A written protocol is important in clinical research as it helps to decrease bias with early findings as the process of the trial has been documented in advance. To show that the study has been properly undertaken before commencing, the investigators must document their names; they must describe the process, detail the analysis that will be undertaken, and provide a power calculation. The ethics committee does not always need to see the protocol.

The P value is defined as the probability under the assumption of no effect or no difference (null hypothesis), of obtaining a result equal to or more extreme than what was actually observed. The P stands for probability and measures how likely it is that any observed difference between groups is due to chance. Being a probability, P can take any value between 0 and 1. Values close to 0 indicate that the observed difference is unlikely to be due to chance, whereas a P value close to 1 suggests no difference between the groups other than due to chance. Thus, it is common in medical journals to see adjectives such as highly significantť or very significantť after quoting the P value depending on how close to zero the value is.A lower p-value is sometimes interpreted as meaning there is a stronger relationship between two variables. However, statistical significance means that it is unlikely that the null hypothesis is true (less than 5%).To understand the strength of the difference between two groups (control vs. experimental) a researcher needs to calculate the effect size.

The study described in the question is a ‘cohort’ study.Other options:A prospective observational study – When groups are classified according to one or more factors at a given time and followed forward to determine outcomes (usually some health status)- Although there is in one sense a control group (those who do not develop the health problem), this is not generally called a ‘controlled’ trial. – An ecological study would look at outcomes in different groups (countries or regions usually) who follow different practices.

An odds ratio (OR) is a measure of the association between an exposure and an outcome. The OR represents the odds that an outcome will occur given a particular exposure, compared to the odds of the outcome occurring in the absence of that exposure. Odds ratios are most commonly used in case-control studies, however they can also be used in cross-sectional and cohort study designs as well (with some modifications and/or assumptions). Wherea = Number of exposed casesb = Number of exposed non-casesc = Number of unexposed casesd = Number of unexposed non-casesOR=(a/c) / (b/d) = ad/bc

In case control studies, observational studies are made to compare two groups, individuals with a certain condition, to those without the condition, with all other factors being equal. In the situation presented by the question, the study was to determine whether a prior risk factor, a low birth weight, was associated with later developing adolescent hypertension, using a paired t-test to statistically determine whether there was indeed a significant association between the two conditions. Case control studies only help to draw associations between two groups and therefore cannot indicate causation. In this scenario we are also not given the p value to determine whether the association is statistically significant. Therefore there is not enough evidence to conclude whether or not birthweight affects the development of adolescent hypertension.

The sensitivity of a screening test can be described in a variety of ways, typically such as sensitivity being the ability of a screening test to detect a true positive, being based on the true positive rate, reflecting a test’s ability to correctly identify all people who have a condition, or, if 100%, identifying all people with a condition of interest by those people testing positive on the test.The specificity of a test is defined in a variety of ways, typically such as specificity is the ability of a screening test to detect a true negative, being based on the true negative rate, correctly identifying people who do not have a condition, or, if 100%, identifying all patients who do not have the condition of interest by those people testing negative on the test.

An odds ratio (OR) is a measure of association between an exposure and an outcome. The OR represents the odds that an outcome will occur given a particular exposure, compared to the odds of the outcome occurring in the absence of that exposure. Odds ratios are most commonly used in case-control studies, however they can also be used in cross-sectional and cohort study designs as well (with some modifications and/or assumptions). Wherea = Number of exposed casesb = Number of exposed non-casesc = Number of unexposed casesd = Number of unexposed non-casesOR=(a/c) / (b/d) = ad/bc

Randomization is one of the ethical protocols for research work, involving unbiased sample selection. Consent is required before undertaking any research and is taken for the complete research work at once. No separate consent is required for the process of randomization. The consent should explain each and every aspect of the study, including details about the process of randomization. Only particular study designs, like Zelen design, permit the avoidance of consent to randomization but these studies have their setbacks.

Compared to parametric tests, non parametric tests are seen to be less powerful as they often contend with fewer assumptions, and may use less information from the data. Sample sizes can be smaller. The samples do not have to follow a normal distribution as in parametric tests, and data may be ordinal, ranked or contain outliers that cannot be removed. Therefore nonparametric tests are well suited for these instances and do not need to be used as a last resort. They are not any more complex than parametric tests.

