Probability, Statistics and Modelling in Public Health

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Arbeev, S. Ukraintseva, L.

Arbeeva, A. Bagdonavicius, A.


Bikelis, V. Kazakevicius, M. Barberger-Gateau, V. Bagdonavicius, M.


Nikulin, O. Barbu, N. Dabrowska, R. Elashoff, D. Deheuvels, G. Gulati, M. Application to Colorectal Cancer C. Gras, J. Daures, B. Huang, D. Huber-Carol, O. Pons, N. Huber, V. Solev, F. Kahle, H. Klyuzhev, V. Ardashev, N. While these study data only quantify the methods used in the literature, based on its frequent use we advocate for logistic regression to be included in biostatistics education for graduate public health students. Less than half of the studies reviewed mentioned anything about missing data. It is extremely unlikely that missing data is not encountered in the majority of public health research.

This lack of reporting about missing data, including attrition, non-response, and dropouts, may reflect a need for journal submission guidelines to require mention of missing data, including its frequency, and how it was addressed in the statistical analysis. About a third of the studies reported using casewise deletion, a relatively outdated and biased approach for analyzing missing data.

Missing data is a well-recognized challenge with human subject research. Modern methods for handling missing data e. This indicates several possible needs. On one hand, in order for newly developing public health professionals to read and understand the limitations of inadequately handling missing data in a statistical analysis, biostatistics education needs to include training on this topic. And on the other, public health professionals may benefit from an introduction to modern methods for handling missing data in a short course or continuing education workshop.

A result with two asterisks is mistakenly interpreted as more significant than a result with one asterisk [ 7 ]. In fact, all that is meaningful is whether or not the p-value is less than alpha. Use of the asterisks notation indicates a possible misunderstanding of p-values and the classical null hypothesis significance testing process used in determining statistical significance [ 8 ]. The relatively high frequency of this problematic reporting could be avoided with education and training on appropriate statistical reporting of inferential statistics. Statistical software is needed to analyze data.

Exposure to one or both of these packages may be beneficial.

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Online training courses in statistical methods and statistical software have grown in popularity and may be an option for many working professionals seeking additional training in a format that is manageable with a full time position. As the modern data age continues to evolve, with the increasing use of administrative and other large data sources, it seems plausible to expect more observational data not originally intended for research to become available and used in public health research.

Avoiding misuse and ensuring scientific validity of health-related findings from such sources depends on well-educated and trained public health professionals. Although experimental studies remain as the gold standard for enabling causal inference, only a handful were reported. And while there are statistical methods that make causal inference with observational data possible, these approaches were scarcely used in our study sample.

When statistical techniques were used, the vast majority of statistical methods seen in our sample were classical statistical techniques commonly taught in a first or second course in introductory and intermediate statistics. Classical statistics is based on normal theory and rooted in the general linear model GLM , a framework that includes the three t-tests, linear regression, and ANOVA. The GLM paradigm assumes independence between observations. When this assumption is violated, as is the case with repeated measures data, more advanced statistical techniques are needed to account for the data dependencies that arise.

Advanced statistical modeling techniques, including mixed and marginal models, are such methods. However, these techniques, as well as complex statistical modeling techniques such as structural equation modeling and factor analysis, were rarely applied and reported. The scarce reporting of advanced methods could be an indication that these methods are not of importance or relevance in public health studies.

The Crisis Of Evidence, Or, Why Probability & Statistics Cannot Discover Cause

However, since training in these methods has only become available in more recent years, we postulate this may be due to the historic lack of education and training availability on these topics. Many of the advanced statistical techniques rarely observed in our study are methods that were not available in mainstream statistical software ten to twenty years ago.

"Probability, Statistics and Modelling in Public Health"

For example, seasoned researchers may not have been exposed to modernized statistical modeling techniques which now available and appropriate for analyzing dependent or multilevel data [ 9 ]. Education in modernized statistical methods, including advanced modeling and computationally intensive statistical techniques, is necessary for staying current and implementing new advanced and methods.

