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Record W2125748810 · doi:10.82308/16725

Stationarity in a prevalent cohort study with follow-up

2005· article· en· W2125748810 on OpenAlexaboutno aff
Vittorio Addona

Bibliographic record

VenueeScholarship@McGill (McGill) · 2005
Typearticle
Languageen
FieldMathematics
TopicStatistical Methods and Bayesian Inference
Canadian institutionsnot available
Fundersnot available
KeywordsEstimatorStatisticsIncidence (geometry)MathematicsNonparametric statisticsEconometricsConfidence intervalCohortAsymptotic distributionConstant (computer programming)Computer science

Abstract

fetched live from OpenAlex

In a prevalent cohort study with follow-up, the incidence process is not directly observed since only the onset times of prevalent cases can be ascertained. Several important consequences follow if one can establish stationarity of the incidence process: (1) The useful epidemiological relationship between prevalence, incidence, and mean duration holds, (2) There is improved efficiency when estimating the underlying survivor function from a prevalent cohort study with follow-up, (3) The constancy of the incidence rate is established, and (4) The constant incidence rate can be estimated using data from a prevalent cohort study. We propose a formal test for stationarity using data from a prevalent cohort study with follow-up, and establish new characterizations of stationarity, and of useful types of departure from stationarity. A dual to the problem of establishing stationarity by comparing the backward and forward recurrence times is addressed. Assuming stationarity of the underlying incidence process, we use the backward and forward recurrence times to verify whether the underlying survival distribution is independent of the date of onset. In doing so, we characterize specific types of dependence of the underlying survival distribution on calendar time. If the data are consistent with stationarity of the incidence rate, then a natural next step is to estimate the (constant) incidence rate. We derive the nonparametric maximum likelihood estimator of the constant incidence rate, prove that the estimator is weakly consistent, and show how one may construct an asymptotic confidence interval for the incidence rate. One main advantage of our procedure is that it only requires the completion of a single prevalent cohort study with follow-up. We apply our test for stationarity to data obtained as part of the Canadian Study of Health and Aging to verify that the incidence rate of dementia amongst the elderly in Canada has remained constant. Upon concluding that this constancy is, plausible, we estimate the incidence rate.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.044
metaresearch head score (Gemma)0.138
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.956
Threshold uncertainty score0.234

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0440.138
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0020.003
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.044
GPT teacher head0.324
Teacher spread0.279 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designTheoretical or conceptual
DomainMethods
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2005
Admission routes1
Has abstractyes

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