1 / 53

A Taxonomy of Research Design

Statistics for Health Research. A Taxonomy of Research Design. Peter T. Donnan Professor of Epidemiology and Biostatistics. Objectives of session. Realise importance of design Understand difference between experimental and observational design Choose appropriate design

mareo
Download Presentation

A Taxonomy of Research Design

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 for Health Research A Taxonomy of Research Design Peter T. Donnan Professor of Epidemiology and Biostatistics

  2. Objectives of session Realise importance of design Understand difference between experimental and observational design Choose appropriate design Understand main forms of experimental design Understand main forms of observational design

  3. Why is design important? Poor design may not answer question Choice of design determines the type of analysis Incorrect design leads to waste of resources Poor design is unethical if patient exposed to danger for little return

  4. A Taxonomy of Research Design Experimental Randomised Controlled Trials Factorial design Crossover Cluster randomisation n.b. All are prospective since following experimental units forward in time

  5. A Taxonomy of Research Design Non-randomised - Observational 1) Cohort (Prospective and Retrospective) 2) Case-Control 3) Cross-sectional 4) Time series

  6. RANDOMISED CONTROLLED TRIAL (RCT) • Gold standard method to assess Efficacy of treatment or demonstrate causal relationship • First proper clinical trials from 1940s in tuberculosis • Ethical requirements set out in Declaration of Helsinki, 1960

  7. RANDOMISED CONTROLLED TRIAL (RCT) Random allocation to intervention or control so likely balance of all factors affecting outcome Hence any difference in outcome ‘caused’ by the intervention

  8. Randomised Controlled Trial Eligible subjects RANDOMISED Intervention Control

  9. RANDOMISED CONTROLLED TRIAL (RCT) • RCT necessary to demonstrate efficacy • Cost-effectiveness (ICER) • NICE and SMC assessment for recommendation to NHS

  10. Examples of RCT • Trials of tamoxifen for treatment of early breast cancer1 • Reduction in recurrence of 29% • Reduction in mortality by 14% in women with oestrogen receptor positive cancers • Trial published recently in Lancet on herceptin (trastuzumab) 1. Lancet 1998; 351: 1451-67.

  11. RANDOMISED FACTORIAL DESIGN Patients are randomised more than once All possible combinations of interventions are studied Simple 2-way factorial has 4 combinations

  12. RANDOMISED RANDOMISED Intervention 2 Intervention 2 Control 2 Control 2 Randomised Factorial Trial Eligible subjects RANDOMISED Control 1 Intervention 1

  13. Example factorial design • Scottish Trial of steroids or acyclovir in Bell’s Palsy • Recruited 500 patients • Steroids / Placebo • Steroids / Acyclovir • Acyclovir / Placebo • Placebo / Placebo

  14. Strengths and Weaknesses of factorial design • Efficient – two trials for the price of one • Relies heavily on assumption of no interaction between treatments i.e. effects of treatments are additive • Results – Sullivan, Swan, Donnan et al, Early treatment with prednisolone or acyclovir and recovery in Bell’s palsy. NEJM 2007; 357: 1598-607

  15. Crossover designs • In this design each patient receives ALL treatments • Order of receipt is randomised • Requires a wash-out period between treatments • Only applicable to transient effects in chronic conditions e.g. asthma, pain

  16. Crossover Trial Eligible subjects RANDOMISED Intervention Control Wash-out Wash-out Control Intervention

  17. Example of Crossover Trial • 2-period 2-treatment trial of acarbose vs. placebo in type 2 diabetes • Mean difference in HbA1c was 0.3% with SD of 0.5% • No significant effect

  18. Strengths and Weaknesses of crossover design • Within patient characteristics remain same as matched analysis • Smaller sample size needed compared with parallel design • Not suitable for intervention that ‘cures’, used in chronic pain, asthma, diabetes. • Carry-over effect needs wash-out period Senn S. (1993) Crossover trials in clinical research. Chichester, John Wiley

  19. CLUSTER RANDOMISATION Man is a unit of the greater beasts, the phalanx. The individual relates to the large unit or phalanx John Steinbeck

  20. WHY USE CLUSTER RANDOMISATION? • Intervention at the group level e.g.new appointment system in general practice • Sometimes only practical design • Can reduce contamination • Easier to implement intervention (e.gcounselling to reduce smoking)

  21. Intervention at practice level & effects on patients Practice level

  22. IMPLICATIONS OF USING RANDOMISATION BY PRACTICE? Sample size needs to be inflated: Subjects within practice more alike than subjects in different practices so independence assumption of statistical tests is incorrect

  23. IMPLICATIONS OF USING RANDOMISATION BY PRACTICE? Sample size obtained with no clustering is inflated by factor: Where  is the intra-cluster correlation and is the mean cluster (practice) size

