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ObsMinds · Observational Research — a specialized subdomain of RSMinds

STROBE compliant · ~10 observational designs · Confounding-aware

Design Rigorous Observational Studies
From Idea to STROBE Synopsis

Cohort, case-control, cross-sectional, and ecological designs. 11-step workflow with a confounding-adjustment check — AI proposes the design, a deterministic DAG analysis catches Simpson's-paradox traps before you commit. Built on STROBE, RECORD, and TRIPOD.

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obs.rsminds.com / workflow
01Idea & Obs Subtype
02PECO Framework
07Matching & Confounder Plan
08Sample Size & Power
11STROBE Synopsis
+ 6 more sections — see full workflow below
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observational subtypes covered

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workflow sections — idea to STROBE synopsis

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reporting standards: STROBE, RECORD, TRIPOD, MOOSE…

₹0/mo

starts here · 7-day money back

How ObsMinds works

The 11-section workflow

Each section is its own AI-assisted accordion. Outputs flow forward — your PECO informs matching; matching informs sample size; confounders thread through analysis; everything assembles into a STROBE synopsis.

01

Idea & Obs Subtype

Inputs

Research idea text

AI action

predictObsSubtype

Output

Best-fit subtype across 20 observational designs with alternatives + category (Cohort / Case-Control / Cross-sectional / Special).

Saves 30–60 min of design-selection deliberation
02

PECO Framework

Inputs

Subtype context

AI action

draftObsQuestion + PECO extraction

Output

Population / Exposure / Comparator / Outcome — with source population and follow-up window for cohort designs.

Saves 1–2 hours of PECO refinement
03

Research Question & FINER

Inputs

PECO

AI action

auditResearchQuestion

Output

3 question drafts + FINER scoring (Feasibility, Interest, Novelty, Ethics, Relevance).

Saves 2–3 hours vs manual drafting
04

Hypothesis & Variables

Inputs

Question

AI action

draftObsHypothesis + deconstructQuestion

Output

Exposure–outcome H1 / H0 + variables: predictor, outcome, confounders, effect modifiers, mediators.

Saves ~1 hour and surfaces confounders early
05

Theory & Framework

Inputs

QuestionVariables

AI action

discoverTheories + generateFramework

Output

3 theory candidates with citations + conceptual framework / DAG-style diagram.

Saves 3–5 hours of literature triangulation
06

Population & Eligibility

Inputs

SubtypeFramework

AI action

generatePopulationCriteria

Output

Source population, inclusion / exclusion criteria, recruitment sources, attrition assumptions.

Saves 1–2 hours and aligns with STROBE items 6–7
07

Sampling & Matching Strategy

Inputs

PopulationSubtype

AI action

suggestMatchingStrategy

Output

Comparator selection, matching variables, propensity-score plan, bias-control checklist for case-control & cohort.

Saves 2–3 hours and catches selection bias before sampling
08

Sample Size & Power

Inputs

SubtypeEffectIncidencePower

AI action

calculateObsSampleSize

Output

N cases + controls (or cohort N) with incidence-rate or odds-ratio assumptions and confounder-adjustment inflation.

Saves 1–2 hours and aligns with STROBE item 10
09

Exposure & Outcome Measurement

Inputs

PECOSetting

AI action

generateExposureAssessment

Output

Ascertainment methods, timing, instruments, and misclassification controls for exposure and outcome.

Saves 2–4 hours of measurement-protocol drafting
10

Analysis Plan

Inputs

OutcomesConfounders

AI action

generateObsAnalysisPlan

Output

Primary / secondary analyses, confounder-adjustment strategy (regression / matching / IPW), sensitivity analyses.

Saves 2–3 hours and addresses confounding head-on
11

STROBE Synopsis

Inputs

All previous sections

AI action

generateObsSynopsis (parallel SSE)

Output

STROBE-compliant synopsis exportable as DOCX, PDF, or Markdown.

Saves a full day of synopsis drafting

Subtype coverage

20 observational subtypes — all covered

From classic prospective cohorts to nested case-control and registry studies. Every subtype gets its own design guidance, STROBE extension mapping, and sample-size formulas.

Cohort

8 designs

  • Prospective Cohort

    Forward follow-up — exposure today, outcomes later.

  • Retrospective Cohort

    Historic records — exposure and outcome already happened.

  • Ambidirectional Cohort

    Mixes historic baseline with forward follow-up.

  • Birth Cohort

    Track individuals from birth across life stages.

  • Case-Cohort

    Subcohort sample as comparator for incident cases.

  • Closed Cohort

    Fixed membership — no new entries after baseline.

  • Dynamic Cohort

    Open entry-exit — person-time accumulation.

  • Cohort Survey

    Repeated measurement on the same cohort.

Case-Control

5 designs

  • Incident Case-Control

    New cases identified as they occur.

  • Prevalent Case-Control

    Existing cases at a point in time.

  • Nested Case-Control

    Cases & controls drawn from a parent cohort.

  • Case-Time Control

    Subject as their own control across time windows.

  • Case-Only

    No external controls — gene-environment focus.

Case Reports & Series

3 designs

  • Case Report

    Single instance — descriptive, hypothesis-generating.

  • Case Series

    Several cases sharing exposure or outcome.

