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How to present and use statistics Chris Robertson David Young Department of Statistics and Modelling Science, University of Strathclyde Health Protection.

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Presentation on theme: "How to present and use statistics Chris Robertson David Young Department of Statistics and Modelling Science, University of Strathclyde Health Protection."— Presentation transcript:

1 How to present and use statistics Chris Robertson David Young Department of Statistics and Modelling Science, University of Strathclyde Health Protection Scotland Royal Hospital for Sick Children, Yorkhill NHS Trust Y ORKHILL H OSPITAL

2 2 Outline oIntroduction oWhat is statistics oHypotheses oStatistics in Medical Research oStudy Design Principles Main Types oData and Presentation oTypes of data oGraphical Methods oTables

3 3 Introduction owhat is statistics and why do we need it? ostatistics is the science of collecting, analysing, presenting and interpreting data oit enables the objective evaluation of research questions of interest oit provides the means to weigh up how much evidence the collected data provide for and against the research hypothesis of interest

4 4 Examples of Research Hypotheses oThe main aim of this prospective cross-over study was to introduce one additional cleaner into a surgical ward from Monday to Friday and measure the effect on the clinical environment. After 6 months the cleaner was switched to another matched surgical ward so that each ward acted as a control for the other. Measuring the effect of enhanced cleaning in a UK hospital: a prospective cross-over study Stephanie J Dancer, Liza F White, Jim Lamb, E Kirsty Girvan and Chris Robertson BMC Medicine 2009, 7:28 doi: /

5 5 Examples of Research Hypotheses oThe aim of this review was to assess whether the reported rate of infection (i.e. incidence) or reported rate of death among patients with CDAD for any particular month within an acute hospital, or any particular acute hospital, differs from other months, other acute hospitals, or the national average in Scotland. Report on Review of Clostridium difficile Associated Disease Cases and Mortality in all Acute Hospitals in Scotland from December 2007 May 2008 Health Protection Scotland, 2008

6 6 Examples of Research Hypotheses oThe aims of the survey were: To provide the HAITF with baseline information on the total prevalence of HAI in Scottish hospitals and its burden in terms of health service utilisation and costs. This information would be available to guide priority setting in the development of strategy and policy. NHS Scotland National HAI Prevalence Survey. Volume 1 of 2. Final Report Health Protection Scotland, 2007

7 7 Statistics and Medical Research ostatistics plays an increasingly important role in medical research oit is not possible, for example, to have a new drug treatment approved for use without solid, statistical evidence to support claims of efficacy and safety oover the last few decades, many new statistical methods have been developed which have particular relevance for medical researchers othese methods can be applied routinely using statistical software packages oeasy to use but difficult to use correctly

8 8 Importance of Statistics Medical researchers should understand some basic statistical concepts to ensure … oappropriate study design in terms of the number of participants oapplication of the correct method of statistical analysis when using software oaccurate and honest reporting of data gathered from research studies oadequate understanding of claims made by other researchers when reviewing medical literature

9 9 The study design o one of the main areas of research in which statisticians can work with other researchers to design the optimal studies o describe the broad class of study design using standard terminology e.g. case study, cross-sectional study, cohort study, case-control study, clinical trial o study intervention should be explained o objectives should be clearly stated o state the outcome measure of interest o distinction between pre- and post-study hypotheses o inclusion and exclusion criteria of patient population o source of study subjects

10 10 Study design (cont.) o choice of control group – concurrent or historical o blinding – ideal is to use double blinding if possible and justification for not should be given o randomisation with details of any factors by which the stratification had been carried out o power and sample size – details of how the sample size was chosen including the power and minimum clinically important effect

11 11 Study Design 1.research idea 2.broad research questions 3.primary research question 4.primary hypothesis 5.secondary research questions 6.secondary hypotheses

12 12 Randomised Controlled Trials Interventional lpopulation lsample linclusion/exclusion criteria lrandomisation – treatments A or B lcomparison of outcomes between A and B using statistical tests

13 13 Cohort Study (longitudinal/prospective) Observational lsubjects without the disease (cohort) leither exposed (e.g. smoker) or not exposed (e.g. non- smoker) lproportion of each group will develop the disease (e.g. lung cancer) lcompare proportions in each group using statistical tests

14 14 Case/Control (retrospective) Observational lpeople with and without the disease (i.e. cases and controls) lobserve the exposure factor (e.g. past smoking habits) lcompare proportions in cases and control who were exposed to the variable of interest (e.g. smoking)

15 15 Cross-sectional (prevalence) ousually carried out using a survey (questionnaire) oused to quantify specified characteristics of a defined group of people onumber of people with attribute at a specified point in time reported as a proportion of the population of interest (prevalence)

