Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification

Md Abul Bashar; Richi Nayak; Sharon Torao Pingi

REVIEW 3 major objections 9 minor 242 references

Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification

T0 review · 3 major / 9 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read This survey argues that GAN-based imputation of longitudinal data rarely states which missing-data mechanism it assumes, and that this omission, together with neglect of static features, class imbalance, and mixed data types, undermines…

desk verdict A genuinely useful survey of GAN-based longitudinal data imputation whose qualitative gap analysis is plausible, but the quantitative meta-claims rest on a corpus that is not fully disclosed and are undermined by an internal counting inconsistency. read the letter →

arxiv 2506.18007 v1 pith:GTMJDO2R submitted 2025-06-22 cs.LG stat.ML

Sharon Torao Pingi , Md Abul Bashar , Richi Nayak This is my paper · ORCID

classification cs.LGstat.ML MSC 68T0762D1062M10

keywords longitudinaldatamissingimputationgenerativeadversarialnetworksclassificationmissingnessmechanismsclassimbalanceirregularsamplingdeeplearningsurvey

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish that GAN-based imputation of longitudinal data has so far paid more attention to generative architecture than to the statistical assumptions that make imputation valid for classification. Reviewing 56 studies published between 2019 and 2024, it reports that over 70% never state which missingness mechanism they assume, while random masking during training silently imposes a missing-completely-at-random assumption even when real dropout and irregular sampling point to more informative mechanisms. It also finds that static features and instance heterogeneity are mostly ignored, and that class imbalance and mixed data types are rarely tackled inside the imputation model. A sympathetic reader would take the paper's point to be that these omissions, not a lack of generative power, are what keep GAN-based imputation from reliably improving longitudinal classification.

What carries the argument

The argument is carried by three working objects. First, the missingness mechanism trichotomy—missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR)—together with the missingness mask and temporal decay used to encode what is observed and how far apart observations are. Second, the eight-category taxonomy of GAN-based imputation approaches, which the survey uses to organise the 56 papers and to ask, for each family, whether it states an assumption, uses static features, handles imbalance, or supports mixed types. Third, the meta-analytic counts (e.g., over 70% without a stated assumption, 85% combining approaches, 41% using auxiliary data or models) that turn the taxonomy into a quantitative gap analysis. The machinery does the work of converting 'methods vary' into 'methods leave the same foundational choices unstated.'

What would settle it

A reader could rerun the paper's search string on the same five databases and count, from full texts, how many studies explicitly state a missingness assumption; if the share with a stated assumption were above half rather than below 30%, the survey's central gap would be refuted. A second check: if most studies were found to evaluate under MAR or MNAR masking rather than uniform random masking, the claim that the field defaults to MCAR would be wrong.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central claim is that the field of GAN-based longitudinal data imputation has matured in architectural sophistication while leaving foundational data assumptions unspecified. The paper proposes a taxonomy of eight approach families—recurrence-based, attention-enhanced, mask reconstruction, latent-space optimisation, GAN inversion, uncertainty-enhanced, data transformation, and auxiliary data/model-enhanced methods—and then evaluates 56 selected papers against the data-level challenges of longitudinal classification. Its headline quantitative findings are that over 70% of studies do not state a missingness assumption, roughly 85% combine at least two approaches, only a handful address class imbalance directly, and almost none integrate static features to account for instance heterogeneity or jointly generate mixed data types. From a longitudinal-data perspective, the paper argues, these gaps matter because irregular sampling and dropout make missingness informative, and imputers that default to MCAR or ignore the static component can distort the temporal correlations classification depends on.

Load-bearing premise

The review's quantitative claims stand on the assumption that the 56 papers returned by its search and screening criteria fairly represent all GAN-based longitudinal imputation work; if that selection is biased, the reported percentages lose their force.