  • Case Study Research

    In-depth qualitative case investigation.

Surveillance & Records

4 designs

  • Record Review

    Retrospective chart abstraction.

  • Registry Study

    Disease / device / exposure registries.

  • Surveillance Study

    Ongoing population-level monitoring.

  • Cox Proportional Hazards

    Time-to-event modelling design.

Compliance

Built on the observational
standards reviewers expect

Integrated

STROBE 2007

Strengthening the Reporting of Observational Studies in Epidemiology

Integrated

STROBE-NI

Newborn Infections extension

Integrated

STROBE-RDS

Respondent-Driven Sampling extension

Integrated

STROBE-ME

Molecular Epidemiology extension

Integrated

STROBE-AMS

Antimicrobial Stewardship extension

Integrated

RECORD 2015

Routinely Collected Data extension

Integrated

TRIPOD 2024

Prediction Model Studies

Integrated

MOOSE 2000

Meta-analyses of Observational Studies

Integrated

ROBINS-I

Risk Of Bias In Non-randomized Studies — Interventions

Integrated

ROBINS-E

Risk Of Bias In Non-randomized Studies — Exposures

Integrated

GATE Frame

Graphic Appraisal Tool for Epidemiology

Integrated

ROBINS-IRR

Risk-of-Bias for interrater-reliability obs studies

Integrated

CASP Cohort

Critical Appraisal Skills Programme — Cohort

Integrated

CASP Case-Control

CASP — Case-Control

Integrated

Newcastle-Ottawa

Quality scale for non-randomised studies

Integrated

GRADE

Grading of Recommendations Assessment

Your protocol is scored against every applicable observational standard in real time.

Why this matters

Confounders,
caught before Simpson's paradox bites

Other tools

Generate a design. Hope for the best.

If the AI omits a key confounder — age in a smoking-mortality cohort, socio-economic status in a screening study — you won't notice until a reviewer asks why your unadjusted OR points the opposite way from your stratified analysis. Simpson's paradox in slow motion.

ObsMinds

AI proposes. DAG analysis cross-checks.

A deterministic confounder-coverage check inspects your variables against a directed acyclic graph for the chosen subtype. Missing common causes, colliders incorrectly adjusted for, and Simpson's-paradox risk are flagged with an explanation — before you submit.

  • Deterministic DAG audit — not LLM-only opinion.
  • Flags omitted common causes and improperly conditioned colliders.
  • Stratified vs adjusted estimate sanity check.
section 10 / analysis plan · prospective cohort · smoking → CHD

AI confounder set

age, sex, BMI

3 covariates · proposed

DAG audit

+ SES, diet, exercise

3 missing common causes flagged

Final adjusted set

6 covariates

Simpson's-paradox risk: low

Plans

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  • All 20 observational subtypes
  • 11-section workflow
  • STROBE + RECORD exports
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₹699/ 90 days
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FAQ

Frequently asked questions

Common questions from epidemiologists, supervisors, and IEC reviewers.

Which observational designs does ObsMinds cover?

All 20 mainstream subtypes — prospective, retrospective, ambidirectional and birth cohorts; case-cohort, dynamic and closed cohorts; nested, incident, prevalent, case-only and case-time control; case report, case series, case-study research; record review, registry, surveillance; and Cox proportional-hazards designs.

How is confounding handled differently from generic AI?

The AI proposes a confounder set; a deterministic DAG audit cross-checks against the design template for missing common causes and improperly adjusted colliders. Disagreements are flagged with an explainer before you commit to your analysis plan.

Will the synopsis pass STROBE peer review?

Output is structured to match all 22 STROBE items, with the appropriate extension (STROBE-NI / RDS / ME / AMS or RECORD) chosen by subtype. Formal compliance still requires investigator verification — every line stays editable.

Can I use ObsMinds for a registry or routinely-collected-data study?

Yes — RECORD is the default extension for registry and EHR-based work. The workflow asks for data-source provenance, code-list versioning, and missingness handling that RECORD reviewers expect.

How does PECO differ from PICO?

PECO swaps Intervention for Exposure. Observational studies don't assign exposure; they observe it. The tool guides you through source population, exposure ascertainment, and a credible comparator without inviting causal language reserved for RCTs.

Does the sample-size calculator work for case-control and cohort designs?

Yes — separate paths for cohort (incidence-rate, relative risk) and case-control (odds-ratio, matched / unmatched, with confounder-adjustment inflation). The verifier flags assumption mismatches before you lock the number.

Can I import an existing protocol or chart-review template?

Yes. Paste any synopsis or PECO statement and the tool extracts population, exposure, comparator, and outcome — including matching variables — so you continue from where you left off.

What about prediction-model studies?

Prediction-focused observational work uses the TRIPOD 2024 extension, with development / validation / update phases mapped to dedicated sections. Calibration and discrimination plans are part of the analysis-plan output.

What export formats are supported?

DOCX, PDF, and Markdown. All exports preserve STROBE structure and reporting headings for direct ethics-committee or journal submission.

Is there a free way to try it?

Subtype prediction (Step 1) is free with login — identify the right observational design for your idea. Full workflow is unlocked on any access plan, with a 7-day money-back guarantee.

Start your observational study in 5 minutes.

Free subtype prediction. No credit card. Sign in with Google.

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