The hazard ratio describes the relative risk of the complication based on comparison of event rates.

Hazard ratios have also been used to describe the outcome of therapeutic trials where the question is to what extent treatment can shorten the duration of the illness. However, the hazard ratio, a type of relative risk, does not always accurately portray the degree of abbreviation of the illness that occurred. In these circumstances, time-based parameters available from the time-to-event curve, such as the ratio of the median times of the placebo and drug groups, should be used to describe the magnitude of the benefit to the patient. The difference between hazard-based and time-based measures is analogous to the odds of winning a race and the margin of victory. The hazard ratio is the odds of a patient’s healing faster under treatment but does not convey any information about how much faster this event may occur.

A t test is a type of statistical test that is used to compare the means of two groups. It is one of the most widely used statistical hypothesis tests in pain studies. There are two types of statistical inferences: parametric and nonparametric methods. Parametric methods refer to a statistical technique in which one defines the probability distribution of probability variables and makes inferences about the parameters of the distribution. In cases in which the probability distribution cannot be defined, nonparametric methods are employed. T tests are a type of parametric method; they can be used when the samples satisfy the conditions of normality, equal variance, and independence. In this case the data is parametric, and they belong to the same patients, so a paired t test should be used.

Absolute risk reduction (ARR) – also called risk difference (RD) – is the most useful way of presenting research results to help your decision-making. Absolute risk reduction = (Control event rate) – (Experimental event rate) = 0.05 = 5% reduction

PCR is a simple, yet elegant, enzymatic assay, which allows for the amplification of a specific DNA fragment from a complex pool of DNA. PCR can be performed using source DNA from a variety of tissues and organisms, including peripheral blood, skin, hair, saliva, and microbes. Only trace amounts of DNA are needed for PCR to generate enough copies to be analysed using conventional laboratory methods. For this reason, PCR is a sensitive assay. Each PCR assay requires the presence of template DNA, primers, nucleotides, and DNA polymerase. The DNA polymerase is the key enzyme that links individual nucleotides together to form the PCR product. The above mentioned components are mixed in a test tube or 96-well plate and then placed in a machine that allows repeated cycles of DNA amplification to occur in three basic steps. The machine is essentially a thermal cycler. It has a thermal block with holes, into which the test tubes or plates holding the PCR reaction mixture are inserted. The machine raises and lowers the temperature of the block in discrete, precise and pre-programmed steps. The reaction solution is first heated above the melting point of the two complementary DNA strands of the target DNA, which allows the strands to separate, a process called denaturation. The temperature is then lowered to allow the specific primers to bind to the target DNA segments, a process known as hybridization or annealing. Annealing between primers and the target DNA occurs only if they are complementary in sequence (e.g. A binding to G). The temperature is raised again, at which time the DNA polymerase is able to extend the primers by adding nucleotides to the developing DNA strand. With each repetition of these three steps, the number of copied DNA molecules doubles.

The definition of negative predictive value is the probability that the individuals with truly negative screening test don’t have Chlamydia. The equation is the following: Negative predictive value = Truly negative/(truly negative + false negative) = 172 / (172 + 5) = 172 / 177

A t test is a type of statistical test that is used to compare the means of two groups. It is one of the most widely used statistical hypothesis tests in pain studies. There are two types of statistical inferences: parametric and nonparametric methods. Parametric methods refer to a statistical technique in which one defines the probability distribution of probability variables and makes inferences about the parameters of the distribution. In cases in which the probability distribution cannot be defined, nonparametric methods are employed. T tests are a type of parametric method; they can be used when the samples satisfy the conditions of normality, equal variance, and independence. In this case the data is parametric, comparing two independent samples from the same population.

Relative risk = (Incidence in exposed group)/incidence in unexposed group). So in this case RR = (50/100)/(50/200) = 2.

The Chi-squared test evaluates if two variables are related. The other statistical tests mentioned do not perform this function.

