STATISTICS FOR MEDICAL STUDENTS IN THE 1990’s: HOW SHOULD WE APPROACH THE FUTURE?

STEPHEN J. W. EVANS The London Hospital Medical College, Turner Street, London, El 2AD, U.K.

SUMMARY Medical statistics teaching in the U.K. has moved over the past twenty years from needing justification to acceptability. In the future teachers need to involve statistical and medical colleagues to improve the content and presentation of their material. While some techniques must continue to be taught, the conceptual aspects of scientific evaluation and assessment will need to increase

INTRODUCTION

Most teachers would wish to go on trying to improve their courses as well as their own teaching, even if they are reasonably satisfied with the status quo. From the earlier papers we may have an optimistic view that even the good can be improved, while the pessimistic view is that what is done currently is not good. Reviewing the literature tends to lead to a pessimistic view. We do not seem to have made much progress over the past decade and many of the things one might suggest now, seem to have been mentioned before but have not always been put into practice. We have probably done a few things slightly better, but have not had any dramatic successes.

Taking the optimistic view, in what directions can we improve? The possibilities are in terms of the titles of previous papers.

1. Rationale and content 2. Teachers and timing 3. Evaluation of teachers and timing

and I will add

4. Style and motivation

RATIONALE AND CONTENT

Over the past decade the need to justify the very existence of a medical statistics course within medical schools has almost disappeared - they are virtually universally accepted by medical academic staff in the U.K. and in many other countries. The questions of current and future importance relate to what is taught in the course. The arguments for teaching statistics to medical students have, in recent times, emphasized the research use of statistical methods as opposed to public health or clinical applications. The inadequacy of published papers in medicine has been taken as evidence of failure to teach statistics or appropriate statistics to doctors. This is not a

1277-6715/90/091069-07%05.00 Q 1990 by John Wiley & Sons, Ltd.

Received April 1990

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burden which can be borne by medical statistics teachers alone. We might do well to utilize examples and reasoning from areas of public health and clinical treatment of individuals, both of which may have an important influence on students’ attitude to, and knowledge of statistics. Given that failure of statistical application in research is usually due to a failure of thinking rather than arithmetic, we, rightly, want to emphasize the former.

It is easier to pay lip-service to emphasis on statistical thinking as opposed to statistical arithmetic (or ‘concepts’ as opposed to ‘methods’ in Healy’s 1984 description’) to ensure that our teaching reflects the thinking aspects of statistics. Also there is pressure from colleagues (mainly basic scientists rather than clinicians) to teach techniques of analysis to present results of practical work. This latter area of statistical principles and methods was the first ‘WHAT’ in the 1979 WHO publication2 on teaching of medical statistics. There is a reaction against teaching almost any meth~dology,~ but I think that simple principles of data description which are only of an elementary nature are worth communicating to all students. Particular topics which tend not to be taught before coming to medical school are exploratory methods such as Box plots, stem and leaf diagrams, median smoothing, and presentation of significant figures in tables. The knowledge of these techniques is useful in many contexts and the ability to choose and interpret appropriate graphics is worthwhile. In this type of application collaboration with basic medical scientists can be very fruitful. Encouraging teachers of these subjects to learn such techniques and to use them both in practical work done by medical students and in presentation of their own research findings must be mutually advantageous. Dissuading students and teachers from using k for SD or SEM is a task in itself worth pursuing. It is not easy to find textbooks which are concept oriented (Mainland4 and Oldham5 are both out of print while Ehrenberg6 does not cover much medically relevant material), nor is it easy to teach and examine ‘thinking’.

The milieu in which we operate in the medical or dental course tends to lay stress on factual learning in spite of what the General Medical Council (the overal U.K. medical licensing authority) or educational theorists desire. We can respond by simply saying it is virtually impossible and teach a minimum course to all as is done at Newcastle and only teach a subset of students the more conceptual material, or we can try to incorporate statistical thinking into our teaching and examining. One aspect is to be able to recommend written material to help students with this but such books will not necessarily sell well. Part of our future approach should be to find (or write!) books of this type and ensure their commercial success by strong recommenda- tions! Statistical ‘cookbooks’ tend to sell well whatever the standard of their statistical comm~nicat ion,~*~ unfortunately.

