世代追蹤 Cohort Study 國立成功大學 臨床藥學與藥物科技研究所 林子傑 博士後研究員 2014.10.23 Outline
• Overview of pharmacoepidemiology (PE) • Design and structure of cohort studies What is pharmacoepidemiology?
• The study of the use of and effects of drugs in large numbers of people. -- Brain L. Strom, editor Pharmacoepidemiology 4th edition
• The study of the frequency, distribution, and determinants of diseases in humans, with “determinants” being drugs, vaccines or devices. -- Hartzema AG, Tilson HH, Chan KA, editors. Pharmacoepidemiology and Therapeutic Risk Management Pharmacoepidemiology is the study of the use of and effects of drugs in large numbers of people
Pharmaco- Providing information about the epidemiology beneficial and harmful effects of any drug, thus permitting a better assessment of the risk/benefit balance for the use Borrow the research of any particular drug in any methods particular patient
Marriage Clinical Epidemiology pharmacology
Epidemiology is the study of the distribution Pharmacology is the study of the effects of drugs. and determinants of diseases in populations Clinical pharmacology is the study of the effects of drugs in humans.
PHARMACOEPIDEMIOLOGY 4.Ed, edited by BRIAN L. STROM What is cohort study ? • Cohort studies → Studies that identify subsets of a defined population and follow them over time, looking for differences in their outcome --- BRIAN L. STROM • In PE: population-based What a cohort study can do in PE?
• Causal-relationship Effectiveness and safety of drugs • Incidence rate, Number needed to treat / harm
Risk difference, R1 R2 Absolute risk reduction Treatment Control Absolute risk increase NNT / NNH
R1- R2 1/ risk difference Why there is a need of cohort studies in PE ? ~Limitations of RCTs • Carefully selected subjects may not reflect real- life patients in whom drug will be used Age, disease severity • Study subjects may receive better care than real-life patients Monitor by research nurse/staff • Short duration of treatment • Smaller sample size Strengths of population-based cohort studies • Large sample size • Longer duration of follow-up than clinical trials • Increased methodological flexibility Multiple comparison/control groups Compare relative effectiveness/safety of drugs in the same/different pharmacological class • Increased generalizability Examine drug use under real-life settings • Need to combat with confounding and other biases in design and analysis phase Limitations of PE studies
• Databases are not necessarily developed for research purposes For reimbursement purposes → miss / over- or under-coding of diagnosis Validity of diagnosis codes • Need to control bias/confounders Still hard to be controlled through varies study design/statistical methods in some circumstances • Limitations of claims database Insufficient socieoeconomic factors in NHIRD Lack of dental record / medical device in medicaid database • Pharmacy record represents the “refill conditions” instead of the actual patient drug-taking behaviors 10 11
Data sources
• Claims databases Patient registries Veterans Affairs (VA) databases Kaiser Permanente Medical Care Program Medicare/Medicaid databases The UK General Practice Research Database (GPRD) National Health Insurance Research Database (NHIRD) Sweden, Denmark, Canada 12 Study population – (1) • Inception cohort – new user design Similar to RCTs Exclude prevalent users Who are prevalent users Survivors of early period of therapy Long-term users tend to be adherent to a therapy Amplify adherence bias Predictors of prevalent use Plausibly already affected by the therapy Could introduce bias by adjusting for factors on the causal pathway • Limitations Small sample size Over-representation of short-term users Challenging to assess long-term effect 13
Study population – (2)
• Study period, baseline period, follow-up period • Index date, the date met inclusion criteria (the start of follow-up) • Continuous enrollment • Censored points 14
Censoring issues
• Non-informative censoring (ignorable missing) Missing completely at random (MCAR):The propensity for a data point to be missing is completely random. i.e. The missing data are just a random subset of the data. Missing at random (MAR): the propensity for a data point to be missing is not related to the missing data, but it is related to some of the observed data. • Informative censoring (non ignorable missing) Missing not at random (MNAR): the propensity for a data point to be missing depends on the unobserved event time 15 16
Selection of comparison group
• Should be comparable As similar as possible with respect to all factors other than the exposure status In RCTs, the comparability is assured by randomization In observational studies, need to collect data on any potential baseline differences that could affect the outcome Non-users, active controls (need to control confounding by indication) 17
Soko Setoguchi, ICPE 2010 18 Intent-to-treat vs. On-treatment ~ Clinical trials • ITT Include outcome data for all randomized participants regardless of their status regarding non-adherence to assigned treatment protocols and missed assessment encounters • As-treated; On-treatment To account for treatment non-adherence ITT On-treatment Goal The evaluation of the effectiveness To measure the effect of the of the treatment in terms of the experimental treatment relative public health benefits of to the control condition when all administering the treatment in the patients adhere to the assigned community in light of inevitable treatment condition treatment non-adherence 19
Intent-to-treat vs. On-treatment ~ real-life setting
• ITT → exposed to drug throughout follow-up • On-treatment → censored at the time point of non- persistence Date filled + days supplied + grace period+ effect period Gap • Information bias Patient persistence with therapy is differential and linked to outcome Persistence of drug effects may differ between therapies 20 Defining Outcomes
• If and how you can measure (define) the outcome Diagnosis codes Surrogates Clinical expert advice is critical and imperative • What is the validity of the definition ? 21 22
Statistical analyses – (1)
