A Hot-Deck Multiple Imputation Procedure for Gaps in Longitudinal Recurrent Event Histories

Wang, Chia Ning; Little, Roderick J. A.; Nan, Bin; Harlow, Sioḃán D.

doi:10.1111/j.1541-0420.2011.01558.x

Cited by 8 publications

(7 citation statements)

References 23 publications

(26 reference statements)

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“…We first performed the analysis only on patients in our cohort who had complete case information (complete case analysis). We then performed multiple imputation to generate missing values for race using a weighted sequential hot deck method which does not place any restrictions on missing data patterns to create five complete datasets 11 . Pooled values were then utilized to calculate race-specific outcomes.…”

Section: Methodsmentioning

confidence: 99%

Racial Disparities in Comorbidities, Complications, and Maternal and Fetal Outcomes in Women With Preeclampsia/eclampsia

Shahul

Tung

Minhaj

et al. 2015

Hypertension in Pregnancy

123

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Objective The mechanisms leading to worse outcomes in African-American (AA) women with preeclampsia/eclampsia remain unclear. Our objective was to identify racial differences in maternal comorbidities, peripartum characteristics, and maternal and fetal outcomes. Methods/Results When compared to white women with preeclampsia/eclampsia, AA women had an increased unadjusted risk of inpatient maternal mortality (OR 3.70, 95% CI: 2.19-6.24). After adjustment for covariates, in-hospital mortality for AA women remained higher than that for white women (OR 2.85, 95% CI: 1.38-5.53), while, the adjusted risk of death among Hispanic women did not differ from that for white women. We also found an increased risk of intrauterine fetal death (IUFD) among AA women. When compared to white women with preeclampsia, AA women had an increased unadjusted odds of IUFD (OR 2.78, 95% CI: 2.49-3.11), which remained significant after adjustment for covariates (adjusted OR 2.45, 95% CI: 2.14-2.82). In contrast, IUFD among Hispanic women did not differ from that for white women after adjusting for covariates. Conclusions and Relevance Our data suggest that African American women are more likely to have risk factors for preeclampsia and more likely to suffer an adverse outcome during peripartum care. Future research should examine whether controlling co-morbidities and other risk factors will help to alleviate racial disparities in outcomes in this cohort of women.

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Section: Methodsmentioning

confidence: 99%

Racial Disparities in Comorbidities, Complications, and Maternal and Fetal Outcomes in Women With Preeclampsia/eclampsia

Shahul

Tung

Minhaj

et al. 2015

Hypertension in Pregnancy

123

View full text Add to dashboard Cite

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“…While random hot deck imputation has commonly been used in surveys, several extensions to clustered longitudinal data have been described in the literature. Little et al 23 and Wang et al 24 applied this method to multiply impute gaps in recurrent event data, using menstrual patterns as an example. Unlike previous extensions which only borrowed information between individuals, our approach restricts the donor pool to entries between and within individuals where appropriate.…”

Section: Discussionmentioning

confidence: 99%

Implementing Multiple Imputation for Missing Data in Longitudinal Studies When Models are Not Feasible: An Example Using the Random Hot Deck Approach

Wang¹,

Stokes²,

Steele³

et al. 2022

CLEP

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Purpose Researchers often use model-based multiple imputation to handle missing at random data to minimize bias. However, constraints within the data may sometimes result in implausible values, making model-based imputation infeasible. In these contexts, we illustrate how random hot deck imputation can allow for plausible multiple imputation in longitudinal studies. Patients and Methods Our motivating example is the Childhood Health, Activity, and Motor Performance School Study Denmark (CHAMPS-DK), a prospective cohort study that measured weekly sports participation for 1700 Danish schoolchildren. Using observed data on 4 variables (pain, activity frequency, sport, sport counts), we created a gold-standard data set without missing data. We then created a synthetic data set by setting some variable values to missing based on a prediction model that mimicked real-data missingness patterns. To create 5 imputed data sets, we matched each record with missing data to several fully observed records, generated probabilities from matched records, and sampled from these records based on the probability of each occurring. We assessed variability and agreement (kappa) between the imputed data sets and the gold-standard data set. We compare results to common model-based imputation methods. Results Variability across data sets appeared reasonable. The range of kappa for the random hot deck approach was moderate for activity frequency (0.65 to 0.71) and sport (0.59 to 0.85), and poor for common model-based approaches (range 0.00 to 0.11). The range of kappas for sport count was strong (0.87 to 0.97) for random hot deck imputation and weak to moderate (0.55 to 0.71) for common model-based imputation. Agreement was higher when more information was present, and when prevalence was higher for our binary variable sport. Conclusion Random hot deck imputation should be considered as an alternative method when model-based approaches are infeasible, specifically where there are constraints within and between covariates.

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“…Furthermore, Hsu et al [5] perform imputation for all censored patients, while the risk-stratified imputation only imputes values for the withdrawal group. Wang et al [19] propose an alternative approach for hot-deck imputation using predictive mean matching (PMM) to include covariates in the imputation process, albeit in the context of imputing recurrent unobserved events rather than a single terminal event. As with Hsu et al [5], this method focuses primarily on continuous covariates and performs imputation for all missing events.…”

Section: Discussionmentioning

confidence: 99%

Risk-stratified imputation in survival analysis

et al. 2013

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Background Censoring that is dependent on covariates associated with survival can arise in randomized trials due to changes in recruitment and eligibility criteria to minimize withdrawals, potentially leading to biased treatment effect estimates. Imputation approaches have been proposed to address censoring in survival analysis; and while these approaches may provide unbiased estimates of treatment effects, imputation of a large number of outcomes may over- or underestimate the associated variance based on the imputation pool selected. Purpose We propose an improved method, risk-stratified imputation, as an alternative to address withdrawal related to the risk of events in the context of time-to-event analyses. Methods Our algorithm performs imputation from a pool of replacement subjects with similar values of both treatment and covariate(s) of interest, that is, from a risk-stratified sample. This stratification prior to imputation addresses the requirement of time-to-event analysis that censored observations are representative of all other observations in the risk group with similar exposure variables. We compared our risk-stratified imputation to case deletion and bootstrap imputation in a simulated dataset in which the covariate of interest (study withdrawal) was related to treatment. A motivating example from a recent clinical trial is also presented to demonstrate the utility of our method. Results In our simulations, risk-stratified imputation gives estimates of treatment effect comparable to bootstrap and auxiliary variable imputation while avoiding inaccuracies of the latter two in estimating the associated variance. Similar results were obtained in analysis of clinical trial data. Limitations Risk-stratified imputation has little advantage over other imputation methods when covariates of interest are not related to treatment, although its performance is superior when covariates are related to treatment. Risk-stratified imputation is intended for categorical covariates, and may be sensitive to the width of the matching window if continuous covariates are used. Conclusions The use of the risk-stratified imputation should facilitate the analysis of many clinical trials, in which one group has a higher withdrawal rate that is related to treatment.

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A Hot-Deck Multiple Imputation Procedure for Gaps in Longitudinal Recurrent Event Histories

Cited by 8 publications

References 23 publications

Racial Disparities in Comorbidities, Complications, and Maternal and Fetal Outcomes in Women With Preeclampsia/eclampsia

Racial Disparities in Comorbidities, Complications, and Maternal and Fetal Outcomes in Women With Preeclampsia/eclampsia

Implementing Multiple Imputation for Missing Data in Longitudinal Studies When Models are Not Feasible: An Example Using the Random Hot Deck Approach

Risk-stratified imputation in survival analysis

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