What Are Statistical Analysis Services for Researchers?
Statistical analysis services for researchers provide hands-on analytical support where PhD biostatisticians perform data analysis on your behalf, from raw data through publication-ready results. Rather than learning complex statistical software or second-guessing which test is appropriate, you submit your dataset and research question to a specialist who delivers verified results, annotated code, formatted tables, and manuscript-ready figures.
At Research Gold, our statistical analysis team is led by Prof. David Okonkwo (PhD, Biostatistics) and includes doctoral-level statisticians with published work spanning clinical medicine, public health, epidemiology, psychology, education, and the social sciences. We work in R, Stata, SPSS, and SAS, selecting the platform that best fits your project requirements or institutional conventions.
The distinction between a statistical analysis service and statistical software is important. Software gives you the tools. A service means an expert runs the analysis, checks assumptions, handles edge cases in your data, and delivers results that satisfy peer reviewers. This is especially critical for statistical analysis for medical research, where incorrect model specification, violated assumptions, or inappropriate handling of missing data can invalidate conclusions and compromise patient safety.
Whether you are a PhD candidate analyzing dissertation data, a clinical researcher preparing a journal submission, or a grant applicant who needs preliminary results to support a funding proposal, our service covers the full analytical pipeline. We follow the reporting standards recommended by the EQUATOR Network (Simera et al., 2010) and apply methods consistent with current best-practice guidelines in biostatistics and epidemiology.
Learn more about our biostatistics consulting service for a complete overview of our capabilities, or read our guide on when to hire a biostatistician to determine if professional statistical support is right for your project.
Types of Analysis We Perform
Our biostatisticians cover the full spectrum of quantitative methods used in health sciences, social sciences, and applied research. Each analysis is performed with appropriate assumption checking, model diagnostics, and sensitivity testing.
Regression Analysis
Regression modeling forms the backbone of most quantitative research. We perform linear regression for continuous outcomes, logistic regression (binary, ordinal, multinomial) for categorical outcomes, Poisson and negative binomial regression for count data, and robust regression methods when standard assumptions are violated. Every regression analysis includes residual diagnostics, multicollinearity assessment (variance inflation factors), and goodness-of-fit evaluation.
Survival Analysis
Time-to-event data requires specialized methods that account for censoring. We produce Kaplan-Meier survival curves with log-rank tests, fit Cox proportional hazards models with proportional hazards assumption testing (Schoenfeld residuals), and perform competing risks analysis using Fine-Gray subdistribution hazard models. These methods are standard in oncology, cardiology, and infectious disease research.
Mixed-Effects Models
Clustered and hierarchical data structures are common in multicenter trials, educational research, and longitudinal studies. We fit linear mixed-effects models and generalized linear mixed models that correctly account for the correlation structure within clusters, repeated measures, or nested observations. Random intercept and random slope specifications are selected based on your study design and research question.
Propensity Score Methods
Observational studies often require methods to reduce confounding bias when randomization is not possible. We implement propensity score matching, inverse probability of treatment weighting, stratification, and doubly robust estimation. Balance diagnostics (standardized mean differences, variance ratios) are reported to demonstrate covariate balance after adjustment.
Bayesian Methods
When your research question benefits from prior information or when frequentist methods are insufficient for your design, we apply Bayesian approaches. This includes Bayesian regression, hierarchical models, and Bayesian meta-analysis. We use informative or weakly informative priors as appropriate, report posterior distributions with credible intervals, and perform prior sensitivity analysis.
Diagnostic Test Accuracy
For studies evaluating screening tools, biomarkers, or clinical prediction rules, we calculate sensitivity, specificity, positive and negative predictive values, likelihood ratios, and area under the receiver operating characteristic curve. We construct ROC curves and apply DeLong's test for comparing diagnostic accuracy across tests or models.
Time Series and Longitudinal Analysis
For data collected repeatedly over time, we apply growth curve models, generalized estimating equations, autoregressive integrated moving average models, and interrupted time series designs. These methods are common in policy evaluation, pharmacovigilance, and public health surveillance research.
If you are unsure which method is appropriate for your data, read our guide on choosing the right statistical test or request a quote and our team will recommend the optimal analytical approach.
