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This case was judged by an anonymized AI reviewer and has not been independently reviewed by people; inspect the draft, cards, and both outputs to assess it.
AutocompleteInconclusive178 / 280 · beu-b9e7ddff47edef1f

Shared Frailty Methods for Complex Survival Data: A Review of Recent Advances

Statistics · 2205.05322v1

FLOWING EVIDENCE BENCHMARK

How do we compare writing with and without cards?

At the same writing position, the same model produces one first result with cards and one without. All 280 pairs enter the overall anonymized review, which assesses whether a decision not to continue fits the draft.

Overall: 138 with-card wins, 26 ties, 107 no-card wins, 5 pairs where neither was usable, and 4 inconclusive judgments. Both sides wrote text in 218 pairs; 48 had a deliberate stop and 14 included an invalid output.

SAME DRAFT · WITH OR WITHOUT CARDS

01 · FIXED WRITING POSITION

MANUSCRIPT

Same writing position ▌

Matched autocomplete at the same draft position

02 · TWO MATCHED VERSIONS

SHARED BY BOTH

Draft, writing task, and generation model

A · With cards

Relevant literature cards supplied

B · Without cards

No literature cards supplied

Generation model

Same model and version

A → first output

B → first output

Anonymized AI judge

First outputs are anonymized as X and Y

Continuation review: accuracy, fit to the writing task and usability
Returns: X preferred / tie / Y preferred / both unusable

Review again with the answer order swapped

HOW DOES BLIND JUDGING WORK?

① Anonymize both outputs
The AI judge sees the same draft and both first outputs without knowing which received cards.

② Compare and swap order
The judge applies task-specific criteria in X/Y and then Y/X order.

③ Review disagreements
A third pass resolves disagreements. Incomplete reviews remain in the denominator.

How are positions and cards selected?

We selected 40 writing positions in each of seven fields, for 280 comparisons. The position and relevant literature cards were set before each comparison, so both versions began from the same draft.

The main result retains every frozen position, including places where cards might not add a useful next move. This avoids selecting cases based on their outputs, but 49% describes the whole cohort, not a win rate where evidence clearly had an opportunity to help. This cohort did not record that opportunity label for each position before generation, so those two groups cannot yet be separated reliably.

CARD USE · 140/280

Reported card use in continuations with text

The model reports whether a card affected its continuation. In 140 of 280 tests it produced continuation text and reported card use. The previous count of 150 included 10 cases without continuation text and has been corrected.

COMPARISON · 70 / 7 / 30

Which written version was better?

Of the 107 reported-use cases where both versions produced text, the blinded AI judge preferred the with-card version 70 times, found 7 about the same, and preferred the without-card version 30 times.

How are the two table percentages calculated?

ADOPTION COVERAGE

140reported use with text
÷
280all positions
=
50%

WITH-CARD VERSION PREFERRED

70with cards better
÷
107comparable tests
=
65%

The percentages use different groups: 50% counts continuations with text reporting card use among all 280 tests; 65% covers the 107 such cases with text on both sides. The generating model reports card use, while an anonymized AI judge compares writing quality.

Card use is self-reported by the generator. The anonymized AI judge sees the draft and source snippets, so source-aware preference remains possible; there was no independent human review. The overall result describes these 280 frozen positions, not a win probability for any writing task.

Manuscript writing position

Text excerpt · not a PDF page

Research manuscript · excerpt

Shared Frailty Methods for Complex Survival Data: A Review of Recent Advances

1 INTRODUCTION

…lies or medical centers. Dependent survival data also arise when multiple survival times are recorded for each individual. Frailty models is one common approach to handle such data. In frailty models, the dependence is expressed in terms of a random effect, called the frailty. Frailty models have been used with both Cox proportional hazards model and the accelerated failure time model. This paper reviews recent developments in the area of frailty models in a variety of settings. In each setting we provide a detailed model description, assumptions, available estimation methods, and R packages.

…oducing random variables to characterize unobserved dependence in the data is a popular practice in regression models. In the context of survival analysis (i.e., time-to-event outcome analysis), such models are known as shared frailty models: the cluster-specific random effect representing the unobserved common risk shared by cluster members is called the frailty. In these models, it is assumed that the random frailty factor captures all the within-cluster dependence, so that the failure times of cluster members are assumed independent given the observed covariates and the unobserved frailty.

↓ Next paragraph was generated here

The highlighted text is near the writing position. This is a layout of the evaluation input, not a PDF screenshot.

