scholarly journals Accounting for missing data caused by drug cessation in observational comparative effectiveness research: a simulation study

2022 ◽  
pp. annrheumdis-2021-221477
Author(s):  
Denis Mongin ◽  
Kim Lauper ◽  
Axel Finckh ◽  
Thomas Frisell ◽  
Delphine Sophie Courvoisier
Keyword(s):  
Missing Data ◽  
Disease Activity ◽  
Multiple Imputation ◽  
Simulation Study ◽  
Comparative Effectiveness ◽  
Real World Data ◽  
Data Set ◽  
Multiple Imputations ◽  
True Value ◽  
The Absolute

ObjectivesTo assess the performance of statistical methods used to compare the effectiveness between drugs in an observational setting in the presence of attrition.MethodsIn this simulation study, we compared the estimations of low disease activity (LDA) at 1 year produced by complete case analysis (CC), last observation carried forward (LOCF), LUNDEX, non-responder imputation (NRI), inverse probability weighting (IPW) and multiple imputations of the outcome. All methods were adjusted for confounders. The reasons to stop the treatments were included in the multiple imputation method (confounder-adjusted response rate with attrition correction, CARRAC) and were either included (IPW2) or not (IPW1) in the IPW method. A realistic simulation data set was generated from a real-world data collection. The amount of missing data caused by attrition and its dependence on the ‘true’ value of the data missing were varied to assess the robustness of each method to these changes.ResultsLUNDEX and NRI strongly underestimated the absolute LDA difference between two treatments, and their estimates were highly sensitive to the amount of attrition. IPW1 and CC overestimated the absolute LDA difference between the two treatments and the overestimation increased with increasing attrition or when missingness depended on disease activity at 1 year. IPW2 and CARRAC produced unbiased estimations, but IPW2 had a greater sensitivity to the missing pattern of data and the amount of attrition than CARRAC.ConclusionsOnly multiple imputation and IPW2, which considered both confounding and treatment cessation reasons, produced accurate comparative effectiveness estimates.

scholarly journals Imputing missing data of function and disease activity in rheumatoid arthritis registers: what is the best technique?

RMD Open ◽  
2019 ◽  
Vol 5 (2) ◽  
pp. e000994 ◽  
Author(s):  
Denis Mongin ◽  
Kim Lauper ◽  
Carl Turesson ◽  
Merete Lund Hetland ◽  
Eirik Klami Kristianslund ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Missing Data ◽  
Disease Activity ◽  
Multiple Imputation ◽  
Mixed Effects ◽  
Attrition Bias ◽  
Low Disease Activity ◽  
True Value ◽  
The Mean

ObjectiveTo compare several methods of missing data imputation for function (Health Assessment Questionnaire) and for disease activity (Disease Activity Score-28 and Clinical Disease Activity Index) in rheumatoid arthritis (RA) patients.MethodsOne thousand RA patients from observational cohort studies with complete data for function and disease activity at baseline, 6, 12 and 24 months were selected to conduct a simulation study. Values were deleted at random or following a predicted attrition bias. Three types of imputation were performed: (1) methods imputing forward in time (last observation carried forward; linear forward extrapolation); (2) methods considering data both forward and backward in time (nearest available observation—NAO; linear extrapolation; polynomial extrapolation); and (3) methods using multi-individual models (linear mixed effects cubic regression—LME3; multiple imputation by chained equation—MICE). The performance of each estimation method was assessed using the difference between the mean outcome value, the remission and low disease activity rates after imputation of the missing values and the true value.ResultsWhen imputing missing baseline values, all methods underestimated equally the true value, but LME3 and MICE correctly estimated remission and low disease activity rates. When imputing missing follow-up values at 6, 12, or 24 months, NAO provided the least biassed estimate of the mean disease activity and corresponding remission rate. These results were not affected by the presence of attrition bias.ConclusionWhen imputing function and disease activity in large registers of active RA patients, researchers can consider the use of a simple method such as NAO for missing follow-up data, and the use of mixed-effects regression or multiple imputation for baseline data.

scholarly journals Weighted multiple imputation of ethnicity data that are missing not at random in primary care databases

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Tra My Pham ◽  
Irene Petersen ◽  
James Carpenter ◽  
Tim Morris
Keyword(s):  
Primary Care ◽  
Missing Data ◽  
Multiple Imputation ◽  
Simulation Study ◽  
Case Analysis ◽  
Missing At Random ◽  
Complete Case ◽  
Missing Not At Random ◽  
Health Records ◽  
Ethnicity Data

