6.6 examples
- 1. Example – anti-psychotic drugs and venous thromboembolism • Some research suggests antipsychotic drugs might be associated with an increased risk of venous thromboembolism – Drugs also widely prescribed for nausea, vomiting, and vertigo – Venous thromboembolism • Deep vein thrombosis • Pulmonary embolism
- 2. Example – anti-psychotic drugs and VT • Nested case-control study examining whether antipsychotic drugs are associated with an increased rate of venous thromboembolism – Examined by type of antipsychotic, potency, and dose – (BMJ 2010;341:c4245)
- 3. Example – anti-psychotic drugs and VT • Cohort was an open cohort of people registered with UK general practices - QResearch – Data have been collected over 16 years – Includes the anonymised records of over 7 million patients who have been registered with over 500 practices spread throughout the UK – UK has national insurance
- 4. Example – anti-psychotic drugs and VT • Nested case-control included time period 1996- 2007 • Cases – All patients aged 16-100 yrs with first ever record of venous thromboembolism – Identified based on diagnostic codes in cohort database
- 5. Example – anti-psychotic drugs and VT • Controls – Used risk-set sampling to identify controls - matched on calendar time of the incident case – Additionally matched on age, sex and practice – Defined as members of the practices with no diagnosis of venous thromboembolism up to the date of the incident case – Selected up to 4 controls per case (will discuss in matching)
- 6. Example – anti-psychotic drugs and VT • Exposure to drugs assessed with prescriptions on or before the index date – For anti-psychotics collected information on drug name, formulation, dose, instructions and date – For other drugs of concern as confounders collected only whether they were prescribed in the 24 months before
- 7. Example – anti-psychotic drugs and VT • Not eligible for analysis if part of the database for less than 24 months – Due to inadequate exposure data • 25,532 venous thromboembolism cases • 89,491 controls
- 8. Example – anti-psychotic drugs and VT • Analysis with conditional logistic regression (due to matching)
- 9. Example – anti-psychotic drugs and VT • Description of VT rates
- 10. Example – anti-psychotic drugs and VT
- 11. Example – anti-psychotic drugs and VT • Notes on the methods in this example – Primary study base – population defined as those attending a set of general practices, all cases within that population identified – Risk-set sampling • What does OR estimate?
- 12. Example – anti-psychotic drugs and VT • Notes on the methods in this example – Case-control design made collection of data on full prescription details for all antipsychotics (drug name, formulation, dose instructions, dates) a manageable task • Allowed detailed examination of anti-psychotic drugs (types, doses, timing) • Allowed comparison of risk between new users vs longer term users
- 13. Example – anti-psychotic drugs and VT • Notes on the methods in this example – Access to the prescription data due to the larger cohort also critical to success of the exposure assessment – Extensive control for confounding, however… – For most participants could not identify the reason for the prescription • Underlying condition and not the drug prescribed to treat it might be the cause of VT (confounding by indication) • Analyses removing those with schizophrenia and bipolar disorder did not change the results
- 14. Example – anti-psychotic drugs and VT • Authors suggested a case-crossover design to strengthen the evidence on anti-psychotic drugs and VT – What do you think of this suggestion?
- 15. Case-control studies outline • Strengths and challenges • Example: anti-psychotic drugs and venous thromboembolism – nested case-control • Example: diarrhea outbreak in India – cumulative case-control xx • Example: hip fracture – comparison of hospital and community controls • Summary
- 16. Example – diarrhea outbreak in India • On 26 September 2007, the Gayeshpur municipality reported cluster of diarrhea cases • Investigation conducted to identify the agent as well as the source of infection • Cumulative case-control design • Saha et al. 2009 (Natl Med J India. 2009 Sep-Oct;22(5):237-9.)
