 5–7 Days | 7–10 Days |
Study Designs We Support at PhD Assistance Research Lab
Scopus
- Randomized Controlled Trials (RCTs)
- Crossover Designs
- Factorial Designs
- Cluster Randomized Trials
- Single-blind / Double-blind Trials
Observational Designs
- Cohort Studies (prospective / retrospective)
- Case-Control Studies
- Cross Sectional Studies
- Nested Case-Control Studies
- Ecological Studies
Longitudinal & Repeated Measures Designs
- Panel Data Analysis
- Time-series designs
- Growth Curve Models
- Mixed Effects Models for Repeated Measures (MMRM)
Survey-Based Designs
- Population Health Surveys
- Knowledge, Attitude, Practice (KAP) studies
- Likert-scale questionnaire studies
- Instrument validation and/or reliability studies
Diagnostic & Prognostic Studies
- Sensitivity/Specificity and/or ROC Analysis
- Prognostic factor analyses
- Diagnostic test validation
- Risk prediction modelling
Epidemiological & Public Health Models
- Infectious Disease Modelling (i.e. SIR/SEIR models).
- Surveillance & outbreak investigation.
- Community intervention trials.
- Health economics & burden of disease models.
Bioinformatics & Omics Studies
- Case-control studies in genomics.
- Differential expression analysis (RNA-seq, microarrays).
- Genotype-phenotype association studies (GWAS).
- Hierarchical clustering and principal component analysis (PCA).
Health Systems & Services Research
- Service Utilization Studies.
- Health Outcomes Evaluation.
- Cost effectiveness and QALY based evaluations.
Check at what stage you’re into & Analyse how you can fit into our engagement model
Aiden
Bayesian Sample Size Determination with Historical Data Integration We can provide simulation-based Bayesian sample size estimation that uses historical trial information with either power priors or partial borrowing.
Isabella
Bayesian Spatial Modelling & Continuous Surface Reconstruction We help clients assess data quality for environmental variables (e.g. temperature, precipitation, etc.) at the spatial resolution they need (e.g. national parks, other subnational locations, or the scale provided by the data).
Arjun
Cross-Study Validation & Heterogeneity Impact Analysis We assist clients that employ multi-study omics data (e.g., RNA-seq, microarray, metagenomics) to apply cross-study validation (CSV) practices to investigate generalizability.
Dr. Leila
“I want to apply multi-omics data across multiple subtypes of disease, with a flexible modelling framework that incorporates variable sample sizes, and allowing unknown similarities across the networks.”
Bayesian Graphical Modelling for Multi-Group, Multi-Omics Network Inference We support researchers examining complex biomedical networks employing multi-type data (e.g., metabolomics, transcriptomics, proteomics), by implementing Bayesian hierarchical graphical models.
Who We Serve Biostatistics Support Tailored to Your Research Needs
If you are doing quantitative research or biomedical research as part of your PhD or doctoral studies, we can provide you with end-to-end biostatistics support. This includes Identifying appropriate statistical tests Power and sample size calculation Inferential analysis (e.g., ANOVA, regression, survival analysis) Support for SPSS, R, STATA, SAS, or Python to assist you with your thesis or dissertation completion output interpretation and incorporation into chapters
We collaborate directly with department heads, academic supervisors and research boards to improve the statistical quality of institutional research projects. Apart from the work being completed to:
- Any university-specific statistical reporting requirements
- Academic ethics regarding integrity
• Formatting and presentation output according to institutional templates
Post-doctoral researchers, faculty, and independent scholars can avail all the following:
- Appropriate selection of statistical methods for complex data sets
- Multivariate modelling (PCA, factor analysis, MANOVA)
• Assistance with journal publication or grant-funded studies in medicine, psychology, education, and engineering
We assist practitioners and health researchers with biostatistics studies around patient experience, decision-making and health systems.
Our biostatistics services within your area include:
• Development of semi-structured interview and focus group protocols
• Phenomenological analysis of patient narratives
• Thematic synthesis in systematic qualitative reviews
For corporate or industry-based R&D groups focused on biostatistics inquiry we can provide actionable insights and analysis to inform product development, improve user experience or explore organizational culture.
We can assist with:
• Developing and analysing employee or expert panel interviews
• Analysing open-ended survey data
• Thematic and discourse analysis for internal reports
We are happy to support students and researchers from the UK, US, Australia, UAE, India and Europe to conduct biostatistical analysis to meet international academic standards.
