Handling missing data in propensity score estimation in comparative effectiveness evaluations: a systematic review
Abstract
Aim: Even though systematic reviews have examined how aspects of propensity score methods are used, none has reviewed how the challenge of missing data is addressed with these methods. This review therefore describes how missing data are addressed with propensity score methods in observational comparative effectiveness studies. Methods: Published articles on observational comparative effectiveness studies were extracted from MEDLINE and EMBASE databases. Results: Our search yielded 167 eligible articles. Majority of these studies (114; 68%) conducted complete case analysis with only 53 of them stating this in the methods. Only 16 articles reported use of multiple imputation. Conclusion: Few researchers use correct methods for handling missing data or reported missing data methodology which may lead to reporting biased findings.
First draft submitted: 13 September 2017; Accepted for publication: 22 September 2017; Published online: 5 October 2017
Randomized controlled trials (RCTs) are generally considered the gold standard for estimating causal effects of treatments [1]. If well-designed and executed, randomization ensures that both measured and unmeasured factors are comparable between treatment groups, meaning that any effects seen are likely to be due to the treatment and not other factors [2]. However, RCTs may not always be an ethical or practical option, and researchers are increasingly making use of routinely collected (observational) data in an attempt to answer questions that might be difficult to address using a trial design [3], for example to investigate potential benefits and harms of interventions that are already accepted in general practice, or in different populations, or under different conditions, to give results that are potentially more generalizable than those from RCTs [4]. Two key analytic considerations of these studies are: the methods used to account for differences in patient characteristics, usually in an effort to create groups similar to those that might have arisen after randomization, and the methods to handle missing data. In routine clinical practice, patient allocation to alternative treatments for the same illness may be conditioned on varying characteristics, and thus, any two or more treatment groups may be different. To examine differences in treatment outcome(s), there is therefore a need to make groups comparable or ‘enforce’ covariate balance prior to comparative analysis [5]. The ideal approach is to compare individuals with exactly similar values on observed covariates. However, this approach may be inefficient when dealing with a larger number of covariates as it may be difficult to find sufficient individuals with exactly similar covariate values in treatment groups [6]. An alternative that is increasingly being used in the last decade (across many disciplines) is the propensity score (PS) approach [7,8] developed by Rosenbaum and Rubin (1983) [9]. Here, the PS describes the probability of a patient's assignment into a treatment group given the observed covariates. Outcomes for patients with similar PSs (between treatment groups) are then comparable as it is assumed they have similar distribution of observed covariates. Estimation of PSs requires that all covariate data are fully observed. However in case of missing data, the patients with missing data are excluded from analysis particularly when using non-Bayesian models that are most commonly applied in practice. Three methods may be used in the face of missing data, these include: complete case analysis, multiple imputation (MI) and estimation of PS within various patterns of missing data [10]. These methods are briefly discussed.
Complete case analysis
This method excludes all patients if they have missing data in at least one of the covariates (or waves of follow-up in a cohort study) from the analysis leading to loss of power. This approach only results in unbiased estimates when it is demonstrable that missing data are unrelated to the study treatments or design. For instance, if a cancer patient is enrolled in a follow-up study and relocates to another city such that subsequent measurements may not be obtained – then such data may be understood to be missing completely at random (MCAR) and estimation of PS and outcome analysis excluding such patients may be valid.
Multiple imputation
MI involves filling in plausible values for missing data more than once, which accounts for uncertainty in filling in unknown values [11]. MI is commonly practiced under the assumption that data are missing at random (MAR) – and this means that the missing data may potentially be related to observed co-variables but not those that are unobserved [12]. For instance, in a follow-up study, if a patient's condition was consistently improving over previous visits and all of a sudden she/he drops out – then it might be plausible to assume the patient is doing better (based on previous information).
As MI generates multiple datasets, two approaches have been proposed to estimate PS – within and across these [13–15]. In the within approach, PSs (per participant) are estimated for each of the multiple imputed datasets. Then the appropriate PS method is used on each of these datasets to create balanced patient groups with treatment effects also estimated within each dataset. While, in the across approach, the estimated multiple PSs per patient are averaged across the imputed datasets – then used to estimate treatment effects in each imputed dataset. Both the within and across approaches result in multiple treatment effects which are pooled using Rubin rules [14] to account for within and between imputation variability.
Propensity score estimation by patterns of missing data
This method aims to retain all the patients in an analysis by grouping them based on observed covariates and using models to estimate PS in each group. If there are clearly defined groupings in the study related to the design – and some data may not be obtained due to this, then clearly such data are missing not at random (MNAR). And this may be a valid method under the MNAR assumption.
Other less intuitive methods have historically been used to address missing data and these include the use of the: previous value to replace missing data (last observation carried forward [LOCF]), substitution with population mean value (simple imputation) and missing indicator method – where those with missing data are assigned to a group. These methods do not account for the uncertainty involved in filling in plausible values [16], and almost always bias overall estimates particularly if they are not aligned with any missingness assumption.
The three missing data assumptions defined (MCAR, MAR and MNAR) are generally not directly testable but are instead justifiable through understanding and describing the process that generated the data.
Existing systematic reviews [7,8,17] have focused on the use and adequacy of reporting of PS methods with an aim of examining reproducibility of findings. The methodological aspects examined in these reviews included: variable selection, methods for estimating and using PS, checking of covariate balance, methods for analysing outcome(s) after PS estimation among others. However, none of the reviews examined how PS methods are used in the presence of missing data. Analysis may result in biased estimates if missing data are not correctly handled [18] even with proper use and reporting of PS methods. On the other hand, existing missing data reviews have focused on RCTs or observational studies that do not focus on comparative evaluations (for example see [19–21]).
Objective
Building on previous systematic reviews [7,8,17,21,22], we therefore systematically reviewed published observational comparative effectiveness studies primarily to assess the methods for handling missing data in estimation of PS and if the methods used are in line with the STROBE guidelines [23]. The STROBE guidelines make recommendations for how missing data should be reported in observational studies and in particular they require researchers to report on the proportion of missing data, reasons why data are missing and how missing data are addressed. These three elements are examined to determine adherence to STROBE guidelines.
Systematic review methods
Search term & literature search databases
We used the search terms (“observational stud* and propensity score*”) – and with this literature searches were carried out in Embase(OvidSP) [1974 to 30 June 2017] and Medline(OvidSP) [1946 to June 30, 2017]. The searches were further restricted to clinical articles published in the last 7.5 years (since January 2010). The restriction was motivated by the fact that the STROBE guidelines were first published in 2007 [23] and researchers would actively use them post 2007.
