# Quick Start Guide: Optimal Policy Trees with Survival Outcomes

This is an R version of the corresponding OptimalTrees quick start guide.

In this guide we will give a demonstration of how to use Optimal Policy Trees with survival outcomes. For this example, we will use the AIDS Clinical Trials Group Study 175 dataset, which was a randomized clinical trial examining the effects of four treatments on the survival of patients with HIV.

Note: this case is not intended to serve as a practical application of policy trees, but rather to serve as an illustration of the training and evaluation process.

First we load in the data:

df <- read.table("ACTG175.txt", header = T)

  pidnum age    wtkg hemo homo drugs karnof oprior z30 zprior preanti race
1  10056  48 89.8128    0    0     0    100      0   0      1       0    0
2  10059  61 49.4424    0    0     0     90      0   1      1     895    0
gender str2 strat symptom treat offtrt cd40 cd420 cd496 r cd80 cd820 cens
1      0    0     1       0     1      0  422   477   660 1  566   324    0
2      0    1     3       0     1      0  162   218    NA 0  392   564    1
days arms
1  948    2
2 1002    3
[ reached 'max' / getOption("max.print") -- omitted 2137 rows ]

Policy trees are trained using a features matrix/dataframe X as usual and a rewards matrix that has one column for each potential treatment that contains the outcome for each sample under that treatment.

There are two ways to get this rewards matrix:

• in rare cases, the problem may have full information about the outcome associated with each treatment for each sample
• more commonly, we have observational data, and use this partial data to train models to estimate the outcome associated with each treatment

Refer to the documentation on data preparation for more information on the data format.

In this case, the dataset is observational, and so we will use RewardEstimation to estimate our rewards matrix.

## Reward Estimation

First, we separate the dataset into the various pieces:

• the features (X)
• the treatments observed in the data (treatments)
• whether the patient was known to have died (died)
• the time of last contact with the patient (times) - this is the survival time for patients that died, and a lower bound on the survival time otherwise
X <- df[, c("age", "wtkg", "karnof", "cd40", "cd420", "cd80", "cd820", "gender",
"homo", "race", "symptom", "drugs", "hemo", "str2")]

treatment_map <- c(
"zidovudine",
"zidovudine and didanosine",
"zidovudine and zalcitabine",
"didanosine"
)
treatments <- treatment_map[df$arms + 1] died <- as.logical(df$cens)

times <- df$days  Next, we split into training and testing: split <- iai::split_data("prescription_maximize", X, treatments, died, times, seed = 2345, train_proportion = 0.5) train_X <- split$train$X train_treatments <- split$train$treatments train_died <- split$train$deaths train_times <- split$train$times test_X <- split$test$X test_treatments <- split$test$treatments test_died <- split$test$deaths test_times <- split$test$times  Note that we have used a training/test split of 50%/50%, so that we save more data for testing to ensure high-quality reward estimation on the test set. The treatment is a categoric variable with four choices, so we follow the process for estimating rewards with categorical treatments. Our outcome is the survival time of the patient, so we use a categorical_survival_reward_estimator to estimate the expected survival under each treatment option with a doubly-robust reward estimation method, using random forests to estimate both propensity scores and outcomes: reward_lnr <- iai::categorical_survival_reward_estimator( propensity_estimator = iai::random_forest_classifier(), outcome_estimator = iai::random_forest_survival_learner(), reward_estimator = "doubly_robust", random_seed = 1, ) train_rewards <- iai::fit_predict( reward_lnr, train_X, train_treatments, train_died, train_times, propensity_score_criterion = "misclassification", outcome_score_criterion = "harrell_c_statistic") train_rewards$predictions$reward   didanosine zidovudine zidovudine and didanosine zidovudine and zalcitabine 1 914.7447 880.6740 1041.0396 998.1612 2 1705.3515 803.2756 1047.7031 877.2287 3 1079.1379 1003.6351 1082.8073 1116.1556 4 986.4404 380.1726 937.6825 1004.8328 5 -2805.6857 526.3117 837.3645 778.4508 6 752.1108 -748.2132 836.1725 784.4516 7 1204.9082 1143.1498 1091.9074 207.6754 8 881.7123 446.2232 887.0838 -1583.7952 9 811.2369 850.6208 -2278.5258 825.1151 10 982.1498 960.2611 1042.8775 1724.6217 11 1185.6566 1093.9143 1179.8668 1159.8659 12 766.9350 603.5744 -1790.4708 864.6945 13 -133.7400 860.9913 1025.1836 1012.4676 14 1156.9335 1041.1249 -521.1263 1151.5515 15 1067.2024 983.1436 1053.9369 1691.5854 [ reached 'max' / getOption("max.print") -- omitted 1054 rows ] train_rewards$score$propensity  [1] 0.2694025 train_rewards$score$outcome  $zidovudine and zalcitabine
[1] 0.6879994

$zidovudine [1] 0.7026574$didanosine
[1] 0.6867158

$zidovudine and didanosine [1] 0.7127891 train_rewards$score$censoring  $zidovudine and zalcitabine
[1] 0.5693723