We have the capability to customize services to support any of the following:
- Meet institutional expectations and formats
- Use region specific tools (e.g. NVivo for the UK/AUS, MAXQDA for Germany/EU)
• Meet university specific rubrics and assessment criteria.
Our Sample & Example Works
We’ve worked on Several challenging PhD projects for our clients across the globe, and we’re proud of every single task that we carry out
Order process
Submit Your Requirements and Make Payment
You complete the order form by filling out your research requirements, and then you complete a secure online payment. This will allow us to initiate your planning and method construction process immediately.
Order Confirmation & Expert Allocation
Regular Updates & Two-Way Communication
Editing, Proofreading & Plagiarism Checking
Review &
Revisions
Final Delivery & Feedback
Our Guarantee
What We promise, we deliver exactly the same
Plagiarism Free
Unlimited Support
On-time delivery
Subject Matter Expertise
Communicate with your writer
Updated academic resources
Free research articles supply*
Client success stories
PhD Biostatistics Service
Expert Guidance on Biostatistics Methodology
"I was not sure about the statistical method best suitable for my clinical research data—linear regression, ANOVA, or otherwise. PhD Assistance Research Lab guided me through the options and recommended a good analytical plan. Their expert guidance on assumptions and variable choice improved the validity of my results."
— Sophie Laurent, France, PhD Scholar
Timely Submission of Statistical Results
"I did not have time prior to my committee review, and I had to do multivariate analysis on my data as well. My crew in my PhD Assistance Research Lab gave my SPSS outputs with neat data, organized tables, and graphs—all because of the deadline I had imposed. It was a lifesaver."
— Jonas Weber, Doctoral Candidate, Germany
Quality Analysis with Detailed Interpretation
"I chose their advanced biostatistics package. Descriptive and inferential stats were flawless, and they went ahead and added a comparison with similar studies' results in my literature review. Interpretation of odds ratio and confidence interval was very helpful in my thesis write-up."
— Isabella Rossi, Medical Researcher, Italy
Well-Structured Quality Deliverable
"The tables were arranged exactly like my journal's layout, and the statistical outputs arrived in neat form with concise annotations. From correlation matrices to regression diagnostics, the reports were ready for use in final form and easy to integrate into my manuscript."
— Erik Lindholm, Data Analyst, Sweden
Close Inspection of Statistical Validity
"I was concerned about the robustness of my results and whether my model was appropriate for the data. The biostatistics team reviewed my SPSS output and provided me with a rigorous validation—assumptions, multicollinearity, residual plots. My work became even more justifiable because of their feedback."
— Marta Novak, PhD Scholar in Epidemiology, Poland
Affordable Pricing"
“I fully admire PhD assistance's reputation and quality. I especially chose PhD assistance's packages as no other company offers such a service. Their service quality and timely delivery is very helpful in meeting journal deadlines. I am glad that PhD assistance offers packages at affordable price.”
-Kate Merkel-Hess
Supportive brainstorming and topic discovery
"Picking a topic for my PhD felt like looking for a needle in a haystack. I was lost until I found the topic selection guidance service at PhD Assistance. They were like personal academic cheerleaders—helping me discover what I truly cared about and could research meaningfully. It was like a brainstorming session with friends who knew their stuff. Now I’m excited to dive into my research and make a difference!"
PhD topic clarity and direction
"Choosing a PhD topic was a total headache until I stumbled upon the PhD Thesis Dissertation Topics Help UK at PhD Assistance. Let me tell you—these guys are wizards when it comes to turning confusion into clarity. They didn’t just throw random ideas at me; they took the time to understand what I was passionate about and what I wanted to achieve. It felt like having a coffee chat with a smart friend who knows all about PhDs. Thanks to their help, I finally feel like I’m on the right track and ready to tackle anything that comes my way!"
Stress-free topic selection in Computer Science
"I was very confused about selecting a research topic for my PhD in computer science. It was stressful and overwhelming. A friend recommended PhD Assistance—and it was the best decision. The expert helped me choose a topic that my guide accepted without any changes. Thank you, PhD Assistance!"
Confidence-building experience
"I recommend PhD Assistance to anyone needing help with research topic selection. I was hesitant at first, but after meeting the team, I gained confidence in articulating my ideas clearly. The title they provided matched exactly what I had in mind—it felt like they had read my thoughts. Thank you!"
