This clinical trial consists over 1000 patients being evaluated for the response to a new treatment against a currently licensed treatment for ulcerative colitis. Therefore, it is comparing its efficacy to an established therapeutic or control in a larger population of volunteers. These are the characteristics of a phase III clinical trial.

An epidemic is declared when the increase in a give disease is above a certain level in a specific interval of time.

An endemic is the general, usual level of a disease in a population at a particular time.

A pandemic is an epidemic that is spread across many countries and continents.

The following terms are used in medical testing:

True negative – The test is negative and the patient does not have the disease.
True positive – The test is positive and the patient has the disease.
False positive – The test is positive but the patient does not have the disease.
False negative – The test is negative but the patient has the disease.

The sensitivity of a predictive test = true positives / (true positives + false negatives).

The specificity of a test = true negatives / (false positives + true negatives).

The negative predictive value of a test = true negatives / (false negatives + true negatives).

The positive predictive value is the ratio of patients truly diagnosed as positive to all those who had positive test results. In this case, this is 60/(60+20)=75%.

The negative predictive value is the ratio of patients truly diagnosed as negative to all those who had negative test results. In this case, this is 80/(80+40)=67%.

The sensitivity is the ratio of patients with the disease who test positive i.e. true positive patients to the total number of people with the disease. In this case, this is 60/(60+40)=60%.

The specificity is the ratio of people who don’t have the disease who test negative i.e. true negatives to the total number of people without the disease. In this case, this is 80/(20+80)=80%.

70% is not the result of any screening measurements

The Delphi method relies on expert consensus. This method kicks off with an open ended questionnaire and uses its responses as a survey instrument for the next round in which each of the participants is asked to rate the items that the investigators have summarized on the basis of the data collected in the first round. Any disagreement is further discussed in phases to come on the basis of information obtained from previous phases.

The Kappa Statistic or Cohen’s Kappa is a statistical measure of inter-rater reliability for categorical variables. It is used when two raters both apply a criterion based on a tool to assess whether or not some condition occur. A good example can be two doctors rating individuals for diabetes occurrence on the basis of symptoms.

It gives a quantitative measure of the magnitude of agreement between observers.

Kappa can take any value between 0 and 1. 0 implies the observers are in complete disagreement and a value of 1 implies complete agreement.

Attributable proportion is the proportion of disease that is caused due to exposure. It refers to the proportion of disease that would be eradicated from a particular population if the disease rate was diminished to match that of the unexposed group.

Risk ratio (relative risk) compares the probability of an event in an exposed (experimental) group to that of an event in the unexposed (control) group. Thus two are not the same.

The attributable risk is the rate of a disease in an exposed group to that of a group that has not been exposed to it i.e. how many deaths did the exposure cause.

Number needed to treat can be defined as the number of patients who need to be treated to prevent one additional bad outcome.

It can be found as:

NNT=1/Absolute Risk Reduction (rounded to the next integer since number of patients can be integer only).

where ARR= (Risk factor associated with the new drug group) — (Risk factor associated with the currently available drug)

So,

ARR= (43.9-37.3)

ARR= 6.6

NNT= 1000/6.6

NNT=151.5

Number needed to treat (NNT) is a time specific epidemiological measure that indicates how many patients would be require for an intervention to prevent one additional bad outcome. A perfect NNT would be 1, where everyone improves with treatment, thus the higher the NNT, the less effective the treatment.

Thus if you treat 1000 patients then you will expect to have 50 fewer strokes.

Only on rare occasions has it been found that the prevalence and incidence were same. Incidence can be greater than prevalence in acute cases only. In case of chronic diseases prevalence is far greater than incidence. One needs to have a deeper understanding of both the concepts to understand the health literature.

The Weibull distribution are used to describe various types of observed failures of the components. it is used in reliability and survival analysis.

Regression Analysis is used to measure the relationship between among two or more variable. It determines the effect of independent variables on the dependent variables.

Student t-test is one of the most commonly used method to test the hypothesis. It determines the significant difference between the means of two different groups.

A time series is a collection of observations of well-defined data obtained at regular interval of time.

Kaplan-Meier estimator is used to estimate the survival function from lifetime data. It can be derived from maximum likelihood estimation of hazard function. It is most likely used to measure the fraction of patient’s life for a certain amount of time after treatment.

The study is being conducted on a smaller level with only 200 participants and is determining the effectiveness of the drug in comparison to a placebo. These characteristics are in accordance with the second phase of trial.

The data is ideal for chi square test evaluation as it will help determine if observed outcomes are in line with expected outcomes, and also if results are significant or due to chance.

The student’s t test is not ideal as it requires comparison of means from different populations, rather than proportions.

Pearson’s coefficient of linear regression is not ideal as it requires the plotting of a linear regression.

The numbers should be analysed before determining if there are any statistical conclusions that can be drawn from the population.

Statistical analysis is always required to determine the performance of any treatment during a clinical drug trial. Conclusions cannot be drawn simply by looking at the data.