Inclusion & exclusion criteria
Primary research studies of observational comparative effectiveness studies (using actual patient data), published in English, were included. Studies only discussing methods in randomized studies, demonstrating methods using simulated data, meta-analyses and systematic reviews were therefore excluded, although bibliographies from secondary studies were hand searched for additional relevant primary observational studies. Conference abstracts were also excluded due to the limited information they provided. Further, studies on quasi experiments were also excluded since a researcher may sometimes have control over treatment assignment. All the studies excluded as described here are referred to as nonprimary.
Screening of papers & data extraction
Abstracts were selected for text review and final inclusion by one reviewer (L Malla). If any abstract did not offer sufficient information, then the full text was obtained (together with any available corresponding supplementary materials) and scanned through for the relevant aspects of method(s). One reviewer (L Malla) extracted data into structured forms (from the full text of the selected articles) which contained variables specific to: article characteristics; and missing data methods (see Table 1 for full description of the variables). A second reviewer (M Ogero) randomly selected 25% (n = 42) of the articles that met inclusion and exclusion criteria and did an independent data abstraction then reviewed methods, and the degree of agreement was examined using Kappa coefficient [24] and is reported. This comparison in agreement was examined for each of: proportion of missing data reported, reasons for missing data indicated and method used to address missing data. Where there was any disagreement, L Malla and M Ogero jointly reviewed the article and reached a consensus.
| # | Variable | Response options |
|---|---|---|
| Article characteristics | ||
| 1 | Year of publication | – |
| 2 | Title of publication | – |
| 3 | Number of treatment groups compared | (1) 2 (2) >2 |
| 4 | Setting (country/continent) | – |
| Missing data methods | ||
| 5 | Proportion of missing data reported | (1) Yes (2) No |
| 6 | Missing data method reported | (1) Yes (2) No |
| 7 | Missing data mechanism mentioned | (1) Yes (2) No |
| 8 | Reason for missing data given | (1) Yes (2) No |
| 9 | Specific missing data mechanism | (1) MCAR (2) MAR (3) MNAR (4) Not mentioned |
| 10 | Specific missing data method used | (1) Complete case (2) Pattern mixture (3) Multiple imputation (4) Not mentioned |
| 11 | Missing data sensitivity conducted | (1) Yes (2) No |
| 12 | Analysis compared between those with complete and incomplete data | (1) Yes (2) No |
| 13 | Variables included in MI explained (if MI used) | (1) Yes (2) No |
| 14 | Number of imputations specified (if MI used) | (1) Yes (2) No |
| 15 | Methods used to estimate propensity scores after MI | (1) Within (2) Across (3) Not mentioned |
| 16 | Software used for MI | (1) R (Hmisc, MICE, mi, etc.) (2) SAS (MI) (3) STATA (MI) (4) Other (5) Not mentioned |
The values in brackets indicate option numbers.
MAR: Missing at random; MCAR: Missing completely at random; MI: Multiple imputation; MNAR: Missing not at random.
Presentation of findings
Articles were stratified into four publication time periods; 2010–2011, 2012–2013, 2014–2015 and 2016–2017 to explore trends in practice. Frequencies and proportions were used to summarize findings. This review followed the various methodological aspects recommended in the PRISMA statement [25].
Results
Study characteristics
The process of identification and selection of articles is summarized in Figure 1 (adapted from the PRISMA flow diagram). We identified 2422 articles after 973 duplicates were removed, 2255 articles did not meet inclusion criteria leaving 167 articles for full text review.
71% (n = 118) of the eligible articles retrospectively analyzed routine datasets, most of which were derived from medical registry databases. The remaining 29% (n = 49) used prospective designs which were based in hospital settings. Most of the articles (90%; n = 151) conducted comparative analyses using two treatment groups while 10% (n = 16) reported analyses using more than two groups. Almost half of the articles (n = 81) were based on data from North America (USA and Canada) – while 29% (n = 49), 18% (n = 30), 4% (n = 6) and 1% (n = 1) were based on data from Europe, Asia, Australia and Africa, respectively (Table 2). See the extracted data and the corresponding references for reviewed articles presented as Supplementary Appendix 1 & 2.
| Study characteristics | n (%) |
|---|---|
| Design | |
| Retrospective | 118 (71%) |
| Prospective | 49 (29%) |
| Setting | |
| North America | 81 (49%) |
| Europe | 49 (29%) |
| Asia | 30 (18%) |
| Australia | 6 (4%) |
| Africa | 1 (1%) |
| Number of treatments compared | |
| 2 | 151 (90%) |
| >2 | 16 (10%) |
The Kappa estimates of agreement on the selected missing data methodological aspects independently identified by both L Malla and M Ogero ranged from 0.96 to 1 which indicated excellent agreements (see Supplementary Appendix 3 for Kappa calculations).
Reporting of missing data
Of the 167 articles, 37% (n = 62) provided information on the amount of missing data and only 12 provided reasons for missingness. Of the 12, three articles linked the reasons to the type of missing data mechanism: MAR (n = 1) and MCAR (n = 2). None of the remaining articles that provided information on amount of missing data (n = 59) commented on any assumed missingness mechanism.
Missing data methods used
Missing data were addressed in 51% (n = 86) of the articles. The remaining 81 did not mention how they dealt with missing data (Table 3). The most common approach used was complete case analysis (62%; n = 53) – which was implicitly determined if articles mentioned they excluded individuals due to missing data. Among the 81 articles that did not mention any use of missing data methods, 61 explicitly indicated to have used multivariable regression models to estimate PSs using standard software (R, SAS, SPSS and STATA). Therefore, we assumed that these articles must have used complete case analysis as this is the default method for handling missing data in these analytic software. Going by this, the number of articles that used complete case analysis would be 114. Some articles also used ad hoc methods that are likely to result in biased estimates like: imputation to most common category (n = 4); mean value imputation (n = 3); LOCF (n = 2); missing indicator method (n = 1); and truncation which was used in a cohort study (n = 1) where data were utilized only up to the earliest time when the first dropout was experienced, and all the follow-up data beyond this were excluded for everybody.