The incidence rate is the number of new cases per population at risk in a given time period. For example, if a population initially contains 1,000 non-diseased persons and 28 develop a condition over two years of observation, the incidence proportion is 28 cases per 1,000 persons per two years, i.e. 2.8% per two years.
Prevalence is the proportion of a particular population found to be affected by a medical condition (typically a disease or a risk factor such as smoking or seat-belt use). It is derived by comparing the number of people found to have the condition with the total number of people studied, and is usually expressed as a fraction, as a percentage, or as the number of cases per 10,000 or 100,000 people.
Incidence should not be confused with prevalence, which is the proportion of cases in the population at a given time rather than rate of occurrence of new cases. Thus, incidence conveys information about the risk of contracting the disease, whereas prevalence indicates how widespread the disease is.

1B: Individual Randomised Control Trial (with narrow confidence intervals)

A case-control study (also known as case-referent study) is a type of observational study in which two existing groups differing in outcome are identified and compared on the basis of some supposed causal attribute. Case-control studies are often used to identify factors that may contribute to a medical condition by comparing subjects who have that condition/disease (the cases) with patients who do not have the condition/disease but are otherwise similar (the controls).
An odds ratio (OR) is a statistic that quantifies the strength of the association between two events, A and B. The odds ratio is defined as the ratio of the odds of A in the presence of B and the odds of A in the absence of B or vice versa.

The type of significance test used depends on whether the data is parametric (can be measured, usually normally distributed) or non-parametric.
Parametric tests:
Student’s t-test – paired or unpaired*
Pearson’s product-moment coefficient – correlation

Non-parametric tests:
Mann-Whitney U test – unpaired data
Wilcoxon signed-rank test – compares two sets of observations on a single sample
chi-squared test – used to compare proportions or percentages
Spearman, Kendall rank – correlation.

The outcome measured is not normally distributed, i.e. it is non-parametric. This excludes the Student’s t-tests. We are not comparing percentages/proportions so the chi-squared test is excluded. The Mann-Whitney U test is a nonparametric test of the null hypothesis that it is equally likely that a randomly selected value from one sample will be less than or greater than a randomly selected value from a second sample.
This test can be used to investigate whether two independent samples were selected from populations having the same distribution.

Prevalence, sometimes referred to as prevalence rate, is the proportion of persons in a population who have a particular disease or attribute at a specified point in time or over a specified period of time.

The power of a test is defined as 1 − the probability of Type II error. The Type II error is concluding at no difference (the null is not rejected) when in fact there is a difference, and its probability is named β. Therefore, the power of a study reflects the probability of detecting a difference when this difference exists. It is also very important to medical research that studies are planned with an adequate power so that meaningful conclusions can be issued if no statistical difference has been shown between the treatments compared. More power means less risk for Type II errors and more chances to detect a difference when it exists.

Positive predictive value = TP (true positives) / [TP + FP (false positives)] = 40 / (40 + 20) = 0.66

A cohort study is a particular form of longitudinal study that samples a cohort (a group of people who share a defining characteristic, typically those who experienced a common event in a selected period, such as birth or graduation), performing a cross-section at intervals through time.
The relative risk (RR) or risk ratio is the ratio of the probability of an outcome in an exposed group to the probability of an outcome in an unexposed group. Relative risk is used in the statistical analysis of the data of experimental, cohort and cross-sectional studies, to estimate the strength of the association between treatments or risk factors, and outcomes.

The SEM is an indicator of how close the sample mean is to the population mean. In reality, however, only one sample is extracted from the population. Therefore, the SEM is estimated using the standard deviation (SD) and a sample size (Estimated SEM). The SEM computed by a statistical program is an estimated value calculated via this process.

Estimated Standard Error of the Mean (SEM)=SDn?

Steps required in performing the Wilcoxon signed rank test:

1 State the null hypothesis and, in particular, the hypothesized value for comparison
2 Rank all observations in increasing order of magnitude, ignoring their sign. Ignore any observations that are equal to the hypothesized value. If two observations have the same magnitude, regardless of sign, then they are given an average ranking
3 Allocate a sign (+ or -) to each observation according to whether it is greater or less than the hypothesized value (as in the sign test)
4 Calculate:
R+ = sum of all positive ranks
R- = sum of all negative ranks
R = smaller of R+ and R-
5 Calculate an appropriate P value What makes this test the most appropriate for this study is that the data is non-parametric, paired and comes from the same population.

Absolute risk reduction (ARR) – also called risk difference (RD) – is the most useful way of presenting research results to help your decision-making. Absolute risk reduction = (Control event rate) – (Experimental event rate) = 0.15 = 15%