What files do you need to review my project?

Send the study design or methods summary, research questions, dataset, data dictionary, variable definitions and any existing outputs. Include the target journal or reporting requirements if selected. We confirm whether the materials are sufficient before quoting the analysis.

Can EditPi choose the statistical tests for my study?

We can recommend methods after reviewing the research question, design, variables, sample, missingness and intended interpretation. The agreed method and assumptions are recorded in the project scope. The final scientific decisions remain with the researcher and coauthors.

Can you help before data collection begins?

Yes. Scope may include an analysis plan, outcome and variable framework, sample-size or power work, and a data-collection structure. The quote identifies the assumptions and deliverables. Planning cannot guarantee a particular result or compensate for later deviations from the design.

Do you clean and prepare datasets?

Yes, when included in the quote. We can review coding, formats, duplicates, impossible values and missing-data patterns, then document agreed changes. We do not silently replace or delete uncertain values; unresolved questions return to the author.

Can you analyze clinical or patient data?

We can assess de-identified clinical and health-research datasets within the available expertise and agreed scope. You must have authority to share the data. Please discuss any NDA, secure-transfer or institutional handling requirements before sending files. Regulated trial programming, regulatory submissions and database hosting require separate confirmation and are not implied by this page.

Will I receive analysis code or software files?

Your written quote specifies whether code, syntax, native software outputs or a documented workflow will be supplied, alongside the agreed reporting files. Confirm any required editable formats before work begins. Where a workflow record is provided, it describes the analysis steps, software and relevant decisions.

Which statistical software do you use?

Software is selected after the method and file review. Depending on the project, work may use SPSS, R or Python. If another package is essential, we confirm its availability, version and required outputs in the written scope.

Can you prepare tables, figures and manuscript results text?

Yes, these can be quoted as deliverables. Tell us the target journal or university format and whether you need editable figures, captions, statistical notes, methods text or results text. Interpretation remains limited to what the study design and data support.

Can you respond to a reviewer who requests reanalysis?

Yes. Send the reviewer comment, submitted manuscript, original dataset, previous code or outputs and any deadline. We first determine whether the requested analysis is feasible and whether it changes the original scope.

Does every systematic review need a meta-analysis?

No. Quantitative pooling depends on the review question, study designs, available statistics and comparability. We confirm whether meta-analysis is defensible after reviewing the extraction data and protocol.

Can you guarantee significant results or publication?

No. EditPi does not alter data or methods to obtain a preferred result and cannot guarantee statistical significance, reviewer acceptance or publication. We report the agreed analysis, uncertainty, assumptions and limitations for author review.

Can you analyze laboratory or instrument-generated data?

We first assess the experimental design, sample metadata, file formats, processing history and available quality-control records. Raw files, processed outputs or both may be needed. The written quote confirms which preprocessing, analyses, visualizations and reporting tasks can be completed.

How is the project price calculated?

Pricing reflects the dataset, study design, analysis complexity, preprocessing, number of outputs, reporting work and deadline. You receive a written scope and price before the agreed work begins.

HUMAN-LED RESEARCH DATA ANALYSIS

Research Data Analysis Services for Studies, Theses and Manuscripts

Turn your approved research data into clear, traceable results. EditPi supports researchers, clinicians, postgraduate scholars and research teams with analysis planning, data preparation, statistical modelling, tables, figures and results reporting. We review the research question, study design and available files before confirming the methods, deliverables, timeline and price.

Request a Data Analysis Quote

Explore analysis services ->

Methods planning · Statistical analysis · Tables and figures · Results reporting

Laptop displaying research data tables and statistical charts

RESEARCH DATA ANALYSIS

What do you need analyzed?

Quantitative research data

Prepare and analyze a dataset for a thesis, manuscript, report or research decision.

Explore statistical analysis ->

Laboratory and instrument data

Analyze suitable outputs from chromatography, spectroscopy, mass spectrometry, imaging or related experimental workflows.

Explore instrument data support ->

Clinical and epidemiological data

Evaluate health outcomes, associations, time-to-event data or diagnostic performance within the agreed study design.

Explore clinical analysis ->

Review and meta-analysis data

Calculate and synthesize effect estimates when the included studies and extracted data support quantitative pooling.