The main result of a correlation is called the correlation coefficient (or r). It ranges from -1.0 to +1.0. The closer r is to +1 or -1, the more closely the two variables are related.If r is close to 0, it means there is no relationship between the variables. If r is positive, it means that as one variable gets larger the other gets larger. If r is negative it means that as one gets larger, the other gets smaller (often called an inverse correlation).

Correlation is a bivariate analysis that measures the strength of association between two variables and the direction of the relationship.Pearson r correlation: Pearson r correlation is the most widely used correlation statistic to measure the degree of the relationship between linearly related variables. Pearson’s correlation coefficient is the test that measures the statistical relationship, or association, between two continuous variables. It is known as the best method of measuring the association between variables of interest because it is based on the method of covariance. It gives information about the magnitude of the association, or correlation, as well as the direction of the relationship.The non-parametric Spearman or Kendall rank correlation coefficient is used if neither variable has a normal distribution or the sample size is small (i.e. <20).

To quantify differences between percentages you can use Fisher’s exact test.Odds ratios, relative risks and number needed to treat are ways of quantifying differences between percentages in two groups, however are not in themselves significance tests.

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.

Linear regression is a basic and commonly used type of predictive analysis. The overall idea of regression is to examine two things: (1) do a set of predictor variables do a good job in predicting an outcome (dependent) variable? (2) Which variables, in particular, are significant predictors of the outcome variable, and in what way do they-indicated by the magnitude and sign of the beta estimates-impact the outcome variable.These regression estimates are used to explain the relationship between one dependent variable and one or more independent variables. Three major uses for regression analysis are (1) determining the strength of predictors, (2) forecasting an effect, and (3) trend forecasting

Power calculations are a important step in study design and preparation, but do not directly help with the interpretation of the results. They are used to determine how many subjects are needed in order to avoid errors in accepting or rejecting the null hypothesis. These calculations are not usually complex and for the best results, the power of the test should be set to above 80%.

Observational studies fall under the category of analytic study designs and are further sub-classified as observational or experimental study designs. The goal of analytic studies is to identify and evaluate causes or risk factors of diseases or health-related events. The differentiating characteristic between observational and experimental study designs is that in the latter, the presence or absence of undergoing an intervention defines the groups. By contrast, in an observational study, the investigator does not intervene and rather simply observesť and assesses the strength of the relationship between an exposure and disease variable.Three types of observational studies include cohort studies, case-control studies, and cross-sectional studies. Case-control and cohort studies offer specific advantages by measuring disease occurrence and its association with an exposure by offering a temporal dimension (i.e. prospective or retrospective study design). Cross-sectional studies, also known as prevalence studies, examine the data on disease and exposure at one particular time point. Because the temporal relationship between disease occurrence and exposure cannot be established, cross-sectional studies cannot assess the cause and effect relationshipDisadvantage of Cohort study is Susceptible to selection bias.

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.

Before the test, the patient was likely to have the disease being tested for. Their pre test odds were 2, making them twice as likely to have the disease. After testing positive, their pretest odds of 2 and the likelihood ratio of 3 are multiplied to produce 6 as the post test odds. The patient is now 6 times as likely to have the disease after the test. Likelihood ratios above 10 or below 0.1 are strong evidence to rule in and rule out a diagnosis. The negative likelihood ratio is not known here, but does not subtract from the interpretation of the outcomes in this patient.

Box plots can be used to display numerical outcomes and give valid summaries (median and interquartile range or IQR) for any distributional form that the outcomes might take. Additionally, they show outlying values and provide data summaries that are not unduly influenced by those outliersOther options:- The box itself represents the interquartile range, with the two whiskers representing the variability outside the upper and lower quartiles.- Variability is represented with whiskers and feathers are used in fan charts.They are not as informative as showing the actual values but can be used to make comparisons of medians and IQRs between subgroups.

