RCOG guidelines summaries
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RCOG GUIDELINES SUMMARIES
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🎁 emrcog treasure box 🎁 ⭐️⭐️⭐️ important statistical concepts ⭐️⭐️⭐️
if the data is not normally distributed ( skewed data) we depend on median not mean to get the average value or middle value)
95% CI means
range of values that you can be 95% confident contains the true mean of the population.
narrow confidence interval implies that there is a smaller chance of obtaining an observation within that interval. and therefore higher accuracy
thats why when we interpret forest plot, the smaller the diamond ( narrow 95% CI) the more significant are the results of the studies.
null hypothesis and alternative are just hypothesis! they dont give you any number
they proposes that there is no or there is ( respectively) statistical significance exists in a set of given observations.
type 1 means that your report that your findings are significant when in fact they have occurred by chance.
A type 1 error is also known as a false positive and occurs when a researcher incorrectly rejects a true null hypothesis. This means that your report that your findings are significant when in fact they have occurred by chance.
A p-value of 0.05 indicates that you are willing to accept a 5% chance that you are wrong when you reject the null hypothesis
a p-value of 0.01 would mean there is a 1% chance of committing a Type I error.
using a lower value for alpha means that you will be less likely to detect a true difference if one really exists (thus risking a type II error).
The probability of making a type II error is called Beta (β), and this is related to the power of the statistical test (power = 1- β). You can decrease your risk of committing a type II error by ensuring your test has enough power. You can do this by ensuring your sample size is large enough to detect a practical difference when one truly exists.
A p-value higher than 0.05 (> 0.05) is not statistically significant and indicates strong evidence for the null hypothesis.
A statistically significant result cannot prove that a research hypothesis is correct (as this implies 100% certainty).
Instead, we may state our results “provide support for” or “give evidence for” our research hypothesis (as there is still a slight probability that the results occurred by chance and the null hypothesis was correct – e.g. less than 5%).
common measurements of association are
the risk difference, risk ratio, rate ratio and odds ratio.
perfect ( number needed to treat) is 1
it means that all people improved with treatment and none with placebo.
an odds ratio less than 1 indicates that the condition or event is less likely to occur in the first group.and vice verse
A risk ratio less than 1.0 indicates a decreased risk for the exposed group, indicating that perhaps exposure actually protects against disease occurrence. and vice verse
If the confidence interval of odds ratios or risk ratios crosses 1 (e.g. 95%CI 0.9-1.1) this implies there is no difference between arms of the study. ( both studies gave the same outcome)
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4.whichnofnthe following tests required the population to be of normal distribution?
A.chi squared
B.mann whitney u
C.analysisnofnvariance
D.kruskal wallis
E.spearman correlation
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Good news regarding Down syndrome screening and other aneuploidy
Now NIPT is offered by NHS to women with high risk results
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