ArticlePharmacoepidemiology and drug safety2017
A comparison of methods for estimating the temporal change in a continuous variable: Example of HbA1c in patients with diabetes.
Article in Pharmacoepidemiology and drug safety, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Human milk oligosaccharide composition and associations with growth: results from an observational study in the US.Frontiers in nutrition · 2023Article
- Experienced HIV-Related Stigma and Psychological Distress in Peruvian Sexual and Gender Minorities: A Longitudinal Study to Explore Mediating Roles of Internalized HIV-Related Stigma and Coping Styles.AIDS and behavior · 2019Article
- A comparison of methods for estimating the temporal change in a continuous variable: Example of HbA1c in patients with diabetes.Pharmacoepidemiology and drug safety · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
purposeTo compare the more complex technique, functional principal component analysis (FPCA), to simpler methods of estimating values of sparse and irregularly spaced continuous variables at given time points in longitudinal data using a diabetic patient cohort from UK primary care.
methodsThe setting for this study is the Clinical Practice Research Datalink (CPRD), a UK general practice research database. For 16,034 diabetic patients identified in CPRD, with at least 2 measures in a 30-month period, HbA1c was estimated after temporarily omitting (i) the final and (ii) middle known values using linear interpolation, simple linear regression, arithmetic mean, random effects, and FPCA. Performance of each method was assessed using mean prediction error. The influence on predictive accuracy of (1) more homogeneous populations and (2) number and range of known HbA1c values was explored.
resultsWhen estimating the last observation, the predictive accuracy of FPCA was highest with over half of predicted values within 0.4 units, equivalent to laboratory measurement error. Predictive accuracy improved when estimating the middle observation with almost 60% predicted values within 0.4 units for FPCA. These results were marginally better than that achieved by simpler approaches, such as last-occurrence-carried-forward linear interpolation. This pattern persisted with more homogeneous populations as well as when variability in HbA1c measures coupled with frequency of data points were considered.
conclusionsWhen estimating change from baseline to prespecified time points in electronic medical records data, a marginal benefit to using the more complex modelling approach of FPCA exists over more traditional methods.
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.