Statistical Concepts and Applications in Clinical Medicine

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The work of biostatisticians is also required in government agencies and legislative offices, where research is often used to influence change at the policy-making level. In short, these professionals use mathematics to enhance science and bridge the gap between theory and practice. Biostatisticians are required to develop statistical methods for clinical trials, observational studies, longitudinal studies, and genomics:.

The validity of their research results depends on how well they can make meaningful generalizations and how well they can reproduce and apply experimental methods.

Biostatistical and medical statistics graduate education

Informatics, which is actually an emerging field, is also known as bioinformatics, a science that relies on the basic disciplines of science, mathematics, probability and statistics, and computer science to build a solid statistical foundation for making advances, improvements, and even breakthroughs in public health and medicine. Health informatics is often said to meet at the intersection of information science, computer science, and healthcare, as it deals with the resources, devices, and methods required for the effective storage, use, and retrieval of information, while public health informatics includes the application of informatics in public health areas, such as surveillance, prevention, preparedness, and health promotion.

Public health informatics focuses on information and technology issues from the perspective of groups of individuals. Naturally, health informatics tools would include computers, making systems analysts important members of public health informatics research teams. It is the responsibility of expert informaticists to systematically apply information, computer science, and technology into research, learning, and the practice of public health. Systems analysts are called upon to write and troubleshoot the software used by biostatisticians and researchers.

In the study three dierent observers obtained counts on each of 20 plates, the sequence of presentation of plates being a dierent random ordering for dierent ob- servers. The 60 counts are recorded in data set bact and shown in Table 1. How can we quantify this variability and how can we decide whether any such observer variation is of practical importance? Do we have to ask more questions of the research group? Do we need more information before we can start to formulate the problem in statistical terms? This problem is further developed in Section 6. Measurement of a diagnostic ratio from heart X-rays In the examination of heart X-rays radiologists regard the ratio of the trans- verse diameter of the heart to the transverse diameter of the thorax as a useful diagnostic index.

Traditionally the magnitude of this diagnostic ratio is judged visually without any direct measurement being recorded.

Statistics in clinical trials: Key concepts

The question now being posed is whether this ratio could be quantied in the sense that its com- putation from measurements made by one radiologist would be conformable with that from measurements made by another radiologist. Only in such cir- cumstances would such a quantitative index be reliably objective.

To investigate the feasibility of this index as a worthwhile recordable mea- surement an observer error study had been carried out as part of a larger-scale assessment of the measurability of heart X-rays. Five consultant radiologists were each presented with 65 heart X-rays on a standard displaying screen in randomized order and asked to measure with a ruler and record certain lengths and angles whose denitions all had agreed.

For two of the radiologists 15 of the heart X-rays were presented once again in a randomized order with- out the radiologists knowledge that these were repeats. The complete set of measurements is to be found in data set dratio. The immediate question of the reliability of the diagnostic ratio and other aspects of the larger study are considered in detail later in Section 6.

Radioimmunoassay of angiotensin II The direct measurement of the concentration of angiotensin II in a blood plasma specimen would require the isolation of this biochemical, an awkward and costly technique. Assay techniques exist whereby such an unknown con- centration may be assessed by comparing the measurable eect the specimen has on some physical or biological system with the eects produced by spec- imens of known concentration on similar systems.

In the radioimmunoassay of angiotensin II the measurable eect is the percentage bound of radioac- tive tritium when the specimen is allowed to interact with tritium saturated antigen. The data set angio shows the information obtained from a particular ra- dioimmunoassay. In the particular system in operation in the MRC Blood. Pressure Unit in the Western Inrmary Glasgow the data were recorded on paper tape direct from a scintillator counter.

The percentage bounds corre- sponding to standard preparations of volume 10 ml were recorded at a range of concentrations of angiotensin II. For each such concentration two separate preparations were used so that two replicates are available at each concen- tration. For each of the new specimens of unknown concentration duplicate determinations of percentage bound were obtained for aliquots of blood plasma. The questions posed by the steroid chemist are straightforwardly stated. Can you suggest an ecient method of estimating the concentrations of the new specimens, and how reliable are the estimates?