Explore meta-analysis support ->

DATA ANALYSIS READINESS

Check the dataset and analysis plan before running tests

A Data Analysis Readiness Assessment identifies issues that could change the method, timeline or interpretation before full analysis begins. We review the materials you provide, document questions and define the work that can be completed. The assessment does not change your research question or generate a preferred result.

Request a Dataset Assessment

What the assessment can examine

01

Research question and study design

Check whether the proposed comparisons or models match the design, sampling and outcome definitions.

02

Variables and data structure

Review coding, units, repeated measurements, group labels, time points and links between files.

03

Data quality questions

Identify missing definitions, duplicate records, impossible values, inconsistent categories and unclear missing-value codes.

04

Methods and outputs

Define candidate analyses, assumptions, diagnostics, tables, figures and reporting files for the written quote.

Scope note: You receive a scope note and data-query list. Full cleaning, analysis, modelling or manuscript writing is included only when stated in the written quote.

Method note:

We do not select a test only because it is familiar or produces a smaller p value. Where relevant, reporting includes effect estimates, uncertainty, model diagnostics and limitations alongside significance tests.

Discuss My Dataset

STATISTICAL ANALYSIS AND MODELLING

Statistical analysis matched to your research question

Share your study design, variable definitions, research questions and dataset. We can help prepare the data, select defensible statistical methods, check relevant assumptions and report the results in a form you can review. The precise analysis depends on the design, measurement scale, sample size, missingness and intended interpretation.

Preparing and exploring a research dataset

Prepare and explore

Dataset structure, coding and

consistency checks.

Compare and model

Group comparisons and hypothesis

tests suited to the design.

Missing-data review and an

agreed handling approach.

Correlation and regression analyses

with appropriate diagnostics.

Descriptive statistics and

exploratory visualizations.

Distribution, outlier and assumption

checks relevant to the proposed

method.

Repeated-measures, longitudinal or

multilevel methods when the data

structure supports them.

Multivariate or predictive modelling

with validation when the study question

and available sample support it.

CLINICAL AND EPIDEMIOLOGICAL ANALYSIS

Clinical and epidemiological data analysis with clear study boundaries

EditPi supports investigator-led clinical, public-health and epidemiological research using the de-identified data and documentation you are authorized to share. We define outcomes, exposures, comparison groups, time points and planned covariates before analysis, then report the agreed estimates and uncertainties without extending conclusions beyond the study design.

Defining the clinical study and analysis requirements

Define

Clarify the study design, analysis population, outcomes, exposures, censoring, missingness and prespecified comparisons.

Analyze

Support may include risk and odds estimates, regression, repeated measures, diagnostic accuracy, survival analysis, and sample-size or power work when suitable.

Report

Prepare transparent methods, tables, figures and results text aligned with the agreed journal or study-reporting requirements.

Scope note:

Regulated clinical-trial programming, regulatory submissions, database hosting and ongoing safety surveillance are not implied. Include them only if EditPi has separately confirmed the required expertise, systems and contract.

Method resources:

EQUATOR reporting guidelines · CONSORT 2025 · STROBE checklists. These are outbound methodological resources, not endorsements of EditPi.

Discuss Clinical Analysis

LABORATORY INSTRUMENT AND OMICS DATA

Analysis support for laboratory and instrument-generated data

Instrument data need more than an exported chart. We review the experimental design, sample metadata, file type, acquisition or processing history and available quality-control information before confirming what can be analyzed. Support may include agreed preprocessing, exploratory analysis, group comparison, multivariate analysis, feature or compound tables, visualization and reporting.

Chromatography and mass spectrometry

HPLC or UHPLC, LC-MS, GC-MS and related exports when suitable raw or processed files and metadata are available.

Spectroscopy, thermal and materials data

FTIR, UV-Vis, Raman, NMR, DSC, TGA, XRD, XRF or related outputs within the confirmed scope.

Integrity note: We distinguish confirmed measurements, database-supported annotations and exploratory hypotheses. A statistical pattern does not by itself confirm compound identity, biological mechanism, causation or instrument performance.

Microscopy and flow-cytometry data analysis

Microscopy and flow data

SEM, TEM, AFM, image-derived measurements or flow-cytometry exports when the source files and gating or measurement details are adequate.

