Implementation of a Smartphone application in medical education: a randomised trial (iSTART)

To describe the results of different statistical ways of addressing radiographic outcome affected by missing data--multiple imputation technique, inverse probability weights and complete case analysis--using data from an observational study. A random sample of 96 RA patients was selected for a follow-up study in which radiographs of hands and feet were scored. Radiographic progression was tested by comparing the change in the total Sharp-van der Heijde radiographic score (TSS) and the joint erosion score (JES) from baseline to the end of the second year of follow-up. MI technique, inverse probability weights in weighted estimating equation (WEE) and CC analysis were used to fit a negative binomial regression. Major predictors of radiographic progression were JES and joint space narrowing (JSN) at baseline, together with baseline disease activity measured by DAS28 for TSS and MTX use for JES. Results from CC analysis show larger coefficients and s.e.s compared with MI and weighted techniques. The results from the WEE model were quite in line with those of MI. If it seems plausible that CC or MI analysis may be valid, then MI should be preferred because of its greater efficiency. CC analysis resulted in inefficient estimates or, translated into non-statistical terminology, could guide us into inaccurate results and unwise conclusions. The methods discussed here will contribute to the use of alternative approaches for tackling missing data in observational studies.

Background: When missing data are present in clinical outcomes studies, complete-case analysis (CCA) is often performed, whereby patients with missing data are excluded. While simple, CCA analysis may impart selection bias and reduce statistical power, leading to erroneous statistical results in some cases. However, there exist more rigorous statistical approaches, such as single and multiple imputation, which approximate the associations that would have been present in a full dataset and preserve the study's power. The purpose of this study is to evaluate how statistical results differ when performed after CCA analysis versus imputation methods. Methods: This simulation study analyzed a sample dataset consisting of 2204 shoulders, with complete datapoints from a larger multicenter total shoulder arthroplasty database. From the sampled dataset of demographics, surgical characteristics, and clinical outcomes, we created five test datasets, ranging from 100 to 2000 shoulders, and simulated 10-50% missingness in the postoperative American Shoulder and Elbow Surgeons (ASES) score and range of motion in four planes in missing completely at random (MCAR), missing at random (MAR), and not missing at random (NMAR) patterns. Missingness in outcomes was remedied using CCA, three single imputation techniques, and two multiple imputation techniques. The imputation performance was evaluated relative to the native complete dataset using the root mean squared error (RMSE) and the mean absolute percentage error (MAPE). We also compared the mean and standard deviation (SD) of the postoperative ASES score and the results of multivariable linear and logistic regression to understand the effects of imputation on the study results. Results: The average overall RMSE and MAPE were similar for MCAR (22.6 and 27.2%) and MAR (19.2 and 17.7%) missingness patterns, but were substantially poorer for NMAR (37.5 and 79.2%); the sample size and the percentage of data missingness minimally affected RMSE and MAPE. Aggregated mean postoperative ASES scores were within 5% of the true value when missing data were remedied with CCA, and all candidate imputation methods for nearly all ranges of sample size and data missingness when data were MCAR or MAR, but not when data were NMAR. When data were MAR, CCA resulted in overestimates of the SD. When data were MCAR or MAR, the accuracy of the regression estimate (β or OR) and its corresponding 95% CI varied substantially based on the sample size and proportion of missing data for multivariable linear regression, but not logistic regression. When data were MAR, the width of the 95% CI was up to 300% larger when CCA was used, whereas most imputation methods maintained the width of the 95% CI within 50% of the true value. Single imputation with k-nearest neighbor (kNN) method and multiple imputation with predictive mean matching (MICE-PMM) best-reproduced point estimates and intervariable relationships resembling the native dataset. Availability of correlated outcome scores improved the RMSE, MAPE, accuracy of the mean postoperative ASES score, and multivariable linear regression model estimates. Conclusions: Complete-case analysis can introduce selection bias when data are MAR, and it results in loss of statistical power, resulting in loss of precision (i.e., expansion of the 95% CI) and predisposition to false-negative findings. Our data demonstrate that imputation can reliably reproduce missing clinical data and generate accurate population estimates that closely resemble results derived from native primary shoulder arthroplasty datasets (i.e., prior to simulated data missingness). Further study of the use of imputation in clinical database research is critical, as the use of CCA may lead to different conclusions in comparison to more rigorous imputation approaches.