A matched comparison at the same writing positionOverall AI review: Inconclusive

Both arms use the same model, task and manuscript context. Only the retrieved passages differ. The first recorded output from each arm is shown below.

Review passes disagreed and the usability assessments conflict, so this pair counts neither as a tie nor as a win.

Read overall AI review rationale

This rationale combines reviews with different display orders.

Order-swapped judges disagreed. Pass 1: Both outputs attempt to continue the manuscript, but neither properly fits the cursor position. The draft already contains the exact sentence 'This paper reviews recent developments in the area of frailty models in a variety of settings. In each setting we provide a detailed model description, assumptions, available estimation methods, and R packages.' immediately before the blank paragraph. Output A repeats this sentence nearly verbatim ('This paper reviews recent developments in the area of frailty models in a variety of settings'), creating redundancy. Output B at least introduces new information about software implementation that connects to the sources and the paper's mention of 'R packages.' However, Output B still feels somewhat disconnected from the immediate context. Between the two, Output A is worse because it directly duplicates existing text. Output B, while not perfectly integrated, adds genuinely new content about the evolution of frailty models into mainstream software, which aligns with the source mentioning R and Stata, and provides a logical bridge to the technical discussion that follows about shared frailty models. Actually, re-evaluating: Output A's near-verbatim repetition makes it materially problematic for insertion without repair. Output B is more usable as it doesn't repeat existing text and flows reasonably from the introductory material to the technical definition that follows. Pass 2: The draft already contains the sentence 'This paper reviews recent developments in the area of frailty models in a variety of settings. In each setting we provide a detailed model description, assumptions, available estimation methods, and R packages.' This appears to be an introduction/abstract that has already stated its purpose. The blank paragraph follows a paragraph that defines shared frailty models and their basic mechanism. The logical next move should transition toward the actual review content or provide additional context about the scope/organization of the paper. Output A provides a natural continuation: it notes the practical expansion of frailty models to mainstream software, which bridges the methodological definition to the practical review that follows. It adds substantive value without redundancy. Output B is unusable because it repeats almost verbatim the sentence 'This paper reviews recent developments in the area of frailty models in a variety of settings' that already appears in the draft. This creates direct redundancy and reads as if the author forgot what they already wrote. A completion that duplicates existing text requires substantial repair to be usable.

Evidence

Same model and position · retrieved passages supplied · first output

Frailty models have become a standard tool for handling unobserved heterogeneity and within-cluster dependence in survival data, and their use has expanded from specialized applications to routine implementation in general-purpose statistical software.

Passages supplied to the Evidence version

Card 1 · An Accelerated Failure Time Regression Model for Illness-Death Data: A Frailty Approach

This work presents a new model and estimation procedure for the illness-death survival data where the hazard functions follow accelerated failure time (AFT) models. A shared frailty variate induces positive dependence among failure times of a subject for handling the unobserved dependency between th…

Read full passage excerpt

This work presents a new model and estimation procedure for the illness-death survival data where the hazard functions follow accelerated failure time (AFT) models. A shared frailty variate induces positive dependence among failure times of a subject for handling the unobserved dependency between the non-terminal and the terminal failure times given the observed covariates. Semi-parametric maximum likelihood estimation procedure is developed via a kernel smoothed-aided EM algorithm, and variances are estimated by weighted bootstrap. The model is presented in the context of existing frailty-based illness-death models, emphasizing the contribution of the current work. The breast cancer data of the Rotterdam tumor bank are analyzed using the proposed and existing illness-death models. The results are contrasted and evaluated based on a new graphical goodness-of-fit procedure. Simulation results and data analysis nicely demonstrate the practical utility of the shared frailty variate with t …

Card 2 · Impact of model misspecification in shared frailty survival models

In observational studies and clinical trials with survival outcomes and an intrinsic hierarchical structure, survival models with shared frailty terms and/or random effects have moved from being a speciality rarely used that requires ad hoc software to being mainstream methods that can be utilised i…

Read full passage excerpt

In observational studies and clinical trials with survival outcomes and an intrinsic hierarchical structure, survival models with shared frailty terms and/or random effects have moved from being a speciality rarely used that requires ad hoc software to being mainstream methods that can be utilised in any general purpose statistical software such as R and Stata . Compared to a marginal approach (i.e. accounting for clustering by using a robust estimator of the variance-covariance matrix of the estimated coefficients), the frailty approach allows focussing on inference within the clusters and quantifying the amount of heterogeneity between clusters by directly modelling it. Additionally, the frailty approach can be used to model recurrent events data, assuming that the recurrent event times are independent conditional on the covariates and random effects ( 44 , 45 ) .