16 16 Examples oa survey of HPV Prevalence among school children in Scotland ocross sectional survey oevaluation of the effect of an additional cleaner on the total coliform count on hand touch sites in a ward orandomised trial oexposure to unfiltered water increases the risk of campylobacter infection ocase control study oassociation between H1N1v Influenza vaccination and the risk of hospitalisation for flu like symptoms ocohort study

17 Presentation of Statistical Data l Keep it clear and simple l Methods used depend upon the types of data recorded l do not include graphs of relatively unimportant data l where the number of observations is small, plots are not useful (e.g. mean values with error bars) 17

18 18 Types of Data categorical data nominal – the data can be classified into a number of specific categories with no particular ordering e.g. blood type, HAI (Yes/No), Test Outcome (Positive/Negative/Ambiguous) ordinal – the data can be classified into a number of specific categories which can be placed in some order of importance e.g. pain scores (mild, moderate, severe or unbearable), deprivation category score within Glasgow (ranges 1–7 from affluent to poor classified by postcodes)

19 19 Types of Data numerical data discrete – data are recorded as a whole number and usually only take specific values e.g. number of cigarettes smoked in a day, number of children, number of admissions continuous – data are recorded to the precision of the measuring instrument and usually take any value within a certain range e.g. height, weight, blood pressure,

20 20 Numerical presentation of data o appropriate numerical summaries should give an overall unambiguous impression of the data o means should be quoted to at most one extra decimal place relative to the precision with which the original measurements were recorded o standard deviations and standard errors may be quoted with two additional decimal places o medians and IQR handled as for means o avoid use of since there is no convention – use e.g. mean (SD 10.35)mmHg

21 21 Presentation of the analysis results o formal analysis should be chosen to most efficiently answer the study hypotheses o actual p-values should be quoted e.g. p=0.21, p=0.003, p<0.001 o confidence intervals are preferable to p-values o assumptions must be validated (e.g. normally distributed) o problems arising from multiple testing should be addressed

22 22 NHS Scotland National HAI Prevalence Survey. Volume 1 of 2. Final Report Health Protection Scotland, 2007

23 23 Whenever you have a table with percentages or means ALWAYS INCLUDE The number of observations on which the mean or percentage is based Gives you an impression of precision

24 24 Weekly Influenza Situation Report (Including H1N1v) Wednesday 28 October influenza/situation-reports/weekly-influenza-sitrep pdf Doubled Increase Over 75s immune Decrease

25 25 Using graphs without numbers or precision is just as bad

26 26 Weekly Influenza Situation Report (Including H1N1v) Wednesday 28 October influenza/situation-reports/weekly-influenza-sitrep pdf

27 27 Weekly Influenza Situation Report (Including H1N1v) Wednesday 28 October influenza/situation-reports/weekly-influenza-sitrep pdf

28 28 Weekly Influenza Situation Report (Including H1N1v) Wednesday 28 October influenza/situation-reports/weekly-influenza-sitrep pdf Most hospitalisations among Relatively flat distribution up to 54

29 29

30 30 Easily See Peak at 0-4 in both males and female More boys 0-4 in hospital than girls 0-4 Peak year old women

31 31 Report on Review of Clostridium difficile Associated Disease Cases and Mortality in all Acute Hospitals in Scotland from December 2007 May 2008 Health Protection Scotland

32 32 Measuring the effect of enhanced cleaning in a UK hospital: a prospective cross-over study Stephanie J Dancer, Liza F White, Jim Lamb, E Kirsty Girvan and Chris Robertson BMC Medicine 2009, 7:28 doi: /

33 33 Summary o Statistical presentation of results must provide scientific evidence to back up claims made in a report o conclusions must be reliable and based upon data l key aspect of statistics is design, analysis and presentation of results in the presence of VARIABILITY. l honest presentation requires the inclusion of information to assess precision (variability) of results n sample sizes, n standard deviations, n confidence intervals

34 34 Recommended Text Books oAn introduction to medical statistics – J. Martin Bland oPractical statistics for medical research – Douglas G. Altman oEssential medical statistics – Betty Kirkwood and Jonathan Sterne oBMJ series of statistical methods (Martin Bland/Douglas Altman) ohttp://openwetware.org/wiki/BMJ_Statistics_Notes_serieshttp://openwetware.org/wiki/BMJ_Statistics_Notes_series ohttp://www-users.york.ac.uk/~mb55/pubs/pbstnote.htmhttp://www-users.york.ac.uk/~mb55/pubs/pbstnote.htm


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