1 / 20

Statistics

Statistics. By Z S Chaudry. Why do I need to know about statistics ?. Tested in AKT To understand Journal articles and research papers. Data. Qualitative (Descriptive) Quantitative(Numeric) Discrete Continuous (range) Mean/Median/Mode Mean : Average Median : middle value of data

jeremy-good
Download Presentation

Statistics

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Statistics By Z S Chaudry

  2. Why do I need to know about statistics ? • Tested in AKT • To understand Journal articles and research papers

  3. Data • Qualitative (Descriptive) • Quantitative(Numeric) • Discrete • Continuous (range) • Mean/Median/Mode • Mean : Average • Median : middle value of data • Mode : Most Frequent occurring value

  4. Distributions and Ranges • Gaussian distribution normal • Positively Skewed • Negatively Skewed • Range • Lower quartile • Upper quartile • Interquartile range – around median

  5. Standard deviation – spread around mean • Square root of the variance • Variance = sum of the square deviations from the mean / n • 65% of values lie within 1 SD • 95% of values lie within 2 SD • 99% of values lie within 3 SD

  6. Key Terms • Probability - likelihood or uncertainty of an event occurring • Add probabilities if EITHER/OR events • Multiply probabilities if AND events • Power • Related to size of study if study too small may not be able to detect a significant significance • Errors • Random Error • Systematic Error (bias)

  7. Key Terms - contd • Hypothesis • Null hypothesis – NO DIFFERENCE between 2 groups under study • Rejecting Hypothesis when true –Type 1 error • Accepting Hypothesis when false – Type 2 error • Compare test results • T-test • Chi-squared test • Produce p-value • Probability of result occurring by chance alone • p<0.05 significant • p<0.01 highly significant

  8. Key Terms - contd • Confidence interval • Level of uncertainty in following : • Odds ratios, relative risk,risk difference,sensitivity,specificity • The wider the range the less certain/significant the results • CI usually 95 % i.e. 2 SD from mean in either direction. • Provided study not biased true value can be expected to lie in the CI.

  9. Key Terms - contd • The more people in a study the smaller the CI. • CI range including zero not statistically significant or if results expressed as ratios a CI including 1 is not statistically significant.

  10. Measures of Risk • INCIDENCE – New cases • (New cases/population at risk over specific time) X 100 • PREVALENCE-Existing cases • (No of individuals with disease/population size during specific time) X 100

  11. Measures of Association • Risk varies from 0 to 1 • Risk = probability of disease/death (R) • Risk = No with disease/no at risk of disease • Risk Difference = R1 – R2 • Relative Risk = R1/R2 • <1 intervention reduces risk of outcome • =1 no effect on outcome • >1 intervention increases risk of outcome • Absolute Risk = R1 – R2 / R2

  12. ODDs and ODDs Ratios • Odds – ratio of probability of an event happening to that of it not happening • Odds Ratio – measure of effectiveness of treatment compared to control • OR = ODDs in treated grp/ODDs in control grp • <1 effects of treatment less than control group • =1 effect of treatment same as control group • >1 effect of treatment greater than control group

  13. Diagnostic Testing • SENSITIVITY – Positive test /total number of positives • SPECIFICITY- Negative test when disease free • Positive Predictive Value – likelihood that positive test will be a true positive • Negative Predictive Value – likelihood that a negative test is a true negative • NNT= Number needed to treat = 1/ ARR So the smaller the ARR the greater the NNT

  14. Bias • Publication –positive results more likely to be published • Selection – systematic differences between sample and target population. • Information – systematic errors in measures of outcome or exposure • ? Language – may be bias in inclusion of studies to be selected in meta-analysis.(combine results of several studies to answer a question)

  15. Validity • Study validity • Internal and external bias • Internal validity • Extent to which conclusions in a study are legitimate. • External validity • Degree to which conclusions generated from a study can be generalised to a target population.

  16. Study designs • Experimental • RCT • Cohort • Longitudinal follow-up of 2 or more groups with recorded exposure to risk • Provides comparative incidence estimates between groups • Can have surveillance bias • Case controlled • Used when prevalence low

  17. Study designs • Observational • Cross-sectional • Gives prevalence estimates

  18. Forest plots • Pictorial representation of ODDs ratios in form of a horizontal line • If horizontal line crosses vertical line results are not significant! • Horizontal line represents the 95% CI of each trial being plotted

  19. Further Reading • High-Yield Biostatistics by Lippincott Williams and Wilkins • The Complete nMRCGP Study Guide by Sarah Gear • CASP tools – Critical Analysis to review papers – available on the web

  20. THE END THANK YOU

More Related