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1. How to collect relevant data Databases Data mining Registers Questionnaires
2. Clinical research data Patient CRF Physician, nurse, monitor CRF Database Data manager, systems programmer Database Report Statistician, statistical programmer
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4. Anecdotal evidence (Case reports) Evidence based medicine (The Cochrane collaboration 1993) Cohort study of smoking and lung cancer (1954) (Bradford Hill) Case-control study of smoking and lung cancer (1950) (Bradford Hill) Randomised clinical trial of streptomycin and tubercolosis (1948) (Bradford Hill)
6. Why statistics? 1. Describing data (statistics in plural) 2. Interpreting uncertain data (statistics in singular)
7. Statistics in singular Two kinds of uncertainty 1. Uncertainty of measurement 2. Uncertainty of sampling
8. 1. Uncertainty of measurement The precision of the used measurement instrument. The precision of the Finapres non-invasive blood pressure monitor is on the average 12.1 mm Hg. The same phenomenon affects biochemical and other common analyses performed in laboratories. Statistical analyses are different. They relate to sampling uncertainty, which affects all results.
9. 2. Uncertainty of sampling Assume that the cumulative 10-year revision rate of the Oxford knee prosthesis is 8% and that two groups of 100 patients receiving the prosthesis are randomly selected and followed over time. One of the groups is given bisphosphonates. Does the treatment affect the revision rate? Patients with knee prostheses Not revised Revised
11. bisphosphonates 6% revised placebo 12% revised Hypothesis test H0: The two samples represent the same population H1: The two samples represent different populations
12. P-value 12/100 vs. 6/100, Fisher's exact test p = 0.22 This means that the chance of getting at least the observed difference in revision rate between two random samples from the same population is 22%.
16. Sampling uncertainty 1. Individual effects vary between subjects. Different samples of subjects leads to different findings. 2. The between-subject variation in the population can be estimated using the information in the sample. 3. The probability that an effect observed in the sample only reflects sampling uncertainty can be calculated.
18. P-values are often misunderstood They cannot - describe clinical relevance (they depend on sample size) - show that a difference “does not exist”, because n.s. is absence of evidence, not evidence of absence
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20. Confidence intervals are better than p-values In contrast to p-values they do - describe clinical relevance - show when a difference “does not exist” because they present lower and upper limits of potential clinical effects/differences
21. 0 Effect Clinically significant effects Statistically and clinically significant effect Statistically, but not necessarily clinically, significant effect Inconclusive Neither statistically nor clinically significant effect Statistically significant reversed effect p < 0.05 p < 0.05 n.s. n.s. p < 0.05 P-value Conclusion from confidence intervals P-value and confidence interval Statistically but not clinically significant effect p < 0.05
24. Collect data to facilitate statistical inference - External validity representativity (inclusion/exclusion) - Internal validity randomization and blinding statistical modelling for bias adjustment - Precision width of confidence intervals significance and power sample size
25. Collect data to facilitate statistical inference Remember 1. Source of subjects 2. Inclusion/exclusion 3. Random sampling (study design) 4. Modelling ( include confounders in the database ) 5. Number of subjects to include
26. Collect data in a safe manner - Monitoring of data collection process - Audit trail - GCP (Documented quality assurance system) - EU Clinical Trials Directive 2001 - ICH-GCP (USA, EU and Japan)
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28. Store data in a safe database Excel worksheet Relational (SQL) database Clear variable definitions (string, numeric, date) Defined variable codes (numeric) Documented revisions/updating Coordination
30. Analyze and report in a safe way - Methodologically correct - ICH/FDA/EMEA Guidelines - ICMJE Guidelines (“Vancouver convention”) - Study registration (ICMJE + WHO) - Results registration (ICMJE)
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37. ICMJE Scientists have an ethical obligation to submit creditable research results for publication. Moreover, as the persons directly responsible for their work, researchers should not enter into agreements that interfere with their access to the data and their ability to analyze them independently, and to prepare and publish manuscripts.
38. ICMJE Authors should identify individuals who provide writing or other assistance and disclose the funding source for this assistance. Editors should ask corresponding authors to declare whether they had assistance with study design, data collection, data analysis, or manuscript preparation. If such assistance was available, the authors should disclose the identity of the individuals who provided this assistance and the entity that supported it in the published article.
39. Recommendations - Use a professional statistics package - Use double data entry - Use variable and value labels - Use written programmes - Include lots of comments in the programmes - Use independent programme validation - Do not hardcode - Back up data regularly - Comply with local archiving instructions
