2009
DOI: 10.1016/j.annepidem.2009.08.002
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Multiple Imputation for Missing Laboratory Data: An Example from Infectious Disease Epidemiology

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Cited by 14 publications
(12 citation statements)
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“…Attrition at one-year follow-up was high given the hard-to-reach population under study with a total of 284 participants completing the assessment (48% completed one-year follow-up). However, theoretically, large amounts of missing data should not be a critical problem as long as the sample size is large enough and the missing values are missing at random (Graham, 2009; Mulla et al, 2009). The analytic sample excluded those who indicated condom use was not applicable (n = 71) or had no sex in the past four months (n = 13).…”
Section: Methodsmentioning
confidence: 99%
“…Attrition at one-year follow-up was high given the hard-to-reach population under study with a total of 284 participants completing the assessment (48% completed one-year follow-up). However, theoretically, large amounts of missing data should not be a critical problem as long as the sample size is large enough and the missing values are missing at random (Graham, 2009; Mulla et al, 2009). The analytic sample excluded those who indicated condom use was not applicable (n = 71) or had no sex in the past four months (n = 13).…”
Section: Methodsmentioning
confidence: 99%
“…Hence, in EMR systems we might find cholesterol levels or blood pressure measurements for some patients but not all. The presence of such information may be related to the patient's disease state; consequently, missing values cannot be considered random 41 , 42 …”
Section: Methodological Challengesmentioning
confidence: 99%
“…The presence of such information may be related to the patient's disease state; consequently, missing values cannot be considered random. 41,42 soMe solutions…”
Section: Data Issuesmentioning
confidence: 99%
“…All cases had subtype information available in gag , env or both gene regions, but among controls, 43/501 (8.6%) were missing all subtype data, including 34/332 (10.2%) from eastern African and 9/169 (5.3%) from southern Africa, due to low HIV-1 plasma viral loads preventing adequate viral amplification. To avoid bias because of control exclusion due to missing subtype data, we performed multiple imputation with 20 datasets imputed using Markov chain Monte Carlo methods 26 .…”
Section: Methodsmentioning
confidence: 99%