Missing data such as appropriateness ratings in clinical research are a common problem and this often yields a biased result. This paper aims to introduce the multiple imputation method to handle missing data in clinical research and to suggest that the multiple imputation technique can give more accurate estimates than those of a complete-case analysis. The idea of multiple imputation is that each missing value is replaced with more than one plausible value. The appropriateness method was developed as a pragmatic solution to problem of trying to assess "appropriate" surgical and medical procedures for patients. Cataract surgery was selected as one of four procedures that were evaluated as a part of the Clinical Appropriateness Initiative. We created mild to high missing rates of 10%, 30% and 50% and compared the performance of logistic regression in cataract surgery. We treated the coefficients in the original data as true parameters and compared them with the other results. In the mild missing rate (10%), the deviation from the true coefficients was quite small and ignorable. After removing the missing data, the complete-case analysis did not reveal any serious bias. However, as the missing rate increased, the bias was not ignorable and it distorted the result. This simulation study suggests that a multiple imputation technique can give more accurate estimates than those of a complete-case analysis, especially for moderate to high missing rates (30 - 50%). In addition, the multiple imputation technique yields better accuracy than a single imputation technique. Therefore, multiple imputation is useful and efficient for a situation in clinical research where there is large amounts of missing data.

BackgroundThere is no consensus on the most appropriate approach to handle missing covariate data within prognostic modelling studies. Therefore a simulation study was performed to assess the effects of different missing data techniques on the performance of a prognostic model.MethodsDatasets were generated to resemble the skewed distributions seen in a motivating breast cancer example. Multivariate missing data were imposed on four covariates using four different mechanisms; missing completely at random (MCAR), missing at random (MAR), missing not at random (MNAR) and a combination of all three mechanisms. Five amounts of incomplete cases from 5% to 75% were considered. Complete case analysis (CC), single imputation (SI) and five multiple imputation (MI) techniques available within the R statistical software were investigated: a) data augmentation (DA) approach assuming a multivariate normal distribution, b) DA assuming a general location model, c) regression switching imputation, d) regression switching with predictive mean matching (MICE-PMM) and e) flexible additive imputation models. A Cox proportional hazards model was fitted and appropriate estimates for the regression coefficients and model performance measures were obtained.ResultsPerforming a CC analysis produced unbiased regression estimates, but inflated standard errors, which affected the significance of the covariates in the model with 25% or more missingness. Using SI, underestimated the variability; resulting in poor coverage even with 10% missingness. Of the MI approaches, applying MICE-PMM produced, in general, the least biased estimates and better coverage for the incomplete covariates and better model performance for all mechanisms. However, this MI approach still produced biased regression coefficient estimates for the incomplete skewed continuous covariates when 50% or more cases had missing data imposed with a MCAR, MAR or combined mechanism. When the missingness depended on the incomplete covariates, i.e. MNAR, estimates were biased with more than 10% incomplete cases for all MI approaches.ConclusionThe results from this simulation study suggest that performing MICE-PMM may be the preferred MI approach provided that less than 50% of the cases have missing data and the missing data are not MNAR.

Missing values in databases is a common issue and almost inevitable, however, how works deal with it are rarely mentioned in most publications. Multiple imputation (MI) is an efficient method for statistical estimates of missing values from incomplete data. The objective of this study was to evaluate the efficiency of the MI using the MICE (Multivariate Imputation by Chained Equations) algorithm to fill in missing data in a database of soil physico-hydrical properties, and to show that it is more feasible to perform the imputation than the complete case analysis (CCA). Preliminary analysis of the database was performed to check the suitability of the proposed algorithm. Imputation of the missing data of each variable was adjusted using linear regression models. The variables with missing data comprise the model as the dependent variable and the other variables, which were correlated with the same, enter as covariates. The analysis was performed by comparing the values of the estimates, their standard errors and 95% confidence intervals. It was concluded that MICE presented better performance than CCA, since, although the statistical comparison of the two methods was similar, multiple imputation maintains the size of the database and preserves the general distribution. MI is a very prominent method to handle missing data. With this study, we aim to help more soil researchers to get started with implementing MI techniques instead of inferior approaches in order to improve statistical analysis accuracy. Our study confirmed that multiple imputation is applicable to missing data in soil properties database.

