For a healthcare literature review, compare articles by aligning PICO, standardizing data extraction, rating bias, and synthesizing results.
Why This Guide Works
Readers want a clean method they can repeat. The steps below keep choices transparent, limit bias, and make the final comparison easy to trust.
Set The Aim With A Sharply Framed Question
Start with one clear question. PICO is a handy way to frame it: Population, Intervention, Comparator, Outcome. Add Setting and Time if they shape the answer. Write the primary outcome in one sentence. List any secondary outcomes you will extract but rank below the main one.
Define What Gets In And What Stays Out
Create inclusion and exclusion rules before screening. Use study design, setting, age range, condition definition, follow-up window, and language. Decide how you will handle preprints, conference abstracts, and grey sources. Keep a log of every rule and the reason for each choice.
Build A Consistent Extraction Sheet
Plan data fields once, then use them for every article. Core fields include citation, design, setting, sample size, eligibility criteria, intervention or exposure details, comparator, outcome definitions, measurement timing, effect estimate with unit, precision measure, and notes on funding or conflicts. Add fields for subgroup notes only if you plan to compare across studies.
What To Compare Across Common Study Types
The table below shows what to line up across frequent designs and why each point matters for a fair head-to-head read.
| Study Type | Core Things To Line Up | Why It Helps |
|---|---|---|
| Randomized controlled trial | Random sequence, concealment, blinding, attrition, adherence, ITT vs per-protocol, outcome timing | Reduces bias and keeps effect estimates comparable |
| Cohort | Eligibility, exposure measurement, confounder control, follow-up length, outcome assessment method | Limits confounding and timing mismatches |
| Case-control | Case definition, control source, matching, exposure ascertainment, blinding of assessors | Cuts selection and recall issues |
| Cross-sectional | Sampling frame, response rate, measurement validity, timing of measures | Keeps prevalence estimates on the same footing |
| Diagnostic accuracy | Index test protocol, reference standard, blinding, spectrum, thresholds | Prevents inflated sensitivity or specificity |
| Qualitative | Methodology, sampling approach, reflexivity, coding steps, saturation checks | Keeps themes trustworthy and comparable |
| Systematic review or meta-analysis | Protocol, search span, inclusion criteria, risk-of-bias approach, heterogeneity plan, model choice | Aligns review quality across sources |
| Economic evaluation | Perspective, time horizon, discounting, cost sources, sensitivity tests | Keeps cost-effect pairs commensurate |
Comparing Articles In Healthcare Literature Reviews: A Simple Map
Below is a plain route from screening to synthesis. Follow it in order for a smoother, faster read and a result others can repeat.
Screen In A Two-Stage Flow
Do a title and abstract screen first, then full text. Record counts and reasons for exclusion. A PRISMA 2020 flow diagram keeps this tidy and reproducible for readers and editors. Pair the diagram with a short methods paragraph that lists databases, dates, and a full search string example; editors and readers can trace every step and spot mismatches early without guesswork.
Score Quality And Bias Before Any Numbers
Judge risk of bias using tools that fit the design. For trials, RoB 2 flags issues in randomization, deviations from intended treatment, missing data, measurement, and selection of the reported result. For non-randomized intervention studies, ROBINS-I steps through confounding, selection, classification of interventions, deviations, missing data, outcome measurement, and selection of the reported result. For observational reporting, STROBE is handy. For qualitative work, COREQ lists what readers look for in interviews and focus groups. Apply the same tool set to every included paper. Report item-level calls, not just a single label.
Align Outcomes And Units
Pick one index outcome per theme so the comparison stays focused. Examples: pain score at 12 weeks, HbA1c at 6 months, readmission within 30 days, time to event, or sensitivity for a specific threshold. State the unit and time window. If trials use different scales for the same construct, convert to a standard mean difference. For dichotomous outcomes, stick to one effect type across studies, such as risk ratio. For time-to-event data, prefer hazard ratios. When authors report medians without dispersion, note the limitation and avoid forced conversions.
Map Effect Estimates Cleanly
Build a master table of effect size, precision, and direction for each study. Use the same sign for benefit across all rows. If a lower value is better, invert where needed so the direction stays consistent. Add a short note when a study uses a composite outcome, then list the parts so readers can spot mismatches.