Omics and multivariate workflows

Assess My Files

Metabolomics and other omics datasets when normalization, annotation resources, comparison groups and validation requirements are defined.

1

Meta-analysis and evidence-synthesis

Meta-analysis when the studies and extracted data can be combined

Quantitative synthesis begins with a defined review question, eligible studies and checked extraction data. We assess the proposed effect measure, study designs, comparability and available statistics before confirming a model. A meta-analysis is not automatically appropriate for every systematic review.

Selecting the meta-analysis effect measure

Preparing meta-analysis figures and results

2

3

4

5

Effect measure
Agree the outcome measure

Checked data
Use eligible studies with checked data

Modeling
Apply fixed or random effect models

Results and figures
Present forest plots and other agreed figures

Interpretation
Summarize key findings and limitations

Possible deliverables when included

Checked effect-size calculations

Fixed or random-effects modeling

Heterogeneity and subgroup analyses

Forest plot and other agreed figures

Methods, results and limitations

Related service: For protocol, searching, screening, risk-of-bias assessment and full review writing, see the Evidence Reviews section

Plan My Meta-analysis

DELIVERABLES AND RESULTS REPORTING

Know exactly what the analysis will return

The written quote lists each file and reporting task included in the project. Deliverables vary by study and may include the items below; they are not automatically included in every order.

Analysis Records

Agreed analysis plan
Data query and decision log
Cleaned or analysis-ready dataset
Code, syntax, software output or a workflow record when included
Assumption checks, diagnostics and limitations relevant to the model

Reporting files

Tables and figures with titles, labels, units and notes.
Statistical findings linked to the research questions.
Methods and results text for a manuscript, thesis or report when included.
Questions requiring author or investigator decisions.
Revision or reviewer-response analysis only when included in the quote.

Software and output files: Software follows the method and file type. The scope identifies the software, version where relevant, and whether editable code, syntax or native output files will be delivered.

What to send for an accurate scope

Send enough information to understand the design, variables and expected outputs. If something is missing, we will identify it before confirming the analysis.

Research question, hypotheses or decision the analysis should address.

Study design, protocol or methods summary, including sampling and groups.

Dataset in an agreed format together with a data dictionary or codebook.

Definitions for outcomes, exposures, covariates, units, time points and missing-value codes.

Any previous analysis, code, output, tables or reviewer comments relevant to the request.

Target journal, reporting guideline or institutional format if already selected.

Required deliverables, preferred software if essential, and the real deadline.

Privacy note: Remove direct personal identifiers before uploading human or clinical data unless a separate secure arrangement has been agreed. You must have the authority to share the files. Ask about confidentiality terms or an NDA before transfer when needed.

Send My Project Details

How your data analysis project moves forward

Share de-identified files, the study design, research questions and required outputs.

A suitable analyst completes the agreed work and records relevant decisions and limitations.

We review data structure and documentation, then send any essential questions.

A second reviewer checks the output where that review is included in the order.

We confirm the methods, checks, deliverables, software, timeline and price in writing.

Step 6: Return files for author approval

You receive the agreed files, findings and questions; revisions follow the terms of the quote.

Tell Us About My Project

SCOPE AND PRICING

Data analysis is quoted from the actual files and methods

Data analysis is priced after a scope review. Complexity depends on the dataset structure and quality, number of outcomes, groups and time points, required preprocessing, statistical methods, diagnostics, tables and figures, interpretation, reporting files and deadline. The written quote confirms the included files, analysis tasks, deliverables, timeline, price and revision terms before work begins.

What determines scope

• Dataset structure and quality
• Outcomes, groups and time points
• Required preprocessing
• Statistical methods and diagnostics
• Tables, figures and interpretation
• Reporting files and deadline

Confirmed analysis deliverables, timeline and price

What the written quote confirms

• Included files and analysis tasks
• Statistical methods and checks
• Deliverables and reporting format
• Timeline
• Price
• Revision terms

!

A preliminary estimate is not a fixed price for every dataset. Additional analyses or new files are assessed as a scope change.

View Pricing Information

Request a Written Analysis Quote

FAQs

Tell us about your dataset and research question