Evaluating FIML and multiple imputation in joint ordinal-continuous measurements models with missing data

Abstract Background/Aims Psoriatic arthritis (PsA) is a heterogeneous disease comprising musculoskeletal and dermatological manifestations, especially plaque psoriasis. Secukinumab, an interleukin17A inhibitor, provided significantly greater PASI75/100 responses in two head-to-head trials versus etanercept or ustekinumab, a tumour necrosis factor inhibitor (TNFi), in patients with moderate-to-severe plaque psoriasis. The EXCEED study (NCT02745080) investigated whether secukinumab was superior to adalimumab, another TNFi, as monotherapy in biologic-naive active PsA patients with active plaque psoriasis (defined as having ≥1 psoriatic plaque of ≥ 2 cm diameter, nail changes consistent with psoriasis or documented history of plaque psoriasis). Here we report the pre-specified skin outcomes from the EXCEED study in the subset of patients with ≥3% body surface area (BSA) affected with psoriasis at baseline. Methods In this head-to-head, Phase 3b, randomised, double-blind, active-controlled, multicentre, parallel-group trial, patients were randomised to receive subcutaneous secukinumab 300 mg at baseline and Weeks 1-4, followed by dosing every 4 weeks until Week 48, or subcutaneous adalimumab 40 mg at baseline followed by the same dosing every 2 weeks until Week 50. The primary endpoint was superiority of secukinumab versus adalimumab on ACR20 response at Week 52. Pre-specified outcomes included the proportion of patients achieving a combined ACR50 and PASI100 response, PASI100 response, and absolute PASI score ≤3. Missing data were handled using multiple imputation. Results Overall, 853 patients were randomised to receive secukinumab (n = 426) or adalimumab (n = 427). At baseline, 215 and 202 patients had at least 3% BSA affected with psoriasis in the secukinumab and adalimumab groups, respectively. At Week 52, more patients achieved simultaneous improvement in ACR50 and PASI100 response with secukinumab versus adalimumab (30.7% versus 19.2%, respectively; P = 0.0087). Greater efficacy was demonstrated for secukinumab versus adalimumab for PASI100 responses and for the proportion of patients achieving absolute PASI score ≤3 (Table 1). Conclusion In this pre-specified analysis, secukinumab provided higher responses compared with adalimumab in achievement of combined improvement in joint and skin disease (combined ACR50 and PASI100 response) and in skin-specific endpoints (PASI100 and absolute PASI score ≤3) at Week 52. P189 Table 1:Skin-specific outcomes at Week 52Endpoints, % responseSEC 300 mg (N = 215)ADA 40 mg (N = 202)P value (unadjusted)PASI10046300.0007Combined ACR50 and PASI10031190.0087Absolute PASI score ≤379650.0015P value vs ADA; unadjusted P values are presented. Multiple imputation was used for handling missing data. ADA, adalimumab; ACR, American College of Rheumatology; N, number of patients in the psoriasis subset; PASI, Psoriasis Area and Severity Index; SEC, secukinumab. Disclosure A. Gottlieb: Grants/research support; A.G. has received research support, consultation fees or speaker honoraria from Pfizer, AbbVie, BMS, Lilly, MSD, Novartis, Roche, Sanofi, Sandoz, Nordic, Celltrion and UCB. F. Behrens: Consultancies; F.B. is a consultant for Pfizer, AbbVie, Sanofi, Lilly, Novartis, Genzyme, Boehringer Ingelheim, Janssen, MSD, Celgene, Roche and Chugai. Grants/research support; F.B. has received grant/research support from Pfizer, Janssen, Chugai, Celgene, Lilly and Roche. P. Nash: Consultancies; P.N. is a consultant for AbbVie, Bristol Myers Squibb, Celgene, Eli Lilly, Gilead, Janssen, MSD, Novartis, Pfizer Inc., Roche, Sanofi and UCB. Member of speakers’ bureau; for AbbVie, Bristol Myers Squibb, Celgene, Eli Lilly, Gilead, Janssen, MSD, Novartis, Pfizer Inc., Roche, Sanofi and UCB. Grants/research support; P.N. has received research support from AbbVie, Bristol Myers Squibb, Celgene, Eli Lilly and Company, Gilead, Janssen, MSD, Novartis, Pfizer Inc, Roche, Sanofi and UCB. J. Merola: Consultancies; J.F.M. is a consultant for Merck, AbbVie, Dermavant, Eli Lilly, Novartis, Janssen, UCB Pharma, Celgene, Sanofi, Regeneron, Arena, Sun Pharma, Biogen, Pfizer, EMD Sorono, Avotres and LEO Pharma. P. Pellet: Corporate appointments; P.P. is an employee of Novartis. Shareholder/stock ownership; P.P. is a shareholder of Novartis. L. Pricop: Corporate appointments; L.P. is an employee of Novartis. Shareholder/stock ownership; L.P. is a shareholder of Novartis. I. McInnes: Consultancies; I.M. is a consultant for AbbVie, Bristol Myers Squibb, Celgene, Eli Lilly and Company, Gilead, Janssen, Novartis, Pfizer and UCB. Grants/research support; I.M. has received grant/research support from Bristol Myers Squibb, Celgene, Eli Lilly and Company, Janssen and UCB.