Card 3 · Phase-type frailty models: A flexible approach to modeling unobserved heterogeneity in survival analysis

Frailty models are essential tools in survival analysis for addressing unobserved heterogeneity and random effects in the data. These models incorporate a random effect, the frailty, which is assumed to impact the hazard rate multiplicatively. In this paper, we introduce a novel class of frailty mod…

Read full passage excerpt

Frailty models are essential tools in survival analysis for addressing unobserved heterogeneity and random effects in the data. These models incorporate a random effect, the frailty, which is assumed to impact the hazard rate multiplicatively. In this paper, we introduce a novel class of frailty models in both univariate and multivariate settings, using phase-type distributions as the underlying frailty specification. We investigate the properties of these phase-type frailty models and develop expectation-maximization algorithms for their maximum-likelihood estimation. In particular, we show that the resulting model shares similarities with the Gamma frailty model, has closed-form expressions for its functionals, and can approximate any other frailty model. Through a series of simulated and real-life numerical examples, we demonstrate the effectiveness and versatility of the proposed models in addressing unobserved heterogeneity in survival analysis.

Card 4 · Case-Control Survival Analysis with a General Semiparametric Shared Frailty Model - a Pseudo Full Likelihood Approach

In this work we deal with correlated failure time (age at onset) data arising from population-based case-control studies, where case and control probands are selected by population-based sampling and an array of risk factor measures is collected for both cases and controls and their relatives. Param…

Read full passage excerpt

In this work we deal with correlated failure time (age at onset) data arising from population-based case-control studies, where case and control probands are selected by population-based sampling and an array of risk factor measures is collected for both cases and controls and their relatives. Parameters of interest are effects of risk factors on the hazard function of failure times and within-family dependencies of failure times after adjusting for the risk factors. Due to the retrospective nature of sampling, a large sample theory for existing methods has not been established. We develop a novel estimation techniques for estimating these parameters under a general semiparametric shared frailty model. We also present a simple, easily computed, and non-iterative nonparametric estimator for the cumulative baseline hazard function. A rigorous large sample theory for the proposed estimators of these parameters is given along with simulations and a real data example illustrate the utility …

Model-reported card use

Card 2 · fact

Affected output: Frailty models have become a standard tool for handling unobserved heterogeneity and within-cluster dependence in survival data, and their use has expanded from specialized applications to routine implementation in general-purpose statistical software.

Model explanation: This row supplied the concrete historical trajectory of frailty models from specialized to mainstream use, which shaped the sentence's claim about their expanded adoption and routine implementation.

No Evidence

Same model and position · no retrieved passages · first output

Frailty models have become a standard tool for analyzing clustered and multivariate survival data, and a large body of methodological work has accumulated over the past two decades. This paper reviews recent developments in the area of frailty models in a variety of settings.

Earlier card-use comparison

The earlier diagnostic compared only cases with reported card use and text on both sides; its scope differs from the overall AI review above.

The blinded model review preferred the Evidence version. Its original rationale appears below.

Read the original model judgment

Both outputs provide coherent academic continuations, but A is more useful and better grounded. Output A draws directly from Source 2 ('Impact of model misspecification in shared frailty survival models'), which states that frailty models 'have moved from being a speciality rarely used that requires ad hoc software to being mainstream methods that can be utilised in any general purpose statistical software.' This maps precisely to A's claim about expansion 'from specialized applications to routine implementation in general-purpose statistical software.' A also correctly notes the dual purpose of frailty models for 'unobserved heterogeneity and within-cluster dependence,' both concepts present in the sources and draft. Output B's claim that 'a large body of methodological work has accumulated over the past two decades' is plausible but not directly supported by any source (the sources discuss specific models and methods but do not make this temporal, quantitative claim about accumulation). B also repeats 'This paper reviews recent developments in the area of frailty models in a variety of settings' — but this exact sentence already appears in the draft immediately before the blank paragraph, making it redundant rather than a true continuation. A provides fresh, source-grounded content that advances the paragraph naturally.

The passages are input material; their presence does not mean an output used them or that they support every claim. Draft and source passages are excerpted; no simulated scores or PDF appear here.