ABSTRACT BackgroundEthnicity is an important factor to be considered in health research because of its association with inequality in disease prevalence and the utilisation of healthcare. Ethnicity recording has been incorporated in primary care electronic health records, and hence is available in large UK primary care databases such as The Health Improvement Network (THIN). However, since primary care data are routinely collected for clinical purposes, a large amount of data that are relevant for research including ethnicity is often missing. A popular approach for missing data is multiple imputation (MI). However, the conventional MI method assuming data are missing at random does not give plausible estimates of the ethnicity distribution in THIN compared to the general UK population. This might be due to the fact that ethnicity data in primary care are likely to be missing not at random. ObjectivesI propose a new MI method, termed ‘weighted multiple imputation’, to deal with data that are missing not at random in categorical variables.MethodsWeighted MI combines MI and probability weights which are calculated using external data sources. Census summary statistics for ethnicity can be used to form weights in weighted MI such that the correct marginal ethnic breakdown is recovered in THIN. I conducted a simulation study to examine weighted MI when ethnicity data are missing not at random. In this simulation study which resembled a THIN dataset, ethnicity was an independent variable in a survival model alongside other covariates. Weighted MI was compared to the conventional MI and other traditional missing data methods including complete case analysis and single imputation.ResultsWhile a small bias was still present in ethnicity coefficient estimates under weighted MI, it was less severe compared to MI assuming missing at random. Complete case analysis and single imputation were inadequate to handle data that are missing not at random in ethnicity.ConclusionsAlthough not a total cure, weighted MI represents a pragmatic approach that has potential applications not only in ethnicity but also in other incomplete categorical health indicators in electronic health records.

scholarly journals Multiple Imputation of Missing Data: A Simulation Study on a Binary Response

2013 ◽  
Vol 03 (05) ◽  
pp. 370-378 ◽  
Author(s):  
Jochen Hardt ◽  
Max Herke ◽  
Tamara Brian ◽  
Wilfried Laubach
Keyword(s):  
Missing Data ◽  
Multiple Imputation ◽  
Simulation Study ◽  
Binary Response

scholarly journals Multiple imputation in big identifiable data for educational research: An example from the Brazilian education assessment system

2020 ◽  
Vol 28 (108) ◽  
pp. 599-621
Author(s):  
Maria Eugénia Ferrão ◽  
Paula Prata ◽  
Maria Teresa Gonzaga Alves
Keyword(s):  
Missing Data ◽  
Multiple Imputation ◽  
Educational Research ◽  
Missing Values ◽  
Assessment System ◽  
Policy And Practice ◽  
Real World Data ◽  
Missing Completely At Random ◽  
Almost All ◽  
Identifiable Data

Abstract Almost all quantitative studies in educational assessment, evaluation and educational research are based on incomplete data sets, which have been a problem for years without a single solution. The use of big identifiable data poses new challenges in dealing with missing values. In the first part of this paper, we present the state-of-art of the topic in the Brazilian education scientific literature, and how researchers have dealt with missing data since the turn of the century. Next, we use open access software to analyze real-world data, the 2017 Prova Brasil , for several federation units to document how the naïve assumption of missing completely at random may substantially affect statistical conclusions, researcher interpretations, and subsequent implications for policy and practice. We conclude with straightforward suggestions for any education researcher on applying R routines to conduct the hypotheses test of missing completely at random and, if the null hypothesis is rejected, then how to implement the multiple imputation, which appears to be one of the most appropriate methods for handling missing data.

scholarly journals How oil income and missing hydrocarbon rents data influence autocratic survival: A response to Lucas and Richter (2016)

2017 ◽  
Vol 4 (3) ◽  
pp. 205316801771979 ◽  
Author(s):  
Joseph Wright ◽  
Erica Frantz
Keyword(s):  
Missing Data ◽  
Multiple Imputation ◽  
Data Set ◽  
Oil Rents

This paper re-examines the findings from a recently published study on hydrocarbon rents and autocratic survival by Lucas and Richter (LR hereafter). LR introduce a new data set on hydrocarbon rents and use it to examine the link between oil income and autocratic survival. Employing a placebo test, we show that the authors’ strategy for dealing with missingness in the new hydrocarbon rents data set – filling in missing data with zeros – creates bias in the reported estimates of interest. Addressing missingness with multiple imputation shows that the LR findings linking oil rents to democratization do not hold. Instead, we find that hydrocarbon rents reduce the chances of transition to a new dictatorship, consistent with the conclusions of Wright et al.

scholarly journals Multiple Imputation with Missing Indicators as Proxies for Unmeasured Variables: Simulation Study

2020 ◽  
Author(s):  
Matthew Sperrin ◽  
Glen P. Martin
Keyword(s):  
Missing Data ◽  
Multiple Imputation ◽  
Simulation Study ◽  
Causal Effect ◽  
Missing At Random ◽  
Directed Acyclic Graphs ◽  
Missing Not At Random ◽  
Routinely Collected Health Data ◽  
Effect Estimation ◽  
Minimal Bias

Abstract Background : Within routinely collected health data, missing data for an individual might provide useful information in itself. This occurs, for example, in the case of electronic health records, where the presence or absence of data is informative. While the naive use of missing indicators to try to exploit such information can introduce bias, its use in conjunction with multiple imputation may unlock the potential value of missingness to reduce bias in causal effect estimation, particularly in missing not at random scenarios and where missingness might be associated with unmeasured confounders. Methods: We conducted a simulation study to determine when the use of a missing indicator, combined with multiple imputation, would reduce bias for causal effect estimation, under a range of scenarios including unmeasured variables, missing not at random, and missing at random mechanisms. We use directed acyclic graphs and structural models to elucidate a variety of causal structures of interest. We handled missing data using complete case analysis, and multiple imputation with and without missing indicator terms. Results: We find that multiple imputation combined with a missing indicator gives minimal bias for causal effect estimation in most scenarios. In particular the approach: 1) does not introduce bias in missing (completely) at random scenarios; 2)reduces bias in missing not at random scenarios where the missing mechanism depends on the missing variable itself; and 3) may reduce or increase bias when unmeasured confounding is present. Conclusion : In the presence of missing data, careful use of missing indicators, combined with multiple imputation, can improve causal effect estimation when missingness is informative, and is not detrimental when missingness is at random.