Editorial extensions

If this is right

If the gap analysis is right, published imputation accuracy numbers are often optimistic: training under random masking certifies performance mainly for MCAR, not for the dropout and irregular-sampling patterns that dominate real longitudinal data.
Joint end-to-end models that attach a classification objective to the imputation GAN should be expected to beat two-phase pipelines because the auxiliary task counteracts error propagation and conditions imputation on class information, a pattern the survey documents across several recent methods.
Attention-based and latent-space optimisation approaches will keep growing as the default response to long, sparse sequences, while uncertainty quantification is an early-stage direction with little standardised evaluation.
Researchers adopting GAIN-style mask reconstruction should state their missingness assumption explicitly; the survey's reading is that default MCAR conflicts with informative missingness and needs theoretical guarantees under MAR and MNAR.
The absence of a universally accepted quality metric for imputed longitudinal data means that RMSE/MAE improvements should not be read as classification improvements; the paper's metric-usage tables give a baseline for forming such standards.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A testable extension of the survey's claim: re-evaluating the same 56 methods under MNAR-generated dropout (e.g., monotone missingness) should shrink the reported performance gap between GAN imputers and simpler baselines, since most methods were tuned under MCAR-style random masking.
The survey's finding that static features are ignored suggests a concrete benchmark: on EHR-style data with strong baseline covariates, a GAN that conditions imputation on static features should improve downstream classification for minority subgroups, something the reviewed methods rarely measure.
The paper's own metric analysis implies that imputation error and classification performance can diverge; a reader could rank the surveyed methods by change in AUC rather than RMSE and get a materially different leaderboard.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The paper fills a real niche: it is the first survey I know that specifically ties GAN-based imputation to longitudinal data and to the downstream classification task, and the eight-category taxonomy is sensible. The discussion of missingness mechanisms (MCAR/MAR/MNAR) and how they interact with GAN training defaults is informed and practically useful, and Section 6.2 gives grounded advice on approach selection. If you work in this area, you will come away with a fair picture of what exists and what is missing.

The soft spots are mostly about the meta-analysis, and they matter. The claim that over 70% of studies do not specify a missingness assumption, that 85% combine approaches, and that RMSE is used in 80% of studies are the paper's load-bearing quantitative outputs. Those numbers depend on a 56-paper corpus whose selection and coding are not transparent: there is no PRISMA flow diagram, no full list of included studies, no coding protocol, and no inter-rater reliability. The inclusion criteria also involve judgment calls that could shift the percentages noticeably — univariate time series are excluded, yet image-transformed methods and even MNIST (P-BiGAN) appear in the tables, and city-level air quality is treated as longitudinal.

There is also a genuine internal inconsistency that the authors need to fix. Section 5.11 says only one method directly addressed class imbalance, citing [205]; Section 6.3.3 says only one study jointly addressed class imbalance and missing values, citing [9]; and the tables show both [187] and [188] using SMOTE. A survey whose own counts contradict each other cannot support precise meta-analytic conclusions.

That said, the qualitative gap analysis — that most GAN-based LDI methods ignore missingness mechanisms, static features, and instance heterogeneity — is probably correct and matches my reading of the literature. The paper is a solid first draft of a useful survey, not a flawed one. I would send it to a serious referee, but require the authors to supply the PRISMA flow diagram, the full list of 56 studies, the coding protocol, and corrected, consistent counts before publication. After those revisions, it would be a valuable reference for anyone working on imputation for clinical or longitudinal tabular data.

Referee Report

3 major / 9 minor

Summary. This manuscript surveys GAN-based imputation for longitudinal data, claiming to be the first such survey from a longitudinal-data-classification perspective. It defines key terms (longitudinal data, irregular sampling, missingness mechanisms, temporal decay), reviews seminal GAN architectures, organizes 56 papers into eight approach categories, tabulates recurrence-based, attention-enhanced, and non-recurrence methods in Tables 3-5, and uses these to quantify gaps: over 70% of studies do not state a missingness assumption, 85% combine approaches, static features and instance heterogeneity are mostly ignored, and class imbalance and mixed data types are rarely jointly handled. It concludes with practical guidance on approach selection, metric usage, and future research directions.

Significance. The survey addresses a genuine gap: prior surveys cover GAN imputation in general, time series, or EHR data, but not the longitudinal-data-specific problems of instance heterogeneity, irregular sampling, and missingness assumptions. Its formal definitions and taxonomy are useful, and the summary tables give a compact map of the field. The paper is honest about limitations and offers concrete future directions, such as MNAR guarantees, random effects, and standardized evaluation of imputed-sample quality. The main load-bearing element is the gap analysis; those percentages are only as good as the reproducibility of the corpus and coding, and that reproducibility is currently missing. With a full study list, a PRISMA flow diagram, and a coding protocol, the paper could become a valuable reference. The paper does not ship code or a reproducible artifact, so the missing artifact is the study corpus itself, which is more damaging in a survey than in a methods paper.

major comments (3)

