{"id":"9d85963b-87f6-4ddd-9034-f72dad292024","arxiv_id":"2608.02105","paper_version":1,"verdict":"ACCEPT","confidence":"MODERATE","novelty_score":3.0,"correctness_risk":"low","formal_verification":"none","parameter_count":1,"one_line_summary":"Anytime-valid meta-analysis is presented as a practical way to keep repeated or result-dependent updating statistically valid, with wider intervals and familiar forest plots.","lead":"This paper explains ALL-IN meta-analysis, which uses wider anytime-valid confidence intervals to keep coverage valid when evidence is updated repeatedly. It argues the method fits both pandemic and non-pandemic settings, including retrospective meta-analyses shaped by trial decisions outside the analyst's control.","discovery_kind":"review","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Coverage 'guarantee' is exact for known-variance normal summary data; the paper applies it to aggregate meta-analysis with estimated SEs without flagging this approximation.","rationale":"The reader's weakest_assumption already names the known-variance, estimated-SE gap, and I agree it is the only serious vulnerability targeting the central claim. It is an inherited assumption of the normal-theory model, not an internal contradiction: the paper is an invited commentary that accurately imports the anytime-valid theory from Ramdas et al. (2023) and ter Schure & Grunwald (2025), and its own limitation section (Section 3) acknowledges when the wider intervals are unnecessary. The proposed simulation would settle whether the estimated-SE transfer actually costs coverage; without it, the appropriate review stance is to preserve the reader's ACCEPT while noting that the word 'guaranteed' should be read within the standard normal-approximation model. I therefore recommend no change to the verdict, with the caveat recorded for the authors.","tokens_in":9113,"tokens_out":18502,"duration_ms":199240,"concrete_test":"Simulate the Figure 1 workflow: for each of K trials (e.g., K=1 to 10, group sizes 200-2000, event probabilities 1-10%), generate binomial outcomes under a common log-hazard-ratio; at each update form the fixed-effect estimate with estimated SE(logHR) and add the ALL-IN interval of half-width 3.037 x SE. Stop according to a data-dependent rule (e.g., stop when the interval excludes the null, or when K=10). Repeat at least 10,000 times and record empirical coverage of the final interval for the true effect. If coverage is below 0.94, the 'guaranteed' claim requires a stated known-variance/asymptotic qualifier; if it is at least 0.95, the estimated-SE transfer is benign.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The load-bearing condition for the central claim is that the anytime-valid e-value theory, applied to the forest-plot workflow of Figure 1, gives exact simultaneous coverage when updated indefinitely. That theory is exact for normal observations with known variances. In the paper's applied setting, the summary statistic is the inverse-variance fixed-effect estimate and the weights use SE(logHR) values estimated from each trial (Figure 1 caption). Replacing known variances by estimates breaks the martingale property on which the guarantee rests; coverage is then asymptotic rather than exact. The Introduction states the guarantee unconditionally ('Type-I error ... and coverage ... guaranteed for unlimited updating'), and Section 2.2 says ALL-IN 'absorbs all of these scenarios.' The paper does not flag the known-variance condition or test the estimated-SE transfer. This is a real soft spot, not a fatal flaw: conventional meta-analysis makes the same known-variance approximation, the anytime-valid factor is conservative, and the cited theory is sound. But if the approximation interacts with data-dependent stopping to produce undercoverage in small or sparse meta-analyses, the headline promise overstates validity for exactly the audience who will use forest plots.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The manuscript is a commentary advocating ALL-IN meta-analysis, an anytime-valid approach that widens meta-analytic confidence intervals so that the meta-analysis can be updated indefinitely while preserving error control. It presents the BCG-COVID case study, argues for applications in both pandemic and non-pandemic settings, and discusses a key limitation: the wider intervals are wasteful when there is a fixed, pre-planned set of trials with no updating. The paper does not derive new statistical theory; it imports anytime-valid confidence sequences from Ramdas et al. (2023) and ter Schure & Grünwald (2025) and illustrates their use in a familiar forest-plot workflow.","tokens_in":9345,"tokens_out":6188,"duration_ms":57624,"significance":"If the central claim holds as stated, ALL-IN meta-analysis would be a practically valuable tool for living and prospective systematic reviews, because it preserves coverage without requiring a pre-specified stopping rule or maximum sample size. The