{"id":"65bd8514-1fe2-4315-bf22-7b24bc1495f0","arxiv_id":"2606.25670","paper_version":2,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":6.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":0,"one_line_summary":"A multiple imputation framework based on Beta regression is proposed for imputing missing correlations in meta-analysis, shown effective via simulation compared to mean imputation and complete-case analysis under MAR.","lead":"The paper proposes a multiple imputation method using stochastic Beta regression to handle partially missing correlations in bivariate and surrogate meta-analyses under the MAR assumption. A smart generalist might read it to see how statistical synthesis of studies can avoid bias and inefficiency from incomplete data without discarding studies.","discovery_kind":"new_method","skeptic_critique":{"model":"grok-4.3","headline":"No significant objection identified","rationale":"The reader's weakest_assumption correctly isolates the MAR condition that the method requires. Because the full manuscript (including simulation code, exact Beta-regression specification for bounded correlations, and multivariate handling) is not supplied, no additional load-bearing technical flaw can be diagnosed. The verdict therefore stays UNVERDICTED.","tokens_in":1672,"tokens_out":246,"duration_ms":15035,"concrete_test":"Re-run the simulation study under the MAR scenarios described in the abstract, comparing bias, coverage, and efficiency of the Beta-regression MI estimator against complete-case and mean-imputation baselines; if the reported gains disappear under correctly implemented MAR data generation, the effectiveness claim does not hold.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim is a proposal for multiple imputation of partially missing correlations via stochastic Beta regression under an explicitly stated MAR mechanism, with effectiveness shown in simulation versus mean imputation and complete-case analysis. The MAR assumption is required for unbiasedness and is acknowledged in the abstract; no internal inconsistency, unstated modeling assumption, or derivation gap is visible in the provided description that would undermine the conditional claim.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The paper proposes a multiple imputation framework for handling partially missing correlations in bivariate and surrogate meta-analyses. Imputation is performed stochastically via a Beta regression model that explicitly incorporates the missing-at-random (MAR) mechanism. The method is compared to mean imputation and complete-case analysis, with effectiveness assessed through simulation studies under various scenarios.","tokens_in":1742,"tokens_out":281,"duration_ms":28522,"significance":"If the simulations establish reduced bias and improved efficiency relative to the comparators while respecting the MAR assumption, the approach would address a practical gap in meta-analysis methodology where complete-case restrictions lead to inefficiency or bias. The choice of Beta regression is well-motivated for bounded correlation coefficients, and the stochastic (multiple) imputation framework is a standard improvement over deterministic mean imputation.","major_comments":[{"comment":"Simulation study section: the central claim of effectiveness rests on 'an extensive simulation study across various scenarios' (abstract), yet the manuscript provides no description of the data-generating process, missingness rates, number of replications, specific performance metrics (bias, MSE, coverage), or quantitative results comparing the Beta regression MI to mean imputation and complete-case analysis. This prevents verification that the method delivers the claimed gains under MAR.","section":"Simulation study"}],"minor_comments":[],"recommendation":"major_revision","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for their thoughtful review and for highlighting the need for greater transparency in the simulation study. We address the major comment below and will revise the manuscript accordingly.","responses":[{"response":"We agree that the current manuscript does not include a sufficiently detailed description of the simulation design. This omission prevents readers from fully assessing the results. In the revised manuscript we will expand the Simulation Study section to specify: (i) the full data-generating process (including how bivariate or surrogate outcomes and correlations are simulated under the MAR mechanism), (ii) the range of missingness rates examined, (iii) the number of Monte Carlo replications, (iv) the exact performance metrics (bias, MSE, coverage probability, and possibly interval width), and (v) quantitative tables or figures that directly compare the proposed Beta-regression multiple imputation procedure against mean imputation and complete-case analysis. These additions will allow independent verification of the claimed gains.","revision_made":"yes","referee_comment":"[Simulation study] Simulation study section: the central claim of effectiveness rests on 'an extensive simulation study across various scenarios' (abstract), yet the manuscript provides no description of the data-generating process, missingness rates, number of replications, specific performance metrics (bias, MSE, coverage), or quantitative results comparing the Beta regression MI to mean imputation and complete-case analysis. This prevents verification that the method delivers the claimed gains under MAR."