Treatment heterogeneity
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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.

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Treatment Heterogeneity

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Treatment heterogeneity

Treatment Heterogeneity

Cheryl Rossi

VP BioRxConsult, Inc.

What is heterogeneity of treatment effects hte

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

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

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

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 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

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

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

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




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

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)



  • 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



  • 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)

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