• To identify differences between comparison groups Chi-square, Student t-test The significance levels are sensitive to sample size The differences may be significant but not clinically meaningful • Standardized differences or effect size Differences between groups are divided by the pooled standard deviation of the two groups Not sensitive to sample size, providing a sense of the relative magnitude of differences 23
Statistical analyses – (2)
• Kaplan-Meier method Give you event rates and event curves Assumes censored patients would have the same probability of experiencing a subsequent event as non-censored patients (MCAR or MAR) 24
Statistical analyses – (2) • Cox proportional hazard model 25
Variables needed for Cox model
• Follow-up time • Outcome • Exposure & covariates
Proc PHREG DATA=test; MODEL time*outcome(0) = Exposure & covariates; RUN; 26
Assumption: “Proportional Hazards” 27
Always check for the PH assumption -1
• Graphical method The log(-log(survival function)) plots 28
Always check for the PH assumption -2 • Graphical method Use the PH graph in the ASSESS option of proc PHREG Cumulative sums of martingale residuals over follow-up times 29 Always check for the PH assumption -3 • Schoenfeld (1982) proposed the first set of residuals for use with Cox regression packages Instead of a single residual for each individual, there is a separate residual for each individual for each covariate 30
Always check for the PH assumption – (4)
• Time-dependent covariates Add time*covariate “interactions” to the model to fit non-PH If the coefficient for the time-dependent variable is significantly different from zero, non-PH is present Stratification 31
Sensitivity analysis
• Guidelines for Good Pharmacoepidemiology Practices (GPP) 2007
“Sensitivity analyses should be conducted to examine the effect of varying the study population inclusion/exclusion criteria, the assumptions regarding exposure, potential effects of misclassification, unmeasured confounders”
33 Method
• Study design and population: cohort, new users • Alendronate, calcitonin/raloxifene Classification : the 1st exposure after their osteoporotic fractures Calcitonin/raloxifene : the reference group (active control) • Primary analysis → on-treatment scenario Patients were censored if they switched to other treatment groups after treatment initiation Non-persistent on their therapy (last date covered by drug plus 30 days, allowing for a 30-day gap between prescriptions) Excluded short-term users 34
Method – ONJ def. and covariates 1. Previous proposed possible ONJ diagnosis codes (ICD-9: 73008, 73000, 73340, 73349, 73018, 73010, 73020, 73345, 73399, 52689, 7339, 5264, 5289, 5259, 5269) 2. Adopted the AAMOS definition Cases with persisted ONJ symptoms for more than 8 weeks and no history of radiation to the jaws 3. Potential cases had to receive any broad-spectrum oral antibiotics • Co-morbid conditions previously proposed to be related to ONJ Diabetes, hyperlipidemia, pancreatitis, gingival and periodontal diseases; other diseases and conditions of teeth and supporting structures, dentoalveolar surgery, rheumatoid arthritis, systemic lupus erythematosus, renal disease, hypertension, Alzheimer’s disease Osteoporos Int, 2013. 24(1): p. 237-44 35
Method – statistical analysis
• Cox regression, PS-matching, KM-plot • Sensitivity analyses 1. An intent-to-treat analysis 2. Patients with MPR above 50% 3. Subgroups: female patients only, different age groups, types of osteoporotic fracture, and excluding patients with periodontal disease 4. Cumulative doses: defined daily doses (DDDs), 183≤ DDDs <365, 365≤ DDDs <730, and DDDs ≥730, DDDs<183 (REF) 2 36
3
Osteoporos Int (2014) 25:1503–151 37
Poor adherence
Figure 1 Kaplan-Meier Analysis for Risk of ONJ
Osteoporos Int (2014) 25:1503–151 4 38
No dose response relationship
Osteoporos Int (2014) 25:1503–151 39
Time-dependent exposure Propensity score 40
Immortal time bias • Refers to a period of follow-up during which, by design, death or the study outcome cannot occur ~ Modern epidemiology, Kenneth J Rothman • In pharmacoepi → typically arises when the determination of an individual’s treatment status involves a delay or wait period during follow-up “Immortal” → Individuals who end up in the treated or exposed group have to survive until the treatment definition is fulfilled 41 Immortal time bias
“Biases the results in favor of the treatment under study by conferring a spurious survival advantage to the treated group.” HR, 0.74; 95%CI, 0.56-0.97 42
Solutions • A time-dependent analysis If time Time-dependent Cox regression Samy Suissa et al. BMJ 2010;340:b5087 44 Time-dependent covariate -2 • Variables change over time • More robust because it utilizes all available data • Programming statements / counting process ID Entry Exit censor sex exposure 01KAIJ0087 0 90 0 0 0 01KAIJ0087 90 390 0 0 1 01KAIJ0087 390 746 0 0 0 01KEEE0550 0 139 0 0 1 01KEEE0550 139 504 0 0 1 01KEEE0550 504 910 0 0 0 01KEEE0550 910 1113 0 0 1 * Data input for counting process 45 Definition of PS 46 Properties of PS • An important balancing property that underlies its value for observational analysis If large enough groups of exposed and unexposed subjects are found → the same distributions of all components of covariates Allows direct estimation of unconfounded risk ratios and risk differences in cohort studies Stratification or matching on the propensity score can yield a better balance of measured covariates • Limitation: does not share with randomization the ability to balance unmeasured confounders Basic & Clinical Pharmacology & Toxicology 2006, 98, 253–259 47 Applications of PS • Stratification or Subclassification Before calculating • Matching the treatment effects • Covariate/Regression adjustment While determining • Weighting the treatment effects • Impractical in situations when there are a large number of covariates or strata PS provide a scalar summary of all the covariate information and there is no limit on the number of covariates for adjustment 48 49 Challenges in conducting cohort study • Data sources • New user design Valid but inefficient (loss of power ) • Comparability of exposed vs. unexposed Selection bias, confounding • Exposure definition Time-varying nature (stopping, switching, restarting) • Avoiding other common problems: Healthy user bias Immortal time bias 50 51 52 Thanks for your attention !