| Missing data methods used | Number of papers | ||||
|---|---|---|---|---|---|
| 2010–2011 | 2012–2013 | 2014–2015 | 2016–2017 | Total | |
| Methods not mentioned | 11 (52%) | 20 (56%) | 25 (45%) | 25 (45%) | 81 (49%) |
| Methods mentioned | 10 (48%) | 16 (44%) | 30 (55%) | 30 (55%) | 86 (51%) |
| Complete case | 10 (100%) | 11 (31%) | 16 (29%) | 16 (29%) | 53 (62%) |
| Multiple imputation | 0 (0%) | 2 (6%) | 5 (9%) | 9 (16%) | 16 (19%) |
| Pattern mixture | 0 (0%) | 1 (3%) | 1 (2%) | 1 (2%) | 3 (3%) |
| Imputation to most common category | 0 (0%) | 0 (0%) | 4 (7%) | 0 (0%) | 4 (5%) |
| Simple imputation | 0 (0%) | 0 (0%) | 2 (4%) | 1 (2%) | 3 (3%) |
| Imputation (type not specified) | 0 (0%) | 0 (0%) | 2 (4%) | 1 (2%) | 3 (3%) |
| LOCF | 0 (0%) | 1 (3%) | 0 (0%) | 1 (2%) | 2 (2%) |
| Truncation | 0 (0%) | 0 (0%) | 0 (0%) | 1 (2%) | 1 (1%) |
| Missing indicator | 0 (0%) | 1 (3%) | 0 (0%) | 0 (0%) | 1 (1%) |
Some of the percentages did not add up to 100% due to rounding off. The percentages for missing data methods are based on the number of articles that mentioned use of methods per reporting time period.
LOCF: Last observation carried forward.
19 articles (11%) used more appropriate methods like: estimation of PS by various patterns of missing data (n = 3); and MI, which was used in 16 of the articles among those that reported missing data methods. Among the articles that used MI; eight (50%) reported the number of imputed datasets – with the least being five and maximum 2000, and 12 (75%) explained the variables that were included in the imputation models. No article reported diagnostics on the plausibility of multiple imputed values. After conducting MI, only five articles (31%) reported on how PS and outcome analysis proceeded: four articles indicated to have estimated PS and outcome analysis per imputed dataset then pooled estimates of treatment effectiveness using Rubin rules; and one article averaged PS across the imputed datasets – then used this in the adjusted analysis for all the imputed datasets with effects also pooled using Rubin rules.
Across the four reporting time periods, the proportion of articles using complete case analysis was higher in the articles published in 2010–2013 compared with the use in articles published in 2014–2017. The proportion of articles using MI increased across the four reporting time periods, from none in 2010–2011 to 16% in 2016–2017.
Missing data sensitivity analysis
The only form of sensitivity analysis that was conducted in five articles was the comparison of treatment effectiveness estimates derived from analyses of multiple imputed datasets and those from complete cases – or comparison of estimates between completers and noncompleters in cohort studies. However, no thorough sensitivity analyses aiming to rule out the possibility of MNAR were reported.
Adherence to STROBE guidelines of reporting missing data
As presented above, adherence to different aspects of STROBE guidelines pertaining to reporting of missing data were as follows: indicating reasons for missing data (n = 12; 7%), reporting amount of missing data (n = 62; 37%), and indicating missing data method(s) used (n = 86; 51%). Overall, only eight (5%) articles adhered to all these three aspects.
Discussion
Among 167 studies, only 86 (51%) discussed missing data issue and among them only 16 (19%) used MI methods to account correctly for the missing data. However even in these studies, reporting was incomplete as only five (31%) described how results were generated across the imputed datasets. Approximately 68% of the articles based effect estimates on complete case analysis but in the majority of the cases, this was an implicit not explicit analysis strategy. This result is consistent with the findings of Karahalios (2012) [21] and Eekhout (2012) [22] who reviewed the use of missing data methods in epidemiologic studies (though not specific to the use of PS) and also found that most researchers used complete case analysis in practice. Complete case analysis may provide unbiased estimates only if data are MCAR. However, only one of the studies provided evidence for MCAR and thus appropriately estimated PS and treatment effectiveness on complete cases. In other cases, researchers used complete case analysis but offered no explanation for why data were missing nor the underlying data generation mechanism. This use of complete case analysis may result in elimination of substantial numbers of observations and the implicit assumption of MCAR can potentially give biased results. In situations where it is not known how data were generated (as could be the case when using registry datasets), it is plausible to assume that data are MAR – then use MI in estimating PSs. Thereafter sensitivity analysis should be used to help validate this assumption because the existence of the MNAR assumption may not be ruled out where MAR is thought to hold [26,27].
Imputation is aimed at maximizing the use of all available data. However, less principled methods like simple imputation, LOCF, imputation to most common category, truncation and missing indicator (as were used in some of the articles in this review) should be avoided [21].
This review has a number of strengths and limitations. It is the first study to systematically examine how missing data are handled with PS methods. As comparative effectiveness is often an implied concept rather than being explicitly stated, we did not restrict our search term to further include ‘comparative effectiveness’, and we were able to obtain a potentially more representative number of articles than if this was included as part of the search. However, we do acknowledge that some articles may have been excluded as searches were not conducted for the grey literature. Study selection was only carried out by one reviewer, however inclusion criteria were clearly defined and most studies were excluded as they were not clinical observational studies comparing treatment outcomes. Moreover, additional review of a random selection of included articles resulted in a very high level of agreement.
Conclusion
Researchers should pay attention to how missing data are methodologically addressed to avoid unknowingly reporting biased results. These would include: (a) identifying the most appropriate missing data mechanism (either MCAR or MAR or MNAR), and if unknown then researchers may assume data are MAR; (b) applying an appropriate missing data method based on the identified/assumed missing data mechanism and; (c) conducting sensitivity analysis in the MNAR framework if missing data mechanism was unknown. As also suggested by Karahalios (2012) [21], authors and editors should follow STROBE guidelines to increase the reliability of findings. That is, reasons for missing data should be indicated,amount of missing data and methods used to handle missing data should be reported.
Majority of articles do not report how missing data are addressed in propensity score estimation.
Among articles that report missing data methods, majority estimate propensity scores on complete cases.
The use of complete case analysis in propensity score estimation may result in biased findings if data are not missing completely at random.
As the underlying data generation mechanism may often be unknown at the time of analysis, researchers are encouraged to assume that data are missing at random and use imputation techniques (then estimate propensity scores on imputed datasets) followed by sensitivity analyses.
Acknowledgements
The authors would like to thank N Roberts, an information specialist with Oxford University, for her guidance in constructing the search strategies and overall use of the search engines.
Author contributions
L Malla did an initial draft of this manuscript with the support of R Perera-Salazar, E McFadden, M Ogero, K Stepniewska and M English. Thereafter, all authors edited subsequent versions and approved the final copy.
