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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

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

1(

assumptions
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
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
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
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)

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