PDF | On Jan 1, , Tiny Nair and others published Medical Statistics Made Easy for the Medical Practitioner. MADE EASY. Michael Harris. General Practitioner and Lecturer in. General Practice, Bath, UK and. Gordon Taylor. Senior Research Fellow in Medical Statistics. 50 Scientifically. Proven Ways to. Be Persuasive. Noah J. Goldstein, Steve J. Martin,. Robert B. Cialdini. Bestselling Word Power Made Easy.

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MEDICAL STATISTICS MADE EASY, 3RD EDITION. Reviewed by Dr John Purvis . Additional article information. Medical Statistics Made Easy. 3rd Edition. Important Note from the Publisher. The information contained within this book was obtained by Scion Publishing. Ltd from sources believed by us to be reliable. Principles of medical statistics / Alvan R. Feinstein. . “computer-intensive” work can be done quickly and easily, requiring no more effort than.

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Forgot password? All credit should be accredited to DK Company - b. Plus, we regularly update and improve textbook solutions based on student ratings and feedback, so you can be sure you're getting the latest information available. How is Chegg Study better than a printed Medical Statistics Made Easy, third edition student solution manual from the bookstore?

Our interactive player makes it easy to find solutions to Medical Statistics Made Easy, third edition problems you're working on - just go to the chapter for your book. Hit a particularly tricky question? Bookmark it to easily review again before an exam.

The best part? As a Chegg Study subscriber, you can view available interactive solutions manuals for each of your classes for one low monthly price. You will find that the examples help you to understand the principles. If there is a word that you do not understand, check it out in the glossary.

How important is it? We noted how often statistical terms were used in randomly selected papers in mainstream medical journals. We grouped the terms into concepts and graded them by how often they were used. This helped us to develop a star system for importance. We also took into account usefulness to readers. For example, numbers needed to treat are not often quoted but are fairly easy to calculate and useful in making treatment decisions. Concepts which are used in the majority of medical papers.

Important concepts which are used in at least a third of papers. Less frequently used, but still of value in decisionmaking. Rarely used in medical journals.

Medical Statistics Made Easy

How easy is it to understand? We have found that the ability of health care professionals to understand statistical concepts varies more widely than their ability to understand anything else related to medicine. This ranges from those that have no difficulty learning how to understand regression to those that struggle with percentages. One of the authors not the statistician!

He graded each section by how easy it is to understand the concept. With a little concentration, most readers should be able to follow these concepts. Some readers will have difficulty following these.

You may need to go over these sections a few times to be able to take them in. Quite difficult to understand.

Only tackle these sections when you are fresh. Statistical concepts that are very difficult to grasp. When is it used? One thing you need to do if critically appraising a paper is check that the right statistical technique has been used. This part explains which statistical method should be used for what scenario. This explains the bottom line what the results mean and what to look out for to help you interpret them.

Examples Sometimes the best way to understand a statistical technique is to work through an example. Simple, fictitious examples are given to illustrate the principles and how to interpret them. Watch out for Exam tips This includes more detailed explanation, tips and common pitfalls.

We have given tips on how to approach these concepts. An understanding of percentages is probably the first and most important concept to understand in statistics! How easy are they to understand? When are they used? Percentages are mainly used in the tabulation of data in order to give the reader a scale on which to assess or compare the data. What do they mean? Per cent means per hundred, so a percentage describes a proportion of To calculate a percentage, divide the number of items or patients in the category by the total number in the group and multiply by The researcher wanted to compare their ages.

The data for age were put in decade bands and are shown in Table 1. Table 1. Authors can use percentages to hide the true size of the data. So, percentages should be used as an additional help for the reader rather than replacing the actual data.

A mean appeared in 2 3 papers surveyed, so it is important to have an understanding of how it is calculated. However, in most groups that we have taught there has been at least one person who admits not knowing how to calculate the mean, so we do not apologize for including it here.

It is used when the spread of the data is fairly similar on each side of the mid point, for example when the data are normally distributed. The normal distribution is referred to a lot in statistics.

It s the symmetrical, bell-shaped distribution of data shown in Fig. The normal distribution. The dotted line shows the mean of the data.