Supplementary data
To view the supplementary data that accompany this paper please visit the journal website at: www.futuremedicine.com/doi/full/10.2217/cer-2017-0071
Financial & competing interests disclosure
The authors are grateful for the funds from the Wellcome Trust (#097170) that support M English through a fellowship and additional funds from a Wellcome Trust core grant awarded to the KEMRI-Wellcome Trust Research Programme (#092654) that supported this work. L Malla is supported by a Nuffield Department of Medicine Prize DPhil Studentship and Clarendon Scholarship (Oxford University). The funders had no role in drafting or submitting this manuscript. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed. Also the authors declare they have no competing interests.
No writing assistance was utilized in the production of this manuscript.
Open access
This work is licensed under the Attribution-NonCommercial-NoDerivatives 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/
Supplementary Material
References
Papers of special note have been highlighted as: • of interest; •• of considerable interest
1.
Ye C, Beyene J, Browne G, Thabane L. Estimating treatment effects in randomized controlled trials with noncompliance: a simulation study. BMJ Open 4(6), e005362 (2014).
2.
Kausto J, Solovieva S, Virta LJ, Viikari-Juntura E. Partial sick leave associated with disability pension: propensity score approach in a register-based cohort study. BMJ Open 2(6), e001752 (2012).
3.
West SG, Duan N, Pequegnat W et al. Alternatives to the randomized controlled trial. Am. J. Public Health 98(8), 1359–1366 (2008).
4.
Berger ML, Mamdani M, Atkins D, Johnson ML. Good research practices for comparative effectiveness research: defining, reporting and interpreting nonrandomized studies of treatment effects using secondary data sources: the ISPOR Good Research Practices for Retrospective Database Analysis Task Force Report–Part I. Value Health 12(8), 1044–1052 (2009).
5.
Rosenbaum PR, Silber JH. Matching and thick description in an observational study of mortality after surgery. Biostatistics 2(2), 217–232 (2001).
6.
Stuart EA. Matching methods for causal inference: a review and a look forward. Stat Sci. 25(1), 1–21 (2010).
7.
Thoemmes FJ, Kim ES. A systematic review of propensity score methods in the social sciences. Multivariate Behav. Res. 46(1), 90–118 (2011).
• Provides a current description of how propensity score (PS) methodological aspects are used.
8.
Zakrison TL, Austin PC, Mccredie VA. A systematic review of propensity score methods in the acute care surgery literature: avoiding the pitfalls and proposing a set of reporting guidelines. Eur. J. Trauma Emerg. Surg. (2017) (Epub ahead of print).
• Provides a current description of how PS methodological aspects are used.
9.
Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. JSTOR 70, 41–55 (1983).
• Describes how PS approach was developed.
10.
Toh S, Garcia Rodriguez LA, Hernan MA. Analyzing partially missing confounder information in comparative effectiveness and safety research of therapeutics. Pharmacoepidemiol. Drug Saf. 21(Suppl. 2), 13–20 (2012).
11.
Lee KJ, Simpson JA. Introduction to multiple imputation for dealing with missing data. Respirol. 19(2), 162–167 (2014).
12.
Carpenter JR, Kenward MG, White IR. Sensitivity analysis after multiple imputation under missing at random: a weighting approach. Stat. Methods Med. Res. 16(3), 259–275 (2007).
13.
Mitra R, Reiter JP. A comparison of two methods of estimating propensity scores after multiple imputation. Stat. Methods Med. Res. 25(1), 188–204 (2016).
• Compares various methods of estimating PS after multiple imputation.
14.
Rubin DB, Schenker N. Multiple imputation in healthcare databases: an overview and some applications. Stat. Med. 10(4), 585–598 (1991).
15.
Mayer B, Puschner B. Propensity score adjustment of a treatment effect with missing data in psychiatric health services research. Epidemiol. Biostatistics Public Health 12(1), (2015) (Epub ahead of print).
16.
Nakai M, Ke W. Review of the methods for handling missing data in longitudinal data analysis. Int. J. Math 5(1), 1–13 (2011).
17.
Yao XI, Wang X, Speicher PJ et al. Reporting and guidelines in propensity score analysis: a systematic review of cancer and cancer surgical studies. J. Natl Cancer Inst. 109(8), (2017) (Epub ahead of print).
18.
Kang H. The prevention and handling of the missing data. Korean J. Anesthesiol. 64(5), 402–406 (2013).
19.
Fielding S, Maclennan G, Cook JA, Ramsay CR. A review of RCTs in four medical journals to assess the use of imputation to overcome missing data in quality of life outcomes. Trials 9, 51 (2008).
20.
Bell ML, Fiero M, Horton NJ, Hsu CH. Handling missing data in RCTs; a review of the top medical journals. BMC Med. Res. Methodol. 14, 118 (2014).
• Reviews how missing data are addressed, though not with respect to the use of PSs.
21.
Karahalios A, Baglietto L, Carlin JB, English DR, Simpson JA. A review of the reporting and handling of missing data in cohort studies with repeated assessment of exposure measures. BMC Med. Res. Methodol. 12, 96 (2012).
• Reviews how missing data are addressed, though not with respect to the use of PSs.
22.
Eekhout I, De Boer RM, Twisk JW, De Vet HC, Heymans MW. Missing data: a systematic review of how they are reported and handled. Epidemiology 23(5), 729–732 (2012).
• Reviews how missing data are addressed, though not with respect to the use of PSs.
23.
Vandenbroucke JP, Von Elm E, Altman DG et al. Strengthening the reporting of observational studies in epidemiology (STROBE): explanation and elaboration. Epidemiology 18(6), 805–835 (2007).
24.
Mchugh ML. Interrater reliability: the kappa statistic. Biochem. Med. (Zagreb) 22(3), 276–282 (2012).
25.
Moher D, Liberati A, Tetzlaff J, Altman DG, Group P. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. J. Clin. Epidemiol. 62(10), 1006–1012 (2009).
26.
Molenberghs G, Beunckens C, Sotto C. Every missing not at random model has a missingness at random counterpart with equal fit. J. R. Stat. Soc. 70(2), 371–388 (2008).
27.
Molenberghs G, Thijs H, Jansen I et al. Analyzing incomplete longitudinal clinical trial data. Biostatistics 5(3), 445–464 (2004).
Information & Authors
Information
Published In
Copyright
© 2017 Lucas Malla.
History
Published online: 5 October 2017
Keywords:
Topics
Authors
Metrics & Citations
Metrics
Article Usage
Article usage data only available from February 2023. Historical article usage data, showing the number of article downloads, is available upon request.