In addition to solid training in classical statistics, we suggest that graduate public health programs may also benefit from providing advanced biostatistics education and training opportunities to their students. Statistical software and computing power now enables researchers to readily access and make use of advanced statistical methods. Public health professionals may benefit greatly from continuing education training opportunities that provide a structured foray into such methods, coupled with statistical software training to show how to apply these methods to real world data.

Reporting of a statistical method does not necessarily mean its use was appropriate or correct. We did not evaluate the appropriateness or correctness of application. The work presented here is limited to an assessment of statistical methods currently used in the general public health literature. Methods applied in research studies may not be adequate, correct, or appropriate. Our work did not assess these aspects, and instead focused on quantifying which methods were used. It is also important to note that the language used by authors to describe some statistical methods varied.

For example, classical linear regression was referred to in many ways, including fixed-effects regression, linear regression, least-squares regression, and general linear model. In a few cases, the description of statistical methods used was unclear and necessitated group discussion to come to a consensus. Finally, our study is limited to studies accepted for publication. It would be interesting to assess any possible publication bias resulting from statistical methods used in accepted as compared to rejected manuscripts.

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Since articles were selected only from , the cross-sectional nature of this study limits an ability to consider how the use of statistical methods has changed over time. Statistics knowledge is essential for reading and understanding public health research. About three quarters of the articles reviewed reported inferential statistics e. In addition, classic and advanced statistical models were reported in more than a third of the publications. A working knowledge of descriptive and inferential statistics is essential to comprehend, evaluate, and interpret the results for most research studies.

Graduate training for public health students and continuing education in biostatistics education for public health professionals are essential for acquiring and maintaining statistics knowledge, as well as continuing to develop new skills as more complex methods are increasingly used and reported. There is a noticeable lack of an evidence basis to make curricula decisions about biostatistics education.

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However, little is known about the methods used in the literature. The work presented here may be useful to curriculum committees deciding on course and content offerings. National Center for Biotechnology Information , U. PLoS One. Published online Jun 7. Matthew J. Cadwell 3.

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  6. Betsy L. Mary Schooling, Editor. Author information Article notes Copyright and License information Disclaimer. Competing Interests: The authors have declared that no competing interests exist. Formal analysis: MJH. Visualization: MJH. Received Nov 25; Accepted May Copyright notice. This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

    Abstract Statistical literacy and knowledge is needed to read and understand the public health literature. Introduction Public health practice relies on the peer reviewed public health literature for current research and findings that support an evidence basis for effective practice. Methods The data collection form used in this study was created by the study authors and designed to gather information on statistical methods described in each randomly selected article. Table 1 Public health journals reviewed and number of articles from included in the study sample. Open in a separate window. Sample size determination The goal of this study was to quantify the types and frequencies of use of statistical methods in the public health literature.

    Data collection and analysis We randomly sampled with probability proportional to the number of articles contributed by each journal [ S1 File ]. Results A total of articles were reviewed. Discussion In order to properly and adequately train public health professionals to access scientific publications, it is essential to, at a minimum, be teaching statistical methods actually used and reported in top tier public health journals. Limitations Reporting of a statistical method does not necessarily mean its use was appropriate or correct.

    Conclusions Statistics knowledge is essential for reading and understanding public health research. Supporting information S1 File This is the study data in an excel file format. XLSX Click here for additional data file. Funding Statement The authors received no specific funding for this work. Data Availability All relevant data are within the paper and its Supporting Information files. References 1. Statistics Education Research Journal , 6 2 , 28โ€” Statistical trends in the Journal of the American Medical Association and implications for training across the continuum of medical education.

    PLoS One , 8 10 :e doi: Council on Education for Public Health Effective practices for teaching the biostatistics core course for the MPH using a competency-based approach. Public Health Rep โ€”92 doi: Hayat M.