What does it mean? The mean is the sum of all the values, divided by the number of values. Add these ages together: If a value or a number of values is a lot smaller or larger than the others, skewing the data, the mean will then not give a good picture of the typical value. In this case, the median may be a more suitable mid-point to use see page E X A M T I P A common multiple choice question is to ask the difference between mean, median see page 12 and mode see page 14 make sure that you do not get confused between them.

It is given in over a third of mainstream papers. It is used to represent the average when the data are not symmetrical, for instance the skewed distribution in Fig. A skewed distribution. The dotted line shows the median.

Compare the shape of the graph with the normal distribution shown in Fig. It is the point which has half the values above, and half below. However, in the second example with six patients aged 52, 55, 56, 58, 59 and 92 years, there are two middle ages, 56 and The median is halfway between these, i. This gives a better idea of the mid-point of this skewed data than the mean of The median may be given with its inter-quartile range IQR.

The 1 st quartile point has the 1 4 of the data below it, the 3 rd quartile has the 3 4 of the sample below it, so the IQR contains the middle 1 2 of the sample. This can be shown in a box and whisker plot. One ward had two patients that were nil by mouth. The median was Box and whisker plot of energy intake of 50 patients over 24 hours.

The ends of the whiskers represent the maximum and minimum values, excluding extreme results like those of the two nil by mouth patients. Rarely quoted in papers and of limited value. It is used when we need a label for the most frequently occurring event. The mode is the most common of a set of events.

Graph of eye colour of patients attending an eye clinic. In this case the mode is brown, the commonest eye colour. Generally when this is mentioned in papers it is as a concept rather than from calculating the actual values, e. The data appear to follow a bi-modal distribution. See Fig. Graph of ages of patients with asthma in a practice. The arrows point to the modes at ages and Bi-modal data may suggest that two populations are present that are mixed together, so an average is not a suitable measure for the distribution.

Quoted in half of papers, it is used as the basis of a number of statistical calculations. LLL It is not an intuitive concept. Standard deviation SD is used for data which are normally distributed see page 9 , to provide information on how much the data vary around their mean.

SD indicates how much a set of values is spread around the average. The mean weight of the patients was 80 kg. For this group, the SD was calculated to be 5 kg. Graph showing normal distribution of weights of patients enrolling in a trial with mean 80 kg, SD 5 kg. Compare this with the example above Number of patients Weight kg Fig. Graph showing normal distribution of weights of patients enrolling in a trial with mean 80 kg, SD 3 kg. SD should only be used when the data have a normal distribution.

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However, means and SDs are often wrongly used for data which are not normally distributed. A simple check for a normal distribution is to see if 2 SDs away from the mean are still within the possible.

For example, if we have some length of hospital stay data with a mean stay of 10 days and a SD of 8 days then: This is clearly an impossible value for length of stay, so the data cannot be normally distributed. The mean and SDs are therefore not appropriate measures to use. Good news it is not necessary to know how to calculate the SD. Keeping the normal distribution curve in Fig. Again, try to memorize those percentages.

Important given in 3 4 of papers.

Medical Statistics Made Easy

LL A difficult concept, but one where a small amount of understanding will get you by without having to worry about the details. Confidence intervals CI are typically used when, instead of simply wanting the mean value of a sample, we want a range that is likely to contain the true population value. This true value is another tough concept it is the mean value that we would get if we had data for the whole population.

Statisticians can calculate a range interval in which we can be fairly sure confident that the true value lies. For example, we may be interested in blood pressure BP reduction with antihypertensive treatment.

From a sample of treated patients we can work out the mean change in BP. If we took another group of patients we would not expect to get exactly the same value, because chance can also affect the change in BP.

The CI gives the range in which the true value i. After treatment with the new drug the mean BP dropped by 20 mmhg. This CI includes zero no change. The size of a CI is related to the sample size of the study.

Larger studies usually have a narrower CI. Where a few interventions, outcomes or studies are given it is difficult to visualize a long list of means and CIs. Some papers will show a chart to make it easier.

For example, meta-analysis is a technique for bringing together results from a number of similar studies to give one overall estimate of effect. Many meta-analyses compare the treatment effects from.

An example is given in Fig. Plot of 5 studies of a new antihypertensive drug. See how the results of studies A and B above are shown by the top two lines, i. The vertical axis does not have a scale. It is simply used to show the zero point on each CI line. The statistician has combined the results of all five studies and calculated that the overall mean reduction in BP is 14 mmhg, CI This is shown by the combined estimate diamond.

