MM105 Biostatistics, Clinical Methodology & Epidemiology
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1 Graduate Programme The Molecular Basis of Human Disease MM105 Biostatistics, Clinical Methodology & Epidemiology Coordinator: Assist Prof Joanna Moschandreas Co-coordinators: Assoc Prof D Mavroudis, Dr M Vassilaki 1 MM105 Biostatistics, Clinical Methodology & Epidemiology 1. Study design: types of clinical trials, types of epidemiologic studies, sample size considerations 2. Data presentation: descriptive statistics, measures of disease frequency 3. Data analysis & interpretation: statistical inference, diagnostic tests 4. Other aspects of clinical research: ethics issues, legal framework 2 ΕΙ Η ΜΕΛΕΤΩΝ EXAMPLE 1. A classic flawed large data set. The Lanarkshire Milk Experiment. Biometrika 1931;23: Study of the effect of daily milk consumption (for 4 months) on the weight & height of children aged 5-12 yrs (57 schools). Milk distributed to children and served as controls. Larger increase in weight in children who consumed milk. 1 pint = 568 ml σελ 30 Petrie & Sabin. 3 4 Intervention group Altman (1980) BMJ Questions to be asked: - How were the children allocated to groups? (Ans: allocated by ballot, but teachers allowed to correct for balance) - How were the schools sampled? - Is weight the best measure of growth? - Definition with clothes (initial weight in Feb, final in June)? Problem? - Dropouts? 1 lb =0.45 kg, 5 14 lb =1 stone 6 1
2 Introduction to Biostatistics - Examples - Use of statistics in medical publications - Overview of course content - Reading material «Statistics is the science of making effective use of numerical data relating to groups of individuals or experiments. It deals with all aspects of this, including not only the collection, analysis and interpretation of such data, but also the planning of the collection of data, in terms of the design of surveys and experiments.» Dodge, Y. (2003) The Oxford Dictionary of Statistical Terms, OUP. 7 8 «Τhe essential skill required for the collection, analysis and evaluation of numerical data is statistics» JM Bland 2000 You can prove anything with statistics Not if you want your analysis to be correct! With some knowledge of statistics you can critically appraise published statistical analyses (and they can often be considered at best incomplete). {We do not prove at the level of the individual. e.g. smoking and lung cancer Not all smokers get lung cancer. But smoking is a risk factor for the development of lung cancer. R Doll et al (2004) BMJ } 10 Statistics is based on variability in medical research it is usually the variability between people that is of interest Systolic blood pressure Intra-individual variability - Trends are often hidden by variability e.g. sifferences in the response to 2 different treatments Stress State (asleep/awake) - Sometimes it is the variability itself that is of interest e.g. likely biochemical measurements in healthy subjects Day to day variation Time of day e.t.c. Inter-individual variability Age Sex e.t.c. 2
3 Using statistical methods variation can be separated into - random variation (variation with no obvious explanation) and - variation due to known causes. EXAMPLE 2. Systolic blood pressure Treatments A and B given to 2 groups of hypertensive men: mean decrease 10mmHg in group that took Α and 4mmHg in group that took Β. The difference could be due to 1) The treatments 2) bias due to other factors RCT 3) random variation e.g. 30% of the variation in BMI in a group of young men was found to be explained by differences in their physical activity level. 13 EXAMPLE 3. On average, men have higher intraocular pressure (IOP) than women How can we investigate this hypothesis? Would 2 IOP measurements on men and 2 on women be enough? What information on IOP do we need to be able to decide how many measurements we need? Use of statistics in medical publications The proportion of articles in American Naturalist with mathematical & statistical content, Σελ. 5, Sokal & Rohlf D Altman (1998) Stats in Med 17,
4 Great progress in the last decade CONSORT 2010 Checklist of Information to Include When Reporting a Randomized Trial*. In 2000, guidelines published on the reporting of RCTs, updated in CONSORT (Consolidated Standards of Reporting Trials) Statement item checklist + flow diagram Also, STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) Statement KF Schulz et al by American College of Physicians Flow diagram of the progress through the phases of a parallel randomized trial of 2 groups (that is, enrollment, intervention allocation, follow-up, and data analysis). Schulz K F et al. Ann Intern Med doi: / by American College of Physicians 22 12% of Nature Neuroscience articles reviewed had pseudoreplication, 36% had possible pseudoreplication