  24. Inflation or DESIGN EFFECT depends on size of intra-cluster correlation Assume 40% reach lipid target on new statin and 30% on old statin • Total(ind)  Inflation Total(Cl) • 944 0.000 1 944 • 944 0.001 1.029 971 • 944 0.01 1.29 1218 • 944 0.05 2.45 2312

  25. How do you know degree of intra-cluster correlation? • Obtain from pilot work • Previous published values • HSRU, Aberdeen • Outcomes  0.05 • Process  0.05 – 0.15

  26. IMPLICATIONS OF USING RANDOMISATION BY PRACTICE? Ethical issues: Cluster-cluster – guardian decides on behalf of all patients Cluster-individual – both patient and GP decide on consent

  27. IMPLICATIONS OF USING RANDOMISATION BY PRACTICE? Analysis: Need to take clustering into account e.g. Multi-level modelling

  28. Study Design - Observational • Cohort • Case-Control • Cross-sectional survey • Time series

  29. Walker & Stampfer ...”should not denigrate the observational nature of the data. Most of what we learn, and will continue to learn, about adverse drug effects are from observational studies” Lancet 1996;348:489

  30. Cohort Design A cohort consists of a group of individuals from a well-defined population (exposure and characteristics known) followed up over time to observe what happens to which groups and when n.b.sociologists call these panels

  31. How many smokers and non-smokers died? Non-smokers Smokers Events

  32. Simple Cohort study Smokers vs. non in Tayside population Non-smoker 1/1/2006 TIME

  33. Framingham cohort study Followed up 5,573 people - white, initially free of CVD, but including people with hypertension and diabetes from 1968 onwards Developed predictive algorithms for CHD

  34. Summary Statistics in Cohort Study • Drug safety study of NSAIDS and hospitalisation for GI bleed • Rate of hospitalisations for GI bleed in NSAID and non-NSAID users over 1 year • Relative risk (RR) is ratio of rates

  35. Relative Risk Hosp GI bleed Yes No Exposed NSAIDS a b c d Unexposed RR = a/(a+b) / c/(c+d)

  36. Example Relative Risk Hosp GI bleed Yes No Exposed NSAIDS 136 4,390 2,104 126,060 Unexposed RR = a/(a+b) / c/(c+d) = 1.83

  37. Example Relative Risk Risk in exposed = 136/(136+4390) = 0.0300 or 30 per 1000 people Risk in unexposed = 2104/(2104+126060) = 0.0164 or 16 per 1000 people Hence RR = 0.0300/0.0164 = 1.83

  38. Interpretation RR = 1.83 and 95% CI (1.19, 2.82) which is highly statistically significant (p < 0.001) 83% higher risk of hospitalisation for GI bleed in those exposed to NSAIDS compared with those not exposed

  39. Limitations In reality subjects have different length of follow-up – so need event rates per person-years follow-up e.g. 30 events per 1000 person years Raw unadjusted rates and Relative Risk – Need to take account of confounding through regression

  40. Retrospective Cohort Design • In this design group of subjects or cohort is identified in the past • Follow-up is then to the present • Advantage that most data already collected and events have occurred • Cheaper to perform as do not have to wait long period before analysis

  41. Log-rank test 21 = 10.6, p = 0.001 generalised Wilcoxon 21 = 7.5, p = 0.006 Donnan et al Prognosis following first acute myocardial infarction in type 2 diabetes: A comparative population study. Diabetic Med 2002; 19: 448-455.

  42. Case-Control Design • In this design group of subjects with disease or condition are identified (Cases) • Suitable Control group identified without the condition • Frequency of Exposure or risk factor compared in cases and controls

  43. Case-Control Design Past Present Unexposed CASES Exposed Unexposed CONTROLS Exposed

  44. Example of Case-Control Study • Early studies of leukaemia around nuclear power stations • Consumption of red meat and risk of colorectal cancer • Use of mobile phones and RTAs

  45. Case-Control Design • May be matched or unmatched • Selection of cases and controls prone to bias • Sometimes have both hospital and population controls • Ascertainment of exposure prone to bias (e.g. recall bias) • Relatively cheap to carry out butdifficulties with confounding

  46. Cross-sectional survey Present Events Exposed Unexposed No Events Unexposed Exposed

  47. Cross-sectional Design • Both exposure and events measured at same time • Often questionnaire–based surveys • Prone to volunteer bias and poor response • Relatively cheap to perform • Main difficulty in ascertaining temporal direction • Examples are postal surveys such as census

  48. Time series • Can be considered a set of regular cross-sectional surveys over time • Examples are rainfall on daily basis, performance of stocks and shares, no. of hospitalisations on daily basis • Requires sophisticated analyses which account for autocorrelation – ARIMA models • Interrupted time series can be powerful method of assessing change following policy or organisational changes

  49. Example Time series Change in mean HbA1c following introduction (vertical line) of web-based Managed Clinical Network for diabetes in Tayside

More Related