Statistics Flashcards
How do we calculate relative risk?
Relative risk (RR) is calculated by dividing the risk of remission in the adjuvant group (89 / 186) by the risk of remission in the standard treatment group (84 / 182).
How do we calculate absolute risk reduction?
This is the absolute risk reduction (ARR). This is calculated by the difference between the risk of remission in both groups.
What is a type I error?
A type I error is when the null hypothesis is true and was wrongly rejected. The greater the p-value, the greater the chance that the null hypothesis will be rejected. With a high p-value of 0.25, there is a large chance the null hypothesis will be accepted. The null hypothesis might be that there is no significant difference between treatment A and B. Saying that treatment A is better than B, when it’s not is committing a type I error.
What is a type II error?
A type II error refers to accepting the null hypothesis when we should have rejected it i.e. there is a difference. The greater the p-value, the greater the chance that the null hypothesis will be rejected. The risk of a type II error occurring is dependent on the power of the study, which is not commented on here.
What is the positive predictive value?
Positive predictive value is defined as the likelihood of having the disease if tested positive by the diagnostic test
How do we calculate positive predictive value?
Positive predictive value is the ratio of patients truly diagnosed as positive to all those who had positive test results (including healthy patients who were falsely diagnosed - ‘false positives.’)
What is the power of a study?
A type 2 error is when an investigator finds no difference even though a difference exists i.e. incorrectly accepting the null hypothesis. The power of the study is the ability of a study to detect a difference if a difference exists. Therefore, the power is 1 minus the probability of making a type 2 error so is calculated as 1 - p (type 2 error).
What is a case control study?
A case control study design is a type of observational study where two existing groups are selected. The two groups are patients with and without the outcome under investigation (dermatomyositis in this example). The two groups are then compared in terms of the exposure under investigation (in this case, high sun exposure) to determine if there is any significant difference.
What is a cohort study?
A cohort study is also a type of observational study. However, in a cohort study, participants are selected and divided into groups based on the exposure (e.g. participants with high and low sun exposure). The participants are then followed over time to examine the outcome occurrence (e.g. number of participants in each group to get diagnosed with dermatomyositis).
What is a cross-sectional study?
A cross-sectional study is also a type of observational study. However, in a cross-sectional study, the relationship between multiple outcomes (e.g. dermatomyositis, lupus) and multiple exposures of interest (e.g. sun exposure, family history, smoking etc) is investigated. The exposure and outcome are both measured at the same time.
How do we calculate the odds ratio?
The odds ratio is calculated by dividing the odds of the first group (the odds of hypertension in caffeine drinkers) by the odds in the second group (the odds of hypertension in non-caffeine drinkers).
How do we calculate the risk ratio?
Risk in A / risk in B
What is the power of a study?
The ability of a study to detect a difference between study groups if such a difference exists
What is the benefit of a crossover study?
Crossover studies are those in which subjects are given two treatments at different time points. As each subject will receive a drug and a placebo by the end of the trial, each patient will therefore, act as a case and a control. A benefit of these studies is that they often require fewer participants because of this