Are we making the most of the facilities available to us in the design of these radioimmunoassays? Here if we denote by D the data containing concentration and corresponding percentage bound response for the standards and by v the percentage bound measurements for the specimen of unknown concentration u then ideally we would like a realistic assessment of the conditional probability density function p u v, D, M u U , where U is the set of possible concentrations and the inclusion of M after the vertical bar reminds us that our assessment may well depend on the model assumptions adopted.

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We shall investigate this problem in full detail in Section 7. Foetal age from crown rump length of foetus It is often dicult from information obtained from pregnant women to. Some estimate of the foetal age is important for a number of reasons. What is an approximate date of birth? If an abortion is being requested, is the foetal age greater than some minimum period beyond which a legal abortion is impossible?

If we do not know the foetal age how can we assess whether the foetus is growing normally? Sonar screening has now provided a safe method of making measurements on the foetus, and one such measurement is of the crown rump length.

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In a study of the interrelationship of crown rump length and foetal age pregnant women for whom foetal ages were reliable to within 3 days were screened, some on a number of dierent occasions, and the crown rump lengths determined. Altogether the study provided pairs of observations for the women according to the schedule recorded in data set foetal.

A recent referral to the clinic is a pregnant woman whose conception date is uncertain. Sonar screening reveals a crown rump length of 35 mm for the foetus.

What can be deduced about the age of the foetus? This is obviously a problem of indirect measurement, because we are trying to infer the foetal age u of the new case from information about the crown rump length v of her foetus and the data D contained in the data set foetal.

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One such form of inference would be to attempt to provide some assessment of the conditional density function p u v, D, M u U , where U is the set of possible foetal ages and M is again retained to emphasize that model assumptions may have a central role to play in the evaluation. With this conditional density function available we would obviously be in a strong position to make inferential statements about foetal age.

We continue our analysis of this problem in Section 7. The following further examples are presented to indicate the varying nature of the problems in this area of medicine. Dierential diagnosis of Cushings syndrome Cushings syndrome is due to the over-secretion of cortisol by the adrenal glands. There are, however, four dierent forms that this syndrome may take, namely: a: adrenal adenoma, b: bilateral hyperplasia, c: adrenal carcinoma, d: ectopic carcinoma.

Type v3 v4 v5 v14 Type v3 v4 v5 v14 a1 3. The question of the diagnostic value of the urinary excretion rates of the fourteen steroid metabolites already referred to in Section 1.

“Applying Statistics in the Medical Field,” F. Arved Harding, M.S.

The data on four of the steroid metabolites are shown in Table 1. A glance at the pregnenetriol column, for example, shows that for bilateral hyperplasia patients the levels are moderate whereas for ectopic carcinoma patients these are high, and so this diagnostic test is of some value in dis- tinguishing between hyperplasia and ectopic carcinoma. To what extent does this dierential ability extend to the other tests and with respect to the other forms, and indeed to distinguishing between normal individuals and cases of Cushings syndrome.

Is it in fact necessary to use all the tests? Are there any features of these tests which could make a misleading diagnosis possible? Could we perhaps obtain as rm a diagnostic opinion with only a subset of the tests?

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  • The reader may at this stage wish to attempt an intuitive diagnostic assess- ment for the four undiagnosed cases I1I4 whose data are given in Table 1. The problem here diers from that of Conns syndrome. Obvious aspects are the larger number of disease types and the increased dimension of the feature vector.

    More substantial dierences, however, exist in the fact that the abnormal cases comprise an aggregated set from dierent clinics with possible variation in clinic referral processes.


    The selection process for normal cases is yet again dierent. As indicated in Section 1. Dierential diagnosis of non-toxic goitre Non-toxic goitre is a disease which may take one of three possible forms: a: simple goitre, b: Hashimotos disease, c: thyroid carcinoma. The case records for past cases showing the results of 4 tests are set out in data set goitre. All the cases have been correctly diagnosed by histopatho- logical examination. The questions to be asked are again of the same form as previously. Do the 4 tests provide an adequate diagnostic basis, and if so, what diagnosis do they provide for the as yet undiagnosed cases given in data set goitre?