[§3.4, §6.1, §6.3.1, Table 5] The central quantitative claims—'85% of works employ a combination of at least two approaches' (Section 6.1), 'over 70% of the studies did not specify a missing data assumption' (Section 6.3.1), and '75% of studies applying GAIN do not specify the assumed missing data mechanism' (Section 6.2.1)—are not auditable because the corpus is not fully disclosed. Section 3.4 jumps from screening directly to 'there remained 56 articles' without a PRISMA flow diagram, and no list of the 56 included studies is provided in the paper or a supplement. There is also no coding protocol defining what counts as 'specifying a missing data assumption' or 'combining approaches,' and no inter-rater reliability is reported. The judgment calls in Section 3.3 make this non-trivial: univariate time series are excluded, yet city-level air quality is treated as longitudinal (Section 6.1.3), and Table 5 lists MNIST for P-BiGAN despite criterion 8 excluding image data. Without an audit trail, the percentages may reflect screening bias rather than properties of the literature. Please provide the full study list, a PRISMA flow diagram, the coding instrument, and either dual coding or a clearly delimited single-coder protocol with sensitivity checks.
[§5.11, §6.3.2, §6.3.3, Tables 3-5] The gap claims about class imbalance and static features are internally inconsistent. Section 5.11 says 'only one method covered directly addressed class imbalance [205]', while Section 6.3.3 says 'Only one study has attempted to jointly address the class imbalance and missing values [9]'; these are different claims. Both conflict with the tables: Table 3 marks [187] as 'Imbalanced SMOTE', Table 4 marks [188] as 'Imbalanced SMOTE', and Table 5 lists [9] as addressing class imbalance via class-specific imputation. Similarly, Section 5.11 says 'A couple of methods [188, 187] addressed instance heterogeneity', but Section 6.3.2 says 'Most existing methods, except [187, 188, 6], overlook static features and instance heterogeneity', and the tables only flag static features for [187] and [188]. The paper must define 'directly addressed' versus 'jointly address', reconcile the counts with the tables, and correct the narrative to match its own coding.
[§6.2.1, §6.3.1] The missingness-assumption variable is not defined consistently across the paper. Section 6.3.1 reports 'over 70% of the studies did not specify a missing data assumption', while Section 6.2.1 reports '75% of studies applying GAIN do not specify the assumed missing data mechanism'; the denominator of the latter is unclear. The text also alternates between 'did not specify' and 'default to MCAR through random masking', which are different observations: one concerns what the authors of the reviewed papers stated, and the other concerns what the training regime implies. Please provide the exact coding rule (what counts as specifying, e.g., an explicit statement versus train-time masking), report per-study coding, and state the denominators for each percentage.

minor comments (9)

[§3.2] The text says 'Scorpus'; this should be 'Scopus'.
[§4, first paragraph] The sentence 'In particular, we focus on Additionally, we discuss...' is an incomplete sentence and should be rewritten.
[Abstract] The phrase 'with a focus whether GANS have adequately addressed' should read 'with a focus on whether GANs have adequately addressed'.
[§6.1.2] The same subsection reports that GAN inversion appeared in 'only 1% of studies over the years' and later that '9% used GAN inversion'; these numbers should be harmonized.
[§2.1.1] The definitions are labeled 1, 2, 4, and 5, with no Definition 3; the numbering should be fixed.
[§5.3] The text says 'GCGAI attempts to improve imputation quality'; this should be 'FCGAI' for consistency.
[References and §5.3/Table 5] References [155] and [156] appear to be the same paper by Psychogyios et al., yet [156] is cited as the 'iGAIN' method in Section 5.3 and Table 5; please verify the citation mapping and remove the duplicate entry.
[§6.3.3 and Table 7] Section 6.3.3 says 'Less than 15% of studies reviewed used AUC', which appears inconsistent with Table 7 reporting 50% AUC usage; clarify whether this refers specifically to using AUC to evaluate minority-class robustness rather than general classification evaluation.
[§5.11 and Figure 4] Section 5.11 refers to 'Figure 4 presents a taxonomy of common GAN-based LDI challenges', but Figure 4 is the topology of LDC challenges; the reference or the caption should be corrected.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity; survey claims are empirical meta-analytic summaries, and the paper's self-citations are illustrative rather than load-bearing.

full rationale

This is a survey with no formal derivation chain whose conclusions could reduce to its inputs by construction. The central claims—that over 70% of reviewed GAN-based LDI studies do not state a missingness assumption, that 85% combine at least two approaches, and that static features, class imbalance, and mixed data types are rarely addressed—are empirical meta-analytic summaries of a 56-paper corpus assembled under described inclusion criteria. They are not definitions of the phenomena they report, nor fitted parameters renamed as predictions. The paper does self-cite its own prior works [187, 188] as examples of methods that address instance heterogeneity and static-feature fusion, and [154] for the non-existence of a universally optimal imputation technique, but these citations are illustrative supporting evidence within a literature review, not the load-bearing justification for a derived result. Even if the corpus selection were biased or the percentages unstable, that would be a correctness or reproducibility concern, not circularity. No equation or category in the paper is defined in terms of the outcome it is used to establish.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

