Big Data and Machine Learning in RCTs An overview

Transcription

1 Big Data and Machine Learning in RCTs An overview Dario Gregori Unit of Biostatistics, Epidemiology and Public Health Department of Cardiac, Thoracic and Vascular Sciences University of Padova

2 Papers in Pubmed (machine learning OR data mining)

3 "Some studies in machine learning using the game of checkers." IBM Journal of research and development (2000): Evolved from the study of pattern recognition and computational learning theory in artificial intelligence, machine learning explores the study and construction of algorithms that can learn from and make predictions on data such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions, through building a model from sample inputs

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6 Methodology Classify the scientific issue Supervise or not-supervise Associate available MLT methods Data Input/outputs characteristics Data size (n vs p) Missing data processing Define the problem Data driven choice Targets Complexity Effect estimation Prediction What is needed from the analysis EFSA supporting publications,

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8 ML and Clinical Trials Pattern recognition (unsupervised learning) Patients classified into subtypes using unsupervised learning, and then monitored for different treatment responses to a variety of interventions with supervised learning to predict future treatment responses Prediction of treatment response (supervised learning) Decision making (reinforcement learning) Computer learning decision making by repeatedly walking through win-lose scenarios, learning from wins and losses, and replaying the scenarios The idea is that if there is enough available information from prior clinical trials, including positive and negative results, a computer could use that data to improve the conduct of the trial as it is being conducted. Furthermore, the treatments could be personalized based on each patient s scenario that had been modeled by a reinforcement learning algorithm.

9 Clinical Trial Enrichment It seeks out a sub-sample of subjects who might be better candidates for demonstrating therapeutic effects, at least from a statistical standpoint Selection on a machine learning-based classifier that combines numerous biomarkers, which include e.g. neuroimaging measures. Combinations of disease markers are more likely to achieve sample size reductions than using single measures.

10 Enrichment strategies Strategies to decrease heterogeneity These include selecting patients with baseline measurements in a narrow range (decreased inter-patient variability) and excluding patients whose disease or symptoms improve spontaneously or whose measurements are highly variable (decreased intra-patient variability). The decreased variability provided by these strategies increases study power. Prognostic enrichment strategies Choosing patients with a greater likelihood of having a disease-related endpoint event (for event-driven studies) or a substantial worsening in condition (for continuous measurement endpoints). These strategies will increase the absolute effect difference between groups but will not alter relative effect. Predictive enrichment strategies Choosing patients more likely to respond to the drug treatment than other patients with the condition being treated. Such selection can lead to a larger effect size (both absolute and relative) and permit use of a smaller study population. Selection of patients could be based on a specific aspect of a patient s physiology or a disease characteristic that is related in some manner to the study drug s mechanism, or it could be empiric (e.g., the patient has previously appeared to respond to a drug in the same class).

11 n80 estimates (i.e., sample sizes required to detect a 25% slowing of the rate of atrophy with 80% power) as a function of restricting the sample to likely decliners. (a) Samples are based on the top k% classified, based on all biomarkers, as most likely to have AD (lower k gives smaller samples). (b) Here samples are based on the top k% of MCI subjects predicted by the classifier as most likely to decline Kohannim O, Hua X, Hibar DP, et al. Boosting power for clinical trials using classifiers based on multiple biomarkers. Neurobiology of aging 2010;31:

12 Framework of application When a large amount of data collected in a previous trial or current trial is available for analysis. May provide novel insight into which patients are most likely to respond to the investigational therapeutic. Requires external validation. Requires a large amount of data and treatment scenarios to train the algorithm effectively in order to make predictions of treatment response.

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14 Fleming & Harrington (1991) RPCT of D penicillamine, for the treatment of primary biliary cirrhosis (PBC) of the liver (n=312, 10yrs) Drug: 1=D penicillamine, 0=placebo. Age: age in years. Sex: 0=male, 1-female. Ascites: presence of ascites (0=no, 1=yes). Hepatom: presence of hepatomegaly (0=no, 1=yes). Spiders: presence of spiders (0=no, 1=yes). Edema: presence of edema (0=no edema and no diuretic therapy for edema; 0.5=edema present for which no diuretic therapy was given, or edema resolved with diuretic therapy; 1=edema despite diuretic therapy. Bili: Serum bilirubin, in mg/dl. Albumin: in gm/dl. Alkphos: alkaline phosphatase, in U/liter. Platelet: platelet count, in number of platelets per cubic milliliter of blood divided by Protime: prothrombin time, in seconds.

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16 Phenomapping

17 Circulating biomarkers of distinct pathophysiological pathways in heart failure with preserved vs. reduced left ventricular ejection fraction European Journal of Heart Failure Volume 17, Issue 10, pages , 16 OCT 2015 DOI: /ejhf.414

18 Heart Failure Survival Model using SRF Hsich E et al. Circ Cardiovasc Qual Outcomes. 2011;4:39-45 Illustration of minimal depth of a variable in a random tree from a 2000-tree forest. Highlighted are the 3 top variables: peak Vo2 (red), BUN (green), and exercise time (yellow). Depth of a node is indicated by numbers 0, 1, 2, and 3 to 8. The minimal depths are 0, 1, and 2 for exercise time, peak Vo2, and BUN, respectively.

19 Boosting prediction performances Training data consists of a set of training examples Ideal target function Hypothesis that best approximate Learning algorithm Hypothesis set Predict new inputs: Unknown target function f Training examples Hypothesis set Learning Algorithm Final hypothesis Neural Networks SVMs Decision Trees Random Forest Naive Bayes Bayesian Networks Regression

20 Allyn J, Allou N, Augustin P, et al. A comparison of a machine learning model with EuroSCORE II in predicting mortality after elective cardiac surgery: a decision curve analysis. PloS one 2017;12:e

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22 Reinforcement learning designs The basic reinforcement is modeled as a Markov decision process a set of environment and agent states, S a set of actions, A, of the agent P ( s, s ) = Pr( s = s s = s, a = a) a t+ 1 t t is the probability of transition from state s to state s' under action a R (, ) a s s is the expected immediate reward after transition from s to s with action a. rules that describe what the agent observes State Agent Reward Environment Action

23 Zhao Y, Kosorok MR, Zeng D. Reinforcement learning design for cancer clinical trials. Statistics in medicine 2009;28:

24 Reward function Dose levels Q-learning function a finite, reasonably small set of decision times is identified for each decision time, a set of possible treatments to be randomized is identified the choice of treatments can be a continuum or a finite set and can include restrictions that may be functions of observed variables such as biomarkers a utility function is identified that can be assessed at each time point and contains an appropriately weighted combination of outcomes available at each interval between decision times and at the end of the final treatment interval

25 Zhao Y, Kosorok MR, Zeng D. Reinforcement learning design for cancer clinical trials. Statistics in medicine 2009;28:

26 Past and future challenges Enrichment trials and trials targeted by design: n-of-1 trials Post RM, Luckenbaugh DA. Unique design issues in clinical trials of patients with bipolar affective disorder. Journal of psychiatric research 2003;37: Beyond static machine learning models: relational learning Getoor L, Taskar B. Introduction to statistical relational learning. MIT press; 2007 Beyond optimal prediction: targeted machine learning of causal effects Athey S, Imbens GW. Machine learning methods for estimating heterogeneous causal effects. stat 2015;1050.