Precision medicine: Statistical methods for estimating adaptive treatment strategies

Abstract

The beauty of science is that all the important things are unpredictable. Freeman Dyson In the typescript which follows, Moodie and Krakow tackle the topical issue of precision medicine and statistical methods for estimating adaptive treatment strategies. This may be the most difficult typescript in our series so far for non-statisticians to understand. It even has equations! But please bear with the authors and give it a chance. One needs not to understand the equations to get the thrust of the strategy. Precision medicine as we discuss elsewhere, is misnamed. In statistics and mathematics precision refers to getting the same answer again and again. It does not mean getting the correct answer, the term for which is accuracy, not precision. However, precision is the current buzz word so there's no point trying to get this straight. When we think about precision we need to consider two elements, reproducibility and replicability. Reproducibility means you give me your data and computer code and I come to the same conclusion you did. Replicability is another matter. I try to replicate your experiment and hopefully reach the same conclusion. In medicine, replicability is obviously more important than reproducibility but things which cannot be reproduced are unlikely to be replicated. As the authors discuss, one can think about precision medicine as one does a family vacation. A best vacation depends on several covariates: where you live, your prior travel experiences, advice from family and friends, online reviews, Wikitravel, cost, your travel budget, if you have kids and many other co-variates. Consequently, there is unlikely to be a best vacation for everyone. Yours might be a week at the Ritz Carlton Cancun with dinner at Careyes and ours, a week at the Pfister Hotel in Milwaukee with dinner at Mader's German Restaurant (bring simvastatin). Similarly, it is unlikely there is a best therapy of acute myeloid leukemia, a best donor, a best conditioning regimen, a best posttransplant immune suppressive regimen etc. and certainly no best combination of these co-variates for your patient. The question Moodie and Krakow tackle is how we can determine the best therapy or combination of therapies for someone receiving a haematopoietic cell transplant. Although the default answer is typically: randomized clinical trials are the gold standard, these inform us of the outcome of a cohort of subjects, not individuals. In many instances, although a new therapy may be shown to be better than an old one in a controlled randomized trial the benefit is not uniformly distributed. Some subjects in the experimental cohort may do worse with the new therapy compared with controls, others better. The question is who are the winners and losers? We cannot do a controlled randomized trial of one person. Moodie and Krakow discuss statistical tools to help us sort this out. Again, please do not be put off by the equations; forgetaboutit. The overriding message is not so complex, and important. We are always standing by on twitter @BMTStats t...

Doubly-Robust Dynamic Treatment Regimen Estimation for Binary Outcomes

“…We first introduce Q-learning for binary outcomes as motivation and elucidation, then provide our proposed method. To identify the optimal DTRs, Q-learning recursively solves treatment decision problems starting from the last stage, and at each stage, the Q-function is defined as follows ( [Moodie et al, 2014], [Moodie and Krakow, 2020]):…”

“…For binary outcomes, the recently proposed DTR estimation approaches are reliant on either Q-learning, which offers relatively straightforward implementation, or G-estimation, which is doubly robust in the sense of offering a consistent estimator of a treatment effect if at least one of two nuisance models is correctly specified. For example, considering cases of cancer and graft-versus-host disease treatment, to maximize the probability of the binary outcome of two-year disease-free survival, Moodie and Krakow [Moodie and Krakow, 2020] implemented Q-learning in a multi-stage treatment decision analysis, employing logistic regression at each stage. This method was shown to be easy to implement, but suffered from problems of sensitivity to misspecification of the outcome model.…”

A practical approach to assessing existing evidence for specific conservation strategies

“…In particular, the approaches used to assess treatment options in evidence‐based medicine do not always translate to more complex conservation systems (Salafsky et al, 2021). If medical researchers have a good experimental design and/or sufficient statistical power, they can show a significant causal relationship between implementation of Treatment X and achievement of Desired Outcome Y without necessarily understanding the underlying treatment mechanism (Moodie & Krakow, 2020; Thomas D. Cook, personal communication). It is more problematic, however, to apply this “black‐box” approach to medical situations in which there is more individual variation (e.g., the adaptive treatment strategies described by Moodie & Krakow, 2020).…”

“…If medical researchers have a good experimental design and/or sufficient statistical power, they can show a significant causal relationship between implementation of Treatment X and achievement of Desired Outcome Y without necessarily understanding the underlying treatment mechanism (Moodie & Krakow, 2020; Thomas D. Cook, personal communication). It is more problematic, however, to apply this “black‐box” approach to medical situations in which there is more individual variation (e.g., the adaptive treatment strategies described by Moodie & Krakow, 2020). And this is likewise a problem for many conservation strategies that take place in complex ecological and socio‐economic systems and involve different combinations of actions, multiple intermediate outcomes, long time frames, and a host of confounding variables (Salafsky et al, 2021).…”

Improving Research through Avoiding Common Statistical Errors: The Case of Piosphere

“…Inappropriate use of statistical methods in piosphere research has not been discussed and challenged, contrary to other scientific disciplines. In these different disciplines, researchers have tried to shed lights on some crucial aspects of the applications of statistical methods, for example, wildlife management [23], crop science [24], rangeland science [25], ecology [26], medicine [27], biology [28] and engineering [29]. There is no specific protocol regarding statistical analysis of piosphere data (which can be used by other disciplines).…”