paper's strengths are that it connects established anytime-valid theory to a concrete applied workflow, provides R code and a replication package for the figures, and is explicit about the efficiency cost when no updating occurs. The main caveat is that the unconditional guarantee as stated in the Introduction is stronger than what the cited theory delivers in the exact aggregate-data workflow shown in Figure 1.","major_comments":[{"comment":"The Introduction states that 'Type-I error for tests and coverage for confidence intervals are guaranteed for unlimited updating, with no maximum sample size.' The cited anytime-valid theory (Ramdas et al., 2023) gives exact coverage for normal observations with known variances, but the worked example in Figure 1 uses an inverse-variance fixed-effect estimate whose weights are computed from SE(logHR) values estimated from each trial, and the same aggregate-summary setting is used throughout Sections 2.1 and 2.2. Replacing known variances by estimates breaks the martingale property on which the exact guarantee rests, so the coverage guarantee is at best approximate or asymptotic in the actual workflow. The authors should state the known-variance condition explicitly and add a caution about small or sparse meta-analyses where estimation error in the weights is non-negligible.","section":"Introduction and Figure 1 caption"},{"comment":"Section 2.1 claims that anytime-valid intervals 'are guaranteed to cover the true effect under any such rule,' referring to result-dependent decisions to start new trials, and Section 2.2 says ALL-IN 'absorbs all of these scenarios' for trial stopping. The anytime-validity literature directly guarantees validity under arbitrary stopping times, but the transfer to a meta-analytic setting with estimated trial variances and potential heterogeneity or selection mechanisms that change the distribution of the included effect estimates requires modeling assumptions that the paper does not state. The authors should either specify the exact model under which the claim is a theorem (for example, fixed-effect normal summary statistics with known variances and decisions that depend only on past summaries) or rephrase the claim as a heuristic extension of anytime-valid validity.","section":"Sections 2.1 and 2.2"}],"minor_comments":[{"comment":"The words 'meta-analyist' and 'analysist' in the Abstract and the Introduction should be corrected to 'meta-analyst' and 'analyst'.","section":"Abstract and Section 2"},{"comment":"The country abbreviations in the Figure 1 caption use inconsistent spelling (for example, 'South-Africa' versus 'South Africa' elsewhere) and the list is difficult to parse; consider adding semicolons or making the structure more uniform.","section":"Figure 1 caption"},{"comment":"The reference 'Moldvay, J. er Schure, J.A., (2026)' appears to have a typo; this should be 'ter Schure'.","section":"References"},{"comment":"The phrase 'the disappointing effect of the BCG vaccine' in Section 2.2 is informal for a methods commentary; consider replacing it with 'the observed null result' or similar.","section":"Section 2.2"},{"comment":"The limitation section acknowledges that the wider intervals are an avoidable cost when there is nothing to update, but it does not mention the known-variance approximation issue raised above; adding a sentence there would help readers who focus on the limitations section.","section":"Section 3"}],"recommendation":"major_revision","confidential_remarks":"This is an invited commentary by the developers of ALL-IN, and the replication package is a clear strength. The main concern is that the Introduction's unconditional guarantee is not qualified for the estimated-variance forest-plot setting; this is a load-bearing issue for the paper's headline promise, but it appears fixable by adding explicit modeling assumptions and a limitation statement rather than by new derivations."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"This is a commentary, not a new statistical contribution: the anytime-valid machinery comes from earlier papers by the same group. What this paper adds is a non-pandemic motivation, a concrete BCG example with reproducible code and data, and a genuinely useful counterexample (mpox) where conventional intervals are the right choice. The central claim—that repeated updating does not inflate error rates and coverage holds without a maximum sample size—is accurately stated and faithful to the published theory. The limitation section is candid about the efficiency cost when no updating occurs. Credit where due: the paper does not overreach beyond its cited theory, and the replication materials are a real plus for readers who want to try the method.