Structured support for academic success
"I was feeling overwhelmed and lost when it came to choosing a PhD topic that would truly excite me and contribute to the field. Thankfully, the topic selection service at PhD Assistance gave me invaluable support. They took the time to understand my interests, strengths, and goals and curated a list of focused research areas. I found a topic that reignited my passion. Thank you, team!"
John, France
Flexible and student-friendly service
"PhD Assistance offered me great flexibility in choosing courses and research areas. The specialists helped me identify a unique and meaningful topic. It gave me a strong foundation to move confidently into the next stage of my research."
Liam
Supportive brainstorming and topic discovery
"Picking a topic for my PhD felt like looking for a needle in a haystack. I was lost until I found the topic selection guidance service at PhD Assistance. They were like personal academic cheerleaders—helping me discover what I truly cared about and could research meaningfully. It was like a brainstorming session with friends who knew their stuff. Now I’m excited to dive into my research and make a difference!"
Supportive brainstorming and topic discovery
"Picking a topic for my PhD felt like looking for a needle in a haystack. I was lost until I found the topic selection guidance service at PhD Assistance. They were like personal academic cheerleaders—helping me discover what I truly cared about and could research meaningfully. It was like a brainstorming session with friends who knew their stuff. Now I’m excited to dive into my research and make a difference!"
Quality & Compliances
We’ve worked on so many great PhD projects for our clients across the globe, and we’re proud of every single task that we carry out
Frequently Asked Questions
No. We do not charge for revisions. We allow unlimited revisions for all statistical analyses deliverables to ensure the satisfaction of our clients and the compliance of their university or journal. We only want to produce perfect products. We are willing to do everything possible to get it right. It doesn’t matter whether it is re-running a test, modifying a figure or changing the interpretation of an analysis. We want it to be perfect.
At PhD Assistance Research Lab, we offer tailored end-to-end biostatistical assistance for PhD scholars, medical researchers, and education professionals. We deliver biostatistical services that consist of:
- Choosing appropriate statistical tests given your research design
- Data exploration: tables, frequency distributions, and descriptive statistics
- Inferential statistics: hypothesis tests, z-tests, t-tests, chi-square, and ANOVA
- Interpreting confidence intervals, p-values, and effect size
- Regression analysis (linear, logistic, Cox, Poisson, etc.)
- Survival analysis: time-to-event modeling
- Multivariate methods: PCA, factor analysis, cluster analysis
- Graphical presentation of the data and presenting results (APA or journal specific)
We also support interpretation of outputs produced by tools like SPSS, R, STATA, SAS, and Python, helping to understand results as well as clearly present findings.
The biostatistics team will use methods that allow them to calculate sample size using the following variables:
- One-tailed vs. two-tailed hypothesis testing
- Type I and Type II error rates (α and β)
- Effect size
- Assumptions of parametric and non-parametric statistics
- Sample size comparisons for means, proportions, or survival curves
- Power analysis for logistic, linear, and Cox regression analysis
- Repeated measures and longitudinal studies
- Account for dropouts, cluster randomization, and multiplicity
Our sample size calculations are customized to your study design, outcome, and software platform.
Absolutely! We are proponents of the team model. When your project is ready for analysis, the biostatistician assigned to your project will be scheduled to communicate with you directly via Zoom, Skype, WhatsApp, or email as you prefer. This is to remove ambiguity, allow for quality assurance, and be aligned with your research/analysis expectations.
We work with numerical and categorical variables; the following are examples:
- Numerical (Continuous/Discrete): age, BMI, blood pressure, temperature, laboratory test values
- Categorical (Ordinal/Nominal): gender, disease stage, treatment type, activity level
- Derived variables: odds ratios, hazard ratios, risk scores, composite scores
We will help you code, recode and transform your variables to be compatible with your analysis method and research model.
Yes, our pricing is value based and transparent to the client when our biostatistics services are retained, depending on the level of analysis and expertise that is required for a particular engagement. We will not compete on price alone; we guarantee quality and timely biostatistics services that meet ethical standards. We will honour established discounts from time-to-time and offer referral benefits. Call our project team to get a project specific quote.
Yes. We provide one-on-one mentorship and training on biostatistical concepts and the use of software. Whether you’re struggling to interpret SPSS outputs, perform regression in R, or understand survival analysis, our PhD-level biostatisticians are here to help you step-by-step. This is particularly useful for students preparing for viva voce, thesis defence, or journal resubmission.
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