In terms of detail, we probably all teach more about confidence intervals and require less calculation than we did 10 years ago. (The first exam paper to ask a question on confidence intervals at The London Hospital Medical College was in 1980; we have asked about design of studies and type I1 errors since 1975.) These trends, together with an emphasis on what is clinically relevant will continue, but may not reach an ideal because of pressure to teach the mechanics of a t-test. It has been argued’ that medical students do not need to be taught how to calculate standard deviations by hand. I think I would agree, but I do think that they need to be taught what a standard deviation is and to be able to contrast it with a standard error. I am not sure how well I can teach students to choose which to use in general without using some jargon. Similar arguments apply to t-tests, chi-square tests, regression and correlation.

One, at least, of my colleagues in London teaches students to calculate Poisson probabilities and I believe that some other universities have such calculations forming part of exams. I think we must eliminate this in the future. The continuation of meetings of this type will provide some impetus in this direction but we could do better by regular assessment of course and examination content by colleagues. This will be discussed further below. How many of us have had regular

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reviews of what we teach with clinical colleagues? How many of us have invited clinicians to attend our lectures and ask for comments? I found having a statistical colleague attend my lectures and then give a ‘post-mortem’ (perhaps all too apt a phrase?) after each one, was a salutary but useful experience. How many of us have a clinician who introduces the subject in the first lecture and says why he or she thinks it is worth learning?

Techniques of evaluation, including audit, are of importance at all stages of a doctor’s career and in all branches of medicine. While statisticians do not have a monopoly in this field” they have a contribution to make in raising awareness of scientific method.

TEACHERS AND TIMING

The involvement of non-statisticians in medical statistics courses is not necessarily disadvanta- geous and if clinicians participate it can be a positive advantage provided that the statistical quality remains high. The worst crime is to make the topic boring, since if it is made very interesting the students will have the enthusiasm to pursue the subject further and correct slight misapprehensions later.

In a very few U.K. medical schools the overall control of the statistics teaching to under- graduates has been in the hands of non-statisticians. These people have not usually been practising doctors but anatomists or physicists who have amateur knowledge of both statistics and medicine which is a bad combination. This situation has improved and is improving in several medical schools at the moment. The quality of any course is strongly dependent on the quality of individual teachers, even more than on the material taught, and we need to ensure that we, and the colleagues we use, are clear and enthusiastic communicators.

It will require time and energy from both those of us who are statisticians and also from clinical colleagues if the latter are to be more involved in the teaching itself. When we at The London Hospital Medical College first started teaching for the examined course in 1974 we had two tutors for every seminar group (of 10 students), one a clinician and the other a statistician; this proved too difficult to sustain in later years. I expect that this will equally be the case now but to experiment every five years or so with dual statisticianwAinician seminars may be instructive, particularly for newer teachers of medical statistics. This could have benefits with respect to statistical consultancy or research with the clinicians. Making joint teaching in seminars a prerequisite for continuing consultation may reduce the volume of consultancy or improve teaching (or both).

The timing of statistical teaching during the medical course cannot be considered independ- ently of its content. ‘Thinking’ aspects are more easily appreciated later in the course while graphical and other techniques can complement basic science teaching in the early terms. Clinical and public health applications may need to be in the middle, when their relevance can be seen more easily by students.

In terms of ease of teaching it is simpler to manage lectures and seminars on statistics early in the medical course, particularly in the first two years, but it is fair to say that, given that we wish to emphasize clinical relevance, having courses later allows for this. The situation with regard to postgraduate teaching and examination is at least as unsatisfactory overall as the undergraduate scene. While there are some Royal Colleges (which examine and provide certification for postgraduate doctors in the U.K. and Ireland) which seem to have moved in the right direction, not all have done so.

For the new course at the new building of CELC (City & East London Consortium, that is, Barts, London, Queen Mary Colleges) commencing in 1990, we have planned for teaching in all three phases of the course. The three phases last 5 terms, 2 terms and 8 terms, respectively. We

1072 S. J. W. EVANS

hope that there will be a decreasing trend of arithmetic and an increasing trend of thinking in the three phases of the course, and our current outline syllabus is given in the Appendix. Teaching during the clinical phase seems to be desirable" from several points of view, though not all agree'* and there are considerable practical problems. The amount of time ought to be governed by what is needed to be taught but the reverse is more frequently the case I suspect.