Under the intention-to-treat principle, all randomized subjects should be analyzed according to their randomly assigned treatment, regardless of treatment actually received or protocol compliance. Adherence to this principle requires that even subjects with missing outcome data be included in the analysis; in fact, the exclusion of such subjects can have important implications on power and bias. Statistical methods for dealing with missing data exist, but many questions remain unclear. Much statistical research has been devoted to the development and assessment of various methods for handling missing data.1 The choice of appropriate methodology requires assumptions on the mechanism underlying the missing data. All of these decisions should be made a priori, preferably before the trial starts but certainly before unblinding the trial. Related conversations between clinical investigators and the study statistician during the design phase often focus on more practical questions. Is there some threshold for the missing data rate below which the trial’s conclusions are unlikely to be affected? Under what circumstances can the missing data be excluded from the analysis without biasing estimation, or is imputation always the preferred approach? In this article, we discuss implications of missing outcome data from a practical standpoint. We describe potential reasons for missing data and suggest strategies to minimize its occurrence. We also present common imputation approaches and emphasize that because none of these approaches are universally preferred, the best analytic plan includes a series of sensitivity analyses. In any longitudinal trial where subjects are followed over some extensive period of time, lengthy follow-up makes missing data somewhat unavoidable. In stroke clinical trials, the primary outcome assessment often occurs at 90 days although there is evidence to suggest that additional follow-up may be beneficial. Subjects may expire, or withdraw informed consent, before primary outcome ascertainment. Subjects may become lost to the …

Fossil-based estimates of diversity and evolutionary dynamics mainly rely on the study of morphological variation. Unfortunately, organism remains are often altered by post-mortem taphonomic processes such as weathering or distortion. Such a loss of information often prevents quantitative multivariate description and statistically-controlled comparisons of extinct species based on morphometric data. A common way to deal with missing data involves imputation methods that directly fill the missing cases with model estimates. Over the last years, several empirically-determined thresholds for the maximum acceptable proportion of missing values have been proposed in the literature, whereas other studies showed that this limit actually depends on various properties of the study data set and of the selected imputation method, and is by no way generalizable. We evaluate the relative performances of seven multiple imputation (MI) techniques through a simulation-based analysis under three distinct patterns of missing data distribution. Overall, Fully Conditional Specification and Expectation-Maximization algorithms provide the best compromises between imputation accuracy and coverage probability. MI techniques appear remarkably robust to the violation of basic assumptions such as the occurrence of taxonomically or anatomically biased patterns of missing data distribution, making differences in simulation results between the three patterns of missing data distribution much smaller than differences between the individual MI techniques. Based on these results, rather than proposing a new (set of) threshold value(s), we develop an approach combining the use of MIs with procrustean superimposition of principal component analysis results, in order to directly visualize the effect of individual missing data imputation on an ordinated space. We provide an R function for users to implement the proposed procedure.

