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What Your Future Doctor Should Know About Statistics: Must-Include Topics for Introductory Undergraduate Biostatistics
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| Pengarang | : | - |
| Nama Majalah/Jurnal | : | The American Statistician |
| Volume / Edisi | : | 69 (No. 3) |
| Halaman | : | 231--240 |
| Abstrak | : | The increased emphasis on evidence-based medicine creates a greater need for educating future physicians in the general domain of quantitative reasoning, probability, and statistics. Reflecting this trend, more medical schools now require applicants to have taken an undergraduate course in introductory statistics. Given the breadth of statistical applications, we should cover in that course certain essential topics that may not be covered in the more general introductory statistics course. In selecting and presenting such topics, we should bear in mind that doctors also need to communicate probabilistic concepts of risks and benefits to patients who are increasingly expected to be active participants in their own health care choices despite having no training in medicine or statistics. It is also important that interesting and relevant examples accompany the presentation, because the examples (rather than the details) are what students tend to retain years later. Here, we present a list of topics we cover in the introductory biostatistics course that may not be covered in the general introductory course. We also provide some of our favorite examples for discussing these topics. |
What Your Future Doctor Should Know About Statistics: Must-Include Topics for Introductory Undergraduate Biostatistics
| Pengarang | : | Brigitte Baldi |
| Nama Majalah/Jurnal | : | The American Statistician |
| Volume / Edisi | : | 69 (No. 3) |
| Halaman | : | 231--240 |
| Abstrak | : | The increased emphasis on evidence-based medicine creates a greater need for educating future physicians in the general domain of quantitative reasoning, probability, and statistics. Reflecting this trend, more medical schools now require applicants to have taken an undergraduate course in introductory statistics. Given the breadth of statistical applications, we should cover in that course certain essential topics that may not be covered in the more general introductory statistics course. In selecting and presenting such topics, we should bear in mind that doctors also need to communicate probabilistic concepts of risks and benefits to patients who are increasingly expected to be active participants in their own health care choices despite having no training in medicine or statistics. It is also important that interesting and relevant examples accompany the presentation, because the examples (rather than the details) are what students tend to retain years later. Here, we present a list of topics we cover in the introductory biostatistics course that may not be covered in the general introductory course. We also provide some of our favorite examples for discussing these topics. |
On Two General Classes of Discrete Bivariate Distributions
| Pengarang | : | Hyunju Lee |
| Nama Majalah/Jurnal | : | The American Statistician |
| Volume / Edisi | : | 69 (No. 3) |
| Halaman | : | 221-230 |
| Abstrak | : | In this article, we develop two general classes of discrete bivariate distributions. We derive general formulas for the joint distributions belonging to the classes. The obtained formulas for the joint distributions are very general in the sense that new families of distributions can be generated just by specifying the “baseline seed distributions.” The dependence structures of the bivariate distributions belonging to the proposed classes, along with basic statistical properties, are also discussed. New families of discrete bivariate distributions are generated from the classes. Furthermore, to assess the usefulness of the proposed classes, two discrete bivariate distributions generated from the classes are applied to analyze a real dataset and the results are compared with those obtained from conventional models. |
The Challenges in Developing an Online Applied Statistics Program: Lessons Learned at Penn State University
| Pengarang | : | Seungjin Kim |
| Nama Majalah/Jurnal | : | The American Statistician |
| Volume / Edisi | : | 69 (No. 3) |
| Halaman | : | 213-220 |
| Abstrak | : | Numerous professional fields have an increasing need for individuals trained in statistics and other quantitative analysis techniques. Today there exists great potential to fulfill this need by providing opportunities through online learning. However, to provide a high-quality education for returning adult professionals seeking advanced degrees in applied statistics online, many challenges need to be overcome. Based on our experience developing Penn State University’s online program in applied statistics, we discuss the evolution of the program’s curriculum, recruitment and development of online faculty, and meeting the requirements of students as important areas that require consideration in the development of an online program. We also highlight program evaluation strategies employed to ensure innovation and improvement in online education as cornerstones to a program’s success. |
A Statistical Framework for Hypothesis Testing in Real Data Comparison Studies
| Pengarang | : | Anne-Laure Boulesteix |
| Nama Majalah/Jurnal | : | The American Statistician |
| Volume / Edisi | : | 69 (No. 3) |
| Halaman | : | 201-212 |
| Abstrak | : | In computational sciences, including computational statistics, machine learning, and bioinformatics, it is often claimed in articles presenting new supervised learning methods that the new method performs better than existing methods on real data, for instance in terms of error rate. However, these claims are often not based on proper statistical tests and, even if such tests are performed, the tested hypothesis is not clearly defined and poor attention is devoted to the Type I and Type II errors. In the present article, we aim to fill this gap by providing a proper statistical framework for hypothesis tests that compare the performances of supervised learning methods based on several real datasets with unknown underlying distributions. After giving a statistical interpretation of ad hoc tests commonly performed by computational researchers, we devote special attention to power issues and outline a simple method of determining the number of datasets to be included in a comparison study to reach an adequate power. These methods are illustrated through three comparison studies from the literature and an exemplary benchmarking study using gene expression microarray data. All our results can be reproduced using R codes and datasets available from the companion website http://www.ibe.med.uni-muenchen.de/organisation/mitarbeiter/020_professuren/boulesteix/compstud2013. |