The standard error of the mean (SEM) is a measure of the spread expected for the mean of the observations – i.e. how ‘accurate’ the calculated sample mean is from the true population mean. The relationship between the standard error of the mean and the standard deviation is such that, for a given sample size, the standard error of the mean equals the standard deviation divided by the square root of the sample size.

SEM = SD / square root (n)

where SD = standard deviation and n = sample size

Therefore, the SEM gets smaller as the sample size (n) increases.

If we want to depict how widely scattered some measurements are, we use the standard deviation. For indicating the uncertainty around the estimate of the mean, we use the standard error of the mean. The standard error is most useful as a means of calculating a confidence interval. For a large sample, a 95% confidence interval is obtained as the values 1.96×SE either side of the mean.

A 95% confidence interval:

lower limit = mean – (1.96 * SEM)

upper limit = mean + (1.96 * SEM)

Results such as mean value are often presented along with a confidence interval. For example, in a study the mean height in a sample taken from a population is 183cm. You know that the standard error (SE) (the standard deviation of the mean) is 2cm. This gives a 95% confidence interval of 179-187cm (+/- 2 SE).

Hence, it would be wrong to say that confidence levels do not apply to standard error of the mean.

Work up bias involves comparing the novel diagnostic test with the current standard test. A portion of the patients undergo the standard test while others undergo the new test as the standard test is costly. The result can be alteration in specify and sensitivity.

Selection bias is when randomisation is not achieved.

Attention bias refers to the person’s failure to consider various alternatives when he pre occupied by some other thoughts.

Instrument bias is related to the experience and extent of familiarization of the participating individuals with the test.

Co intervention bias is characterized by the groups receiving different co interventions.

A forest plot is a graphical representation that summarizes the findings of several research, such as a meta-analysis of a series of randomized controlled trials.

The Kaplan-Meier estimate shows survival over time, for example, plotting the number of patients still alive seven years after chemotherapy for lung cancer.

Fisher’s exact test similarly uses contingency tables to assess statistical significance, however, it is typically used when sample sizes are small.

Chi-square test assesses whether an association exists between two categorical variables using the observed and expected frequencies. For instance, is social class (I-V) related to body mass index (BMI) category? Using the observed and anticipated frequencies, the Chi-square test determines whether a connection exists between two categorical variables. For example, is socio-economic status related to BMI category?

Linear regression is a technique which attempts to model the relationship between two variables by fitting a linear equation to observed data. Linear regression uses correlation between two continuous variables. As correlation only indicates the strength of an association only, it cannot be used to forecast the change in one variable when a second variable is altered.

This equation takes the form y = mx + c, where ‘y’ is the dependent variable, ‘x’ is the independent variable, ‘m’ is the slope of the line and ‘c’ is the intercept. In this example, for a range of heights, it would be possible to map a line of best fit to a scatter plot and thus predict the forced expiratory volume (FEV1) for an individual.

99.7% of the values within 3 standard deviations of the mean.

For 99.7% confidence interval, you can find the range as follows:

1. Multiply the standard error by 3.

2. Subtract the answer from mean value to get the lower limit.

3. Add the answer obtained in step 1 from the mean value to get the upper limit.

For a confidence interval of 68%, multiply the standard error with 1 and repeat the process. For a 95% confidence interval, Standard Error is multiplied by 1.96 to get the interval.

Both incidence and prevalence are indicators of the disease frequency. While incidence tells us about the number of cases reported per population in a provided time period, prevalence is the factor you should be vigilant about as it tells us about the total number of cases that have been reported in a population at a particular point of time.

The Number Needed to Treat (NNT) is the number of patients you need to treat to prevent one additional bad outcome. For example, if a drug has an NNT of 5, it means you have to treat 5 people with the drug to prevent one additional bad outcome.

To calculate the NNT, you need to know the Absolute Risk Reduction (ARR); the NNT is the inverse of the ARR:

NNT = 1/ARR

Where ARR = CER (Control Event Rate) – EER (Experimental Event Rate).

NNTs are always rounded up to the nearest whole number.

In this case, the NNT can be computed as follows:

ARR = 10% – 5% = 0.05

NNT = 1/0.05 = 20

A prospective randomised double blind controlled trial against cyclizine in multiple centres is the most likely to change your practice.

Case controlled studies are efficient in identifying an association between a drug treatment and outcome and are usually conducted retrospectively. They are generally less valued than prospective randomised trials. They cannot generate incidence data, are subject to bias, have difficult selection of controls and can be made more difficult if note keeping is not reliable.

The gold standard in intervention-based studies is randomised controlled double blind trials. Its features are:

Treating all intervention groups identically
Reduction of bias by random allocation to intervention groups
Patients and researchers unaware of which treatment was given until at completion of study
Patients analysed within the group to which they were allocated, and
Analysis focused on estimating the size of the difference in predefined outcomes between intervention groups.

New healthcare interventions should be evaluated through properly designed randomised controlled trials (though there are some potential ethical disadvantages)

Conducting trials in multiple centres is an accepted way of evaluating a new drug as it may be the only way of recruiting sufficient number of patients within a reasonable time frame to satisfy the objectives of the trial. Type II statistical errors will occur if a small numbers of patients is used in study group.