\n\nThe soft spot is one the authors do not flag. The anytime-valid guarantee is exact for normal summaries with known variances. In the forest-plot application, the SE(logHR) values are estimated from each trial, and the inverse-variance weights treat those estimates as fixed. That replacement breaks the exact martingale property; coverage becomes asymptotic rather than exact. The paper states the guarantee unconditionally (\"Type-I error ... and coverage ... guaranteed for unlimited updating\") and says ALL-IN \"absorbs all of these scenarios\" without mentioning this condition. For large meta-analyses the approximation is fine, but in small or sparse settings the headline promise is stronger than what the theory delivers. This is a real soft spot, though not a fatal one—conventional meta-analysis makes the same approximation, and the anytime-valid factor adds conservatism, so the practical risk is likely modest. A sentence acknowledging the known-variance assumption would fix it.\n\nThe citation pattern is heavily self-referential, but that is appropriate here: the method and the accumulation-bias concept are the authors' own prior work, and the key validity results are externally published and verifiable. I do not see a circularity problem.\n\nWho is this for? Anyone in evidence synthesis who wants a concise, non-technical introduction to automatic sequential meta-analysis and a worked example. It deserves a serious referee, and the referee should ask for the one clarifying sentence about estimated standard errors. I would send it to review rather than desk reject.","headline":"A clear, honest commentary on the authors' own anytime-valid meta-analysis method, worth publishing but with one unstated approximation about estimated standard errors.","tokens_in":701,"tokens_out":1254,"would_cite":false,"duration_ms":24894,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"Widened confidence intervals can keep a repeatedly updated meta-analysis at 95% coverage with no maximum sample size.","keywords":["ALL-IN meta-analysis","anytime-valid inference","sequential analysis","living systematic review","prospective meta-analysis","accumulation bias","confidence intervals","forest plot"],"falsifier":"Simulate a meta-analysis in which trials accrue one by one and their sizes, stopping times, or inclusion are triggered by the current interim estimate; construct ALL-IN intervals from aggregate log hazard ratios with estimated standard errors exactly as in Figure 1; if, under any such decision rule, the interval covers the true effect in fewer than 95% of repetitions, the central coverage claim is refuted.","tokens_in":8948,"feed_emoji":"📊","tokens_out":13169,"duration_ms":96618,"temperature":0.7,"pith_summary":"This commentary argues that ALL-IN meta-analysis, built from anytime-valid confidence intervals, keeps its promised 95% coverage no matter how often or how adaptively the evidence is updated, with no maximum sample size and no pre-specified stopping rule. The practical payoff is that a familiar forest plot with widened intervals can serve as a living, prospective, or real-time synthesis, and can remain valid when trial stopping and starting decisions respond to accumulating results. The paper illustrates the approach with the BCG-vaccine COVID-19 collaboration and draws the line at settings where nothing is updated and no decisions are data-dependent: there the extra width is avoidable waste.","feed_headline":"Widened intervals keep meta-analysis valid through unlimited updates","feed_subtitle":"Wider intervals let living and prospective meta-analyses stay valid under unplanned stops and accumulation bias.","key_machinery":"The engine of the argument is the anytime-valid confidence interval, a confidence sequence: a nested family of intervals constructed so that, no matter when the analyst stops and looks, the true effect lies inside with probability at least 95%. In ALL-IN meta-analysis this appears as a single widened multiplier replacing the familiar $1.96$ times the standard error — about $3.037$ in the BCG example at a reasonable sample size — and the construction needs no maximum sample size or stopping rule. That multiplier is what converts an ordinary cumulative meta-analysis into one whose updates are all simultaneously valid.","core_discovery":"The paper's central claim is that ALL-IN meta-analysis is anytime-valid: type-I error for tests and coverage for confidence intervals are guaranteed simultaneously at every update, for unlimited updating, with no maximum sample size. Conventional meta-analysis loses this guarantee because each update is another chance to be wrong, and result-dependent decisions about which trials exist and when they stop — accumulation bias and unplanned early stopping — make the problem worse. ALL-IN absorbs these scenarios by widening intervals; in the paper's example a fixed-effects estimate with standard error 0.084 is reported with an anytime-valid interval roughly three standard errors wide, $[0.76;\\,1.27]$, instead of the usual $1.96$-standard-error interval. The same forest plot can be read as before, and the wider interval automatically discourages strong conclusions when data are still thin.","pith_inferences":["One could extend