EVALUATION

Intercalated BSc degrees (an extra year taken in the middle of the medical course) provide an opportunity to give expanded courses on research methods and statistical contributions to them. These have been done in Belfast, Newcastle and Cambridge, though our course at The London Hospital on Biometry and Medical Statistics has been the oiily complete BSc on the topic, I believe. This has become an intercollegiate BSc in Epidemiology and Statistics in 1989/90 involving teachers from seven medical schools and students from five.

Most schools use a variety of methods for evaluating students and one method I have found to be appreciated by students as well as teachers is a critical essay on the current medical literature. We have used this for several years at The London Hospital and it has needed care to prevent cheating but is good for emphasizing the medical relevance of statistics and its thinking aspects. We have also used summaries of papers from the British Medical Journal, Lancet and New England Journal of Medicine, with questions regarding their interpretation in final essay examination papers. Its only problem is the person-hours required to mark it!

External examiners tend to be able to comment on the details of an examination paper but are not usually expected to step back and query whether the overall balance of the course is appropriate, as evidenced by the content of several years of examinations. I think that this approach does merit some effort.

The evaluation of teaching needs much further work. There are three basic sources of evaluation:

(a) students (b) clinical colleagues (c) statistical colleagues.

A few of us have used occasional questionnaires for course evaluation but I do not think we have used either our clinical (not epidemiological) nor our statistical colleagues enough. Certainly in London University comments from external examiners tend to be taken very seriously by medical school authorities so that friendly criticism is less easily made in this context. Other medical schools in the U.K. are less likely to have external examiners for basic statistics within medical degrees and might like to invite informal review which I suggest ought to take place in London as well. This is threatening and time absorbing but could lead to improvement. Exchange of posts between teachers in different schools for a year provide a cheap sabbatical in these days of economic problems particularly for those in isolated positions and for whom the conventional sabbatical is not possible. There are generally no rights to any sabbatical time in many U.K. universities (including London as a whole).

STYLE AND MOTIVATION

I have the feeling that it is the individual teacher who makes a greater contribution to the effectiveness of teaching than any of the other topics. Someone may come up with a study design which allows the components of variance to be measured! The use of problem-oriented methods

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has been successful in clinical medicine and although it is time consuming has advantages in medical statistics. The fully problem-oriented approach has considerable appeal particularly when medical statisticians are full participants in the whole course but this is not practised in the U.K. at the moment. Problem oriented approaches to biostatistics have been described but have not yet received a full evaluation.’ 39 l4

The use of a variety of teaching methods is always helpful though the impact of computer aided teaching has not been what was expected a few years ago. It would be good to have some worthwhile material in this field as well as some videos or tape-slide sequences which were of a high standard. Interactive video is now fashionable and could be very successful but I have not seen it working in medical statistics yet.

We tried an experiment of dividing students into seminar groups according to their previous exposure to statistics, and though this was favoured at the start of the year it was not favoured at the end. No difference was detectable in exam results (though the comparison was of low power) but the style was such that those with a strong background were taught interesting (to the teacher and some students at least) material which was beyond the examined syllabus.

Most preclinical medical students are motivated by exams but I think we need some research carried out on their motivation to learn statistics at various stages of their career, including postgraduate and in-service training.

CONCLUSION

I remain optimistic: we can go on doing better. The advent of compulsory statistics in the medical curriculum has providedjobs for some of us! I think that overall we have done as well as or better than some other subjects introduced into medicine and the success of BSc Courses in Newcastle, Belfast and London confirm that to some degree.

The main problem is finding adequate time to give to teaching, and the current emphasis on U.K. universities being given credit for research but not teaching makes life difficult. I think that over the next few years there will be a swing back to emphasis on teaching and particularly to its evaluation (audit of teaching is coming even if it is not yet done in all schools!). We must be ready with good designs to help others evaluate their teaching and we should have had ‘piloting’ experience of the evaluation of our own courses.