}],"tokens_in":1212,"tokens_out":310,"duration_ms":16675,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The core of this paper is a multiple imputation scheme that draws missing correlations stochastically from a Beta regression model rather than using complete-case deletion or plugging in the observed mean. That modeling choice fits the bounded nature of correlations and builds in the MAR assumption directly.\n\nIt improves on the two common shortcuts by preserving variability across imputations and avoiding the bias that comes from ignoring the missingness mechanism. The authors compare the new approach to mean imputation and complete-case analysis in simulations under MAR, which is the right baseline.\n\nThe limitation is that the abstract gives no numbers on bias, coverage, or efficiency gains across the scenarios. Without those, it is difficult to judge how large the practical gain is or whether the Beta regression step adds much beyond simpler stochastic imputation. If the full simulations are well-designed and reported, this is a useful incremental tool; if they are thin, the advantage stays mostly theoretical.\n\nThis is aimed at statisticians and applied researchers who run bivariate or surrogate meta-analyses and regularly face partial correlation data. A methods reader who needs a reproducible way to handle MAR missingness in correlations will get something concrete from it.\n\nSend it for peer review. The idea is straightforward, the target problem is real, and the proposed fix is a clear step beyond current practice even if the evidence needs tightening.","headline":"A practical Beta regression imputation method for missing correlations in meta-analysis that improves on mean imputation and complete cases under MAR, though simulation details are thin in the abstract.","tokens_in":2221,"tokens_out":344,"would_cite":false,"duration_ms":17434,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"Multiple imputation with Beta regression recovers missing correlations in meta-analyses under missing-at-random conditions.","keywords":["missing data","meta-analysis","multiple imputation","Beta regression","correlation coefficients","surrogate endpoints","MAR assumption"],"falsifier":"A simulation in which missingness depends on the actual value of the unobserved correlation itself, producing systematic bias in the imputed values and downstream meta-analytic estimates.","tokens_in":2572,"feed_emoji":"📊","tokens_out":391,"duration_ms":14866,"temperature":0.7,"pith_summary":"The paper develops a multiple imputation procedure that uses stochastic draws from a Beta regression model to fill in partially missing correlations for bivariate and surrogate meta-analyses. This replaces both complete-case restriction, which discards information and can bias pooled estimates, and deterministic mean imputation, which understates uncertainty. The method is shown through simulation to maintain valid inference when the observed missingness follows the missing-at-random mechanism. Readers care because many meta-analyses lose studies or precision when correlation coefficients between endpoints are incompletely reported.","feed_headline":"Beta regression imputation recovers missing correlations","feed_subtitle":"Stochastic multiple imputation under MAR outperforms complete-case analysis and mean imputation in meta-analysis simulations.","key_machinery":"Stochastic multiple imputation driven by a Beta regression model that treats the observed missingness as missing at random.","core_discovery":"The authors propose a multiple imputation framework in which imputation is performed via stochastic procedures based on a Beta regression model, thereby explicitly accounting for the missing at random assumption underlying the observed missingness mechanism, and demonstrate its effectiveness through an extensive simulation study comparing the proposal with simple mean imputation and complete-case analysis under the MAR assumption.","pith_inferences":[],"forward_implications":[],"fun_headline_variants":["Beta regression stochastic imputation for missing correlations","Stochastic beta regression imputation for partial missing correlations","Beta regression multiple imputation handles missing meta-analysis correlations","Multiple imputation using beta regression in meta-analyses"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"The mechanism producing the missing correlations is missing at random, so that a regression fitted only to observed data yields unbiased imputations.","fun_headline_variants_meta":{"raw":{"variants":["Beta regression stochastic imputation for missing correlations","Stochastic beta regression imputation for partial missing correlations","Beta regression multiple imputation handles missing meta-analysis correlations","Multiple imputation using beta regression in meta-analyses"]},"model":"grok-4.3","cost_usd":0.009199,"raw_usage":{"total_tokens":4074,"prompt_tokens":574,"num_sources_used":0,"completion_tokens":54,"cost_in_usd_ticks":91987000,"prompt_tokens_details":{"text_tokens":574,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":3446,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":574,"tokens_out":54,"duration_ms":29585,"temperature":1.0,"reasoning_tokens":3446,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-06-29T04:59:20.379703+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"A simulation in which missingness depends on the actual value of the unobserved correlation itself, producing systematic bias in the imputed values and downstream meta-analytic estimates.","supporting_citations":[],"review_version":2}