Citations
How to Cite
Handling missing data in propensity score estimation in comparative effectiveness evaluations: a systematic review. (2017) Journal of Comparative Effectiveness Research. DOI: 10.2217/cer-2017-0071
Export citation
Select the citation format you wish to export for this article or chapter.
Citing Literature
- Sunao Shimada, Masataka Taguri, Handing missing data using multiple imputation in hybrid control clinical trials with modified power prior, Journal of Biopharmaceutical Statistics, 10.1080/10543406.2026.2676020, (1-24), (2026).
- Jennifer L. Lee, Chris Billovits, Shih-Yin Chen, Robert E. Wickham, Bob Kocher, Connie E. Chen, Anita Lungu, Using Machine Learning to Match Clients and Therapy Providers: Evaluating Clinical Quality and Cost of Care, Value in Health, 10.1016/j.jval.2025.06.002, 28, 9, (1327-1334), (2025).
- Sevinc Puren Yucel Karakaya, Ilker Unal, Incorporation of missing indicator with multiple imputation in propensity score analysis with partially observed covariates: A simulation study, Statistical Methods in Medical Research, 10.1177/09622802251338365, 34, 7, (1293-1302), (2025).
- Anna Giolito, Corinne Levy, Emmanuelle Varon, Robert Cohen, Sidonie Hanna, Zein Assad, Léa Lenglart, Stephane Bechet, Stephane Bonacorsi, François Dubos, Elise Launay, Morgane Pelleter, Alexis Rybak, Francois Angoulvant, Michael Levy, Naïm Ouldali, Véronique ABADIE, Mohamed ABDANNE, Mohamed ABDELHADI, Said ABERRANE, Steffen ACKERMANN, Marie-Noëlle ADAM, Latif ADAMON, Ghassan ADI, Sylvia AKITANI, Ziad AL JAZAYRI, Ashraf AL JUNAIDI, Céline AL MAZINI, Catherine ALBA-SAUVIAT, Marie-Thérèse ALBERTINI, Abdelatif ALLIA, Chantal ALLOUCHE, Habib AMAMA, Marlene AMARA, Patrick AMIRAULT, Daniel AMSALLEM, Maud ANDRE, Isabelle ANDRIANTAHINA, Raphaël ANXIONNAT, Guillaume ARLET, Samar ARMOUCHE, Dominique ASTRUC, Brigitte AUBURTIN, Elena AUDRY DEGARDIN, Fanny AUTRET, Nabil AYADI, Naima AYAZ-BENSULTANA, Marie-Nadège BACHELIER, Julien BALEINE, Jadot BALITALIKE, Olivia BANDIN, Danielle BANEGAS, Anandadev BANERJEE, Marie BARET, Guilène BARNAUD, Sandrine BARON-JOLY, Alain BARRANS, Catherine BARREY, Katia BARSOTTI, Marie-Anne BARTHEZ, REMI BARUTEAU, Romain BASMACI, Joachim BASSIL, Paula BATTAGLINI, Soizic BEAUMIER, Nathalie BEDNAREK, Abdelmalek BELGAID, Elisabeth BELIVIER, Sophia BELLULO, Mohamed BEN SAID, Faouzi BENABDELMALEK, Mohamed BENBIH, Khalil BENCHEKROUN, Catherine BENEZECH, Mabrouk BENGRINA, Abdelkader BENMAHAMMED, Lahbib BENMONSOUR, Ismail BENMOULAI, Gregoire BENOIST, Catherine BENOIT, Philippe BENSAID, Zehaira BENSEDDIK, Patrick BERCHE, Guenaëlle BERETTA-SALAUN, Jean BERGOUNIOUX, Pauline BERNEAU, Dominique BERTEROTTIERE, Anne BERTROU, Hélène BIESSY, Marie BILGER, Nicolas BILLAUD, Patrick BILLION, André BINA, Philippe BINEAU, Valérie BIRAN, Camille BLACNKAERT, Philippe BLANC, Stéphane BLANC, Amandine BLASQUEZ, Elodie BLONDEL, Gilles BLONDIN, Nathalie BLOT, Pascal BOILEAU, Philippe BOIZE, Pascal BOLOT, Stéphane BONACORSI, Ruffin BONGO BOINA, Martine BONNIN, Patrice BOQUET, Emmanuelle BOSDURE, Claude BOSI, Cécile BOST-BRU, Nourredine BOUKEZIA, Samira BOUKHRIS, Stephane BOULARD, Djamila BOULFOUL, Hocine BOUMECID, Jacqueline BOUR, Malek BOURENNANE, Nadège BOURGEOIS-NICOLAOS, Hafidha BOUSSADIA, Noel BOUSSARD, Gerald BOUSSICAULT, Morgane BOUTRY, Emilie BOUVY, Sophie BOYER, Catherine BRANGER, Caroline BRECHET, Violaine BRESSON, Jacques BREUIL, Claire BRIENNON, Natalie BRIEU, Sarah BRISSET, Anne BROCARD, Emmi BROENEN, Jacques BROUARD, Anne-Laure BRUNA, Julie BUCHER, Benjamin BUENO, Caroline BUISSON-TOUATI, Marine BUTIN, Blandine CABARET, Lucile CADOT, Jocelyne CAILLON, Pierre CALLAMAND, Dominique CAMBON, Gilles CAMBONIE, Frédérique CANIS, Anne-Marie CANZI, undefined CAPDEVILLE, Ricardo CARBAJAL, Bernard CARBONNELLE, Joelle CARRE-CAVELLIER, Amélie CARRER, Christian CARRIERE, Guy CARROGER, Bernadette CARTIER-RIVIERE, Françoise CASCARIGNY, Paul CASHA, Pierre CASTELNAU, Christian CATTOEN, Vincent CATTOIR, Bernard CAURIER, Ariane CAVALIER, Clémence CAZAU, Agnès CECILLE, Gaelle CELTON, Aouatef CHAABANE, Brigitte CHABROL, Anne CHACÉ, Jamilé CHAHINE, Yves CHAIX, Martin CHALUMEAU, Arnaud CHALVON DEMERSAY, Jean-Marc CHAMOUILLI, Valérie CHAMPION, Pascal CHANTELAT, Julie CHANTREUIL, Chantal CHAPLAIN, Sylvie CHARACHON, Anne