Software We Use
We perform all analyses using industry-standard statistical software recognized by Cochrane, the Joanna Briggs Institute, and leading peer-reviewed journals. The table below summarizes each platform and its typical applications.
| Software | Strengths | Common Research Applications |
|---|---|---|
| R | Open source, extensive package ecosystem, advanced visualization, Bayesian methods | Meta-analysis (metafor, meta), regression modeling, survival analysis, publication-quality ggplot2 figures, machine learning, text mining |
| Stata | Intuitive syntax, strong epidemiological tools, panel data capabilities | Clinical trial analysis, survey-weighted analysis, longitudinal panel models, epidemiological methods (metan, stcox, melogit) |
| SPSS | Menu-driven interface, widely taught in graduate programs | Descriptive statistics, ANOVA, chi-square tests, logistic regression, scale reliability analysis, common in social science and nursing research |
| SAS | Regulatory acceptance, macro programming, large dataset handling | Clinical trial reporting for FDA and EMA submissions, pharmaceutical research, health insurance claims data, CDISC-compliant outputs |
All code is fully annotated with inline comments explaining each analytical step. We select the software that fits your project, or use the platform required by your institution, funder, or target journal. If you have no preference, we default to R for its flexibility and open-source reproducibility.
Our Analysis Process
Our workflow follows a structured, transparent sequence from initial consultation through final deliverables. Every step is documented so you and your reviewers can trace the analytical decisions.
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Consultation and study review. We discuss your research question, study design, outcome variables, and analytical goals. We review your dataset structure and identify any immediate data quality issues such as missing values, outliers, or coding inconsistencies.
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Statistical analysis plan. We draft a formal analysis plan documenting all primary and secondary analyses, covariates and confounders, handling of missing data (listwise deletion, multiple imputation, or maximum likelihood), planned sensitivity analyses, and the significance threshold. This plan serves as a methodological roadmap and is useful for ethics applications and journal submissions.
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Data cleaning and preparation. We clean and restructure your data as needed, including variable recoding, data transformation, outlier assessment, and merging of multiple data files. We document every data manipulation step in the code for full transparency.
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Analysis execution. We run all planned analyses with appropriate model diagnostics and assumption checks. Each model is evaluated for correct specification, and alternative approaches are tested when assumptions are not met.
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Results compilation. We produce formatted tables (descriptive statistics, regression coefficients, odds ratios, hazard ratios), publication-quality figures (forest plots, Kaplan-Meier curves, ROC curves, scatter plots), and a narrative interpretation of every result.
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Delivery and revision. We deliver the complete package: annotated code, statistical outputs, tables, figures, and results narrative. All tiers include revisions, and we handle additional analyses requested by journal peer reviewers at no extra cost within the engagement scope.
What You Receive
Every statistical analysis project from Research Gold includes a comprehensive deliverable package designed for immediate use in your manuscript, thesis, or grant application.
Reproducible Code
Fully annotated R scripts, Stata do-files, or SPSS syntax files documenting every step from data import through final output. Your code runs independently on any machine with the specified software installed, enabling you and your reviewers to verify and replicate every result.
Publication-Ready Tables
Formatted tables following the conventions of your target journal. This includes Table 1 (baseline characteristics with means, standard deviations, frequencies, and percentages), regression output tables (coefficients, standard errors, confidence intervals, p-values), and any additional summary tables required by your study design.
Publication-Ready Figures
High-resolution figures delivered in PNG, PDF, and editable vector formats. Common figures include forest plots, Kaplan-Meier survival curves, ROC curves, scatter plots with regression lines, residual diagnostic plots, and box plots. All figures are sized to match standard journal column widths.
Narrative Results Interpretation
A written summary of your results in academic prose, suitable for direct inclusion in the results section of your manuscript. This narrative covers the magnitude and direction of effects, statistical significance, confidence intervals, effect sizes, and clinical or practical interpretation. We explain what the numbers mean in the context of your research question, not just whether a p-value crossed a threshold.
Methods Section Draft
A draft of the statistical methods paragraph for your manuscript, describing the analytical approach, software version, packages used, and reporting standards followed. This section is written to satisfy peer reviewer scrutiny and meets the statistical reporting requirements of CONSORT, STROBE, or other applicable EQUATOR Network guidelines.