Understanding and Addressing the Unbounded “Likelihood” Problem
| Pengarang | : | Yossi Ben-Zion |
| Nama Majalah/Jurnal | : | The American Statistician |
| Volume / Edisi | : | 69 (No. 3) |
| Halaman | : | 191-200 |
| Abstrak | : | The joint probability density function, evaluated at the observed data, is commonly used as the likelihood function to compute maximum likelihood estimates. For some models, however, there exist paths in the parameter space along which this density-approximation likelihood goes to infinity and maximum likelihood estimation breaks down. In all applications, however, observed data are really discrete due to the round-off or grouping error of measurements. The “correct likelihood” based on interval censoring can eliminate the problem of an unbounded likelihood. This article categorizes the models leading to unbounded likelihoods into three groups and illustrates the density-approximation breakdown with specific examples. Although it is usually possible to infer how given data were rounded, when this is not possible, one must choose the width for interval censoring, so we study the effect of the round-off on estimation. We also give sufficient conditions for the joint density to provide the same maximum likelihood estimate as the correct likelihood, as the round-off error goes to zero. |
A New Test for Short Memory in Long Memory Time Series
| Pengarang | : | Timothy A. C. Hughes |
| Nama Majalah/Jurnal | : | - |
| Volume / Edisi | : | 69 (No. 3) |
| Halaman | : | 182-190 |
| Abstrak | : | This article considers short memory characteristics in a long memory process. We derive new asymptotic results for the sample autocorrelation difference ratios. We used these results to develop a new portmanteau test that determines if short memory parameters are statistically significant. In simulations, the new test can detect short memory components more often than the Ljung-Box test when these short memory components are in fact within a long memory process. Interestingly, our test finds short memory autocorrelations in U.S. inflation rate data, whereas the Ljung-Box test fails to find these autocorrelations. Modeling these short memory autocorrelations of the inflation rate data leads to improved model accuracy and more precise prediction. |
Discreteness Causes Bias in Percentage-Based Comparisons: A Case Study From Educational Testing
| Pengarang | : | Darrick Yee |
| Nama Majalah/Jurnal | : | - |
| Volume / Edisi | : | 69 (No. 3) |
| Halaman | : | 174-181 |
| Abstrak | : | Discretizing continuous distributions can lead to bias in parameter estimates. We present a case study from educational testing that illustrates dramatic consequences of discreteness when discretizing partitions differ across distributions. The percentage of test takers who score above a certain cutoff score (percent above cutoff, or “PAC”) often describes overall performance on a test. Year-over-year changes in PAC, or ΔPAC, have gained prominence under recent U.S. education policies, with public schools facing sanctions if they fail to meet PAC targets. In this article, we describe how test score distributions act as continuous distributions that are discretized inconsistently over time. We show that this can propagate considerable bias to PAC trends, where positive ΔPACs appear negative, and vice versa, for a substantial number of actual tests. A simple model shows that this bias applies to any comparison of PAC statistics in which values for one distribution are discretized differently from values for the other. |
Bayesian Variable Selection Under Collinearity
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | - |
| Volume / Edisi | : | 69 (No. 3) |
| Halaman | : | 165-173 |
| Abstrak | : | In this article, we highlight some interesting facts about Bayesian variable selection methods for linear regression models in settings where the design matrix exhibits strong collinearity. We first demonstrate via real data analysis and simulation studies that summaries of the posterior distribution based on marginal and joint distributions may give conflicting results for assessing the importance of strongly correlated covariates. The natural question is which one should be used in practice. The simulation studies suggest that posterior inclusion probabilities and Bayes factors that evaluate the importance of correlated covariates jointly are more appropriate, and some priors may be more adversely affected in such a setting. To obtain a better understanding behind the phenomenon, we study some toy examples with Zellner’s g-prior. The results show that strong collinearity may lead to a multimodal posterior distribution over models, in which joint summaries are more appropriate than marginal summaries. Thus, we recommend a routine examination of the correlation matrix and calculation of the joint inclusion probabilities for correlated covariates, in addition to marginal inclusion probabilities, for assessing the importance of covariates in Bayesian variable selection. |
A Classroom Approach to the Construction of an Approximate Confidence Interval of a Poisson Mean Using One Observation
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | - |
| Volume / Edisi | : | 69 (No. 3) |
| Halaman | : | 160-164 |
| Abstrak | : | Even elementary statistical problems may give rise to a deeper and broader discussion of issues in probability and statistics. The construction of an approximate confidence interval for a Poisson mean turns out to be such a case. The simple standard two-sided Wald confidence interval by normal approximation is discussed and compared with the score interval. The discussion is partly in the form of an imaginary dialog between a teacher and a student, where the latter is supposed to have studied mathematical statistics for at least one semester. |