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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.
AutocompleteAbout the same30 / 280 · beu-07a22b6b12713437

A tutorial on individualized treatment effect prediction from randomized trials with a binary endpoint

Statistics · 2106.05588v1

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

A tutorial on individualized treatment effect prediction from randomized trials with a binary endpoint

1 Introduction

… its essentials in the setting of a randomized trial, discuss the importance of a clear definition of the estimand of interest, provide insight into the required assumptions, and give guidance with respect to modeling and estimation options. Simulated data illustrates the potential of different modeling options across scenarios that vary both average treatment effect and treatment effect heterogeneity. Two applied examples illustrate individualized treatment effect prediction in randomized trial data. Keywords : prediction, causal inference, treatment effect, regression, personalized medicine

…rticular, included predictor effects in the model are not intended to reflect the extent to which their removal or modification would change an individual’s prediction. On the other hand, predictions of treatment effect do express an expected difference due to modification of the treatment condition. They have classically been studied on a group level (e.g. treated group versus control group), often assume a constant effect across individuals, have a causal interpretation, and are traditionally expressed using relative effect measures ( e.g. odds ratio, relative risk, or hazard ratio) [ 2 ] .

↓ 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: About the same

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.

Read overall AI review rationale

This rationale combines reviews with different display orders.

Order-swapped judges disagreed. Pass 1: Both outputs are nearly identical and equally usable. They both successfully complete the paragraph by contrasting individualized treatment effect prediction with the group-level predictions discussed in the preceding text. Both maintain the academic tone, correctly identify that individualized prediction requires modeling treatment effect variation across individuals, and use appropriate terminology ('baseline characteristics' vs 'covariate values'—both are standard and acceptable in this context). The only difference is 'baseline characteristics' (A) versus 'covariate values' (B), which are functionally equivalent in this methodological context. Neither output contains material errors or unsupported claims. The paragraph was clearly incomplete and needed this continuation to contrast group-level versus individual-level prediction, so 'complete' with text is appropriate rather than a bare 'complete' action. Pass 2: Both outputs are nearly identical and equally usable, but Output A edges ahead on precision. The key difference is 'covariate values' (A) versus 'baseline characteristics' (B). The preceding text discusses 'included predictor effects' and the draft's broader context involves modeling with predictors/covariates. 'Covariate values' more precisely matches the statistical modeling terminology already established in the manuscript (the abstract mentions 'regression' and predictor effects). 'Baseline characteristics' is slightly more clinical/epidemiological in flavor and marginally less aligned with the modeling focus. Both correctly contrast individualized prediction with group-level effects, appropriately continue the argument, and require no repair. The difference is subtle but A has marginally better terminological consistency with the draft's established vocabulary.

Evidence

Same model and position · retrieved passages supplied · first output

Individualized treatment effect prediction instead targets the difference in expected outcome under treatment versus control for a given set of covariate values, and thus requires a model that explicitly allows the treatment effect to vary across individuals.

Passages supplied to the Evidence version

Card 1 · The Choice of Effect Measure for Binary Outcomes: Introducing Counterfactual Outcome State Transition Parameters

Standard measures of effect, including the risk ratio, the odds ratio, and the risk difference, are associated with a number of well-described shortcomings, and no consensus exists about the conditions under which investigators should choose one effect measure over another. In this paper, we introdu…

Read full passage excerpt

Standard measures of effect, including the risk ratio, the odds ratio, and the risk difference, are associated with a number of well-described shortcomings, and no consensus exists about the conditions under which investigators should choose one effect measure over another. In this paper, we introduce a new framework for reasoning about choice of effect measure by linking two separate versions of the risk ratio to a counterfactual causal model. In our approach, effects are defined in terms of "counterfactual outcome state transition parameters", that is, the proportion of those individuals who would not have been a case by the end of follow-up if untreated, who would have responded to treatment by becoming a case; and the proportion of those individuals who would have become a case by the end of follow-up if untreated who would have responded to treatment by not becoming a case. Although counterfactual outcome state transition parameters are generally not identified from the data witho …

Card 2 · Disagreement Concerning Effect-Measure Modification

Stratifying factors, like age and gender, can modify the effect of treatments and exposures on risk of a studied outcome. Several effect measures, including the relative risk, hazard ratio, odds ratio, and risk difference, can be used to measure this modification. It is known that choice of effect m…