scholarly journals Use of Missing Indicators as Proxies for Unmeasured Variables: Simulation Study

2020 ◽  
Author(s):  
Matthew Sperrin ◽  
Glen P. Martin
Keyword(s):  
Missing Data ◽  
Multiple Imputation ◽  
Simulation Study ◽  
Predictive Accuracy ◽  
Causal Effect ◽  
Model Performance ◽  
Directed Acyclic Graphs ◽  
Regression Imputation ◽  
Effect Estimation ◽  
Improved Model

Abstract Background Within routinely collected health data, missing data for an individual might provide useful information in itself. This occurs, for example, in the case of electronic health records, where the presence or absence of data is informative. While the naive use of missing indicators to try to exploit such information can introduce bias when used inappropriately, its use in conjunction with other imputation approaches may unlock the potential value of missingness to reduce bias and improve prediction.Methods We conducted a simulation study to determine when the use of a missing indicator, combined with an imputation approach, such as multiple imputation, would lead to improved model performance, in terms of minimising bias for causal effect estimation, and improving predictive accuracy, under a range of scenarios with unmeasured variables. We use directed acyclic graphs and structural models to elucidate causal structures of interest. We consider a variety of missingness mechanisms, then handle these using complete case analysis, unconditional mean imputation, regression imputation and multiple imputation. In each case we evaluate supplementing these approaches with missing indicator terms. Results For estimating causal effects, we find that multiple imputation combined with a missing indicator gives minimal bias in most scenarios. For prediction, we find that regression imputation combined with a missing indicator minimises mean squared error.Conclusion In the presence of missing data, careful use of missing indicators, combined with appropriate imputation, can improve both causal estimation and prediction accuracy.

scholarly journals How to Apply Multiple Imputation in Propensity Score Matching with Partially Observed Confounders: A Simulation Study and Practical Recommendations

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Albee Ling ◽  
Maria Montez-Rath ◽  
Maya Mathur ◽  
Kris Kapphahn ◽  
Manisha Desai
Keyword(s):  
Missing Data ◽  
Propensity Score ◽  
Multiple Imputation ◽  
Propensity Score Matching ◽  
Simulation Study ◽  
Observational Studies ◽  
Auxiliary Variables ◽  
Imputation Model ◽  
Partially Observed ◽  
Practical Recommendations

Propensity score matching (PSM) has been widely used to mitigate confounding in observational studies, although complications arise when the covariates used to estimate the PS are only partially observed. Multiple imputation (MI) is a potential solution for handling missing covariates in the estimation of the PS. However, it is not clear how to best apply MI strategies in the context of PSM. We conducted a simulation study to compare the performances of popular non-MI missing data methods and various MI-based strategies under different missing data mechanisms. We found that commonly applied missing data methods resulted in biased and inefficient estimates, and we observed large variation in performance across MI-based strategies. Based on our findings, we recommend 1) estimating the PS after applying MI to impute missing confounders; 2) conducting PSM within each imputed dataset followed by averaging the treatment effects to arrive at one summarized finding; 3) a bootstrapped-based variance to account for uncertainty of PS estimation, matching, and imputation; and 4) inclusion of key auxiliary variables in the imputation model.

scholarly journals Considerations for using multiple imputation in propensity score-weighted analysis

2021 ◽  
Author(s):  
Andreas Halgreen Eiset ◽  
Morten Frydenberg
Keyword(s):  
Missing Data ◽  
Propensity Score ◽  
Confidence Interval ◽  
Multiple Imputation ◽  
Computational Time ◽  
Standardized Mortality Ratio ◽  
Data Set ◽  
Propensity Score Model ◽  
Weighted Analysis ◽  
Percentile Confidence Interval

We present our considerations for using multiple imputation to account for missing data in propensity score-weighted analysis with bootstrap percentile confidence interval. We outline the assumptions underlying each of the methods and discuss the methodological and practical implications of our choices and briefly point to alternatives. We made a number of choices a priori for example to use logistic regression-based propensity scores to produce standardized mortality ratio-weights and Substantive Model Compatible-Full Conditional Specification to multiply impute missing data (given no violation of underlying assumptions). We present a methodology to combine these methods by choosing the propensity score model based on covariate balance, using this model as the substantive model in the multiple imputation, producing and averaging the point estimates from each multiple imputed data set to give the estimate of association and computing the percentile confidence interval by bootstrapping. The described methodology is demanding in both work-load and in computational time, however, we do not consider the prior a draw-back: it makes some of the underlying assumptions explicit and the latter may be a nuisance that will diminish with faster computers and better implementations.

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