Treatment Heterogeneity

1. Treatment Heterogeneity Cheryl Rossi VP BioRxConsult, Inc.

2. What is Heterogeneity of Treatment Effects (HTE) • Heterogeneity of Treatment Effects implies that different patients can respond differently to a particular treatment. • Statistically speaking it is the interaction between treatment effects and individual patient effects • Average treatment effect reported in RCTs varies in applicability to individual patients

3. Factors Effecting Response to Treatment • Intrinsic variability: physiological • Responsiveness to treatment, vulnerability to treatment effects, patient preferences (utilities), risk without treatment • Patient-related factors: • Sociodemographic factors (age, sex) • Clinical differences (severity of illness, comorbidities) • Genetic/biologic differences • Behavioral differences (i.e. compliance)

4. Reasons for HTE • Drug-related • PK/PD of drug: absorption, distribution, metabolism, rate of elimination • Physiology: Drug concentration at target site, #/functionality of target receptors • Underlying risks: Differing prognosis, # of comorbidities, type of comorbidities Patient reported outcomes: expectations, preference, cultural differences

5. Results of HTE • Suboptimal treatment outcomes • Treatments that have no benefit, or cause harm • Reimbursement for ineffective treatments • Failure to account for this can lead to higher costs and poorer outcomes • Inefficient allocation of resources

6. Internal validity vs. external validity • Internal validity – minimize extraneous sources of variability (statistical analyses can control for variability) • External validity (generalization) –stratified analysis – treatment effects for relevant patient populations

7. Approaches to Deal with HTE • Methods based on structural equation modeling [SEM] (measuring unobserved heterogeneity), i.e. but different within-class homogeneity yet different from larger class of patients • Factor-Mixture Modeling (overall population, 2 subpopulation distributions) • Latent classes examined to determine how they differ (assignment for each individual merged with original study data; post hoc comparisons on variables likely to account for heterogeneity) • Cluster Analysis – (outcomes variables continuous), exploratory analysis driven • Growth Mixture Analysis – outcomes variables continuous or categorical– categorize patients based on temporal pattern of changes in latent variable methods • Multiple Group Confirmatory Factor Analysis

8. Statistical Methods (continued) • Use of Instrumental Variables (IV) • IV Methods: “identify internally valid casual effects for individual who’s treatment status is manipuable by the instrument at hand” Angrist May, 2003 • IV methods used heavily in econometrics research, also useful in Comparative Effectiveness Research • Assumptions of exclusion and independence

9. IV methods • Doiand D1iarepotential treatment assignments indexed to binary instrument If Di is indexed to latent-treatment assignment mechanism: Potential treatment assignments: D0i = 1( D1i = Ziis a binary instrument, and ni is a random error independent of treatment. Do is what treatment iwould receive if Zi= 0, and D1i what treatment iwould be receive if Z=1 The observed assignment variable (only one potential assignment is ever observed for a particular individual), Di =Doi (1-Zi) + D1iZi,Paralleling potential outcomes 1(

10. Assumptions For a model without covariates, key assumptions are: • Independence. (Yoi, Y1i, Doi, D1i) ||_ Zi. • First stage. P[Di=1|Zi=1] ≠ P[Di=1|Zi=0]. • Monotonicity. Either D1i >= Doi or vice versa; without loss of generality, assume the former The instrument is as good as randomly assigned, affect probability of treatment (1st stage), and affects everyone the same way (monotonicity) E[Yi| Zi=1]- E[Yi|Zi=0] /E[Di|Zi=1}-E{Di|Zi=0} = E[Y1i-Y0i|D1i>D0i] Left side of equation is the population equivalent of Wald estimator for regression models with measurement error and right side of equation is Local Average Treatment Effects (LATE) – effect on treatment of those whose treatment status is changed by the instrument. The standard assumption of constant causal effects, Y1i= Y0i + α For further theory and application see Angrist article (2004) which links Local Average Treatment Effects (LATE), which is tied to a particular instrument to Average Treatment Effects (ATE), which is not instrument dependent. Reference: Angrist, Joshua “Treatment Effect Heterogeneity in Theory and Practice”, The Economic Journal 114 (March), C52-C83

11. Types of Variable to be Analyzed • Clinical/laboratory • PROs • Clinician-reported outcomes • Proxy/caregiver variables • Resource use • Count variables • Time to events • (multiple variables with covariates – examined simultaneously)

12. Summary • Objectives: maximizing treatment effectiveness and minimizing adverse events • As researchers – take steps to manage heterogeneity • Prior to design of studies leverage information to explain group membership (increase confidence in variability) • Treatment response vary by a number of factors (as mentioned previously) • Identifying patients who respond to treatment can reduce investment in drug development and reduce exposure of patients who are non-responsive improving the benefit/risk profile of product

13. Conclusions • Utilize statisticians in the front end of design to help with how to manage HTE • Inclusion of clinical experts prior to design/conduct regarding the: - inclusion of covariates - advise on anticipated and observed latent classes - advice on characteristics determining class membership (confirm finding – post hoc comparisons)