the same widening construction to effect measures other than the log hazard ratio, such as risk differences or odds ratios, whenever a normal-with-known-standard-error approximation is defensible; the paper itself only demonstrates the log-hazard-ratio case.","The width multiplier depends on a chosen \"reasonable\" sample size, so a natural next step is to make that tuning parameter explicit and study how early-interval width changes with it; the paper does not provide this sensitivity analysis.","Because coverage holds under arbitrary decision rules, a practical guidance rule could tell analysts when to switch from conventional to ALL-IN intervals; the paper leaves that operational choice open.","The same logic should protect meta-analyses used by regulators or guideline panels that combine trials stopped for futility, without needing to reconstruct the actual stopping rules; the paper states the principle but does not apply it to that governance setting."],"forward_implications":["Living and prospective meta-analyses can be updated indefinitely, and each new forest plot still carries valid 95% intervals.","Collaborative pandemic evidence synthesis can conclude from interim trials in real time, as the BCG example concluded early from two unfinished trials.","Retrospective meta-analyses gain validity under accumulation bias and uncontrolled early stopping because coverage holds under any result-dependent continuation rule.","Existing alpha-spending approaches are covered as a special case, but ALL-IN removes the need to pre-specify a maximum sample size.","When the trial set is closed and no update-driven decisions shaped it, conventional intervals are shorter and should be preferred."],"supporting_citations":[{"why":"Defines ALL-IN meta-analysis and its anytime-valid intervals; the method advocated here.","marker":"Ter Schure & Grünwald, 2025"},{"why":"Provides the general anytime-valid inference theory from which the coverage guarantee is imported.","marker":"Ramdas et al., 2023"},{"why":"Shows how repeated updating lets conventional intervals contradict each other, motivating the need for anytime-valid coverage.","marker":"Pace and Salvan, 2019"},{"why":"Supplies the Cochrane Handbook's alpha-spending framework that ALL-IN is contrasted with and the notion of pre-specified stopping rules.","marker":"Higgins et al. 2024"},{"why":"The BCG-COVID living meta-analysis whose aggregate data and forest plots demonstrate ALL-IN in practice.","marker":"Ter Schure et al., 2022"},{"why":"Defines accumulation bias, the across-trial decision dependency that ALL-IN addresses outside pandemic settings.","marker":"Ter Schure & Grünwald, 2019"},{"why":"Offers the comparison with alpha-spending showing that the wider intervals do not require much larger average sample sizes.","marker":"Ter Schure et al., 2024"},{"why":"Planned mpox meta-analysis used to illustrate a closed, update-free setting where conventional intervals are preferable.","marker":"Rojek et al, 2024"}],"fun_headline_variants":["Anytime-valid meta-analysis: wider intervals, endless updates","Meta-analysis that stays valid no matter how often you update","ALL-IN meta-analysis: robust to stops, updates, and bias","Wider intervals make meta-analysis valid under any update","Update meta-analyses forever with anytime-valid intervals"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The anytime-valid coverage guarantee is imported from abstract anytime-valid inference and is assumed to remain exact when the method is applied to aggregate meta-analysis summaries in which log hazard ratios are treated as normally distributed with known standard errors, even though those standard errors are estimated from the data.","fun_headline_variants_meta":{"raw":{"variants":["Anytime-valid meta-analysis: wider intervals, endless updates","Meta-analysis that stays valid no matter how often you update","ALL-IN meta-analysis: robust to stops, updates, and bias","Wider intervals make meta-analysis valid under any update","Update meta-analyses forever with anytime-valid intervals"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000199,"raw_usage":{"total_tokens":1357,"prompt_tokens":917,"completion_tokens":440,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":533,"completion_tokens_details":{"reasoning_tokens":357}},"tokens_in":533,"tokens_out":440,"duration_ms":3880,"temperature":1.0,"reasoning_tokens":357,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-15T15:01:02.595445+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Simulate a meta-analysis in which trials accrue one by one and their sizes, stopping times, or inclusion are triggered by the current interim estimate; construct ALL-IN intervals from aggregate log hazard ratios with estimated standard errors exactly as in Figure 1; if, under any such decision rule, the interval covers the true effect in fewer than 95% of repetitions, the central coverage claim is refuted.","supporting_citations":[],"review_version":2}