CHARBONNEAU, Hubert CHARDON, Sabine CHARPENTIER, Claire CHARRETON, Pamela CHELLUN, Claude CHENEL, Mireille CHERON, Emmanuel CHEURET, Bertrand CHEVALLIER, Jean CHEVREL, Laurent CHEVRET, Didier CHOGNOT, Francis CHOMIENNE, Anais CHOSIDOW, Delphine CHRISMENT, Catherine CIUPEK, Perrine CLAIR, Olivier CLARIS, Catherine CLAVEL, Pierre COCHAT, Delphine COCHE, Edeline COINDE, Jean-Claude COLOMBANI, Jean Charles COMBE, Anne-Marie CORDIER, Philippe CORMIER, Francoise CORNIAU, Guillaume COSTA, Yannick COSTA, Jane COTTIN, Pascal COUMENGES, René COURCOL, Laurence COURDAVAULT, Gery COUROUBLE, Henri COURTADE, Audrey COUTABLE, Irina CRAIU, Françoise CREPET, Edwige DA, Isabelle DAIRE-CHABANON, Fabienne DALMON, Claude DANAN, Nevena DANEKOVA, Marie-Pierre DANJEAN-DEGUIN, Anne Laure DANQUIGNY, Anne DAO-DUBREMETZ, Patrick DAOUD, Florence DAOUDI, Emile DARAI, Cécile DARNAUD, Jean Philippe DARRAS, Gilles DASSIEU, Stéphane DAUGER, Marie-Christine DE BARBENTANE, Christophe DE CHAMPS, Laurent DE DECKER, Henri DE MONTCLOS, Loïc DE PONTUAL, Dominique DEBRIEL, Anne DECOSTER, Vanessa DEGAS, Thierry DELACOUR, Marie DELAFAY, Catherine DELAMARE, Gilles DELAPORTE, Jean-Marie DELARBRE, Chantal DELAVELLE, Emmanuel DELBEKE, Sophie DELESALLE, Delphine DELIGNE, Marylin DELIMELE, Delphine DELORME, Daniele DELUCA, Céline DELVART, Marie-Claude DEMACHY, Marie-Joelle DEMARCQ, Nathalie DEMARQUE, Nadia DEMAYER, Gaelle DEMAY-LEGROS, Diane DEMONCHY, Delphine DESBOIS, Nina DESCHAMPS, Luc DESFRERE, Romain DESHAYES DE CAMBRONNE, Blandine DESSE, Rodrigue DESSEIN, Emmanuelle DESSIOUX, Julienne DESSON, Ghislaine DESVIGNE, E. DEVILLE, Aude DEWITTE, Tahar DHAOUI, Catherine DIDIER-WRIGHT, Katherine DIECKMANN, Catherine DOIT, Hélène DOLFI FIETTE, Philippe DOLHEM, Marie-Aliette DOMMERGUES, Elodie DORANGEON, Florence DOUCET-POPULAIRE, Franck DUBOIS, Béatrice DUBOURDIEU, Sarah DUCROCQ, Anwar DUDIN, Marie-José DUFOUR, Bruno DUMOULARD, Jean-Michel DUPRE, Marie-France DURAND, Arnaud DUTHILLY, Sarah DUTRON, Véronique DUVAL, Emmanuel EICHER, Laurence EITENSCHENCK, Mohamed EL HAMRI, Martine ELENA-DAUMAS, Zoubida ELHARRIF, Ossam ELIAS, Jean-Philippe EMOND, Sophie ENCHERY, Ralph EPAUD, Simon ESCOBA, Laurence ESTAPA, Vincent ESTEVE, Cyrielle ESTEVEZ, Jerôme ETIENNE, Annie EVERS, Dominique EVRARD, Françoise EVREUX, Didier EYER, Stéphanie EYSSETTE-GUERREAU, Christelle FABBRO, Frédéric FAIBIS, Anne FARGEOT ESPALIAT, Anne FARGES, Skander FARHAT, Valérie FAUL, Giovanni FAVARETTO, Marion FAVIER, Albert FAYE, Marc FELDMANN, Janine FEREY, Agnès FERRONI, Hélène FIETTE, Jean-François FILIPPI, Anne FILLERON, Jean-Louis FLIPO, Vincent FLURIN, Michaela FOCA, Laurence FOIX, Marie Pierre FOS, Catherine FRANCOIS-CHERVET, Ulrike FREY, Jean-Louis GAILLARD, Théophile GAILLOT, Vincent GAJDOS, Serge GALLET, Gildas GALLOU, Idris GANIVALA, Caroline GARANDEAU, Antoine GARBARG-CHENON, Yves GARCERA, Claudio GARCIA SANCHEZ, Fabien GARNIER, Eulalie GARRABE, Anne GASCHET, Jean GASCHIGNARD, Géraldine GASCOIN, Joël GAUDELUS, Valérie GAUDUCHON, Martine GAVIGNET, Françoise GEFFROY, Laurent GENIEZ, Emilie GEORGET, Sophie GERARD, Justine GERAUD, Ezat GHOSN, Vannina GIACOBBI MILET, Isabelle GILLES, Yves GILLET, Catherine GIORGI, Florent GIRARD, Juliette GIRARD, Adeline GIRAUDON, Nathan GIROUX, Isabelle GLATZ, Elodie GLEIZE, Sophie GOBET, Annie GOGUELIN, Celine GOISSEN, Alain GOUDEAU, Anne GOUGEON, Pierre GOUMY, François GOURAUD, Frédérique GOURIET, Alain GOUX, Aurélie GRAILLES, Houria GRAINE, Céline GRANCHER, Anna GRANDO, Christelle GRAS-LE-GUEN, Magali GRECO, Bernard GRESSIER, Isabelle GRIMAL, Emmanuel GRIMPREL, Geneviève GRISE, Richard GRUSS, François GUERIN, Sophie GUIBERT, Henriette GUICHARD, Tamazoust GUIDDIR, Pascal GUIET, Vincent GUIGONIS, Julia GUILBERT, Christine GUILLERMET FROMENTIN, Olivier GUILLUY, Corinne GUITTON, Andreas HAAS, Hervé HAAS, Alexandre HABIB, Abdelatif HACHANI, Guilda HACHEM, Sarah HADJADJ, Isabelle HAEGY-DOEHRING, Mahmad HAGE, Mohamed HAGE CHEHADE, Houria HALLAGE, Fazia HALLALEL, Muriel HALNA, Farida HAMDAD, Severine HAOUISEE, Samir HARCHAOUI, Rihad HATAHET, Fréderique HAY, Noureddine HEDJEM, Laure HEES, Annie HEIDT, Véronique HENTGEN, Marie-Christine HERAUD, Christian HERVE, Joelle HEURTE, Emmanuelle HEUSSE, Rachel HEYMAN, Céline HIVERT, Anne-cécile HOCHART, Cécile HOMBROUCK-ALET, Stéphanie