Intensive short courses in biostatistics for fellows and physicians

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<ul><li><p>STATISTICS IN MEDICINEStatist. Med. 2002; 21:27392756 (DOI: 10.1002/sim.1212)</p><p>Intensive short courses in biostatistics for fellows andphysicians</p><p>Walter T. Ambrosius1;; and Amita K. Manatunga2</p><p>1Section on Biostatistics; Department of Public Health Sciences; Wake Forest University;Medical Center Boulevard; Winston-Salem; NC 27157; U.S.A.</p><p>2Department of Biostatistics; Rollins School of Public Health; Emory University; 1518 Clifton Road NE;Atlanta; GA 30322; U.S.A.</p><p>SUMMARY</p><p>At both of our universities we teach (with colleagues) introductory courses in statistics for fellows andphysicians. We do not expect that those taking these courses will be able to do their own statisticalwork, but rather the intention is for them to learn the language and to facilitate future collaboration.Basic principles of study design are introduced in the courses, as well as some of the most commonstatistical procedures. We will discuss the factors (what works and what does not) that may contributeto a successful course, a comparison to other courses, and our self-evaluation strategy. Finally, we willcover the nancial arrangements that we have made when teaching these courses. Copyright ? 2002John Wiley &amp; Sons, Ltd.</p><p>KEY WORDS: biostatistics; teaching; short courses; physicians</p><p>1. INTRODUCTION</p><p>At both Indiana and Emory Universities we teach basic biostatistics to physicians in a shortcourse format. Prior to these courses there was virtually no teaching of biostatistics to physi-cians at either institution. The major goal of these courses is to improve the overall researchatmosphere at each university. This is accomplished in two ways. First, we expect that manyof those taking the courses will be able to perform simple comparisons without the help ofa statistician. Second, we hope that participants will gain a better appreciation for what theydo not know. With this knowledge, we hope that physicians will recognize when they needto include a statistician on their research team.The statistical background of the students varies greatly. Some have had a course or two</p><p>during their undergraduate studies or in medical school and others have had no statistical</p><p>Correspondence to: Walter T. Ambrosius, Section on Biostatistics, Department of Public Health Sciences, WakeForest University, Medical Center Boulevard, Winston-Salem, NC 27157, U.S.A.</p><p>E-mail:</p><p>Contract=grant sponsor: National Institutes of Health; contract=grant numbers: PHS M01-RR00750, M01-RR00039</p><p>Received May 2000Copyright ? 2002 John Wiley &amp; Sons, Ltd. Accepted November 2001</p></li><li><p>2740 W. T. AMBROSIUS AND A. K. MANATUNGA</p><p>training. From information provided at registration we had 65 MDs, 9 PhDs, 1 DNS (Doctorof Nursing Science), 2 PharmDs, 1 DDS, 3 MD-PhDs, 2 PhD-RNs and 5 with unknowneducation take the 2001 Indiana University (IU) course. A more thorough description of theirstatistical background is discussed in Section 6.1.The intention of these courses is to familiarize physicians with statistical methods that</p><p>often appear in the medical literature and to enable them to have a fruitful collaborativerelationship with an applied statistician. To that end, we have sacriced depth in any one areaof biostatistics to enable us to cover a very broad range of statistical ideas and methods.In this paper we will rst discuss the approaches to short courses in biostatistics that others</p><p>have taken and then we will describe the courses we teach. These two courses are similar toeach other and have been successfully implemented over the last seven years. We will discussthe course objectives, the syllabus, and the necessary logistical and nancial arrangements.Over the years, the courses have been rened. We will discuss the pros and cons of theserenements.</p><p>2. LITERATURE REVIEW</p><p>Very little on teaching short courses in biostatistics has appeared in the literature outside of afew papers in the American Statistical Association Proceedings of the Sections on StatisticalEducation and Teaching Statistics in the Health Sciences. The one exception to this is aseries of papers which appeared in The American Statistician [13] in 1995.Birch [1] described a course he taught for the Institute for Professional Education to en-</p><p>gineers, social scientists, managers, and medical researchers. The course is titled Linear andnonlinear regression and its applications. His course is taught by one instructor over threedays with seven hours of lecture per day. His suggestions include be prepared and exible,use multiple media, present each topic from the ground up, plant seeds before getting to aparticular topic and tie a new topic back to previous lectures, use real problems for motivation,emphasize application of techniques rather than theory, use repetition in lecture, encouragediscussion, and be enthusiastic. These suggestions are applicable to almost any discipline ingeneral and to applied statistics in particular. Birch does not recommend