GRAMMS (Good Reporting of A Mixed Methods Study)

What it is

GRAMMS — Good Reporting of A Mixed Methods Study — is a six-item reporting checklist introduced by O'Cathain, Murphy, and Nicholl (J Health Serv Res Policy, 2008) and listed on the EQUATOR Network. It was derived from a structured review of mixed-methods studies in health services research that found their reporting was frequently incomplete, particularly around why the methods were mixed and how the strands were brought together. GRAMMS asks authors to (1) justify the mixed-methods design (what the combination adds over a single method); (2) describe the design — its priority (which strand dominates), sequence (concurrent vs sequential), and purpose; (3) describe each method (sampling, data collection, and analysis) for the quantitative and qualitative strands separately; (4) describe where, how, and by whom integration occurred; (5) describe any limitation associated with one component arising from the presence of the other (e.g., a reduced sample because of dual data collection); and (6) describe the insights gained by mixing or integrating the methods. It is a reporting tool — a transparency checklist for the manuscript — not a critical-appraisal instrument, a risk-of-bias tool, or a numeric quality score. It is maintained as a community resource via EQUATOR rather than through a formal versioned update process.

When to use

Use GRAMMS when the deliverable is a mixed-methods study report or protocol that genuinely combines a quantitative and a qualitative strand — for example, a real-world effectiveness or health-services evaluation paired with patient or clinician interviews, a survey nested in a registry analysis, or a process evaluation accompanying an observational comparative-effectiveness study. The natural decision contexts are peer-reviewed journal submission and, secondarily, an HTA/payer dossier where qualitative patient-experience or implementation evidence is integrated with quantitative outcomes to inform value. Decision rule for picking GRAMMS over a sibling guideline: if the study is purely observational quantitative RWE (claims/EHR cohort, comparative effectiveness), reach for STROBE/RECORD-PE or HARPER, not GRAMMS; if the study is purely qualitative, reach for COREQ or SRQR; choose GRAMMS only when integration of two strands is itself a reported object of the study. GRAMMS is best used alongside the strand-specific guideline for each component (e.g., GRAMMS + RECORD-PE for the quantitative arm + COREQ for the interviews), because GRAMMS deliberately does not specify how to report the internals of either strand in depth.

What it requires

GRAMMS enforces six reporting domains, and only some have analogs in quantitative RWE practice: - Design transparency — the justification, priority, sequence, and purpose of the mixed-methods design must be explicit. This is the domain that overlaps most with RWE design-transparency expectations (pre-specified design, clear research question). - Per-strand methods — for the quantitative strand specifically, the catalog's substantive RWE standards apply: data fitness-for-use, phenotype/algorithm definition, time-zero alignment, the estimand and intercurrent events, confounding control, and attrition/missing data. GRAMMS itself states only that each strand's methods be described; it does not enumerate these RWE-specific items, so a credible report borrows them from the appropriate quantitative guideline. - Integration — the distinctive GRAMMS requirement: report where (design, methods, or interpretation level), how (e.g., triangulation, following a thread, a joint display), and by whom integration was performed. This domain has no counterpart among the quantitative methods in this catalog. - Strand-interaction limitations and integration insights — what one strand cost or contributed to the other, and what was learned that neither strand could have produced alone.

When NOT to use — limitations and common misapplications

- Treating GRAMMS as a risk-of-bias or quality-scoring tool. It is a reporting checklist; a fully GRAMMS-compliant paper can still describe a biased, underpowered, or poorly integrated study. Completeness of reporting is not validity. For appraisal of the quantitative strand use ROBINS-I; do not tally GRAMMS items into a "quality score." - Believing the checklist makes the study causal or rigorous. Reporting that integration occurred does not make the quantitative strand confounding-controlled; the design must earn that separately. - Wrong guideline for the design. Using GRAMMS for a single-method observational study (where STROBE/RECORD-PE belongs), or using STROBE alone for a study whose central claim rests on integrating qualitative and quantitative findings (where the integration items GRAMMS polices would otherwise go unreported). - Checklist-as-theater. The most common failure GRAMMS was designed to catch is naming a study "mixed methods" while reporting two parallel strands that are never actually integrated — item 4 (integration) and item 6 (insights from mixing) exist precisely to expose this, and submitting a checklist with those items hand-waved defeats the purpose.

How it maps to this catalog

GRAMMS sits one level above the quantitative methods catalog: it governs the report structure of a mixed-methods study, and its quantitative strand must still satisfy this catalog's design standards. Map only the items that genuinely overlap, and treat the integration/qualitative items as out of catalog scope (a finding in itself): the per-strand methods requirement for the quantitative arm is implemented by `active-comparator-new-user` (defensible design and time-zero alignment), `diagnosis-phenotype-algorithm-1ip-2op-time-window-rwe` (phenotype/algorithm validation), `estimands-ate-att-intercurrent-events-rwe` (estimand and intercurrent events), `high-dimensional-propensity-score-hdps-rwe` (confounding control), and `attrition-and-loss-to-follow-up-rwe` (attrition/missing data); the design-transparency item is reinforced by `target-trial-emulation` (pre-specification discipline); and `claims-analysis` carries the data-fitness considerations for a claims/EHR/registry quantitative strand. GRAMMS' integration, strand-interaction limitation, and integration-insight items have no implementing concept here because the catalog scopes quantitative RWE only — authors should pair GRAMMS with COREQ/SRQR for the qualitative strand and report integration per GRAMMS directly.

Applied note (claims/EHR/registry RWE)

A typical use is a sequential explanatory design: a claims-based active-comparator new-user cohort estimates a comparative safety effect, then clinician interviews explain an unexpected channeling pattern. GRAMMS forces the report to state that the quantitative finding drove the qualitative sampling (integration at the methods level), to acknowledge that recruiting interviewees from the cohort narrowed the quantitative window (strand-interaction limitation), and to articulate the insight (e.g., prescribing rationale that residual-confounding diagnostics could not surface). The cohort itself is still held to `active-comparator-new-user` and `attrition-and-loss-to-follow-up-rwe` standards — GRAMMS does not relax them.