APPENDIX: OUTLINE SYLLABUS FOR COURSE AT QUEEN MARY AND WESTFIELD COLLEGE IN BIOMETRY AND MEDICAL STATISTICS FOR

MEDICAL AND DENTAL STUDENTS

Phae I, Term 1, Weeks 2-9 inclusive

The general pattern will be a 45 minute lecture followed by either a one and a half hour practical session in a laboratory with tutors present or a 45 minute seminar. (The seminar ought ideally to be one hour but, due to constraints on teachers and seminar rooms, it is likely that 45 minute seminars will be given twice, once to each half of the class of 255 students. Availability of laboratory space with computer facilities will also be a requirement for the practical sessions). Each week the teaching will be based on a problem of biological or clinical interest with relevant statistical material introduced. This will relate to the modules of basic medical science being taught concurrently such as molecules, cells and tissues.

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Topics for each week from 2 to 9:

Week 2 Introduction to the scope of the course. Measurement of concentrations (related to molecules, cells and tissues (MCT) module). Introduction to com- puter facilities in laboratory. Statistical graphics.

Classification of tissues (MCT). Measurement scales, descriptive statistics, measurement of variability. Seminar on meaning of standard deviation and interquartile range.

Birthweight and gestation. Shapes of distributions. Introduction to the Gaus- sian distribution. Centiles (Seminar).

Dental surveys. Populations and samples. Standard errors. Confidence inter- vals (Practical).

Experiments in rheumatology (locomotion, bones, joints - LBJ). Measurement errors, randomization, bias (Seminar).

Clinical trials in rheumatology (LBJ). Comparison of samples. Confidence intervals on differences. Size of sample (Practical).

Drug dose response in haematology (MCT) or cardiology. Elementary regres- sion and correlation (Seminar).

Smoking and health. Further regression and correlation (Practical).

Examination. MCQ and Essay. Tutors’ assessments will also be taken into account.

Week 3

Week 4

Week 5

Week 6

Week 7

Week 8

Week 9

Week 10

Ideally ten and no more than 15 students will be in each seminar group.

Phase II, Term 6, Weeks 1-5 and 7-10

The general pattern will be similar to that in Phase I with the problem topics being even more clinically orientated. There will be 45 minute lecture sessions followed by two one hour seminars (to half the students) or one to two hour practicals. Within the overall time there will be periods of self-directed, problem-based, learning. There will also be an opportunity for application of elementary statistical methods to the data collected for projects.

Cardiac mortality and morbidity. Diagnostic tests, death certification, rates, indirect standardization.

Sugar and health. Dental disease, diabetes. Comparison of treatments. Decid- ing on causes of disease. Case-control studies.

Cerebral palsy. Classification, prognosis. Longitudinal studies. Elementary survival analysis.

Critical reading of medical and dental literature.

Weeks 1-3

Weeks 4-5

Weeks 7-8

Weeks 9-10

Comments on Phase I1 Teaching, particularly in relation to dentistry

I would recommend that there are four seminar groups for the (55) dental students (requiring two tutors and two rooms) and that this arrangement covers both Phase I and Phase 11. This will allow greater emphasis on dental problems within the seminars.

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The biometry course will not be statistical topic orientated but medical or dental problem orientated. The aim is that students will learn how to evaluate and present data in elementary situations and to evaluate diagnostic methods, treatments and suggested causes of disease.

The detailed description of each lecture/seminar session is then not appropriate. It will, in any event, require some input from clinicians or basic scientists regarding the problems.

To take one example from Week 5. We might begin with an example of a person with partial dentures. We will discuss the idea of finding out the prevalence of missing teeth. The idea of taking a random sample from a defined population begins to be introduced. Having taken a sample, we wish to estimate the prevalence or mean number of missing teeth. We relate this to a population and then need to know the precision of the estimate. Standard errors of means and proportions and confidence intervals are discussed and the effects of sample size illustrated.

Pbase 111, Clinical Course, Terms S15

There is at present no formal arrangement for biometry to be taught at specific times in the clinical course, but within the period allocated for epidemiology some further applications will be given and there will be some statistical input to academic half days on various topics and to grand rounds.

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