Treatment Heterogeneity

Cheryl RossiVP BioRxConsult, Inc.

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

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)

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 comorbiditiesPatient reported outcomes: expectations, preference, cultural differences

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

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

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

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

IV methods •D

oi and D1i are potential treatm

ent assignments indexed

to binary instrument

If Di is indexed to latent-treatm

ent assignment m

echanism:

Potential treatment assignm

ents:

D0i = 1(

D1i =

Zi is a binary instrument, and n

i is a random error

independent of treatment.

Do is w

hat treatment i w

ould receive if Zi = 0, and D

1i w

hat treatment i w

ould be receive if Z=1

The observed assignment variable (only one potential

assignment is ever observed for a particular individual),

Di =D

oi (1-Zi) + D

1iZi, Paralleling potential outcom

es

1(

AssumptionsFor 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

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)

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

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)