HONORÉ, Cosmina HOREA, Nathalie HUBERT, Philippe HUBERT, Frédéric HUET, Nicole HUIN, Hélène HUVENNE, Nourredine IDRES, Patrick IKOUNGA, Tara INGRAO, Jacques IRAKOTOH, Jean Louis JACOB, Coralie JACQUET, Mohamad JALLOUL, Didier JAN, Anne-Christinne JAOUEN, Vincent JARLIER, Pierre-Henri JARREAU, Benoit JAULHAC, Etienne JAVOUHEY, Josette JEHAN, Cecile JENSEN, Eric JEZIORSKI, Mikael JOKIC, Marie-Laure JOLY-GUILLOU, Lydie JOLY-SANCHEZ, Nicolas JORAM, Claude JORION, Eric JULLIAN, Marie-Emmanuelle JUVIN, Nicolas KALACH, Samer KAYAL, Mohamed KHALED, Ali KHALFI, Slimane KHORSI, Francois KIEFFER, Marie-Dominique KITZIS, Rémi KOM, Isabelle KONE-PAUT, Tamas KOVACS, Stanislas KOZISEK, Johana KUCEROVA, Bernard KUNTZEL, Francois LABARTHE, Marc LABENNE, Marie-Jose LABORIE, Philippe LABRUNE, Delphine LACROIX, Abdelhamid LAFENDI, Evelyne LAGIER, Jeremy LAGIER, Marion LAGREE, Hakim LAHRACH, undefined LAIDJ, Norbert LAISNEY, Mustapha LAKHDARI, Marie LAMANT, Mélanie LAMARCQ, Christine LAMBERT, Sylvie LAMOUREUX, Danielle LANDRAGIN, Philippe LANOTTE, Yacine LAOUDI, Delphine LAPEYRE, Fabrice LAPEYRE, Claire LAPOSTOLLE, Vincent LAUGEL, Afaf LAULAN, Elise LAUNAY, Jean-Philippe LAVIGNE, Marc LE BIDEAU, Alain LE COUSTUMIER, Bertrand LEBOUCHER, Thierry LECINE, Sylvie LEDRU, Fabrice LEFEVRE, Michel LEGAGNEUR, Marie-Ange LEGALLE, Pierre-Louis LEGER, Antoine LEGROS, Philippe LEHOURS, Nathalie LELIEVRE, Jean LELIOUX, Philippe LELUAN, Nadine LEMAITRE, Carole LEMARIÉ, Chantal LEMBLE, Olivier LEMENAND, Michel LENEVEU, Sophie LEOTARD, Jm LEPAGE, Nathalie LERET, Marion LEROUGE-BAILHACHE, Pascal LEROUX, Fabrice LESAGE, Emmanuel LESCANNE, Claire LETELLIER, Stéphane LETEURTRE, Muriel LETREUST, Maeva LEUVREY, Marion LEVAST, Marc LEVY, Michael LEVY, Pierre-Yves LEVY, Anne LIENHARDT, Agnès LIGIOS, Laurence LONG, Mathie LORROT, Sylvie LOUF, Alain LOZNEWSKI, Pierre LUREAU, Zoha MAAKAROUN-VERMESSE, Pascal MACCHI, Fouad MADHI, Guy MAGER, Jean-François MAGNY, Laurence MALIGE, Olivier MAMMES, Alexis MANDELCWAJG, Aziza MANDJEE, Alain MANGEOL, Cécile MANIN, Thierry MANSIR, Céline MANTEAU, Valérie MARCOU, Christophe MARGUET, Sylvie MARIETTE, Mariana MARIN, Alain MARMONIER, Stéphane MARRET, Sandrine Anne MARTHA, Claude MARTIN, Laurence MARTINAT, Muriel MARTINEZ OZENDA, Sophie MARTY, Caroline MASSEROT, Laurent MATHIEU, Pierre MATHIEU, Zied MATOUSSI, Thierry MAUGARD, Damien MAURIN, Richard MAZATAUD, Mustapha MAZEGHRANE, Célestin M'BAMBA, Joelle MELLIER, Monique MELON, Cedric MENAGER, Anne-Claude MENGUY, Mohamed MENOUAR, Odile MENOUNI-FORAY, Etienne MERLIN, Elodie MERLOT, Fabienne MERMET JEANVOINE, Sylvain MERMOND, Pauline MESLIN, Patricia MEUNIER, Ellia MEZGUELDI, Maite MICAELO, Fabrice MICHEL, Sophie MIGNARD, Loïc MIGNOT, Izzat MIKAIL, Karen MILCENT, Mathieu MILH, Marie-José MILLERET-PROYART, Catherine MILLOU, Philippe MINODIER, Luc MONCEAU, Olivier MOQUET, Michel MOREIGNE, Cyril MORISOT, Sophie MORLE, Pierre MORNAND, Eric MOULENE, Pierre MOULIN, Laurence MOULY, Audrey MOWENDABEKA, Stéphanie MULLER, Valérie MURBACH, Jean NAKHLEH, Sylvie NATHANSON, Xavier NAUDOT, Caroline NAVARRO, Raluca NEMES, Dominique NERI SCHIAVINI, Simone NEROME, Catherine NEUWIRTH, Guy NGALULA, DZU Joel NGUYEN, Nawal NICOLA, Alain NINOT, Céline NOBILI-DUBARRY, Marie-Noëlle NOULARD, Hatem OSMAN, Zafer OSMAN, Houria OULD HOCINE, Naim OULDALI, Abdelaziz OULEBSIR, Olivier OULES, Antoine OUZIEL, Anne-Sophie PAGES, Marie-Christine PAGES, Corinne PAGET, Xavier PALETTE, Marie PANCHER, Letitia PANTALONE, Jean-christophe PAON, Fabienne PATEYRON, Pierre PATOZ, Gérard PAUL, Jean-Gabriel PAUL, Emilie PAUQUET, Chantal PEIGNE, Hélène PEJOAN, Béatrice PELLEGRINO, Isabelle PELLOUX, Antoni PENAUD, Delphine PENEL, Bastien PERE, Noémie PEREZ, Coline PERRIER, Delphine PESENTI, Nelly PETITBOULANGER, Florence PHAN, Isabelle PHARAON, Marie-Hélène PIERRE, Denys PIERREJEAN, Christine PIETREMENT, Patrick PINA, Béatrice PINDI, Elisabeth PINLOU, Didier PINQUIER, Marie-Frédérique PINTURIER, Franck PLAISANT, Deborah PLANCHENAULT, Claire PLASSART, Marie-Caroline PLOTON, Emmanuel PLOUVIER, Isabelle POGGIOLI, Isabelle POILANE, Charlotte PONS, Thi Ngoc PORCHERET HUONG, Charlotte PORDES, Guillaume POUESSEL, Claire POYARD, Laurent PRADEAUX, Francoise PRERE, Laurence PRIQUELER, Muriel PRUDENT, Christèle QUEINNEC, Cécile