STATISTICAL METHOD
Prevalence Meta-Analysis
Statistical Consulting for Pooled Prevalence
We pool prevalence across the studies you have identified, choosing the transformation or binomial model by the data, fitting random effects, and reporting the pooled proportion with its confidence interval, a prediction interval and the heterogeneity.
No Commitment Required
Strictly Confidential
In-House PhD-Level Statisticians
Fixed Project Fee
Same-Day Response
METHOD OVERVIEW
What Is a Prevalence Meta-Analysis?
A prevalence meta-analysis combines the proportion of people with a condition reported by several studies into one pooled estimate with its confidence interval, and describes how much the studies differ. Each study contributes a case count and a sample size, and there is no comparison group, so the result is a proportion, not an effect. Proportions are modeled on a transformed scale, such as the logit, or with a binomial-normal mixed model, so that intervals stay between zero and one. Because populations and case definitions differ, heterogeneity is expected. OnlineSPSS, a specialist statistical consultancy for academic, clinical and professional research, provides this analysis within your review design, with the relevant checks, estimates, uncertainty measures, interpretation and publication-ready reporting.
WHEN TO ENGAGE
When Is Prevalence Meta-Analysis Appropriate?
This analysis fits a set of studies that each estimate how common one condition is, and these are the situations in which researchers most often ask us to plan, run or confirm it.
Pooling Prevalence Estimates Studies estimate how common a condition is in a population, and a pooled proportion with its uncertainty is needed for a review or a policy report.
Rare or Very Common Outcomes Several studies report proportions near zero or one, where raw proportions behave poorly and the choice of transformation or model changes the result.
Prespecified Prevalence Question The condition, population and case definition were fixed in a protocol, and the model and subgroup plan must be set before the pooled results are seen.
Reviewing a Prevalence Synthesis A pooled prevalence exists and a reviewer questioned the transformation. Independent Statistical Review re-examines the model and the definitions.
ANALYTICAL APPROACH
How We Apply Prevalence Pooling
Within the family of Evidence Synthesis & Meta-Research, prevalence is pooled only after case definitions, populations and the transformation have been examined.
Comparable Case Definitions We check that studies measure the same quantity in comparable populations. Incidence, risk and prevalence are never mixed in one synthesis, and differing definitions are grouped.
Transformation and Model The Freeman-Tukey double arcsine transformation has well-known criticisms, so we prefer the logit or a binomial-normal mixed model, especially for proportions near zero or one.
Heterogeneity and Prediction High heterogeneity is common in prevalence, so I-squared alone says little. We report tau-squared and a prediction interval, and we qualify any single pooled number.
Representativeness and Bias Sampling frame, response rates and case ascertainment can bias prevalence, so we appraise studies with the Joanna Briggs Institute checklist and test their influence.
DELIVERABLES
What You Receive
As part of our Meta-Analysis & Evidence Synthesis service, the pooled prevalence is delivered with the estimates, checks and files needed to report and defend it.
Data & Variable Review Case counts, denominators, case definitions, sampling frames and time periods are reconciled, and any incidence or risk data are separated before a model is fitted.
Method-Specific Checks Transformation and binomial-model fits are compared, with leave-one-out and risk-of-bias analyses, and the influence of studies with extreme proportions is examined.
Statistical Results The back-transformed pooled prevalence with 95% confidence and prediction intervals, between-study variance, and subgroup prevalence by case definition and setting.
Publication-Ready Tables & Figures A prevalence forest plot with subgroup panels, a study characteristics table and a risk-of-bias figure, formatted to PRISMA 2020, MOOSE or the journal's requirements.
Interpretation The pooled value is written for a defined population and period, with its variation explained and the certainty of evidence rated in the GRADE framework.
Reproducible Analysis Files R scripts, Stata do-files or SAS programs are provided with the case counts and denominators, so the pooled prevalence and subgroups can be refitted as studies are added.
ALTERNATIVE METHODS
When Another Method May Be More Appropriate
We choose the method by what the studies measured and what the review must answer. One population, a comparison between groups or definitions that differ in kind need another approach.
01
Incidence & Prevalence Analysis
For prevalence or incidence in one dataset or population, direct estimation with standardization and confidence intervals applies, with no synthesis across studies.
02
Meta-Analysis
When groups are compared, for example exposed and unexposed, an effect measure such as a risk ratio is pooled instead of a single proportion per study.
03
Meta-Regression
To explain why prevalence differs, study-level moderators such as region or case definition can be modeled, given enough studies for each covariate.
04
SWiM (Synthesis Without Meta-Analysis)
When definitions or populations differ too much to justify one pooled number, a structured synthesis reports prevalence by group without pooling.
CASE EXAMPLE
How We Work in Practice
An illustration of how a pooled prevalence moves from a written plan to a reportable, carefully qualified summary estimate.
ILLUSTRATIVE EXAMPLE · CHRONIC DISEASE EVIDENCE REVIEW · UNITED STATES
An evidence review team at a public health agency needed a pooled prevalence of chronic kidney disease among adults with type 2 diabetes to inform a screening recommendation.