providing studentswith handouts because he believes that it discourages student participation in the learningprocess.Cornell et al. [2] have described the short courses given by the Department of Statistics at</p><p>the University of Florida. Their course is primarily directed at people in industry. The coursetopics vary with each course. Each course is 2.5 days long which allows participants to taketwo courses back-to-back in one week. Their paper details their experiences in setting upthese classes but does not discuss in much depth what is taught and how it is taught.Kleinbaum [3] has taught 71 short courses over more than 20 years. He was invited by a</p><p>sponsor to give many of these courses. The format of his courses varied; many were doneas a business venture and with and without a co-teacher. He found that most participantsand instructors prefer classes of 24 days during the work week. Kleinbaum states that adisadvantage of short courses is that participants usually do not have time between lecturesto do homework or think about what they have learned. He strongly recommends providingcopies of handouts to the students.Roberson et al. [4] describe a Research skills course taught at the University of Arkansas.</p><p>The rst edition of the course met for two one-hour sessions per week for 8 weeks. A second</p><p>Copyright ? 2002 John Wiley &amp; Sons, Ltd. Statist. Med. 2002; 21:27392756</p></li><li><p>INTENSIVE SHORT COURSES IN BIOSTATISTICS 2741</p><p>version met for two one-hour sessions per week for 12 weeks. Topics included design, sam-pling, population and samples, descriptive statistics, regression, ANOVA, interrater agree-ment, and others. The instructors assigned and graded homework although no scores weregiven. Somewhat surprisingly, the students were very good about completing their home-work in time. The authors concluded by listing nine suggestions for anyone designing acourse for a similar audience. Included in this list was use a textbook, require homework,use at most two instructors, provide handouts, use software, and intersperse new and oldconcepts. To encourage high attendance, participants and their mentors or supervisors wererequired to sign a commitment that the participant would attend at least 80 per cent of thelectures.OBrien et al. [5] have a unique approach to teaching biostatistics. Their seminar was</p><p>entitled Concepts in research design and biostatistics for clinical scientists. In their seminar,most of the participants were residents and fellows from a single department or program(Department X) but others include research nurses and experienced investigators. The groupsize was limited to twelve with two biostatisticians participating. One of the biostatisticiansworks collaboratively with Department X. Their seminar met weekly at 7 a.m. for six weeks.Sessions lasted at least one hour but were often extended if the participants were engrossedin discussion. Readings were assigned prior to class from Medical Uses of Statistics [11]which served to focus the discussions. The technical level was not high and a simple rule ofthumb was used: Any equation or other technical matter that all biostatisticians would notknow quickly from memory is almost certainly too complex: : :Peterson et al. [6] describe a course in clinical research methods taught in eight one-</p><p>hour sessions. The course was designed as an introductory course and covers study design,distributions, descriptive statistics comparative statistics, and clinical signicance.Ahn et al. [7] describe a course entitled Clinical Research Design Course at the University</p><p>of Texas Health Science Center at Houston. Their course met weekly for nine 1.5 hoursessions. Like OBrien [5], they focused on general design issues and principles rather thanon the mathematical details. Topics included hypothesis tests, design, randomization, samplesize, interval estimation, equivalence and meta-analysis. No formal evaluation was made butstudent suggestions resulted in the addition of lectures on outcomes research, drug developmentand sampling strategies for studies in human populations.Finally, Deutsch and Ahn [8] describe courses taught at the University of California at</p><p>San Diego and the University of Texas Health Science Center at Houston. These coursesranged from 8 to 10 weeks in length. The goals of the course were to familiarize clinicianswith research design, allow them to read the literature eectively, conduct simple analyseson their own, and communicate more eectively with biostatisticians. The UCSD lectureswere 1.5 hours once per week for 8 weeks. Topics included design, descriptive statistics,hypothesis testing, condence intervals, power, linear and logistic regression, survival analysis,ANOVA and ANCOVA, non-parametric statistics, and frequency data. Overhead projectorsand handouts were used. The course did not emphasize computation but a new course oncomputation was planned that would be a prerequisite for the existing course. At UTMS-Hthe course was taught as a part of a research design course that has been described previously[7]. The purpose of this course is similar to that oered at UCSD except that UCSD focusedmore on teaching how to read medical journals and the UTMS-H course also focused onethics and obtaining grant funding.Some of the features of these courses will be contrasted to our courses in Section 9.