A guide to missing data for the pediatric nephrologist

Nicholas G. Larkins, Jonathan C. Craig, Armando Teixeira-Pinto

Research output: Contribution to journalReview article

2 Citations (Scopus)

Abstract

Missing data is an important and common source of bias in clinical research. Readers should be alert to and consider the impact of missing data when reading studies. Beyond preventing missing data in the first place, through good study design and conduct, there are different strategies available to handle data containing missing observations. Complete case analysis is often biased unless data are missing completely at random. Better methods of handling missing data include multiple imputation and models using likelihood-based estimation. With advancing computing power and modern statistical software, these methods are within the reach of clinician-researchers under guidance of a biostatistician. As clinicians reading papers, we need to continue to update our understanding of statistical methods, so that we understand the limitations of these techniques and can critically interpret literature. © 2018 IPNA
Original languageEnglish
Pages (from-to)223-231
Number of pages9
JournalPediatric Nephrology
Volume34
Issue number2
DOIs
Publication statusE-pub ahead of print - 13 Mar 2018
Externally publishedYes

Fingerprint

Pediatrics
Reading
Software
Research Personnel
Research
Nephrologists

Cite this

Larkins, Nicholas G. ; Craig, Jonathan C. ; Teixeira-Pinto, Armando. / A guide to missing data for the pediatric nephrologist. In: Pediatric Nephrology. 2018 ; Vol. 34, No. 2. pp. 223-231.
@article{51c3fe9c7e6443cab3afb15110a7964e,
title = "A guide to missing data for the pediatric nephrologist",
abstract = "Missing data is an important and common source of bias in clinical research. Readers should be alert to and consider the impact of missing data when reading studies. Beyond preventing missing data in the first place, through good study design and conduct, there are different strategies available to handle data containing missing observations. Complete case analysis is often biased unless data are missing completely at random. Better methods of handling missing data include multiple imputation and models using likelihood-based estimation. With advancing computing power and modern statistical software, these methods are within the reach of clinician-researchers under guidance of a biostatistician. As clinicians reading papers, we need to continue to update our understanding of statistical methods, so that we understand the limitations of these techniques and can critically interpret literature. {\circledC} 2018 IPNA",
author = "Larkins, {Nicholas G.} and Craig, {Jonathan C.} and Armando Teixeira-Pinto",
year = "2018",
month = "3",
day = "13",
doi = "10.1007/s00467-018-3932-4",
language = "English",
volume = "34",
pages = "223--231",
journal = "Paediatric Nephrology",
issn = "0931-041X",
publisher = "Springer",
number = "2",

}

A guide to missing data for the pediatric nephrologist. / Larkins, Nicholas G.; Craig, Jonathan C.; Teixeira-Pinto, Armando.

In: Pediatric Nephrology, Vol. 34, No. 2, 13.03.2018, p. 223-231.

Research output: Contribution to journalReview article

TY - JOUR

T1 - A guide to missing data for the pediatric nephrologist

AU - Larkins, Nicholas G.

AU - Craig, Jonathan C.

AU - Teixeira-Pinto, Armando

PY - 2018/3/13

Y1 - 2018/3/13

N2 - Missing data is an important and common source of bias in clinical research. Readers should be alert to and consider the impact of missing data when reading studies. Beyond preventing missing data in the first place, through good study design and conduct, there are different strategies available to handle data containing missing observations. Complete case analysis is often biased unless data are missing completely at random. Better methods of handling missing data include multiple imputation and models using likelihood-based estimation. With advancing computing power and modern statistical software, these methods are within the reach of clinician-researchers under guidance of a biostatistician. As clinicians reading papers, we need to continue to update our understanding of statistical methods, so that we understand the limitations of these techniques and can critically interpret literature. © 2018 IPNA

AB - Missing data is an important and common source of bias in clinical research. Readers should be alert to and consider the impact of missing data when reading studies. Beyond preventing missing data in the first place, through good study design and conduct, there are different strategies available to handle data containing missing observations. Complete case analysis is often biased unless data are missing completely at random. Better methods of handling missing data include multiple imputation and models using likelihood-based estimation. With advancing computing power and modern statistical software, these methods are within the reach of clinician-researchers under guidance of a biostatistician. As clinicians reading papers, we need to continue to update our understanding of statistical methods, so that we understand the limitations of these techniques and can critically interpret literature. © 2018 IPNA

U2 - 10.1007/s00467-018-3932-4

DO - 10.1007/s00467-018-3932-4

M3 - Review article

VL - 34

SP - 223

EP - 231

JO - Paediatric Nephrology

JF - Paediatric Nephrology

SN - 0931-041X

IS - 2

ER -