State of the art pharmacoepidemiological study designs for post-approval risk assessment

Transcription

1 State of the art pharmacoepidemiological study designs for post-approval risk assessment Cardiac Safety Research Consortium Think Tank Round Table Meeting Thursday, March 6, 2014 Jennifer L. Lund, PhD Department of Epidemiology Gillings School of Global Public Health 1

2 Why are pharmacoepidemiology studies of postapproval risk needed? Common criticism of pharmacoepidemiology studies Designing pharmacoepidemiology studies like RCTs Take Away #1: New user design Take Away #2: Active comparator Summary and Discussion 2

3 Why do we need post approval pharmacoepidemiology studies? RCTs are the gold standard for establishing treatment efficacy and safety Limitations of RCTs: the 5 S s Too Small To detect rare outcomes Too Simple To detect interactions Too Selected To be generalizable Too Specific To assess all relevant outcomes Too Short To detect long term outcomes 3

4 Common criticism of pharmacoepidemiology studies Unmeasured confounding (e.g., by indication) leads to bias in non experimental studies of drug effectiveness and safety Potential for Confounding Statins and CVD Harder to study Easier to study Statins and Rhabdomyolysis Unintended Effects Intended Effects 4

5 Example 1: HRT and risk of coronary heart disease Observational studies > 30% lower risk in current users of HRT compared with never users Fig 2. Summary of relative risks and 95% confidence interval estimates for studies of estrogen use and risk of coronary disease, by study design. Stampfer (1991) Women s Health Initiative > 20% higher risk in initiators of HRT compared with non initiators Manson (2003) 5

6 RCT question vs. observational study question RCT: What is the CHD risk in women assigned to initiation of HRT compared with those assigned to placebo? Design: Randomly assigned to HRT or placebo Analysis: Compare risk between initiators and non users of HRT Observational studies: What is the CHD risk in women who are currently taking HRT compared with those who are not? Design: Women asked about their HRT use Analysis: Compare risk between prevalent users and non users of HRT (current vs. never) 6

7 Initiators vs. Prevalent Users Prevalent users are survivors of the early period of pharmacotherapy Covariates for drug users at study entry may be affected by earlier drug use (e.g., LDL cholesterol) TAKE AWAY #1: New User Design Should Be Implemented to Enhance Study Validity Eliminates these biases by restricting the analysis to persons under observation at the start of the current course of treatment. Ray (2003) 7

8 Hernan (2008) 8

9 Example 2: Insulin glargine use and cancer risk Study Comparator Adjusted hazard ratio Randomised trial Human insulin 0.63 (0.36, 1.09) German database Human insulin 0.86 (0.79, 0.94) UK THIN database Human insulin 0.81 (0.59, 1.11) Swedish database Other insulins 1.07 (0.91, 1.27) Scottish database Other insulins 1.02 (0.77, 1.36) Based on Pocock (2009) 9

10 Further limiting confounding by design: Comparative New User Design Washout Period Drug A Treatment Randomized Drug B Baseline period/ No past use of medication New Users of Drug A Treatment Prescribed New Users of Drug B 10

11 Why use an active comparator? Limits confounding by indication Relevant clinical alternative Similar point in disease progression Reduces confounding by frailty Similar medicalization/access to care Clear baseline for study follow up TAKE AWAY #2: Active Comparators Should Be Used Whenever Available to Enhance Study Validity. Reduces biases, including unmeasured confounding, by restricting the cohorts to persons seeking treatment for the same indication. 11

12 Active comparator: confounding by BMI Stürmer (2013) 12

13 Comparative new user design with an astreated analysis Stürmer (2013) 13

14 Summary TAKE AWAY #1: New user design excludes prevalent users and provides estimates similar to RCTs TAKE AWAY #2: Active comparators can further reduce confounding by indication and frailty Pharmacoepidemiological studies, if properly designed and implemented, represent a valuable tool for post approval risk assessment. 14

15 Thank you for your attention Jennifer L. Lund, PhD Department of Epidemiology McGavran Greenberg Hall Phone: (919)

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17 Treatment switching and discontinuation Treatment Prescribed New Users Drug A New Users Drug B Switching Discontinuation 17

18 Managing treatment switching and discontinuation RCT: Intention to treat Pharmacoepidemiology: First treatment carried forward Assume patients continue to be exposed Tends to be biased towards null Safety studies: both RCTs and pharmacoepidemiology studies must be modified ITT analyses underestimate drug safety effects Censoring at treatment discontinuation ( as treated analysis) TAKE AWAY #3: In studies assessing post approval safety risk, as treated analyses should be conducted alongside intention to treat analyses. 18

19 5Shortcomings of RCTs Too Small to detect rare outcomes Too Simple to detect interactions Too Selected to be generalizable to all users and all indications Too Specific to assess all relevant outcomes Too Short to detect long-term effects 19

20 Detection of ADR: Rule of 3 Upper limit of 95% CI for incidence = 0 if no event occurred in N persons UL 95% CI = 3/N Examples N Upper limit 95% CI 10 30% (3/10 = 0.3)! 30 10% 100 3% 300 1% 1, % 10, % Hanley JA, Lippman-Hand A. If nothing goes wrong, is everything all right? Interpreting zero numerators. JAMA 1983;249:

21 Too Small Number of exposed Probability of detection 99% 95% 90% 1/10 1/100 1/1000 Incidence 21

22 Examples for Incidence of ADR Drug Event Incidence Chinidine Syncopy 1 / 100 Clozapine Agranulozytosis 1 / 1,250 Enalapril Angioedema 1 / 3,000 Lovastatin Rhabdomyolysis 1 / 3,000 Dextrane Anapylactoide reaction 1 / 4,000 Clopidogrel Agranulozytosis 1 / 5,000 Halothane Liver cell necrosis 1 / 30,000 Choramphenicole Aplastic anemia 1 / 40,000 22

23 Too Short Usually weeks or month (rarely: years) Important exceptions: Physicians Health Study (PHS) 325mg aspirin every other day 50mg beta-carotene every other day Women s Health Study (WHS) 100mg aspirin every 2nd day 600 IU vitamin E every other day 50mg beta-carotene every other day Women s Health Initiative (WHI) 0.625mg estrogen, 2.5mg progesterone daily 0,625mg estrogen daily 5* yrs 10 yrs 10 yrs 10 yrs 2* yrs 5* yrs 7* yrs * trial arm stopped 23

24 Nonexperimental Studies of Drug Effects Not restricted by 5 S of RCTs Large enough to study rare outcomes Include people with co-morbidity Include people with co-medication Include elderly, children, pregnant women Include wider indication (e.g., less severe disease), off-label use Wide variety of outcomes Lagged and long term effects 24

25 Unintended Drug Effects Often little confounding Automated Spontaneous reporting systems Large linked healthcare databases FDA sentinel initiative Ad hoc, e.g., Coxibs and CVD Insulin glargine and cancer ARBs and cancer 25