</p><p>Copyright ? 2002 John Wiley &amp; Sons, Ltd. Statist. Med. 2002; 21:27392756</p></li><li><p>2742 W. T. AMBROSIUS AND A. K. MANATUNGA</p><p>3. COURSE OBJECTIVES</p><p>Upon completion of the short course, we expect our students to recognize the dierent typesof basic study design in medical research. This includes understanding the mechanics of astudy design and knowing when it is and is not appropriate. They should be able to identifyappropriate selection and use of basic statistical procedures and be able to interpret the results.Finally, we expect that the participants will learn enough statistics during the course so thatthey can develop communication with statisticians on study design and analysis.Our intention is not that those taking our course would be able to do their own statistical</p><p>work. In fact, we do not even discuss the use of statistical software packages that are necessaryfor all but the simplest of problems. We do describe some of the most common statisticalmethods so that students are aware of more than the ubiquitous correlation coecient, t-testand linear regression.</p><p>4. HISTORY</p><p>The rst short course was given at Indiana in October 1990. At that point there were sevenfaculty members participating. It has since been given a total of six times, most recently inJanuary 2001 with ten faculty each teaching one lecture and one giving two lectures. TheDirector of the Division of Biostatistics serves as the course director and all Division facultyparticipate in the course. The course is given over three half-days with a total of 12 lectures.The rst few times the course was taught there were approximately 50 students. In recentyears, attendance has been limited to 30 students and we have usually had a waiting list. Themost recent course was given in a new auditorium and had 88 enrollees. While most of thestudents have been junior faculty (both clinical and non-clinical), the course has also beenattended by senior faculty, fellows, and nurse researchers. The course has also been giventwice on a contract basis to residents and fellows in the Radiology Department. There wereapproximately 1520 students at the rst lecture and attendance usually tapered down to34 by the end of the course.One of the authors (AKM) was involved with the Indiana course from the beginning and</p><p>used that course as a basis for the course at Emory. Owing to this shared history, there aremore similarities than dierences between the two courses.Since 1994, the course has been given four times at Emory, approximately 18 months</p><p>apart. The course is sponsored by the General Clinical Research Center (GCRC) and theDepartment of Biostatistics at the Rollins School of Public Health. The GCRC biostatisticianis the course director. This course is not considered part of the general teaching load within theBiostatistics Department and consequently it has been dicult to get instructors. Initially, fourfaculty members participated, but this was expanded to six faculty members. The instructorswere given a small honorarium. The number of students attending the course has varied from50 to 75. The student body has fellows, physicians and basic scientists. While the majoritywere physicians from within the university (90 per cent), there were a few students attendingfrom outside the university and these were mainly family practice physicians. For variouslogistical reasons (space, food etc.) the number of participants was limited to approximately75. During the last ve years, this course has attracted a large number of students and theregistration has had to be closed earlier than anticipated.</p><p>Copyright ? 2002 John Wiley &amp; Sons, Ltd. Statist. Med. 2002; 21:27392756</p></li><li><p>INTENSIVE SHORT COURSES IN BIOSTATISTICS 2743</p><p>Table I. Indiana syllabus.</p><p>Day Topic</p><p>1 Welcome and Introduction1 Study design:</p><p>Experimental studies, randomization, blindness, biases, analyses, interpretation1 Observational study design:</p><p>Ecologic, case-control, prospective, outcomes, odds ratios, relative risks1 Descriptive statistics:</p><p>Types of data, measures of location and spread, normal distribution,distribution of a mean, distribution of a proportion</p><p>1 Statistical inference:Hypothesis test, type I and type II errors, statistical signicance versuspractical signicance, t-tests, condence interval, sample size estimation</p><p>2 Statistical inference on categorical variables:Review of binomial distribution and its normal approximation, estimationand testing of single proportions, two proportions, tests of association(2 2 table, RC table), Fishers exact test, sample size estimation</p><p>2 Evaluation of diagnostic tests:Sensitivity, specicity, ROC curves, measures of agreement</p><p>2 Comparison of means:Paired t-test, two-sample t-test, Wilcoxon, MannWhitney, one-way ANOVAmultipl...</p></li></ul>


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