When to Use R Versus Stata Versus SPSS
Choosing the right statistical software depends on your discipline, research design, institutional requirements, and analytical complexity. The comparison below helps you decide which platform best fits your project.
| Factor | R | Stata | SPSS |
|---|---|---|---|
| Cost | Free and open source | Commercial license required | Commercial license required |
| Learning curve | Steeper, code-based | Moderate, code or menu | Gentle, menu-driven |
| Best for | Advanced modeling, Bayesian methods, meta-analysis, custom visualization | Epidemiology, clinical trials, panel data, survey analysis | Descriptive statistics, ANOVA, scale reliability, social science |
| Reproducibility | Excellent (scripts, R Markdown, Quarto) | Excellent (do-files, logs) | Limited (syntax files less commonly used) |
| Visualization | Superior (ggplot2, plotly) | Good (built-in graphics, user-written schemes) | Basic (chart builder) |
| Package ecosystem | Largest (20,000 plus CRAN packages) | Moderate (community-contributed commands) | Limited (extensions available) |
| Regulatory acceptance | Increasingly accepted, FDA R Submissions Working Group | Widely accepted | Accepted for academic research |
| Common in | Biostatistics, genomics, data science, ecology | Epidemiology, economics, public health | Psychology, nursing, education, social work |
When you use our R statistical analysis service, you receive scripts built with tidyverse, ggplot2, and domain-specific packages such as survival, lme4, brms, and metafor. When you choose our Stata analysis service, deliverables include do-files with Stata commands optimized for clinical and epidemiological workflows. Our SPSS analysis service provides syntax files alongside annotated output for researchers whose programs or committees require SPSS-based results.
If you are unsure which software to select, we will recommend the best option based on your discipline, analytical requirements, and target journal conventions.
Who Uses Our Statistical Analysis Services
Our clients span every stage of the research lifecycle and represent a wide range of disciplines and career stages.
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PhD candidates and doctoral researchers. Your dissertation committee expects rigorous statistical analysis, but your training may not have covered the specific methods your data require. Our service delivers defensible results with code your committee can inspect at your viva or defense. This is the most common use case for our data analysis service for PhD researchers.
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Clinical researchers and physician-scientists. You have patient data from a hospital-based study or clinical trial and need a biostatistician to analyze it correctly, handle missing data, and produce results that satisfy journal peer reviewers and institutional review board requirements.
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Grant applicants. Preliminary statistical results strengthen grant proposals by demonstrating feasibility and effect size estimates. We deliver analyses formatted for National Institutes of Health, National Institute for Health and Care Research, and institutional funding applications. See our grant methodology service for additional support.
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Systematic review teams. You have completed a qualitative review and now need quantitative analysis of your extracted data. Our biostatisticians pool results, generate forest plots, and assess heterogeneity. Learn more on our meta-analysis service page.
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Authors responding to peer reviewer comments. Journal reviewers have requested additional analyses, alternative models, or sensitivity checks. Our expedited turnaround helps you meet your revision deadline. See our response to reviewers service for dedicated support.
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Research teams without in-house statistical expertise. Many departments and research groups lack a dedicated biostatistician. We serve as your external statistical arm, providing expert analysis without the overhead of a full-time hire.
Pricing
Our statistical analysis pricing is based on project scope and complexity rather than a fixed menu. Variables that influence the quote include the number of outcome variables, the complexity of the analytical methods, the size and quality of the dataset, and the turnaround timeline.
| Project Type | Typical Price Range | Includes |
|---|---|---|
| Single regression model with descriptive statistics | $400 to $600 | Data cleaning, assumption checks, regression output, formatted table, code |
| Multi-model analysis (3 to 5 models) | $600 to $1,000 | Multiple regression or survival models, tables, figures, narrative interpretation |
| Complex analysis (mixed-effects, propensity score, Bayesian) | $1,000 to $2,000 | Advanced modeling, sensitivity analyses, multiple figures, methods section draft |
| Comprehensive analysis package | $2,000 and above | Full analytical pipeline, multiple outcomes, subgroup analyses, complete results chapter |
All projects include reproducible code, formatted tables, publication-quality figures, narrative interpretation, and revisions for reviewer comments within the engagement scope.
View full pricing details on our transparent pricing page or request a quote for a personalized estimate. We reply with a detailed quote within 2 hours.
Free Statistical Tools
We offer free, browser-based statistical calculators for researchers who want to explore methods, verify calculations, or run preliminary analyses before ordering our full service.
- Power analysis calculator: Determine the sample size needed to detect a meaningful effect with adequate statistical power for your study design.
- Effect size calculator: Calculate Cohen's d and Hedges' g for standardized mean differences, along with odds ratios and risk ratios.
- Chi-square calculator: Run a chi-square calculator test of independence or goodness of fit with expected frequencies and p-values.
- ICC calculator: Compute the ICC calculator intraclass correlation coefficient for inter-rater reliability and measurement agreement studies.
These tools complement our professional service. Use them for teaching, preliminary exploration, or quick verification. For publication-ready results with expert oversight and narrative interpretation, our full statistical analysis service provides the rigor and documentation that peer reviewers expect.