Corso di clinical writing

1. Corso di clinical writing

2. What to expect today? Core modules • Introduction • General principles • Specific techniques • Title/Abstract drafting • Finding out relevantliterature, and Introductiondrafting • Nuts & boltsofstatistics and Methodsdrafting • Practicalsession 1 – Appraisalof a publishedarticle

3. Ultimate goal ofresearch: appraisalofcausation

4. Methodsofinquiry Statistical inquiry may be… Descriptive (to summarize or describe an observation) or Inferential (to use the observations to make estimates or predictions)

5. Descriptivestatistics 100 100 AVERAGE

6. Inferentialstatistics If I become a scaffolder, how likely I am to eat well every day? Confidence Intervals P values

7. Samples and populations This is a sample

8. Samples and populations And this is its universal population

9. Samples and populations This is another sample

10. Samples and populations And this might be its universal population

11. Samples and populations But what if THIS is its universal population?

12. Samples and populations Any inference thus depend on our confidence in its likelihood

13. Accuracy and precision true value measurement distance spread Accuracy measures the distance from the true value Precision measures the spead in the measurements

14. Accuracy and precision

15. Random and systematicerrors Thus Precision expresses the extent of RANDOM ERROR Accuracy expresses the extent of SYSTEMATIC ERROR (ie bias)

16. Bias Bias is a systematic DEVIATION from the TRUTH -in itself it cannot be ever recognized -there is a need for external gold standard and/or permanent surveillance

17. An incomplete listofbias · Selection bias · Information bias · Confounders · Observation bias · Investigator’s bias (enthusiasm bias) · Patient’s background bias · Distribution of pathological changes bias · Selection bias · Small sample size bias · Reporting bias · Referral bias · Variation bias · Recall bias · Statistical bias · Selection bias · Confounding · Intervention bias · Measurement or information · Interpretation bias · Publication bias · Subject selection/sampling bias Simplest classification: 1. Selection bias 2. Information bias Sackett, J Chronic Dis 1979

18. Validity Internal validity entails both PRECISION and ACCURACY (ie does a study provide a truthful answer to the research question?) External validity expresses the extent to which the results can be applied to other contexts and settings. It corresponds to the distinction between SAMPLE and POPULATION)

19. Validity Rothwell, Lancet 2005

20. Navigatingthroughvariables

21. Statisticalvariables Variables CATEGORY QUANTITY nominal ordinal discrete continuous Death: yes/no Race measuring ordered categories counting ranks BMI Blood pressure Device diameter SES

22. Categoricalvariables Exp Ctrl Event a b No event c d Absoluteriskreduction (ARR) = [ a / ( a + c ) ] - [ b / ( b + d ) ] Relative risk (RR) = [ a / ( a + c ) ] / [ b / ( b + d ) ] Relative riskreduction (RRR)= 1 - RR Oddsratio (OR)= (a/c)/(b/d) = ( a * d ) / ( b * c )

23. Categoricalvariables • Absoluteriskreduction (ARR) • 25% (25/100)- 40% (40/100) = -15% • Relative risk (RR) • 25% (25/100)/ 40% (40/100) = 0.62 • (givenanequivalencevalueof 1) • Relative riskreduction (RRR) • 1 – 0.62 = 38% • Oddsratio (OR) • 33% (25/75)/ 66% (40/60) = 0.5 • (givenanequivalencevalueof 1) Laparoscopic surg Open surg 25 40 Bleeding 75 60 Nobleeding 100 100 Total

24. Mean (arithmetic) • Characteristics: • -summarises information well • -discards a lot of information • Assumptions: • -data are not skewed • distorts the mean • outliers make the mean very different • -Measured on measurement scale • cannot find mean of a categorical measure • ‘average’ device diameter may be meaningless

25. Median • What is it? • The one in the middle • Place values in order • Median is central • Definition: • Equally distant from all other values • Used for: • Ordinal data • Skewed data / outliers • E.g. …………………

26. Comparingmeasuresofcentraltendency • Mean is usuallybest • If it works • Useful properties (with standard deviation [SD]) • But… Group 1 Group 2 17 21 19 21 Lesion diameter (mm) 19 21 17 21 18 4 Mean 18 17.6 Median 18 21

27. Comparingmeasuresofcentraltendency It also depends on the underlying distribution… Symmetric? mean=median=mode

28. Comparingmeasuresofcentraltendency It also depends on the underlying distribution… Asymmetric? Mean>Median>Mode 30 Mode Median 25 Mean 20 Frequency 15 10 5 0 0 1 2 3 4 5 6 7 8 9 Number of clips implanted per patient

29. Measuresofdispersion: examples • Range • Top to bottom • Not very useful • Interquartile range • Used with median • ¼ way to ¾ way • Standard deviation (SD) • Used with mean • Very useful 99% Confidence Interval (CI) 75% CI SD

