The case-population strategy in pharmacovigilance

Transcription

1 The case-population strategy in pharmacovigilance 11th ENCePP Plenary Meeting EMA, 18 June 2013 Joan-Ramon Laporte

2 Three premises Protect the patients Early signals, prompt responses Avoid unnecessary action Robust and valid signals Protect public health Estimate the magnitude of risk and population impact

3 New scenarios ADRs 4 th cause of death? Need for a public health perspective Increase in the risk of common diseases

4 Number of cases per million per year Breast cancer Gastrointestinal bleeding Death by myocardial infarction Cerebrovascular accident Hospitalization for heart failure Fall and hip fracture Death by any cancer ,300 2, ,800 1,730

5 Hormone Replacement Therapy (HRT) Non-steroidal antiinflammatory drugs (NSAIDs) Antiplatelet drugs Anticoagulants NSAIDs Rosiglitazone Breast cancer Gastrointestinal bleeding Death by myocardial infarction Cerebrovascular accident Hospitalization for heart failure Cardiovascular mortality Fall and hip fracture Death by any cancer Ezetimibe Antipsychotics Epoetins NSAIDs Glitazones HRT NSAIDs Glitazones Epoetins Omeprazole and analogues New antidepressants Glitazones

6 New scenarios ADRs 4 th cause of death? Need for a public health perspective Increase in the risk of common diseases Higher public health impact of ADRs Refinement of observational research Meta-analysis of RCTs Development and implementation of ICTs, registers, and EHRs

7 Case-population in pharmacovigilance Rationale Examples Conclusions

8 Methods in pharmacovigilance Case reports Series of cases Voluntary reporting

9 Voluntary reporting Disease of interest Yes No Exposed a b Non-exposed c d Just a probably unrepresentative sample

10 Methods in pharmacovigilance Voluntary reporting Anecdotal No risk estimation Clinical perspective Natural history Case-control studies Cohort studies (Meta-analysis of) RCTs Control group Magnitude of association Risk estimation Epidemiological perspective Incidence Public health perspective

11 Case-control study Disease of interest Yes No Exposed a b Non-exposed c d

12 Case-control study Disease of interest Yes No Exposed a b a b OR = c d Non-exposed c d

13 Case-control study Disease of interest Yes No Exposed a b a b OR = c d Non-exposed c d

14 Case-control study Prevalence of use = 1% α = 0.05 β = 0.20 OR = 5 3 controls per case 200 cases 600 controls

15 Case-control study Prevalence of use = 1%o α = 0.05 β = 0.20 OR = 5 3 controls per case 2,000 cases 6,000 controls

16 Case-control studies Automated databases: Best observational approach for estimating the risks of rare diseases But: Low numbers of cases of rare diseases Limited statistical power for subgroup analysis, dose-effect relationships, etc. May Limited not be statistical fully representative power when of the prevalence whole of health the exposure system of interest is low

17 Case-population study Define condition, set diagnostic criteria Ensure full ascertainment of cases, by a process which is independent of previous exposures or suspected causes Estimate the rate of exposure among the cases Estimate rate of exposure among the general population from consumption data Compare rates of exposure among cases with rates of exposure among general population

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19 Case-control study C C

20 Case-population study C

21 N of exposed cases Diseased Population (person-time) Exposed population (EP, person-time) Exposed a PT E N of nonexposed cases Non-exposed c PT NE Total n PT TOT Non exposed population

22 N of exposed cases Diseased PT EP= Consumption (mg) DDD (in mg) 365 days Exposed a PT E N of non-exposed cases Non-exposed c PT NE PT TOT PT E Total n PT TOT

23 N of exposed cases EP= Four pieces Diseased of information: PT Consumption (mg) DDD (in mg) 365 days Cases and their exposures Exposed a PT E Consumption (weight units) Non-exposed c PT NE Total n PT TOT N of non-exposed cases Mean daily dose (not necessarily the DDD) Mean duration of treatment course PT TOT PT E

24 Case-population in pharmacovigilance Rationale Examples Conclusions

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27 Type B adverse and other rare ADRs Agranulocytosis Dobesilate Pirythyldione Case-control ( ) (22.6- ) OR (95%CI) Case-population ( ) ( ) TEN Co-trimoxazole Carbamazepine Phenobarbital Piroxicam Allopurinol 160, , ; ; ( ) 24.4 ( ) 21.9 ( ) 14.5 ( ) 3.4 ( ) PPH Appetite suppressants 23.1 ( ) 31 ( )

28 GI bleeding Case-control vs case-population p: paracetamol p : propyphenazone d: dipyrone I: indomethacin N: naproxen A: ASA D: diclofenac P: piroxicam

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32 Case-population in pharmacovigilance Rationale Examples Conclusions

33 The case-population strategy Consists in comparing past exposure to a given risk factor in subjects presenting a given disease or symptom (cases) with the exposure to this factor in the whole cohort or the source population of cases

34 The case-population strategy Need for drug utilization data, which are rarely available IMS National health systems statistics o Varying accessibility o Rarely offer data on aggregated number of users

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38 The case-population strategy Need for drug utilization data, which are rarely available IMS National health systems statistics o Varying accessibility o Rarely offer data on aggregated number of users Need for complete ascertainment of cases Setting up collaborating networks in defined areas or using electronic registers of the diagnoses of interest

39 Spontaneous reporting Case-population Case-control studies Cohort studies (Meta-analysis of) RCTs Methods in pharmacovigilance Anecdotal Magnitude of association? No risk estimation Clinical perspective Natural history Control group Magnitude of association Risk estimation Control Incidence group Number of cases, population impact Magnitude of association Risk estimation Epidemiological perspective Incidence Public health perspective

40 Strengths Early signal generation Incidence Population impact Limitations Case ascertainment Adjusting population denominators Adjusting exposure denominators Confounding

41 Conclusions Method for early signal generation In certain circumstances, hypothesis generating In certain circumstances, early risk estimation and initial estimate of public health impact Seems appropriate for type A and type B ADRs Need for methodological refinement in relation to: o Type A vs type B adverse reactions o Latency time between exposure and adverse event o Etiological fraction of medicines in the condition of interest