Download Full-text

Wind power prediction with missing data using Gaussian process regression and multiple imputation

Applied Soft Computing ◽

10.1016/j.asoc.2018.07.027 ◽

2018 ◽

Vol 71 ◽

pp. 905-916 ◽

Cited By ~ 24

Author(s):

Tianhong Liu ◽

Haikun Wei ◽

Kanjian Zhang

Keyword(s):

Missing Data ◽

Gaussian Process ◽

Multiple Imputation ◽

Wind Power ◽

Gaussian Process Regression ◽

Power Prediction ◽

Wind Power Prediction

Download Full-text

1230Should multiple imputation be stratified by exposure group when estimating causal effects via outcome regression?

International Journal of Epidemiology ◽

10.1093/ije/dyab168.747 ◽

2021 ◽

Vol 50 (Supplement_1) ◽

Author(s):

Jiaxin Zhang ◽

S. Ghazaleh Dashti ◽

John B. Carlin ◽

Katherine J. Lee ◽

Margarita Moreno-Betancur

Keyword(s):

Missing Data ◽

Multiple Imputation ◽

Observational Studies ◽

Controlled Trial ◽

Simulated Data ◽

Causal Effects ◽

Exposure Group ◽

Complete Case ◽

Main Effect ◽

Health Cohort Study

Abstract Background Outcome regression remains widely applied for estimating causal effects in observational studies, in which causal inference is conceptualised as emulating a randomized controlled trial (RCT). Multiple imputation (MI) is a commonly used method for handling missing data, but while in RCTs it has been shown that MI should be conducted by treatment group to reduce bias, whether imputation should be conducted by exposure group in observational studies has not been studied. Methods We conducted a simulation study to evaluate the performance of seven methods for handling missing data: Complete-case analysis (CCA), MI of main effect, MI with interactions (between exposure and: outcome, a strong confounder, outcome and a strong confounder, all incomplete), and MI conducted by exposure group. We simulated data based on an example from the Victorian Adolescent Health Cohort Study. Three exposure prevalences and seven outcome generation models were considered, the latter ranging from no interaction to strong-positive or negative exposure-confounder interaction. Various missingness scenarios were examined: with incomplete outcome only or also incomplete confounders, and three levels of complexity regarding the missingness mechanism. Results For all scenarios, MI by exposure led to the least bias, followed by MI approaches that included exposure-confounder interactions. Conclusions If MI is adopted in outcome regression, we recommend conducting MI by exposure group and, when not feasible, including exposure-confounder interactions in the imputation model. Key messages Similar to RCTs, MI should be conducted by exposure group when estimating average causal effects using outcome regression in observational studies.

Download Full-text

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

International Journal for Population Data Science ◽

10.23889/ijpds.v1i1.54 ◽

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.

Download Full-text

Comparison of Missing Data Infilling Mechanisms for Recovering a Real-World Single Station Streamflow Observation

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18168375 ◽

2021 ◽

Vol 18 (16) ◽

pp. 8375

Author(s):

Thelma Dede Baddoo ◽

Zhijia Li ◽

Samuel Nii Odai ◽

Kenneth Rodolphe Chabi Boni ◽

Isaac Kwesi Nooni ◽

...

Keyword(s):

Missing Data ◽

Multiple Imputation ◽

Real World ◽

Missing Values ◽

Total Error ◽

Extensive Study ◽

Error Measurement ◽

Missing Data Imputation ◽

Single Station ◽

Real World Datasets

Reconstructing missing streamflow data can be challenging when additional data are not available, and missing data imputation of real-world datasets to investigate how to ascertain the accuracy of imputation algorithms for these datasets are lacking. This study investigated the necessary complexity of missing data reconstruction schemes to obtain the relevant results for a real-world single station streamflow observation to facilitate its further use. This investigation was implemented by applying different missing data mechanisms spanning from univariate algorithms to multiple imputation methods accustomed to multivariate data taking time as an explicit variable. The performance accuracy of these schemes was assessed using the total error measurement (TEM) and a recommended localized error measurement (LEM) in this study. The results show that univariate missing value algorithms, which are specially developed to handle univariate time series, provide satisfactory results, but the ones which provide the best results are usually time and computationally intensive. Also, multiple imputation algorithms which consider the surrounding observed values and/or which can understand the characteristics of the data provide similar results to the univariate missing data algorithms and, in some cases, perform better without the added time and computational downsides when time is taken as an explicit variable. Furthermore, the LEM would be especially useful when the missing data are in specific portions of the dataset or where very large gaps of ‘missingness’ occur. Finally, proper handling of missing values of real-world hydroclimatic datasets depends on imputing and extensive study of the particular dataset to be imputed.

Download Full-text