See how combining a number of studies reduces the CI, giving a more accurate estimate of the true treatment effect. The chart shown in Fig. Standard deviation and confidence intervals what is the difference? Standard deviation tells us about the variability spread in a sample. The CI tells us the range in which the true value the mean if the sample were infinitely large is likely to be.

Which study showed the greatest change? Did all the studies show change in favour of the intervention? Were the changes statistically significant?

In the example above, study D showed the greatest change, with a mean BP drop of 25 mmhg. The wide CI could be due to a low number of patients in the study. A really important concept, P values are given in more than four out of five papers. LLL Not easy, but worth persevering as it is used so frequently. It is not important to know how the P value is derived just to be able to interpret the result. The P probability value is used when we wish to see how likely it is that a hypothesis is true.

The hypothesis is usually that there is no difference between two treatments, known as the null hypothesis. The P value gives the probability of any observed difference having happened by chance.

However, this is an arbitrary figure. If we look at 20 studies, even if none of the treatments work, one of the studies is likely to have a P value of 0. The lower the P value, the less likely it is that the difference happened by chance and so the higher the significance of the finding.

It means that the difference will only have happened by chance 1 in times. This is unlikely, but still possible. It is usually considered to be very highly significant. Say there is a new fertility treatment and we want to know whether it affects the chance of having a boy or a girl. Therefore we set up a null hypothesis that the treatment does not alter the chance of having a girl. Out of the first 50 babies resulting from the treatment, 15 are girls.

We then need to know the probability that this just happened by chance, i. The P value gives the probability that the null hypothesis is true. The P value in this example is Do not worry about how it was calculated, concentrate on what it means. It means the result would only have happened by chance in in 1 or 1 in times if the treatment did not actually affect the sex of the baby.

This is highly unlikely, so we can reject our hypothesis and conclude that the treatment probably does alter the chance of having a girl. Try another example: Patients with minor illnesses were randomized to see either Dr Smith or Dr Jones.

Dr Smith ended up seeing patients in the study whereas Dr Jones saw patients Table 2. Table 2. The null hypothesis is a concept that underlies this and other statistical tests. The test method assumes hypothesizes that there is no null difference between the groups. The result of the test either supports or rejects that hypothesis. The null hypothesis is generally the opposite of what we are actually interested in finding out.

If we are interested if there is a difference between two treatments then the null hypothesis would be that there is no difference and we would try to disprove this.

Try not to confuse statistical significance with clinical relevance. If a study is too small, the results are unlikely to be statistically significant even if the intervention actually works. Conversely a large study may find a statistically significant difference that is too small to have any clinical relevance.

In the example above, only two of the sets of data showed a significant difference between the two GPs. Dr Smith s consultations were very highly significantly shorter than those of Dr Jones. Dr Smith s follow-up rate was significantly higher than that of Dr Jones. Used in one in three papers, they are an important aspect of medical statistics. L The details of the tests themselves are difficult to understand.

Thankfully you do not need to know them. Just look for the P value see page 24 to see how significant the result is. Remember, the smaller the P value, the smaller the chance that the null hypothesis is true. Parametric statistics are used to compare samples of normally distributed data see page 9. If the data do not follow a normal distribution, these tests should not be used. A parametric test is any test which requires the data to follow a specific distribution, usually a normal distribution.

These techniques can also allow for independent variables which may have an effect on the outcome. Again, check out the P value. They test the probability that the samples come from a population with the same mean value.

It is covered separately page The null hypothesis is that there is no difference between the bronchodilator and the placebo.Variables and Data A variable contains data about anything we measure. Photo credits are required where indicated, and some links to sources provided. A negative correlation coefficient means that as the value of one variable goes up the value for the other variable goes down the graph slopes down from left. The CI tells us the range in which the true value the mean if the sample were infinitely large is likely to be.

Generally when this is mentioned in papers it is as a concept rather than from calculating the actual values, e. His main role is in the teaching, support and supervision of health care professionals involved in non-commercial research.

You've measured a variable in two groups, and the means and medians are distinct. Public Health Image Library. The First Guaranty Bank does not have any responsibility or control over any of these external web sites, their content or their privacy policies.