Evidence map›Paper›PMID 39427148›Full record

ArticleBMC medical research methodology2024

Analyzing missingness patterns in real-world data using the SMDI toolkit: application to a linked EHR-claims pharmacoepidemiology study.

Sudha R Raman, Bradley G Hammill, Pamela A Shaw, Hana Lee, Sengwee Toh, John G Connolly, Kimberly J Dandreo, Vinit Nalawade, Fang Tian, Wei Liu and 5 more

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In one paragraph

Article in BMC medical research methodology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Observational
  3. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Sudha R RamanDepartment of Population Health Sciences, Duke University School of Medicine, Durham, USA. Sudha.raman@duke.edu.
Bradley G HammillDepartment of Population Health Sciences, Duke University School of Medicine, Durham, USA.
Pamela A ShawBiostatistics Division, Kaiser Permanente Washington Health Research Institute, Seattle, USA.
Hana LeeOffice of Biostatistics, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, USA.
Sengwee TohDepartment of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, USA.
John G ConnollyDepartment of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, USA.
Kimberly J DandreoDepartment of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, USA.
Vinit NalawadeDepartment of Population Health Sciences, Duke University School of Medicine, Durham, USA.
Fang TianOffice of Surveillance and Epidemiology, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, USA.
Wei LiuOffice of Surveillance and Epidemiology, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, USA.
Jie LiOffice of Surveillance and Epidemiology, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, USA.
José J Hernández-MuñozOffice of Surveillance and Epidemiology, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, USA.
Robert J GlynnDivision of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
Rishi J DesaiDivision of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
Janick WeberpalsDivision of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, USA.

Funding

U.S. Food and Drug Administration Master Agreement 75F40119D10037
6 · The paper itself

Abstract

backgroundMissing data in confounding variables present a frequent challenge in generating evidence using real-world data, including electronic health records (EHR). Our objective was to apply a recently published toolkit for characterizing missing data patterns and based on the toolkit results about likely missingness mechanisms, illustrate the decision-making process for analyses in an empirical case example.

methodsWe utilized the Structural Missing Data Investigations (SMDI) toolkit to characterize missing data patterns in the context of a pharmacoepidemiology study comparing cardiovascular outcomes of initiating sodium-glucose-cotransporter-2 inhibitors (SGLT2i) and dipeptidyl peptidase-4 inhibitors (DPP-4i) among older adults. The study used a linked EHR-Medicare claims dataset from Duke Health patients (2015-2017), focusing on partially observed confounders from EHR data (HbA1c lab and body mass index [BMI] values). Our analysis incorporated SMDI's descriptive functions and diagnostic tests to explore missingness patterns and determine missingness mitigation approaches. We used findings from these investigations to inform estimation of adjusted hazard ratios comparing the two classes of medications.

resultsHigh levels of missingness were noted for important confounding variables including HbA1c (63.6%) and BMI (16.5%). Diagnostic tests resulted in output that described: 1) the distributions of patient characteristics, exposure, and outcome between patients with or without an observed value of the partially observed covariate, 2) the ability to predict missingness based on observed covariates, and 3) estimate if the missingness of a partially observed covariate is differential with respect to the outcome. There was evidence that missingness could be sufficiently described using observed data, which allowed multiple imputation by chained equations using random forests to address missing confounder data in estimating treatment effects. Multiple imputation resulted in improved alignment of effect estimates with previous studies.

conclusionsWe were able to demonstrate the practical application of the SMDI toolkit in a real-world setting. Application of the SMDI toolkit and the resulting insights of potential missingness patterns can inform the choice of appropriate analytic methods and increase transparency of research methods in handling missing data. This type of approach can inform analytic decision making and may increase our ability to generate evidence from real-world data.

Indexed as

Dipeptidyl-Peptidase IV InhibitorsElectronic Health RecordsPharmacoepidemiologyAgedDiabetes Mellitus, Type 2FemaleGlycated HemoglobinHumansMaleMedicareSodium-Glucose Transporter 2 InhibitorsUnited StatesDipeptidyl-Peptidase IV InhibitorsGlycated HemoglobinSodium-Glucose Transporter 2 InhibitorsElectronic health recordsMissing dataReal-world evidence

Identifiers

PMID39427148
PMCPMC11490010

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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.