The protocol, filed before extraction, specified a binomial-normal random-effects model on the logit scale, subgroups by case definition and a prediction interval. Several studies recruited from nephrology clinics, a frame that overstates prevalence, so the protocol’s sensitivity analysis excluded them, and three reports gave annual new diagnoses, which are incidence and were kept out of the pool. Studies defined disease by eGFR alone, albuminuria alone or both, so definition was the prespecified subgroup. Heterogeneity was large, and we described it through tau-squared and the prediction interval and advised against reading one pooled number as universal.
Outcome: The team received the pooled prevalence with its 95% confidence interval and prediction interval, subgroup estimates by case definition, a forest plot, a risk-of-bias summary and the scripts.
This example does not describe a real client or project. We never share details of actual client work, in line with the confidentiality we promise every researcher. This illustrative example is included to show the type, scope, and standard of work involved in this kind of analysis.
SOFTWARE
Statistical Software for Pooled Prevalence
Pooled prevalence is modeled in R, Stata, SAS and Python, with syntax or scripts provided where applicable.
RESEARCH DISCIPLINES
Prevalence Evidence Across Research Fields
Pooled prevalence is used wherever a condition or behavior is measured in many populations, each field with its own definitions, sampling frames and reporting conventions.
A Defined Process for Your Analysis
We review your dataset, research question and design before any work begins, so the scope, deliverables, timeline and fixed project fee are clear from the outset.
01
Share Your Research Materials
Send your dataset, research question, study design and any existing analysis plan or output. We will let you know if anything else is needed to assess the project.
02
Receive Your Assessment
Your statistically trained Research Coordinator reviews the project and confirms the required analysis, deliverables, timeline and fixed project price before work begins.
03
Analysis & Delivery
Once you confirm the scope, an in-house statistician completes the agreed analysis and delivers the outputs, interpretation and reporting files.
RESEARCHER VOICE
What Researchers Say
Feedback from a health technology assessment analyst whose first-pass output was rebuilt carefully before it could be signed off.
“Our first pass at Proportion / Prevalence Meta-Analysis produced output, but I was not comfortable signing it off because of whether the studies were comparable enough to pool and how heterogeneity should be handled. The analysis was rebuilt carefully and the interpretation was much clearer. The final output was concise, clearly interpreted, and ready to use in our research reporting.”
RESEARCH RESPONSIBILITY
Statistical Support Without Taking Over Your Research
We provide the statistical work for your pooled prevalence, and the review, its meaning and its authorship stay entirely with you.
Agreed Statistical Support We provide the agreed methodological, analytical and statistical reporting support for your pooled prevalence, within the scope confirmed before work begins.
No Intellectual Contribution OnlineSPSS claims no intellectual contribution to your research. Our work is statistical support, and authorship remains yours.
Your Research Decisions The review question, case definition and conclusions remain with you, and our statistical interpretation stays within what the pooled estimates and the included studies support.
Confidential Handling Research data and project details are handled confidentially and are not published. An NDA is available where required, and can be arranged before sensitive materials are shared.
FREQUENTLY ASKED QUESTIONS
Questions About Prevalence Meta-Analysis
Short answers to what researchers ask before pooling prevalence, from transformations and heterogeneity to confidentiality and the assessment process.
Which transformation or model suits pooled proportions?
For proportions near zero or one, a binomial-normal mixed model is usually preferable, and the logit transformation is a reasonable alternative when few studies have zero counts. The Freeman-Tukey double arcsine transformation stabilizes variances but has documented problems, including misleading back-transformed estimates when sample sizes differ, so we do not use it as a default. We state the model in the protocol and show a second approach as a sensitivity analysis.
Should prevalence be pooled or reported without a meta-analysis?
Pooling fits when studies estimate the same quantity with a comparable case definition in populations a reader would group together. A very high I-squared is not by itself a reason to stop, because it is expected for prevalence and reflects a share of variability, not its size, so we report tau-squared and the prediction interval instead. When populations or definitions differ in kind, a pooled value would describe no real population, and we present the estimates by group without pooling.
Can I pool incidence rates with prevalence proportions?
No. Prevalence is the proportion of people with a condition at a time or over a period, incidence is a rate of new cases in person-time, and a risk, or cumulative incidence, is the proportion of an initially unaffected group who develop the condition over a stated period. They answer different questions, so each is synthesized separately with its own model, or one measure is excluded for a reason the protocol states.
Can you review an analysis that has already been completed?
Yes, we can review a completed prevalence synthesis before submission or a response to reviewers. We check the extracted counts and denominators, the case definitions, the transformation or model, the heterogeneity reporting and the bias appraisal, and confirm what stands or needs to change.
How do you protect my dataset and research confidentiality?
Your extraction sheets, output files and any protocol or analysis plan are handled confidentially, and project information is not published. An NDA is available where required and can be arranged before sensitive materials are shared. Read more about confidentiality and data security.
What happens when I request an assessment, and how long does it take?
You share your research question, design and relevant materials, and a statistically trained Research Coordinator reviews the request and responds the same day. The analysis, deliverables, timeline and one fixed project fee are then confirmed before any work begins, so the timeline follows from the agreed scope.
Still have a question? Find more answers in our full FAQ.
NEXT STEP
Need Support With
Prevalence Meta-Analysis?
Share your review question, the extracted case counts and your protocol. We assess whether the studies can be pooled and confirm the approach before any work begins.
No Commitment Required
Strictly Confidential
Fixed Project Fee
In-House PhD-Level Statisticians
Same-Day Response
Serving Researchers Since 2014