REVIEW 1 major objections 6 minor 2 references
Early Pregnancy Treatment Decisions: Designing Perinatal Pharmacoepidemiology Studies using Real-World Data
T0 review · 1 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash
Pith's one-line read For studies of medication changes in early pregnancy, follow-up should begin at the healthcare encounter where the treatment decision is made—usually the first prenatal visit—not at conception.
desk verdict Useful methods guidance for a neglected design question; the pre-visit left-censoring issue is real but within scope if the target population is made explicit. read the letter →
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
What carries the argument
The central mechanism is the encounter-anchored time zero: starting follow-up at a prenatal visit or other healthcare encounter where a treatment decision is actually made, rather than at conception. The argument is carried by two analytic devices—clone-censor-weighting, in which each observation is cloned once per strategy and clones whose observed fills deviate from their assigned strategy are censored and reweighted, and sequential trials, in which non-initiators re-enter eligibility at later intervals. Both require defining strategies relative to the pre-pregnancy regimen and fixing a grace period, illustrated as 45 days, so that no future prescription fills are used to decide which strategy a person followed.
What would settle it
A head-to-head emulation of the same treatment-change question in the same administrative data, run once with conception as time zero and once with the first prenatal encounter as time zero, would settle the claim if the two estimates diverge in the direction and magnitude predicted by immortal time bias; if they do not diverge, re-anchoring time zero changes nothing.
Extended reading notes
Core claim
The central claim is that for research questions about changing pregestational medication regimens, the time zero of a target trial emulation should be a healthcare encounter, not conception. Conception is intuitively appealing but does not align with real decision points, because pregnancy recognition and entry into care happen weeks later. The paper specifies treatment strategies relative to the regimen already in use—continuation, adding, switching, and discontinuation—and shows that with a prespecified grace period, encounter-anchored designs can be analyzed with clone-censor-weighting or sequential trials to avoid immortal person time and conditioning on future events. The worked example is a hypothetical trial comparing metformin continuation, metformin plus insulin, and switching to insulin among people with pregestational type 2 diabetes at their first prenatal visit.
Load-bearing premise
The whole design rests on prescription fills within the grace period correctly revealing which treatment strategy a person actually follows; if fills misrepresent true use through stockpiling, leftover medication, provider samples, or non-adherence, the clone censoring and weights assign people to the wrong strategy and the estimates are biased.
Editorial extensions
If this is right
- Studies that anchor at conception and simply compare exposed versus unexposed pregnancies should be replaced or supplemented by encounter-anchored designs when the question is whether to continue, add, switch, or stop a chronic medication.
- Pregnancy identification algorithms based on delivery or live birth records condition on a future event; anchoring at the first pregnancy-related encounter restores the correct risk set of pregnancies at the decision point.
- Using a prespecified grace period to classify strategies reduces the chance that researchers look into future prescription fills to determine a person's strategy.
- Clone-censor-weighting and sequential trials both fit the pregnancy setting, with sequential trials best suited to initiation-versus-no-initiation comparisons and clone-censor-weighting suited to multi-strategy decisions.
- Clinical trial templates from pregnancy trials, such as the metformin-plus-insulin example, can directly inform the design of emulated trials in administrative data.
Reading between the lines
- Editorial inference: the same encounter-anchored logic extends beyond diabetes to any chronic pregestational medication—antidepressants, antihypertensives, immunosuppressants—and beyond pregnancy to any setting where treatment decisions occur at discrete clinical visits.
- Editorial inference: running the same research question on the same database with conception-anchored and encounter-anchored time zero would quantify how much immortal time bias actually shifts estimates in perinatal pharmacoepidemiology.
- Editorial inference: formalizing a data-generating model in which fills misrepresent true use through stockpiling, leftover medication, or non-adherence would yield bounds or bias-calibrated estimates for the clone-censor-weight estimator, addressing the paper's acknowledged uncertainty about fill accuracy.
- Editorial inference: the grace-period length is itself a design choice that may interact with care access; when prenatal visits are infrequent, a fixed 45-day window could misclassify strategies, so adaptive grace periods defined by the next encounter are worth testing.