The selection process is based on five requirements and essentially aims to select a random representative cohort through:1.Isolating a group for which there is a greater or lesser chance of detecting a possible difference between the treatments compared;2.Establishing a homogeneous group in order to reduce the variability of response, thus making statistical comparison more sensitive and decreasing the risk of bias due to the constitution of non-homogeneous groups;3.Obtaining representative samples of the affection studied;4.Defining the rules corresponding to realistic recruitment;5.Respecting ethical obligations.

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

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

The level of statistical significance is often expressed as a p-value between 0 and 1. The smaller the p-value, the stronger the evidence that you should reject the null hypothesis.A p-value less than 0.05 (typically ≤ 0.05) is statistically significant. It indicates strong evidence against the null hypothesis, as there is less than a 5% probability the null is correct (and the results are random). Therefore, we reject the null hypothesis, and accept the alternative hypothesis.However, this does not mean that there is a 95% probability that the research hypothesis is true. The p-value is conditional upon the null hypothesis being true is unrelated to the truth or falsity of the research hypothesis.A lower p-value is sometimes interpreted as meaning there is a stronger relationship between two variables. However, statistical significance means that it is unlikely that the null hypothesis is true (less than 5%).To understand the strength of the difference between two groups (control vs. experimental) a researcher needs to calculate the effect size.

If the 95% confidence interval contains zero (more precisely, the parameter value specified in the null hypothesis), then the effect will not be significant at the 0.05 level.If the 95% CI for the DIFFERENCE between the 2 groups contains the value 0, this means that the p-value will be greater than 0.05. Conversely, if the 95% CI does not contain the value 0, then the p-value will be strictly less than 0.05. The same applies when comparing groups using a ratio, such as an odds ratio or risk ratio. When using a RATIO instead of a DIFFERENCE, the situation of no difference between the 2 groups will be indicated by a value of 1 instead of 0. If the ratio equals to 1, the 2 groups are equal. Hence, if the 95% CI of the ratio contains the value 1, the p-value will be greater than 0.05. Alternatively, if the 95% CI does not contain the value 1, the p-value is strictly less than 0.05.

Logistic regression analysis is an easy to use regression that can serve to estimate how a binary or dichotomous dependent variable, for example whether or not a student passes or fails a medical exam, is related to the number of hours the student studies for the exam. This regression is able to yield an odds ratio, i.e. the probability of passing the exam based on each additional hour of study. As stated, the outcome does not need to be normally distributed but is usually binary. This type of analysis is not complex and does not need to be avoided as it is available most statistical packages.

The Chi squared test is used to find the relationship between two variables, determining whether they are not independent of each other. This test of statistical significance allows you to determine the degree of confidence with which you can accept or reject the null hypothesis. The student t test compares the significant differences in data means, while rank correlation, linear regression and the correlation coefficient all help to measure the linear relationship between variables.

Case control studies are used to determine the relationship between exposure to a risk factor and the resultant outcomes. It finds patients in the general population who have a certain condition and retrospectively searches for past exposure to possible risk factors for the disease. Controls are people who do not have the disease found in the general population. This type of study is useful for rare diseases, and is less time consuming to conduct. While cohort studies can be used to study rare diseases they measure the relative risk of developing the disease over time based on exposure. Ecological or cross-sectional studies attempt take a snapshot of a whole population, and thus are inappropriate for rare diseases as larger sample numbers are needed. A randomised control trial is better suited to determine the effect of an intervention.

The Chi-squared test would have been a useful test to compare the proportions in the scenario. However, due to the small sample size, Fisher’s exact test can be applied to analyse the significance of the difference. Adequate information is not given to determine what sample sizes were used to test the efficacy of the treatment, and to tell whether the treatment should be discontinued without further analysis on the data.

Odds ratios are best described as the odds of an event in one group divided by the odds of the event in another. They form an alternative to the relative risk in case-control studies and are close to the relative risk when events are rare. The odds are the number of the event divided by the number without the event, which is less intuitive than the risk, which is the number with the event divided by the total. Odds ratios are therefore less intuitive than relative risks. Logistic regression analysis outputs variable coefficients which are the log of the odds ratio.

The equation, lung function = 3.2 + (1.7 x height) is linear, but this does not necessarily mean that the relationship itself is linear. The equation is able to predict that taller individuals are more likely to have greater lung functions. No information is given on whether the coefficients and the R value are statistically significant.