Glycemic-Control Outcomes from HbA1c and Glucose Data

Definition and scope

Operational glycemic outcomes based on harmonized HbA1c or glucose measurements, preserving units, timing, collection mechanism, and the distinction between continuous change and a target-defined responder. The target population, use, and analysis remain protocol decisions; this card explains the choices and their consequences rather than approving a study.

Operational specification

Specify the biomarker, assay/unit convention, baseline window, follow-up visit/window, date-selection hierarchy, target population, treatment strategy, and handling of acute illness, missing tests, and repeated values. HbA1c percentage-point change is not a percent relative change. CGM time-in-range is a separate measurement with wear-time and sampling requirements.

Analysis and diagnostics

Analyze an explicitly defined change or follow-up contrast with baseline adjustment and design-appropriate confounding/dependence handling. Inspect the measurement schedule and missingness by arm, confirm unit conversion and plausible values, and separate a laboratory observation from a treatment recommendation. Targets and responder thresholds require clinical and protocol justification.

When NOT to use and common errors

Do not assign normal glycemia to untested people, use a post-treatment test to select a favorable baseline, mix mmol/mol and percent as though identical, or infer treatment benefit from a within-arm pre/post change alone.

Worked example and implementation boundary

A synthetic patient has HbA1c 8.5% at baseline and 7.5% at the defined follow-up. Calculate follow-up minus baseline in percentage points. The observed change is -1.0 percentage point. It is not a 1% relative reduction or a causal treatment effect by itself. The code performs that explicitly stated calculation with synthetic inputs. It is not a complete fitted analysis and does not demonstrate the identification assumptions, estimate sampling uncertainty, or certify application in another population.

Data-source and reporting requirements

Preserve the provenance, dates, units, and observation rule for each input. Claims capture services and transactions under observable benefits; absence of a record is not proof that an event or resource use did not occur. EHR and registry measurements depend on the collection process and may miss care outside the system. Linked sources add reconciliation and selection questions. Primary collection can improve a missing domain but still requires valid measurement and follow-up. Report which of these limitations affects the stated use, which evidence supports the chosen inputs, and what remains uncertain.

Worked example

Scenario

A synthetic patient has HbA1c 8.5% at baseline and 7.5% at the defined follow-up. Calculate follow-up minus baseline in percentage points.

Dataset

Synthetic inputs with units defined in the scenario; no patient data.

Steps

Result

The observed change is -1.0 percentage point. It is not a 1% relative reduction or a causal treatment effect by itself.

Trade-offs

Code example

baseline_a1c = 8.5
followup_a1c = 7.5
result = followup_a1c-baseline_a1c
assert abs(result - -1.0) < 1e-9
print(result)

baseline_a1c <- 8.5
followup_a1c <- 7.5
result <- followup_a1c-baseline_a1c
stopifnot(abs(result - -1.0) < 1e-9)
print(result)

data worked_example;
  baseline_a1c = 8.5;
  followup_a1c = 7.5;
  result = followup_a1c-baseline_a1c;
  put result=;
run;

Citations

1. [1]Nathan DM, et al. Translating the A1C Assay Into Estimated Average Glucose Values. Diabetes Care. 2008.
2. [2]U.S. Food and Drug Administration. Patient-Focused Drug Development: Selecting, Developing, or Modifying Fit-for-Purpose Clinical Outcome Assessments. Final guidance. October 2025.