Signed reviews
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. This manuscript is a methods/guidance paper on designing perinatal pharmacoepidemiology studies that evaluate changes to pregestational medication regimens, using type 2 diabetes mellitus treated with metformin as the running example. The authors argue that for such questions, time zero should be anchored at healthcare encounters (e.g., the first prenatal visit) rather than at conception; that treatment strategies should be defined in relation to existing treatment (continuation, adding, switching, discontinuation); and that clone-censor-weighting or sequential trial designs should be used to avoid immortal person time and conditioning on future events. They also review algorithms for identifying and dating pregnancies in administrative data, discuss how prescription fills can be used to operationalize strategies within a grace period, and enumerate limitations of claims-based measurement of treatment strategies.
Significance. If the proposed design principles are adopted, they would move the field from exposure-oriented 'first-trimester exposed versus unexposed' comparisons toward decision-oriented comparative effectiveness questions that match the timing of clinical care, which is a substantive contribution. The paper is careful to acknowledge residual within-trial immortal time in sequential trials, confounding that is not addressed by design, and measurement limitations of prescription fills. The protocol table and the illustrative figures provide concrete starting points for implementation, and the paper explicitly identifies falsifiable design assumptions (e.g., the grace period and the use of fills to define strategies). The manuscript does not present empirical analyses, simulations, or code, but as a design framework that is not a deficiency.
major comments (1)
- [Section 3, 'Aligning time zero with relevant time points for treatment decisions'; Figure 1] The central recommendation that 'time zero should be anchored on a healthcare encounter' and that 'The first such visit can be used as the first possible time at which a person might be enrolled' does not account for treatment decisions made before the first observed pregnancy-related encounter. A pregnant person with T2DM may stop metformin or start insulin immediately after a positive home pregnancy test, or at a primary care visit that is not coded as prenatal care. Such decisions occur before time zero and are not addressed by the cloning/censoring machinery: if eligibility is operationalized as 'using metformin at the first prenatal visit,' early switchers are excluded and the estimand becomes conditional on survival without treatment change up to that visit; if eligibility is instead inferred only from pre-pregnancy fills, early switchers can be misclassified as 'continuers' because the baseline strategy is read from fills observed after the decision has already been made. The manuscript should either explicitly restrict its scope to decisions made at prenatal or other healthcare encounters, or discuss how earlier decision points could be identified (e.g., pregnancy-test-related visits or primary care visits) and what sensitivity analyses would be needed when the timing of the decision is unknown.
minor comments (6)
- [Table, Causal Contrast row] The Causal Contrast row is confusingly formatted: the target trial column reads 'Intention-to-treat effect; per-protocol effect' and the emulation column also lists 'Per-protocol effect.' Please clarify whether both ITT and per-protocol effects are estimands, or only the per-protocol effect.
- [Section 4, references] The sentence 'residual immortal person time bias may still be present within each trial, since initiators can fill a prescription at any time within the trial interval while non-initiators must complete the full interval without filling a prescription' is cited to reference 35, which concerns gestational age at arrest of development; this reference does not support the statement about sequential trials and should be replaced with a methods citation on sequential trial emulation (e.g., Caniglia et al. 2023 or a standard text).
- [Author affiliations] The author affiliation numbering is jumbled, with duplicate '4' labels for the University of British Columbia and McGill University; please correct the affiliation list.
- [Section 1, Figure 1 discussion] The statement that for Pregnancy 2 'no data would be recorded' and researchers 'would not be aware of its existence until the abortion procedure occurred' is internally inconsistent, since the abortion procedure itself is a healthcare encounter; please rephrase to 'no data would be recorded before the abortion claim.'
- [Section 3, page 9] The phrase 'The first such visit can be used as the first possible time at which a person might be enrolled' should be qualified as 'the first pregnancy-related visit observable in the data at which eligibility can be assessed,' because earlier healthcare encounters (e.g., primary care visits with a pregnancy test) may exist and may already be decision points.
- [Section 2, measurement of strategies] The discussion of stockpiling, leftover medication, and provider samples is useful; consider adding a sentence recommending quantitative bias analysis to explore how such misclassification of strategy adherence would affect estimates, paralleling the sensitivity-analysis recommendation already made for selection bias in the Discussion.