Background:With the increase in the use of secondary data in epidemiological studies, the inquiry of how to manage missing data has become more relevant. Our study applied imputation techniques on traumatic spinal cord injuries data; a medical problem where data is generally sporadic. Traumatic spinal cord injuries due to blunt force cause widespread physiological impairments, medical and non-medical problems. The effects of spinal cord injuries are a burden not only to the victims but to their families and to the entire health system of a country. This study also evaluated the causes of traumatic spinal cord injuries in patients admitted to the University Teaching Hospital and factors associated with clinical complications in these patients.Methods:The study used data from medical records of patients who were admitted to the University Teaching Hospital in Lusaka, Zambia. Patients presenting with traumatic spinal cord injuries between 1stJanuary 2013 and 31stDecember 2017 were part of the study. The data was first analysed using complete case analysis, then multiple imputation techniques were applied, to account for the missing data. Thereafter, both descriptive and inferential analyses were performed on the imputed data.Results:During the study period of interest, a total of 176 patients were identified as having suffered from spinal cord injuries. Road traffic accidents accounted for 56% (101) of the injuries. Clinical complications suffered by these patients included paralysis, death, bowel and bladder dysfunction and pressure sores among other things. Eighty-eight (50%) patients had paralysis. Patients with cervical spine injuries compared to patients with thoracic spine injuries had 87% reduced odds of suffering from clinical complications (OR=0.13, 95% CI{0.08, 0.22}p<.0001). Being paraplegic at discharge increased the odds of developing a clinical complication by 8.1 times (OR=8.01, 95% CI{2.74, 23.99}, p<.001). Under-going an operation increased the odds of having a clinical complication (OR=3.71, 95% CI{=1.99, 6.88}, p<.0001). A patient who presented with Frankel Grade C or E had a 96% reduction in the odds of having a clinical complication (OR=.04, 95% CI{0.02, 0.09} and {0.02, 0.12} respectively, p<.0001) compared to a patient who presented with Frankel Grade A.Conclusion:A comparison of estimates obtained from complete case analysis and from multiple imputations revealed that when there are a lot of missing values, estimates obtained from complete case analysis are unreliable and lack power. Efforts should be made to use ideas to deal with missing values such as multiple imputation techniques.The most common cause of traumatic spinal cord injuries was road traffic accidents. Findings suggest that paralysis had the greatest negative effect on clinical complications. When the category of Frankel Grade increased from A-E, the less likely a patient was likely to succumb to clinical complications. No evidence of an association was found between age, sex and developing a clinical complication.

Since the work of Little and Rubin (1987) not substantial advances in the analysisof explanatory regression models for incomplete data with missing not at randomhave been achieved, mainly due to the difficulty of verifying the randomness ofthe unknown data. In practice, the analysis of nonrandom missing data is donewith techniques designed for datasets with random or completely random missingdata, as complete case analysis, mean imputation, regression imputation, maximumlikelihood or multiple imputation. However, the data conditions required to minimizethe bias derived from an incorrect analysis have not been fully determined. In thepresent work, several Monte Carlo simulations have been carried out to establishthe best strategy of analysis for random missing data applicable in datasets withnonrandom missing data. The factors involved in simulations are sample size,percentage of missing data, predictive power of the imputation model and existenceof interaction between predictors. The results show that the smallest bias is obtainedwith maximum likelihood and multiple imputation techniques, although with lowpercentages of missing data, absence of interaction and high predictive power ofthe imputation model (frequent data structures in research on child and adolescentpsychopathology) acceptable results are obtained with the simplest regression imputation.

BackgroundRandomised Controlled Trials (RCTs) are the gold standard for evaluating treatment effects. However, missing outcomes can threaten the validity of the results. Missing data pose a unique challenge in time-to-event analyses, where the event time may be censored rather than completely missing. Proper handling of missing event times is crucial to ensure unbiased and reliable conclusions in RCTs. This scoping review examines how missing outcomes in time-to-event studies have been addressed in high-impact medical journals and evaluates the implementation and reporting of multiple imputation (MI) techniques in RCTs.MethodThis scoping review assessed methods for handling missing time-to-event outcomes in RCTs published between 2019 and 2024 in three high-impact medical journals: The New England Journal of Medicine, The Lancet, and The Journal of the American Medical Association. Studies were reviewed to identify whether missing outcome data were present and, if so, which methods were used to handle them. Studies that applied MI were examined in detail to assess how the MI approach was implemented and reported. The review also explored theoretical approaches for imputing censored event times.ResultsA total of 834 articles were identified through a PubMed search. After screening, 383 RCTs underwent full-text review. Of these, 354 (92.4%) had no or < 10% missing outcomes without imputation. The remaining 29 studies (7.6%) addressed missing data using statistical approaches: 12 applied MI, 10 used complete case analysis, 6 conducted best-/worst-case sensitivity analyses, and 1 used a propensity score-based method. MI approaches varied, with some studies lacking detailed reporting.ConclusionIn RCTs with survival outcomes, properly handling missing event times is essential. This scoping review reveals that, despite the availability of statistical methods, the treatment of missing time-to-event outcomes remains underutilised and often poorly documented. While many studies reported non-administrative censoring, limited information was provided on whether such censoring was informative or non-informative. Additionally, the reporting of MI techniques is frequently insufficient. These findings highlight a critical gap in the handling and reporting of missing outcomes in survival analysis. Strengthening these practices will enhance the reliability and reproducibility of survival analyses in RCTs.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12874-025-02676-1.