- 17. Example – diarrhea outbreak in India • Cases – Reviewed data on diarrheal diseases available from the municipal health office – Defined a possible case-patient as a resident of the Gayeshpur municipality who had diarrhea (>3 loose stools during a 24-hour period) between September and October 2007 – Asked healthcare workers in all facilities to report similar new cases – Cases for the case-control analyses met the above definition and resided in one of 4 wards (ward numbers 2, 5, 6 and 8)
- 18. Example – diarrhea outbreak in India • Controls – Were matched on age-, sex- and neighborhood to each case • Exposure assessment – Municipal health workers collected information among participants regarding demographic characteristics, date of onset, signs and symptoms, outcome, food- handling practices, water intake and sanitation
- 19. Example – diarrhea outbreak in India • Results
- 20. Example – diarrhea outbreak in India • Results
- 21. Example – diarrhea outbreak in India
- 22. Example – diarrhea outbreak in India
- 23. Example – diarrhea outbreak in India • Notes on the methods in this example – Primary study base – population defined as those residing in one of 4 wards in Gayeshpur municipality, all cases within that population identified – Case-control design facilitated a rapid assessment of differences in exposures among those with diarrheal disease and those without – Self-report of exposures so accuracy of recall may be different between cases and controls
- 24. Example – diarrhea outbreak in India • Notes on the methods in this example – Confounding handled by matching on age, sex and neighborhood • To be discussed in matching module – Concerns about other confounders? – Problematic that OR in study estimates OR in population and not CIR or IDR?
- 25. Example – hip fracture • Case-control study of risk factors for hip fracture – Comparison of results using hospital controls vs community controls – N Engl J Med. 1991 May 9;324(19):1326-31
- 26. Example – hip fracture • Cases – White and black women aged 45 years or more with radiologically confirmed diagnosis of first hip fracture – Selected from 30 participating hospitals in NYC and Philadelphia 1987-1989 – Primary or secondary study base?
- 27. Example – hip fracture • Hospital controls – Hospitalized women from a surgical ward or an orthopedic ward with no previous hip fracture or hip replacement – Frequency matched to cases by age, hospital and race – Admissions diagnoses included cardiovascular disease or peripheral vascular disease, digestive disorders, cancers, osteoarthritis and other musculoskeletal disorders, infections – Why might you use hospital controls?
- 28. Example – hip fracture • Community controls – For cases aged 65 or older, community controls were randomly selected from the Health Care Financing Administration lists of Medicare recipients – frequency matched to cases on age, race and zip code – For cases under 65 years of age, community controls selected by random digit dialing and matched by age, race and telephone prefix – Why might you use community controls?
- 29. Example – hip fracture • Exposure assessment – In-person interviews to assess lower-extremity function, vision, medical and surgical history, use of medications before hospitalization, dietary and reproductive history, height, weight, smoking, alcohol consumption, symptoms related to balance and gait, and sociodemographic information – Cases and hospital controls were generally interviewed in the hospital – Community controls were generally interviewed in their homes
- 31. Example – hip fracture • Associations observed were different depending on the control group selected – Hospital controls likely less healthy than study base – Community controls perhaps healthier than study base • Comparison of community controls with representative sample of elderly in urban northeastern areas – Very similar • Conclude community controls more appropriate
- 32. Example – hip fracture • Notes on the methods in this example – Secondary study base – highlights challenge of finding controls to represent secondary study base – Case-control design provided a less resource intensive approach to identifying risk factors, but challenges with control group identification probably mean a trade off with bias – Self-report of exposures so accuracy of recall may be different between cases and controls • Probably less of an issue for the hospital controls than for the community controls
- 33. Example – hip fracture • Notes on the methods in this example – What does OR estimate? • Cumulative case-control so OR estimates OR in population – What could have been changed about the design to have the OR estimate a different MoA? • Matching controls on time of case occurrence could have produced a density-sampling approach in which OR = IDR • However, the control groups already bringing in bias, so perhaps perceived not to be worth the additional effort
- 34. Example – hip fracture • Notes on the methods in this example – With the cumulative case-control design, when does OR approximate IDR? • Rare disease