RABER, Jeanne RAGOT, Julie RAIGNOUX, Aina RAOBISON, Didier RAOULT, Jean-Pierre RAULT, Nadia RAVANEL, Josette RAYMOND, Marie-Françoise RAYNAUD, Philippe REBAUD, Vialet RENAUD, undefined RENNES, Karine RETORNAZ, Agnes REY, Florence RICHARDIN, Christian RICHELME, Yves RIMET, Didier RIMPICI, Brigitte RIVIERE, Blandine ROBERT, Christine ROCHE, Christophe ROLLAND, Olivier ROMAIN, Bernard ROMEO, Barbara ROS, Denis ROUBIN, Veronica ROUDAUT, Laurent ROUDIERE, Sébastien ROUGET, Catherine ROUGIER, Sylvie ROULLAUD, Nicolas ROULLET RENOLEAU, Florence ROUSSEAU, Patricia ROUSSELLIER, Anne Laure ROUX, Danielle ROYBET, Jean Christophe ROZE, Jonathan ROZENTAL, Ahmed SADIK, Marc SAF, Marie-Hélène SAID-MENTHON, Samir SAKR, Sophie SALOMEZ, Charles SALOMON, Richard SANCHEZ, Jean SARLANGUE, Laurent SARTHOU, Jacques SARTRE, Simone SAUMUREAU, Isabelle SAVOY, Yves SCAT, Jean-Louis SCHAEFER, Fanny SCHNEIDER, Jean-Marc SCHNEIDER, Maxime SCHVARTZ, Cyril SCHWEITZER, Olivier SEBAG, Helene SEE, Odile SEVIN, Elsa SEYS, Annie SFEZ, Cédric SHAWALI, Ludovic SIMON, Catherine SIMONIN, Boualem SIOUALA, Valérie SIVADON-TARDY, Salim SMATI, Aurelie SMETS, André SOMMABERE, Bertrand SOTO, Nicolas SOUSTELLE, Jean STEFANIUK, Robin STEPHAN, Alina SURDU, Anne SWEERTVAEGHER, Marie SZULC, Cedric TAHIRI, Soad Linda TAHRAOUI, Muriel TAMINE, Manon TAUZIN, Jean-Pierre TCHANGA, Mustapha TERKI, Carmell TETE, Anthony TEXIER, Patrice THEROND, Michel THIBAULT, Marion THIEBLEMONT, Gérard THIRIEZ, Jean THORE, Maxime THOUVENIN, Catherine TIRY, Elodie TISSERAND, Pierre TISSIERES, Anne TIXIER, Julie TOUBIANA, Pauline TOUROULT-JUPIN, Anne-Lise TOYER, Tu Anh TRAN, Anne Sophie TRENTESAUX, Frédéric TRONC, Jeanne TRUONG, Nicoletta URSULESCU, Anne VACHEE, Christine VAILLANT, Aurélie VALADE, Patrick VALAYER, Mickaël VALENSI, Eric VALLEE, Christelle VALLET, Philippe VAN DE PERRE, François VANDENESCH, Noémie VANEL, Jacques VAUCEL, Agnès VEAUVY-JUVEN, Christine VENOT, Matthieu VERDAN, Charlotte VERDET, Michel VERGNAUD, Christiane VERGNE, Véronique VERNET-GARNIER, Christine VERVEL, Céline VIALA, Véronique VIALETTE, Philippe VIC, Olivier VIGNAUD, Jérémie VIOLETTE, Catherine VITTECOQ, Hoang VU THIEN, Richard WASELS, Arnaud WIEDEMANN-FODE, Isabelle WORCEL, Mohamed Amine YANGUI, William Azzam YOUSSEF WILLIAM, Hubert YTHIER, Ali ZARZOUR, Arianne ZELINSKY-GURUNG, Neila ZEMOURI, Ferielle ZENKHRI, Brigitte ZIMMERMANN, Justine ZINI, Adjunctive dexamethasone and 30-day all-cause death after hospital admission in paediatric pneumococcal meningitis: a propensity score analysis, The Lancet Child & Adolescent Health, 10.1016/S2352-4642(25)00029-X, 9, 4, (255-261), (2025).
- Hwa Kyung Byun, Hyung Seok Park, Seo Hee Choi, Seho Park, Jee Suk Chang, Ik Jae Lee, Yong Bae Kim, Excluding Upper Axillary Level 1 in Regional Nodal Irradiation Does Not Increase Axillary Recurrence Risk in Patients With Breast Cancer, Practical Radiation Oncology, 10.1016/j.prro.2024.08.010, 15, 2, (144-154), (2025).
- Xiao Liu, Propensity Score Weighting with Missing Data on Covariates and Clustered Data Structure, Multivariate Behavioral Research, 10.1080/00273171.2024.2307529, 59, 3, (411-433), (2024).
- Zachary K. Collier, Kamal Chawla, Olushola Soyoye, Optimizing Imputation for Educational Data: Exploring Training Partition and Missing Data Ratios, The Journal of Experimental Education, 10.1080/00220973.2023.2287447, 93, 3, (607-627), (2024).
- Chong Shen, Minyue Pei, Xiaoxiao Wang, Yiming Zhao, Luning Wang, Jiping Tan, Ke Deng, Nan Li, Robust estimation of dementia prevalence from two-phase surveys with non-responders via propensity score stratification, BMC Medical Research Methodology, 10.1186/s12874-023-01954-0, 23, 1, (2023).
- Daniel T. Vader, Ronac Mamtani, Yun Li, Sandra D. Griffith, Gregory S. Calip, Rebecca A. Hubbard, Inverse Probability of Treatment Weighting and Confounder Missingness in Electronic Health Record-based Analyses: A Comparison of Approaches Using Plasmode Simulation, Epidemiology, 10.1097/EDE.0000000000001618, 34, 4, (520-530), (2023).
- Shu Yang, Peipei Du, Xixi Feng, Daihai He, Yaolong Chen, Linda L. D. Zhong, Xiaodong Yan, Jiawei Luo, Propensity score analysis with missing data using a multi-task neural network, BMC Medical Research Methodology, 10.1186/s12874-023-01847-2, 23, 1, (2023).