Teste de Progresso: ausência de alunos de Medicina é sugestiva de mau desempenho acadêmico / Progress Test: medical student absenteeism is suggestive of poor academic performance

Resumo: Introdução: Não se sabe se a ausência de estudantes de Medicina ao Teste de Progresso (TP) se dá de forma aleatória ou por alguma característica sistemática deles, o que poderia influenciar a representatividade dos resultados obtidos pelos participantes. Objetivo: Este estudo teve como objetivos comparar os índices de desempenho acadêmico, no curso de Medicina da UFSC, dos alunos presentes e ausentes ao TP em 2019; propor uma maneira de estimar, a partir desses índices, quais seriam as notas dos faltantes se tivessem participado do TP; e identificar fatores associados à ausência ao TP. Método: Foram comparadas as médias dos índices de desempenho acadêmico, globais e nas diferentes fases (semestres) dos grupos de alunos presentes e ausentes ao TP, utilizando teste t de Student para amostras independentes. Por meio de uma técnica de regressão linear, foram imputadas as prováveis notas no TP ao grupo de alunos ausentes. Resultado: As médias globais dos três indicadores acadêmicos foram significativamente menores nos alunos ausentes ao TP (p variando de < 0,03 a < 0,0001); em dez das 11 fases (semestres) analisadas do curso, os indicadores acadêmicos dos faltosos foram piores do que dos presentes. A imputação de notas no TP aos ausentes permitiu verificar que existe correlação (R = 0,62) entre a porcentagem destes e a diferença de notas entre os grupos que realizaram e os que faltaram ao TP. Entre os alunos do gênero masculino, 25,8% não fizeram o TP, enquanto no gênero feminino foram 16,6% (diferença com p < 0,01). Conclusão: A ausência de alunos ao TP não se dá de forma aleatória. Entre os faltosos, há uma tendência sistemática de existirem alunos com piores índices de desempenho acadêmico. O uso de imputação múltipla de dados evidencia uma correlação entre a porcentagem de faltosos e a diferença na média da nota no TP, desse grupo, comparada à média da nota dos participantes. A proporção de homens que faltaram ao TP foi significativamente maior do que a de mulheres.

Abstract: Introduction: It is not known whether the absence of medical students at the Progress Test (PT) is random event or if it due to some systematic characteristic of the students, which could influence the representativeness of the results obtained by the participants. Objectives: 1) to compare the academic performance indexes, in UFSC Medical School, of students who were present and absent from the PT in 2019; 2) to propose a way to estimate, based on these indexes, what the absentee's grades would be if they had participated in the PT; 3) to identify factors associated with absence from the PT. Method: The averages of academic performance indexes, overall and in the different phases (semesters) in the groups of students who were present and absent from the PT, were compared using Student's t test for independent samples. Using a linear regression technique, the probable PT scores were assigned to the group of absent students. Results: The global averages of the three academic indicators were significantly lower in students absent from the PT (p ranging from < 0.03 to < 0.0001); in 10 of the 11 analyzed course phases (semesters), the academic indicators of absentees were worse than those present at the test. The attribution of PT grades to the absentees allowed us to verify that there is a correlation (R=0.62) between the percentage of these students and the difference in grades between the groups that took and those that did not take the PT. Among male students, 25.8% did not attend the PT, while among female students the number of absentees was 16.6% (difference with p <0.01). Conclusions: The absence of students at the PT does not occur randomly. Among the absentees, there is a systematic tendency to have students with worse academic performance. The use of multiple imputation of data demonstrate a correlation between the percentage of absentees and the difference in the average of grades in the PT of this group, compared to the average of the participants' grades. The proportion of male students who missed the PT was significantly higher than that of female students.

Multiple imputation of missing data in clinical longitudinal studies and its sensitivity analyses / 中国临床药理学与治疗学

AIM: To guide the multiple imputation of missing data in clinical longitudinal studies and its sensitivity analyses, and highlight the importance of sensitivity analyses by taking the clinical trial of Qizhitongluo Capsule in treating ischemic stroke as an example. METHODS: To implement PROC MI process in SAS to perform multiple imputation and its sensitivity analysis. RESULTS: In the example, after multiple imputation, improvements in lower limb motor scores of the Qizhitongluo group were greater than those of the placebo group (all P<0.01), and the results of two sensitivity analyses under "missing not at random" were consistent with those under "missing at random". CONCLUSION: Multiple imputations combined with sensitivity analyses can ensure a robust result. It is recommended that clinical researchers perform sensitivity analyses after filling missing data.

