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Comments on talks

Marshall M. Joffe University of Pennsylvania. Comments on talks. ATS: What’s new?. Already in use previously in trials, practice Consider study: treatment for blood pressure (BP) Protocol specifies changing initial agent if BP too high ATS Compared with standard treatment using ITT

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Comments on talks

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  1. Marshall M. Joffe University of Pennsylvania Comments on talks

  2. ATS: What’s new? • Already in use previously in trials, practice • Consider study: treatment for blood pressure (BP) • Protocol specifies changing initial agent if BP too high • ATS • Compared with standard treatment using ITT • Adaptive part of strategy not focus or evaluated • Standard trials: • Protocols/strategies allow changes due to side effects • Earlier methods not tailored for evaluating second line therapies

  3. Use Standard Design and Analysis • Trial with 4 arms • A: initial treatment • B: second line treatment plan : example • B1: add second treatment if SBP>140 • B2: add second treatment if SBP>160 • Arms: A1B1, A1B2,A2B1, A2B2 • Adaptive treatment strategies • Compare using ITT • Not way to optimize plan

  4. Compare multiple regimes/plans • Tailoring variables • In clinical trials literature for simple treatments, known as subgroup analysis • Extended here to multiple treatments, times, dynamic regimes • Often has exploratory flavor rather than confirmatory • Viewed (in simpler setting) negatively by • Part of clinical trials community • Regulators • Want prespecified comparisons

  5. Issues with multiplicity • Examples: • Just 1 or 2 covariates • In principle: many at each time point • How does one choose covariates/number for tailoring strategies? • Risks: • Overfitting models • Overoptimism of estimates of predictive ability, value of regime • Presenters: • How would one deal with this? • Speak about opportunities and risks involved

  6. Time-varying treatments, covariates • Estimation methods variants of earlier methods developed by Robins • G-formula • G-estimation (not discussed here, less used in other settings; why?) • Inverse probability of treatment weighting • New focus on determining optimal regimes/strategies • Results in non-regularity • Solutions presented

  7. Design • Less discussed • Can randomize • Up front/at beginning to strategy • Sequentially/multiple times • Analysis strategies similar • Compare options

  8. Approaches to selecting covariates for tailoring • Observational studies • Apply similar G-methods • Clinical trials • Wider

  9. Emphasis on pragmatic trials/decisions • Take individual preferences into account? • Maximize individualized utility functions • Earlier work on effects of time-varying treatments had some emphasis on modeling, understanding causal processes • Interest in incorporating here as part of learning systems

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