As a survey, the paper has no free parameters and introduces no new entities. Its claims rest on assumptions about the representativeness of the literature search and the validity of the authors' categorization scheme. The main epistemic risk is that the meta-analytic percentages and gap analysis depend on the manual interpretation of papers, which is not fully auditable from the preprint.

assumptions (3)

domain assumption The PRISMA-based search and manual screening process selected a representative set of studies for meta-analysis.
The quantitative claims (70% no missingness assumption, 85% combine approaches, metric usage percentages) depend on the completeness and representativeness of the 56-paper pool. Section 3 describes the search but does not provide a flow diagram or exclusion list.
domain assumption The categorisation of methods into eight approaches is a valid partitioning of the literature.
The survey groups methods by their most significant contribution, but many works combine approaches, so the categories rely on the authors' judgment about which contribution is primary. This is acknowledged in Section 5 but remains a subjective modeling choice.
domain assumption Usage percentages across the reviewed papers are meaningful for assessing evaluation practice.
The survey uses the prevalence of metric usage in the 56 papers to classify metrics as standard, widely accepted, or emerging. If the paper pool is biased toward certain application domains, the percentages do not generalize to the whole field.

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Cite this review

Pith. "Pith review of Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification." pith.science (2026). https://pith.science/paper/GTMJDO2R

@misc{pith2026250618007,
  author       = {Pith},
  title        = {Pith review of: Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/GTMJDO2R}},
  note         = {Machine review of arXiv:2506.18007}
}

read the original abstract

Longitudinal data is commonly utilised across various domains, such as health, biomedical, education and survey studies. This ubiquity has led to a rise in statistical, machine and deep learning-based methods for Longitudinal Data Classification (LDC). However, the intricate nature of the data, characterised by its multi-dimensionality, causes instance-level heterogeneity and temporal correlations that add to the complexity of longitudinal data analysis. Additionally, LDC accuracy is often hampered by the pervasiveness of missing values in longitudinal data. Despite ongoing research that draw on the generative power and utility of Generative Adversarial Networks (GANs) to address the missing data problem, critical considerations include statistical assumptions surrounding longitudinal data and missingness within it, as well as other data-level challenges like class imbalance and mixed data types that impact longitudinal data imputation (LDI) and the subsequent LDC process in GANs. This paper provides a comprehensive overview of how GANs have been applied in LDI, with a focus whether GANS have adequately addressed fundamental assumptions about the data from a LDC perspective. We propose a categorisation of main approaches to GAN-based LDI, highlight strengths and limitations of methods, identify key research trends, and provide promising future directions. Our findings indicate that while GANs show great potential for LDI to improve usability and quality of longitudinal data for tasks like LDC, there is need for more versatile approaches that can handle the wider spectrum of challenges presented by longitudinal data with missing values. By synthesising current knowledge and identifying critical research gaps, this survey aims to guide future research efforts in developing more effective GAN-based solutions to address LDC challenges.

Figures

Figures reproduced from arXiv: 2506.18007 by the authors.

**Figure 2.** (a) Traditional GAN [72], (b) Conditional GAN (cGAN) [135], [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗

**Figure 3.** A two-phase GAN framework for LDC showing the input into the [PITH_FULL_IMAGE:figures/full_fig_p016_3.png] view at source ↗

Figures from the paper (7 more)

**Figure 4.** Figure 4: A topology of LDC Challenges faced by GANs when modelling [PITH_FULL_IMAGE:figures/full_fig_p020_4.png]

**Figure 5.** Figure 5: Inputs to GANs for LDI. Often, it is the [PITH_FULL_IMAGE:figures/full_fig_p029_5.png]

**Figure 6.** Figure 6: A taxonomy of common GAN-based Longitudinal Data Imputation [PITH_FULL_IMAGE:figures/full_fig_p030_6.png]

**Figure 7.** Figure 7: The self-feeding uni/bi-directional RNN cell with regression layers [PITH_FULL_IMAGE:figures/full_fig_p031_7.png]

**Figure 8.** Figure 8: Base G model of GANs for LDI feature correlations. Not only is the non-i.i.d. assumption in longitudinal data violated here, but information on global temporal evolution lost. Nevertheless, FCN-based methods have shown strong performance in learning cross-feature depe…

**Figure 9.** Figure 9: Approaches used in GANs for LDI. The Recurrence “Alone” cat [PITH_FULL_IMAGE:figures/full_fig_p053_9.png]

**Figure 10.** Figure 10: Application domains of studies using GANs for LDI [PITH_FULL_IMAGE:figures/full_fig_p054_10.png]

Discussion (0). Continue with ORCID to comment.

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