5 % of the 157 articles in which two experimental effects were compared used an incorrect procedure: they did not use a test of the differences, but 2 separate tests Extended description of statistical procedures had a positive effect on the number of citations received. 16.7% had an inappropriate statistical analysis Nov 30, 2009 (publication date) Only 70% of the articles that used statistical methods described their methods and presented the results with a measure of error or variability
6 ΒΙΟΣΤΑΤΙΣΤΙΚΗ Overview of course content A frequentist approach will be used. This is based on the frequentist probability = the proportion of times an event would occur if the experiment were repeated a large number of times. N( A) p( A) = lim N N where event Α occurs Ν(Α) times in Ν trials. The data are a repeatable random sample, parameters are fixed. 31 cf Bayesian analyses are based on a different definition of probability, personal probability. They are based on the personal degree of belief in the data. The data are fixed, the parameters are described probabilistically. Biostatistics STATISTICAL INFERENCE Descriptive Inferential ESTIMATION HYPOTHESIS TESTING Based on properties of samples Sample = a subgroup of the population that is being studied. From sample to population Population = a group of subjects or objects with certain characteristics that we would like to study. «How will my study be published if there is no p-value?» «But it s not statistically significant!» ότητες Biostatistics component of MM Descriptive statistics: Data types, summarizing frequency distributions, information from summary data, displaying data. SPSS [Thursday 24/11, Friday 25/11] 2. Introduction to statistical inference: Estimation, hypothesis testing & sample size estimation. [Wednesday 30/11, Thurs 1/12] 3*. Analysis of quantitative data: comparisons between groups. Parametric & non-parametric univariate tests & CIs for independent and paired samples. SPSS [Friday 2/12, Tues 6/12] 5*. Correlation: parametric & non-parametric. SPSS [Friday 9/12] 6* Survival analysis: Kaplan-Meier survival curve, Cox regression model [Wednesday 14/12] Paper discussion 19/12 4*. Analysis of qualitative data: comparisons between groups. Tests & CI s for independent & paired proportions, comparing risks. SPSS [Thursday 8/12]
7 The conditions under which common statistical tests are valid and their interpretation will be emphasized during the course. The technical details of statistical calculations are not of interest. A publication contains the following statement: «an independent samples t-test was used to compare the means of the two groups. The difference between means was statistically significant (p=0.003)» - why was an independent samples t-test appropriate? - what does statistically significant mean? - what does p=0.003 mean? FURTHER READING Medical Statistics At a Glance 2nd ed Α. Petrie & C Sabin Blackwell, 2005 (WA 950 P495m 2005)* Medical Statistics A Textbook for the Health Sciences 4th ed D Machin, MJ Campbell & Walters Wiley, 2007 (WA 950 C189m 2007) An Introduction to Medical Statistics 3 rd ed M Bland. OUP 2000 Practical Statistics for Medical Research D.G. Altman. Chapman and Hall, 1991 (R853.S7 A ) Statistical Methods in Medical Research (4th edition). Armitage P, Berry G, Matthews JNS. Blackwell Science 2002 (WA 950 A733s 2007) [more advanced] Biometry: the principles & practice of statistics in biological research (3 rd ed) RR Sokal & FJ Rohlf. WH Freeman & Co, 1995 [more theoretical] 1) Martin Bland s BMJ articles: 2) Statistics at Square One 9th ed. TDV Swinscow (free to download at books section, non-clinical). 38 SPSS SPSS 15.0 Guide to Data Analysis Marija J. Norušis ISBN Prentice Hall (Οπ. & Ορ HA32.N ) ΒΙΟΣΤΑΤΙΣΤΙΚΗ. Τριχόπουλος, Α. Τζώνου, Κ Κατσουγιάννη Εκδόσεις «ΠΑΡΙΣΙΑΝΟΥ» 2001 Discovering statistics using SPSS 3 rd ed Andy Field (2009) ISBN Εισαγωγή στη Στατιστική. Γ Χλουβεράκης (Τµήµα Ιατρικής Πανεπιστηµίου Κρήτης) Εκδόσεις «Ελληνικά Γράµµατα» (2002) 39 Στατιστική µε το SPSS 13 D Howitt & D Cramer Eκδόσεις Κλειδάριθµος ISBN:
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