Valores anômalos e dados faltantes em estudos clínicos e experimentais / Anomalous values and missing data in clinical and experimental studies

Durante a análise dos dados de uma pesquisa científica, é habitual deparar-se com valores anômalos ou dados faltantes. Valores anômalos podem ser resultado de erros de registro, de digitação, de aferição instrumental, ou configurarem verdadeiros outliers. Nesta revisão, são discutidos conceitos, exemplos e formas de identificar e de lidar com tais contingências. No caso de dados faltantes, discutem-se técnicas de imputação dos valores para evitar a exclusão do sujeito da pesquisa, caso não seja possível recuperar a informação das fichas de registro ou reabordar o participante

During analysis of scientific research data, it is customary to encounter anomalous values or missing data. Anomalous values can be the result of errors of recording, typing, measurement by instruments, or may be true outliers. This review discusses concepts, examples and methods for identifying and dealing with such contingencies. In the case of missing data, techniques for imputation of the values are discussed in, order to avoid exclusion of the research subject, if it is not possible to retrieve information from registration forms or to re-address the participant

Comparison of different methods in dealing with HIV viral load data with diversified missing value mechanism on HIV positive MSM / 中华流行病学杂志

Objective To compare results of different methods in organizing HIV viral load (VL) data with missing values mechanism. Methods We used software SPSS 17.0 to simulate complete and missing data with different missing value mechanism from HIV viral loading data collected from MSM in 16 cities in China in 2013. Maximum Likelihood Methods Using the Expectation and Maximization Algorithm (EM), regressive method, mean imputation, delete method, and Markov Chain Monte Carlo (MCMC) were used to supplement missing data respectively. The results of different methods were compared according to distribution characteristics, accuracy and precision. Results HIV VL data could not be transferred into a normal distribution. All the methods showed good results in iterating data which is Missing Completely at Random Mechanism (MCAR). For the other types of missing data, regressive and MCMC methods were used to keep the main characteristic of the original data. The means of iterating database with different methods were all close to the original one. The EM, regressive method, mean imputation, and delete method under-estimate VL while MCMC overestimates it. Conclusion MCMC can be used as the main imputation method for HIV virus loading missing data. The iterated data can be used as a reference for mean HIV VL estimation among the investigated population.

Comparison of different methods in dealing with HIV viral load data with diversified missing value mechanism on HIV positive MSM / 中华流行病学杂志

Objective To compare results of different methods in organizing HIV viral load (VL) data with missing values mechanism. Methods We used software SPSS 17.0 to simulate complete and missing data with different missing value mechanism from HIV viral loading data collected from MSM in 16 cities in China in 2013. Maximum Likelihood Methods Using the Expectation and Maximization Algorithm (EM), regressive method, mean imputation, delete method, and Markov Chain Monte Carlo (MCMC) were used to supplement missing data respectively. The results of different methods were compared according to distribution characteristics, accuracy and precision. Results HIV VL data could not be transferred into a normal distribution. All the methods showed good results in iterating data which is Missing Completely at Random Mechanism (MCAR). For the other types of missing data, regressive and MCMC methods were used to keep the main characteristic of the original data. The means of iterating database with different methods were all close to the original one. The EM, regressive method, mean imputation, and delete method under-estimate VL while MCMC overestimates it. Conclusion MCMC can be used as the main imputation method for HIV virus loading missing data. The iterated data can be used as a reference for mean HIV VL estimation among the investigated population.

No le tema a los datos perdidos: enfoques modernos para el manejo de datos perdidos

La mayoría de los datos en ciencias sociales y educación presentan valores perdidos debido al abandono del estudio o la ausencia de respuesta. Los métodos para el manejo de datos perdidos han mejorado dramáticamente en los últimos años, y los programas computacionales ofrecen en la actualidad una variedad de opciones sofisticadas. A pesar de la amplia disponibilidad de métodos considerablemente justificados, muchos investigadores e investigadoras siguen confiando en técnicas viejas de imputación que pueden crear análisis sesgados. Este artículo presenta una introducción conceptual a los patrones de datos perdidos. Seguidamente, se introduce el manejo de datos perdidos y el análisis de los mismos con base en los mecanismos modernos del método de máxima verosimilitud con información completa (FIML, siglas en inglés) y la imputación múltiple (IM). Asimismo, se incluye una introducción a los diseños de datos perdidos así como nuevas herramientas computacionales tales como la función Quark y el paquete semTools. Se espera que este artículo incentive el uso de métodos modernos para el análisis de los datos perdidos.

