Download Treatment Heterogeneity

Treatment Heterogeneity
Cheryl Rossi
VP 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:
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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 comorbidities
Patient 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
•
Doi and D1i are potential treatment assignments indexed to binary instrument
If Di is indexed to latent-treatment assignment mechanism:
Potential treatment assignments:
(
1(𝛾𝑜 + 𝛾𝑖 > 𝑛𝑖)
D0i
= 1
D1 i
=
Zi is a binary instrument, and ni is a random error independent of
treatment.
Do is what treatment i would receive if Zi = 0, and D1i what treatment i would
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
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
Types of Variable to be Analyzed
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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)