The complete capture of all values for each variable of interest in pharmacy research studies remains aspirational. The absence of these possibly influential values is a common problem for pharmacist investigators. Failure to account for missing data may translate to biased study findings and conclusions. Our goal in this analysis was to apply validated statistical methods for missing data to a previously analyzed data set and compare results when missing data methods were implemented versus standard analytics that ignore missing data effects. Using data from a retrospective cohort study, the statistical method of multiple imputation was used to provide regression-based estimates of the missing values to improve available data usable for study outcomes measurement. These findings were then contrasted with a complete-case analysis that restricted estimation to subjects in the cohort that had no missing values. Odds ratios were compared to assess differences in findings of the analyses. A nonadjusted regression analysis ("crude analysis") was also performed as a reference for potential bias. Veterans Integrated Systems Network that includes VA facilities in the Southern California and Nevada regions. New statin users between November 30, 2006, and December 2, 2007, with a diagnosis of dyslipidemia. We compared the odds ratios (ORs) and 95% confidence intervals (CIs) for the crude, complete-case, and multiple imputation analyses for the end points of a 25% or greater reduction in atherogenic lipids. Data were missing for 21.5% of identified patients (1665 subjects of 7739). Regression model results were similar for the crude, complete-case, and multiple imputation analyses with overlap of 95% confidence limits at each end point. The crude, complete-case, and multiple imputation ORs (95% CIs) for a 25% or greater reduction in low-density lipoprotein cholesterol were 3.5 (95% CI 3.1-3.9), 4.3 (95% CI 3.8-4.9), and 4.1 (95% CI 3.7-4.6), respectively. The crude, complete-case, and multiple imputation ORs (95% CIs) for a 25% or greater reduction in non-high-density lipoprotein cholesterol were 3.5 (95% CI 3.1-3.9), 4.5 (95% CI 4.0-5.2), and 4.4 (95% CI 3.9-4.9), respectively. The crude, complete-case, and multiple imputation ORs (95% CIs) for 25% or greater reduction in TGs were 3.1 (95% CI 2.8-3.6), 4.0 (95% CI 3.5-4.6), and 4.1 (95% CI 3.6-4.6), respectively. The use of the multiple imputation method to account for missing data did not alter conclusions based on a complete-case analysis. Given the frequency of missing data in research using electronic health records and pharmacy claims data, multiple imputation may play an important role in the validation of study findings.

ObjectiveTo compare the effects of accounting for different missing data mechanisms in an investigation of the role of lifecourse socio-economic position (SEP) on later-life verbal cognitive ability.DesignTwo UK prospective cohort...

Students with high scores (top third) on the essay portion of an Advanced Placement examination and low scores (bottom third) on the multiple-choice portion of the examination were compared with students with the opposite pattern (top third on the multiple-choice questions and bottom third on the essay questions). Across examinations in different subject areas (history, English, and biology), students who were relatively strong in the essay format and weak in the multiple-choice format were as successful in their college courses as students with the opposite pattern, especially in those courses where grades are typically not determined by multiple-choice tests. Although differential essay and multiple-choice test performance was not related to course grades, it was related to performance on other tests. Students in the high multiple-choice/low essay group performed much better than the other group on other multiple-choice tests, especially the verbal section of the SAT. In relation to their performance on multiple-choice tests, students in the high essay/low multiple-choice group performed well on other essay tests.

BackgroundResearch using data from large population-based datasets is often hindered by the presence of non-trivial proportions of missing data. Numerous approaches for handling missing data are available, each of which...

<h3>Background</h3> Research using data from large population-based datasets is often hindered by the presence of non-trivial proportions of missing data. Numerous approaches for handling missing data are available, each of...