Most of the social and educational data have missing observations due to either attrition or nonresponse. Missing data methodology has improved dramatically in recent years, and popular computer programs as well as software now offer a variety of sophisticated options. Despite the widespread availability of theoretically justified methods, many researchers still rely on old imputation techniques that can create biased analysis. This article provides conceptual introductions to the patterns of missing data. In line with that, this article introduces how to handle and analyze the missing information based on modern mechanisms of full-information maximum likelihood (FIML) and multiple imputation (MI). An introduction about planned missing designs is also included and new computational tools like Quark function, and semTools package are also mentioned. The authors hope that this paper encourages researchers to implement modern methods for analyzing missing data.

Comparação de métodos de imputação única e múltipla usando como exemplo um modelo de risco para mortalidade cirúrgica / Comparison of simple and multiple imputation methods using a risk model for surgical mortality as example

INTRODUÇÃO: A perda de informações é um problema frequente em estudos realizados na área da Saúde. Na literatura essa perda é chamada de missing data ou dados faltantes. Através da imputação dos dados faltantes são criados conjuntos de dados artificialmente completos que podem ser analisados por técnicas estatísticas tradicionais. O objetivo desse artigo foi comparar, em um exemplo baseado em dados reais, a utilização de três técnicas de imputações diferentes. MÉTODO: Os dados utilizados referem-se a um estudo de desenvolvimento de modelo de risco cirúrgico, sendo que o tamanho da amostra foi de 450 pacientes. Os métodos de imputação empregados foram duas imputações únicas e uma imputação múltipla (IM), e a suposição sobre o mecanismo de não-resposta foi MAR (Missing at Random). RESULTADOS: A variável com dados faltantes foi a albumina sérica, com 27,1 por cento de perda. Os modelos obtidos pelas imputações únicas foram semelhantes entre si, mas diferentes dos obtidos com os dados imputados pela IM quanto à inclusão de variáveis nos modelos. CONCLUSÕES: Os resultados indicam que faz diferença levar em conta a relação da albumina com outras variáveis observadas, pois foram obtidos modelos diferentes nas imputações única e múltipla. A imputação única subestima a variabilidade, gerando intervalos de confiança mais estreitos. É importante se considerar o uso de métodos de imputação quando há dados faltantes, especialmente a IM que leva em conta a variabilidade entre imputações para as estimativas do modelo.

INTRODUCTION: It is common for studies in health to face problems with missing data. Through imputation, complete data sets are built artificially and can be analyzed by traditional statistical analysis. The objective of this paper is to compare three types of imputation based on real data. METHODS: The data used came from a study on the development of risk models for surgical mortality. The sample size was 450 patients. The imputation methods applied were: two single imputations and one multiple imputation and the assumption was MAR (Missing at Random). RESULTS: The variable with missing data was serum albumin with 27.1 percent of missing rate. The logistic models adjusted by simple imputation were similar, but differed from models obtained by multiple imputation in relation to the inclusion of variables. CONCLUSIONS: The results indicate that it is important to take into account the relationship of albumin to other variables observed, because different models were obtained in single and multiple imputations. Single imputation underestimates the variability generating narrower confidence intervals. It is important to consider the use of imputation methods when there is missing data, especially multiple imputation that takes into account the variability between imputations for estimates of the model.

Multiple Imputation Technique Applied to Appropriateness Ratings in Cataract Surgery

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.

Comparison of different methods in management of multivariate missing data / 第二军医大学学报

Objective:To explore the results of different methods for managing multivariate missing data. Methods: Case deletion, simple imputation and multiple imputation were compared when used for analyzing the clinical data of 925 liver cancer patients with medium multivariate missing data. Results: There were differences among the 3 methods. When ?=0.05, the risk factors influencing patients' survival time were clinical staging,history of hepatic cirrhosis, portal vein tumor thrombas, and levels of g-GT and WBC with multiple imputation, and were TNM staging, lipiodol dose, AST and ALP with case deletion. The 3 more factors of simple imputation were TNM staging, ALP and AFP compared with multiple imputation. Conclusion: Simple imputation is superior to case deletion in management of multivariate missing data but tends to make standard error smaller and P value lower. Multiple imputation is more reasonable and scientific than the other 2 methods.