Circularity Check
No significant circularity: the paper is a design-guidance review whose central recommendation is a normative alignment principle, not a derived prediction or fitted result.
full rationale
This paper does not fit parameters, estimate effects, or derive quantitative predictions. Its central claim—that time zero for studies of pregestational medication changes should be anchored on healthcare encounters and that treatment strategies should be defined relative to existing treatment—is an argued methodological recommendation, not an output computed from inputs. The supporting methods (clone-censor-weighting and sequential trials) are introduced with citations to external methods literature (e.g., refs 38–40), and the paper explicitly notes their limitations rather than presenting them as proven by the authors' own prior work. The self-citations present (Latour et al. 2025, Chiodo et al. 2023, and Wood & Edwards 2026) provide background context on selection bias and preconception study design; they are not used as load-bearing proof of the paper's central proposal. No equation in the paper equates a prediction to a fit, and no fitted input is relabeled as a prediction. The paper's own caveats—such as uncertainty about whether prescription fills reflect true use and the challenge of treatment changes before the first observed encounter—are acknowledged limitations, not circular reasoning. The design recommendations remain independent content that could be evaluated by future empirical applications, so the appropriate circularity score is low.
Assumptions & free parameters
free parameters (1)
- Grace period duration for treatment strategies =
45 days (example)
assumptions (4)
- domain assumption Pregnancy episodes can be identified and dated in routinely collected data with sufficient accuracy to assign gestational age to treatment decisions.
- domain assumption Prescription fills within a grace period represent the treatment actually taken, enabling classification of clones to strategies.
- domain assumption Exchangeability (no unmeasured confounding) and positivity hold after adjustment for baseline confounders.
- domain assumption First prenatal visit or healthcare encounter is a clinically meaningful decision point where treatment changes occur.
Cite this review
Pith. "Pith review of Early Pregnancy Treatment Decisions: Designing Perinatal Pharmacoepidemiology Studies using Real-World Data." pith.science (2026). https://pith.science/paper/JTT2AL2W
@misc{pith2026260811108,
author = {Pith},
title = {Pith review of: Early Pregnancy Treatment Decisions: Designing Perinatal Pharmacoepidemiology Studies using Real-World Data},
year = {2026},
howpublished = {\url{https://pith.science/paper/JTT2AL2W}},
note = {Machine review of arXiv:2608.11108}
}
read the original abstract
Research on the use of medications during pregnancy has two primary goals: to detect signals that medications may be harmful to a pregnant individual or fetus, and to support better treatment of pregnant people who require pharmacotherapy. Target trial emulation has been proposed as an approach to estimate the effects of interventions in real world data, with recent extensions to the pregnancy setting. This approach focuses on aligning eligibility and treatment initiation with start of follow up, which is particularly desirable given methodological challenges specific to pregnancy, such as right and left censoring and truncation, competing events, differences in gestational length, and varying etiologically susceptible periods. While previous work on target trial emulation in pregnancy has focused on initiation versus non-initiation of point treatments such as vaccines or antibiotics, this paper focuses on research questions regarding changes to pregestational treatment regimes, and aims to highlight opportunities and approaches to designing studies that align with relevant time points during early pregnancy at which treatment decisions occur in clinical practice. Using the example of treatment for type 2 diabetes mellitus, we review methods for identifying pregnancy episodes in routinely collected healthcare data, introduce possible time zero candidates, and discuss analytic approaches that minimize potential bias due to selection and immortal person time.
Figures
Figures from the paper (1 more)
Reference graph
Works this paper leans on
-
[15]
Andrade, S. E. et al. Administrative Claims Data Versus Augmented Pregnancy Data for the Study of Pharmaceutical Treatments in Pregnancy. Curr. Epidemiol. Rep. 4, 106–116 (2017). 16. Schummers, L. et al. A more accurate approach to define abortion cohorts using linked administrative data: an application to Ontario, Canada. Int. J. Popul. Data Sci. 7, 1700...
work page 2017
-
[29]
Lohse, S. R. et al. Validation of spontaneous abortion diagnoses in the Danish National Registry of Patients. Clin. Epidemiol. 2, 247–50 (2010). 30. Nordeng, H., Lupattelli, A., Engjom, H. M. & van Gelder, M. M. H. J. Detecting and Dating Early Non-live Pregnancy Outcomes: Generation of a Novel Pregnancy Algorithm From Norwegian Linked Health Registries. ...
Reviewed August 12, 2026 · model on record in the stance chip above.
Discussion (0). Continue with ORCID to comment.