30. Standard deviation • Standard deviation (SD): • approximates population σ • as N increases • Advantages: • with mean enables powerful synthesis • mean±1*SD 68% of data • mean±2*SD 95% of data (1.96) • mean±3*SD 99% of data (2.86) • Disadvantages: • is based on normal assumptions - 2 ( x x ) S = SD - N 1

31. Statisticalvariables Variables CATEGORY QUANTITY nominal ordinal discrete continuous Death: yes/no Race measuring ordered categories counting ranks BMI Blood pressure Device diameter SES

32. Comparisons Variables PAIRED OR REPEATED MEASURES UNPAIRED OR INDEPENDENT MEASURES eg Unpaired Student t test Chi square test eg Repeated-measures ANOVA PairedStudent t test

33. Statisticaltests Are data categorical or continuous? Categorical data: compare proportions in groups Continuous data: compare means or medians in groups How many groups? Two or more groups, compare by chi-square test Two groups; normal data, same spread? More than two groups; normal data? Non-normal data; use Mann Whitney test Normal data; use t test Non-normal data; use Kruskal Wallis Normal data; use ANOVA

34. Testingnormalityassumptions • Rules of thumb • Referring to previous data or analyses (eg landmark articles, large databases) • Inspection tables and graphs (eg outliers, histograms) • Checking rough equality of mean, median, mode • Performing ad hoc statistical tests • Shapiro-Wilks test • Kolmogodorov-Smirnov test • …

35. Alpha and type I error Whenever I perform a test, there is thus a risk of a FALSE POSITIVE result, ie REJECTING A TRUE null hypothesis This error is called type I, is measured as alpha and its unit is the p value The lower the p value, the lower the risk of falling into a type I error (ie the HIGHER the SPECIFICITY of the test)

36. Alpha and type I error Type I error is like a MIRAGE Because I see something that does NOT exist

37. Beta and type II error Whenever I perform a test, there is also a risk of a FALSE NEGATIVE result, ie NOT REJECTING A FALSE null hypothesis This error is called type II, is measured as beta and its unit is a probability The complementary of beta is called power The lower the beta, the lower the risk of missing a true difference (ie the HIGHER the SENSITIVITY of the test)

38. Beta and type II error Type II error is like being BLIND Because I do NOT see something that exists

39. Summaryoferrors

40. Inferentialstatistics P values tell you whether there is a DIFFERENCE and its DIRECTION Confidence intervals tell you what is the MAGNITUDE (or SIZE) of such difference

41. Power and sample size Whenever designing a study or analyzing a dataset, it is important to estimate the sample size or the power of the comparison SAMPLE SIZE Setting a specific alpha and a specific beta, you calculate the necessary sample size given the average inter-group difference and its variation POWER Given a specific sample size and alpha, in light of the calculated average inter-group difference and its variation, you obtain an estimate of the power (ie 1-beta)

42. Questions?

43. Materials and methods How was the problem studied?

44. Materials and methods How was the problem studied? The answer is in the Methods

45. Expanded IMRADalgorithm IntroductionBackground Limitations of current evidence Study hypothesis MethodsDesign Patients Procedures Follow-up End-points Additional analyses Statistical analysis Results Baseline and procedural data Early outcomes Mid-to-long term outcomes Additional analyses DiscussionSummary of study findings Current research context Implications of the present study Avenues for further research Limitations of the present study Conclusions

46. Structuredapproach • Study design • Patients (selection) • Procedures • Follow-up • Outcomes (ie end-points, definitions) • Additional analyses (eg IVUS, QCA, CT) • Statistical analysis

47. Materials and methods • Describe what was done to answer the research question • Give full details of the methods • Include a clear statement of study design • “The EXCITE study was a double-blind, randomized, parallel design … designed to compare the efficacy and safety of …” • Include a sentence about IRB approval, informed consent, or compliance with animal welfare regulations • “The protocol was approved by the institutional review board, and all patients gave informed consent …”

48. Materials and methods • State the protocol/procedures. Repeat the question. • “We tested the efficacy of xemilonercept administered subcutaneously in a dose of 30 mg, given three times weekly for up to 6 months.” • “There were 2 primary endpoints. The first was event-free survival at 182 days, with an event defined as…” • Describe materials/methods or subjects adequately • Write in a logical order (usually chronological) • Describe analytical methods

49. Materials and methods • Use subheadings • Do not include results in Methods • Include appropriate figures and tables • Write in past tense • Use active voice whenever possible • Place details in parentheses • BMI decreased 10% (from 32.6 to 29.4, p=0.027)

50. Materials and methods • Use a figure for a complex design • Cite references for published methods • Describe others fully • Discuss learning curve implications • Enable the reader to a comprehensive appraisal of selection, performance, adjudication, and attrition bias