ArticleAmerican journal of epidemiology2015
Efficient estimation of smooth distributions from coarsely grouped data.
Article in American journal of epidemiology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 31 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
31 citing papers in PubMed.
- Projected Aging Among People With HIV in the US.JAMA network open · 2026Article
- Evolution of birth-cohort patterns in lifestyle-associated cancer mortality across high-income countries.International journal of epidemiology · 2026Article
- A Double Decomposition of Standard Deviation Below the Modal Age at Death and the Role of Causes of Death.European journal of population = Revue europeenne de demographie · 2026Article
- Causes of death contributions to the sex gaps in life expectancy: evidence by education levels from Australia.BMC public health · 2026Article
- Getting closer to each other? Convergence and divergence patterns of life expectancy in 277 border regions of Western Europe 1995-2019.European journal of epidemiology · 2025Article
- Competing risks models with two time scales.Statistical methods in medical research · 2025Article
- A demographic assessment of the impact of the war in the Gaza Strip on the mortality of children and their parents in 2023.Population health metrics · 2025Article
- Using Bayesian evidence synthesis to quantify uncertainty in population trends in smoking behaviour.Statistical methods in medical research · 2025Article
- A modal age at death approach to forecasting adult mortality.Population studies · 2025Article
- Estimated Deaths Averted in Adults by COVID-19 Vaccination in Select Latin American and Caribbean Countries.Open forum infectious diseases · 2024Article
- Population age structure dependency of the excess mortality P-score.Population health metrics · 2024Article
- Trends in educational inequalities in smoking-attributable mortality and their impact on changes in general mortality inequalities: evidence from England and Wales, Finland, and Italy (Turin).Journal of epidemiology and community health · 2024Article
- The long-lasting effect of armed conflicts deaths on the living: Quantifying family bereavement.Science advances · 2024Article
- Sex differences in cause-specific mortality: regional trends in seven European countries, 1996-2019.European journal of public health · 2023Article
- Life expectancy changes since COVID-19.Nature human behaviour · 2022Article
- Article
- Probability of males to outlive females: an international comparison from 1751 to 2020.BMJ open · 2022Article
- COVID-19 Open-Data a global-scale spatially granular meta-dataset for coronavirus disease.Scientific data · 2022Article
- Quantifying impacts of the COVID-19 pandemic through life-expectancy losses: a population-level study of 29 countries.International journal of epidemiology · 2022Article
- Excess Mortality During the COVID-19 Pandemic in Guatemala.American journal of public health · 2021Article
Corrections and comments
- Erratum issued
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ungrouping binned data can be desirable for many reasons: Bins can be too coarse to allow for accurate analysis; comparisons can be hindered when different grouping approaches are used in different histograms; and the last interval is often wide and open-ended and, thus, covers a lot of information in the tail area. Age group-specific disease incidence rates and abridged life tables are examples of binned data. We propose a versatile method for ungrouping histograms that assumes that only the underlying distribution is smooth. Because of this modest assumption, the approach is suitable for most applications. The method is based on the composite link model, with a penalty added to ensure the smoothness of the target distribution. Estimates are obtained by maximizing a penalized likelihood. This maximization is performed efficiently by a version of the iteratively reweighted least-squares algorithm. Optimal values of the smoothing parameter are chosen by minimizing Akaike's Information Criterion. We demonstrate the performance of this method in a simulation study and provide several examples that illustrate the approach. Wide, open-ended intervals can be handled properly. The method can be extended to the estimation of rates when both the event counts and the exposures to risk are grouped.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.