Decide When Pooling Makes Sense
Meta-analysis helps only when the question, designs, outcomes, and timing align. If those pieces match, pick a random-effects model per the Cochrane Handbook unless a strong case exists for a fixed model. Check clinical and methodological differences first. If variability is high or reporting is thin, keep the synthesis narrative and show side-by-side differences instead of a forced pooled number.
Group Like With Like
Cluster studies by design, risk-of-bias tier, population, dose, or setting. Within each cluster, compare effect size, precision, and any consistent subgroup signal. When a subgroup looks striking, check whether it appears in more than one study and whether it was pre-specified. Avoid long lists of post-hoc splits.
Write Side-By-Side Narratives That Read Tight
Present the comparison as short blocks. Lead with the strongest evidence, then move to designs with a higher chance of bias. Use plain verbs. Name the population, the exposure or intervention, the comparator, and the direction and size of the effect with its unit. End each block with a one-line takeaway.
Translate Evidence Into Certainty
GRADE helps you state how much confidence readers can place in an effect. Trials start higher on certainty; observational designs start lower. Downgrade for bias, inconsistency, indirectness, imprecision, or publication bias. Upgrade only when a large effect, a clear dose-response, or a strong bias away from the null is present. Share the final certainty rating next to each main outcome.
Turn Methods Into A Repeatable Record
List databases, dates, and all search strings. Name every screening step and who did it. Note how conflicts were resolved. Share the extraction form as a supplement. If you used automation to speed screening or extraction, say where and how it shaped the work. Readers and reviewers should be able to retrace every filter and calculation.
Second Table: Ready-To-Use Appraisal Shortlist
Here is a compact list of tools you can apply by study type. Pick one per design and stick with it for the full review.
| Tool | Best Fit | Core Items You Will Check |
|---|---|---|
| RoB 2 | Randomized trials | Randomization, deviations, missing data, measurement, selection of reported result |
| ROBINS-I | Non-randomized interventions | Confounding, selection, classification, deviations, missing data, measurement, selection of reported result |
| STROBE | Cohort, case-control, cross-sectional | Design clarity, participants, variables, bias, study size, results, funding |
| CONSORT | Randomized trials reporting | Flow, allocation, blinding, numbers analyzed, outcomes, harms |
| COREQ | Qualitative | Research team, methods, study context, analysis, reporting |
| QUADAS-2 | Diagnostic accuracy | Patient selection, index test, reference standard, flow and timing |
| GRADE | Any body of evidence | Factors that raise or lower certainty and the final rating |
Show Numbers In Ways People Read
Tables carry most of the load. Put the extraction table in the main text, not just in a supplement. Use one table for study features and one for results. Use short labels, consistent units, and footnotes for any quirks. If you pool, add a forest plot and a short paragraph that states the pooled effect, the model, and the measure of variability in plain language.
Handle Missing Or Messy Data
If a paper omits a needed number, try contacting the authors once. If no reply arrives, present what you have rather than inventing values. When two papers use the same data set, treat them as one to avoid double counting. When a trial includes multiple arms that map to the same comparator, handle the shared arm carefully to avoid giving it extra weight.
Keep Ethics And Compliance In View
Disclose funding for every included study and your own review. Flag any trial registrations you checked. For patient-level data, confirm consent and approvals in the original papers before drawing strong claims. Avoid language that overstates what the data can support.
Common Pitfalls And Straightforward Fixes
- Screening drift between reviewers → Train on five to ten papers first and agree on rule wording.
- Outcome shopping → Pre-rank outcomes in the protocol and stick with the order.
- Unit chaos → Convert to common units early and document the recipe.
- Unclear bias calls → Quote the method section that led to each call.
- Over-pooling → Keep designs and time points separate when they do not match.
- One loud outlier → Run a leave-one-out check and present both versions.
- Thin reporting → Say so plainly and keep those studies in a separate block.
Write With A Reader’s Pace
Use short paragraphs, front-loaded sentences, and active voice. Replace jargon when a common term exists. Keep figures and tables near the text that refers to them. Label panels and axes clearly. Share data and code where possible so others can fully reproduce every step.
Closing Notes
Comparing healthcare articles well is less about fancy math and more about steady, fair choices. Frame one clear question, extract the same fields every time, rate bias with fit-for-purpose tools, align outcomes and units, and then tell the story in clean, direct prose. Do that, and your review will read tight, look credible, and stand up to close reading under scrutiny.