HomeServicesBioinformatics & Multi-Omics AnalysisMeta-Analysis, Systematic Review & Mendelian Randomization
Ref H11
Computational Service
Confidence Reported
Scoped After Feasibility Review
Service Overview
Evidence synthesis and causal inference answer questions that no single study can. We run systematic reviews and meta-analyses to protocol — search, screening, extraction, risk-of-bias assessment, pooled estimation, heterogeneity and publication bias — including network meta-analysis where several treatments must be compared indirectly. Alongside this we perform one- and two-sample Mendelian randomisation, with the sensitivity analyses needed to test whether a causal estimate survives its assumptions.
Key Features & Advantages
Protocol-Driven And Reportable
Reviews follow PRISMA-aligned procedures with a pre-specified protocol, producing flow diagrams and documentation that journals and reviewers expect.
Heterogeneity Investigated, Not Just Reported
Where studies disagree, subgroup and meta-regression analyses examine why, which is usually more informative than the pooled estimate itself.
MR Assumptions Tested Explicitly
Instrument strength, pleiotropy and heterogeneity are assessed through multiple estimators and sensitivity methods, because a single inverse-variance estimate proves little on its own.
Full Reproducibility
Search strategies, extraction tables, code and datasets are delivered so the analysis can be updated when new studies appear.
Where This Service Is Used
1Synthesising the evidence for a treatment effect across published trials
2Comparing multiple interventions through network meta-analysis without head-to-head trials
3Pooling diagnostic accuracy or prevalence estimates across studies
4Testing whether a biomarker or exposure is causally related to disease using genetic instruments
5Prioritising drug targets by combining genetic causal evidence with trial evidence
Methods and Techniques Applied
1Protocol development, search strategy construction across databases, and deduplication
2Dual screening, data extraction and risk-of-bias assessment with disagreement resolution documented
3Fixed and random effects pooling, with heterogeneity quantification and prediction intervals
4Subgroup analysis, meta-regression, publication bias assessment and evidence certainty grading
5Network meta-analysis with consistency checking and treatment ranking where indirect comparison is required
6Mendelian randomisation with instrument selection, multiple estimators and pleiotropy sensitivity analyses
What We Need From You
1The research question in structured form, with population, intervention, comparator and outcome defined
2Inclusion and exclusion criteria, or a request that we draft them for your approval
3Any existing search results, extraction tables or protocol registration
4For Mendelian randomisation: the exposure and outcome of interest and any preferred GWAS sources
What You Receive
1Complete search strategy, screening records and PRISMA flow diagram
2Extraction tables and risk-of-bias assessments per study
3Pooled estimates with forest plots, heterogeneity statistics and prediction intervals
4Publication bias, subgroup, meta-regression and sensitivity analyses
5Mendelian randomisation results across estimators with pleiotropy diagnostics and plots
What We Compute and Report
1Pooled effect estimates with confidence and prediction intervals
2Heterogeneity statistics with subgroup and meta-regression results
3Publication bias assessment and evidence certainty grading
4Network meta-analysis rankings with consistency checks where indirect comparison applies
5Causal effect estimates across several estimators with pleiotropy diagnostics
Questions We Are Asked Before Scoping
Can you write the manuscript?
▸We produce the complete analysis, figures and methods section, and draft results text. Authorship and the final manuscript remain with your team.
What if the included studies are poor quality?
▸Then the pooled estimate is unreliable and we say so. Risk-of-bias assessment and certainty grading exist precisely to prevent a precise-looking number from masking weak evidence.
How strong is Mendelian randomisation evidence?
▸It is stronger than observational association but rests on assumptions that cannot be fully verified. We report multiple estimators and sensitivity analyses, and where they disagree the causal claim is presented as unresolved.
Interpretation and Limitations
Computational predictions prioritise hypotheses; they do not independently establish binding, biological activity, therapeutic efficacy, safety or clinical validity. Performance depends on input quality, target class, the structural evidence available and the applicability domain of the selected methods. Scores used for ranking are comparative devices and should not be read as measured quantities. All deliverables are for research use only and are not intended for diagnostic or therapeutic use.
Is This the Right Service for You?
Choose this when
A treatment effect must be synthesised across published trials to protocol standard
Several interventions need comparing where head-to-head trials do not exist
A causal claim about an exposure needs genetic evidence rather than association alone
Consider another service when
A single cohort needs analysing rather than the literature
Clinical Prediction Modeling Target-level causal evidence is the goal
Disease Target Mining What Goes Wrong, and How We Handle It
These are the failure modes that produce confident-looking results which do not survive the next experiment. Each one is designed against rather than discovered afterwards.
A precise pooled estimate built from biased studies
Risk-of-bias assessment and certainty grading accompany every pooled result, and a precise number from weak evidence is labelled as such.
Heterogeneity reported and then ignored
Subgroup analysis and meta-regression investigate why studies disagree, which is often more informative than the pooled value.
A single causal estimator treated as proof
Multiple estimators and pleiotropy diagnostics are run; where they disagree the causal claim is reported as unresolved.
A Worked Example
A causal claim that did not survive sensitivity analysis
The situation
An observational association between a circulating biomarker and disease risk was being used to justify a programme.
What the work involved
Two-sample causal analysis was run with instrument strength assessment, several estimators, and pleiotropy and heterogeneity diagnostics, alongside a systematic review of the observational literature.
What came back
The primary estimate was significant; two robust estimators were not, and pleiotropy diagnostics flagged a likely violation. The evidence was reported as insufficient to support a causal claim, before the programme committed.
Illustrative of the shape this work takes. Details are generalised, no client or target is identified, and scope and outcome vary with the system in question.
Service Workflow
1
Scientific Consultation
We discuss the objective, the decision it supports and the data you hold. Nothing is quoted before we understand what the result has to answer.
2
Feasibility Review
We check that the data, target class and available structural evidence can support the proposed approach, and say plainly where they cannot.
3
Technical Proposal
Scope, methods, deliverables, timeline and price are confirmed in writing, together with the limitations we expect to report.
4
Computation and QC
The analysis runs on pinned tool versions in a containerised environment, with quality checkpoints and method comparison where that is informative.
5
Report and Follow-Up
You receive the ranked results, the documented methods, the limitations and a proposed validation plan, plus support in interpreting them.
Service Process
1
Online Consultation
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2
Solution Matching
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3
Service Contract
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4
Project Execution
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5
Data Delivery
Specifications & Turnaround
Note: ICDMO delivers computational (in silico) services. Results prioritise hypotheses for testing; they do not by themselves establish binding, biological activity, efficacy or safety. All deliverables are for research use only and are not intended for diagnostic or therapeutic use. Downstream experimental work is scoped separately and executed through qualified partners.
Get a Custom Quote
Our scientific team responds within 24 hours with a detailed technical proposal and pricing tailored to your project.
Contact Us Online ConsultationStandard Deliverables
✓Sequence-verified final product
✓Full QC documentation
✓Certificate of Analysis (CoA)
✓Electronic data files
✓Project completion report
Why ICDMO?
Computational CDMO Focus
Reproducible, Versioned Pipelines
Confidence & Limitations Reported
Experiment-Aware Recommendations
Response Within One Business Day