Read full passage excerpt

Stratifying factors, like age and gender, can modify the effect of treatments and exposures on risk of a studied outcome. Several effect measures, including the relative risk, hazard ratio, odds ratio, and risk difference, can be used to measure this modification. It is known that choice of effect measure may determine the presence and direction of effect-measure modification. We show that considering the opposite outcome -- for example, recovery instead of death -- may similarly influence effect-measure modification. In fact, if the relative risk for the studied outcome and the relative risk for the opposite outcome agree about the direction of effect-measure modification, then so will the two cumulative hazard ratios, the risk difference, and the odds ratio. When risks are randomly sampled from the uniform (0,1) distribution, the probability of this happening is 5/6. Disagreement is probable enough that researchers considering one relative risk should also consider the other and furt …

Card 3 · Subtleties in the interpretation of hazard ratios

The hazard ratio is one of the most commonly reported measures of treatment effect in randomised trials, yet the source of much misinterpretation. This point was made clear by (Hernan, 2010) in commentary, which emphasised that the hazard ratio contrasts populations of treated and untreated individu…

Read full passage excerpt

The hazard ratio is one of the most commonly reported measures of treatment effect in randomised trials, yet the source of much misinterpretation. This point was made clear by (Hernan, 2010) in commentary, which emphasised that the hazard ratio contrasts populations of treated and untreated individuals who survived a given period of time, populations that will typically fail to be comparable - even in a randomised trial - as a result of different pressures or intensities acting on both populations. The commentary has been very influential, but also a source of surprise and confusion. In this note, we aim to provide more insight into the subtle interpretation of hazard ratios and differences, by investigating in particular what can be learned about treatment effect from the hazard ratio becoming 1 after a certain period of time. Throughout, we will focus on the analysis of randomised experiments, but our results have immediate implications for the interpretation of hazard ratios in obse …

Card 4 · The statistical properties of RCTs and a proposal for shrinkage

Abstract We abstract the concept of a randomized controlled trial as a triple , where is the primary efficacy parameter, b the estimate, and s the standard error (). If the parameter is either a difference of means, a log odds ratio or a log hazard ratio, then it is reasonable to assume that b is un…

Read full passage excerpt

Abstract We abstract the concept of a randomized controlled trial as a triple , where is the primary efficacy parameter, b the estimate, and s the standard error (). If the parameter is either a difference of means, a log odds ratio or a log hazard ratio, then it is reasonable to assume that b is unbiased and normally distributed. This then allows us to estimate the joint distribution of the z‐value and the signal‐to‐noise ratio from a sample of pairs . We have collected 23 551 such pairs from the Cochrane database. We note that there are many statistical quantities that depend on only through the pair . We start by determining the estimated distribution of the achieved power. In particular, we estimate the median achieved power to be only 13%. We also consider the exaggeration ratio which is the factor by which the magnitude of is overestimated. We find that if the estimate is just significant at the 5% level, we would expect it to overestimate the true effect by a factor of 1.7. This …

Card 5 · A Small Sample Correction for Estimating Attributable Risk in Case-Control Studies

The attributable risk, often called the population attributable risk, is in many epidemiological contexts a more relevant measure of exposure-disease association than the excess risk, relative risk, or odds ratio. When estimating attributable risk with case-control data and a rare disease, we presen…

Read full passage excerpt

The attributable risk, often called the population attributable risk, is in many epidemiological contexts a more relevant measure of exposure-disease association than the excess risk, relative risk, or odds ratio. When estimating attributable risk with case-control data and a rare disease, we present a simple correction to the standard approach making it essentially unbiased, and also less noisy. As with analogous corrections given in Jewell (1986) for other measures of association, the adjustment often won't make a substantial difference unless the sample size is very small or point estimates are desired within fine strata, but we discuss the possible utility for applications.

Model-reported card use

The model did not report that any card materially affected this completion.

No Evidence

Same model and position · no retrieved passages · first output

Individualized treatment effect prediction instead targets the difference in expected outcome under treatment versus control for a given set of baseline characteristics, and thus requires a model that explicitly allows the effect to vary across individuals.

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.

This case does not have both with-card continuation text and a model-reported use record. It remains among all 280 tests but does not enter the reported-use comparison.

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.