$zidovudine [1] 0.5932067$didanosine
[1] 0.5603087

$zidovudine and didanosine [1] 0.6173393 We can see that the internal outcome estimation models have c-statistics around 0.7, which gives us confidence that the survival time estimates are of decent quality, and good to base our training on. The accuracy of the propensity model is around the same as random guessing at 25%, which is to be expected as the underlying data comes from a randomized trial. Given this, we may not expect the doubly-robust estimation method to perform significantly differently to the direct method, as there is not likely to be much treatment assignment bias to correct for. ## Optimal Policy Trees Now that we have a complete rewards matrix, we can train a tree to learn an optimal prescription policy that maximizes survival time. Note that we exclude two features from our prescription policy (cd420 and cd820) as these are observed after the treatment assignment is decided. We will use a grid_search to fit an optimal_tree_policy_maximizer: grid <- iai::grid_search( iai::optimal_tree_policy_maximizer( random_seed = 1, minbucket = 10, ), max_depth = 1:5, ) iai::fit(grid, subset(train_X, select = -c(cd420, cd820)), train_rewards$predictions$reward) iai::get_learner(grid)  Fitted OptimalTreePolicyMaximizer: 1) Split: age < 35.5 2) Prescribe: zidovudine and zalcitabine, 595 points, error 4377.8 3) Prescribe: zidovudine and didanosine, 474 points, error 4396.1 Optimal Trees Visualization The resulting tree recommends different treatments based simply on the age of the patient. We can make treatment prescriptions using predict: iai::predict(grid, train_X)   [1] "zidovudine and didanosine" "zidovudine and didanosine" [3] "zidovudine and didanosine" "zidovudine and zalcitabine" [5] "zidovudine and didanosine" "zidovudine and zalcitabine" [7] "zidovudine and zalcitabine" "zidovudine and didanosine" [9] "zidovudine and didanosine" "zidovudine and zalcitabine" [11] "zidovudine and didanosine" "zidovudine and zalcitabine" [13] "zidovudine and didanosine" "zidovudine and didanosine" [15] "zidovudine and zalcitabine" "zidovudine and zalcitabine" [17] "zidovudine and zalcitabine" "zidovudine and didanosine" [19] "zidovudine and zalcitabine" "zidovudine and zalcitabine" [21] "zidovudine and zalcitabine" "zidovudine and didanosine" [23] "zidovudine and zalcitabine" "zidovudine and zalcitabine" [25] "zidovudine and zalcitabine" "zidovudine and didanosine" [27] "zidovudine and zalcitabine" "zidovudine and didanosine" [29] "zidovudine and didanosine" "zidovudine and zalcitabine" [31] "zidovudine and didanosine" "zidovudine and zalcitabine" [33] "zidovudine and zalcitabine" "zidovudine and zalcitabine" [35] "zidovudine and zalcitabine" "zidovudine and zalcitabine" [37] "zidovudine and zalcitabine" "zidovudine and zalcitabine" [39] "zidovudine and didanosine" "zidovudine and didanosine" [41] "zidovudine and didanosine" "zidovudine and zalcitabine" [43] "zidovudine and didanosine" "zidovudine and didanosine" [45] "zidovudine and didanosine" "zidovudine and didanosine" [47] "zidovudine and zalcitabine" "zidovudine and didanosine" [49] "zidovudine and didanosine" "zidovudine and zalcitabine" [51] "zidovudine and zalcitabine" "zidovudine and didanosine" [53] "zidovudine and zalcitabine" "zidovudine and didanosine" [55] "zidovudine and zalcitabine" "zidovudine and zalcitabine" [57] "zidovudine and zalcitabine" "zidovudine and didanosine" [59] "zidovudine and zalcitabine" "zidovudine and zalcitabine" [ reached getOption("max.print") -- omitted 1009 entries ] If we want more information about the relative performance of treatments for these points, we can predict the full treatment ranking with predict_treatment_rank: iai::predict_treatment_rank(grid, train_X)   [,1] [,2] [,3] [1,] "zidovudine and didanosine" "didanosine" "zidovudine and zalcitabine" [2,] "zidovudine and didanosine" "didanosine" "zidovudine and zalcitabine" [3,] "zidovudine and didanosine" "didanosine" "zidovudine and zalcitabine" [4,] "zidovudine and zalcitabine" "didanosine" "zidovudine and didanosine" [5,] "zidovudine and didanosine" "didanosine" "zidovudine and zalcitabine" [6,] "zidovudine and zalcitabine" "didanosine" "zidovudine and didanosine" [7,] "zidovudine and zalcitabine" "didanosine" "zidovudine and didanosine" [8,] "zidovudine and didanosine" "didanosine" "zidovudine and zalcitabine" [9,] "zidovudine and didanosine" "didanosine" "zidovudine and zalcitabine" [10,] "zidovudine and zalcitabine" "didanosine" "zidovudine and didanosine" [11,] "zidovudine and didanosine" "didanosine" "zidovudine and zalcitabine" [12,] "zidovudine and zalcitabine" "didanosine" "zidovudine and didanosine" [13,] "zidovudine and didanosine" "didanosine" "zidovudine and zalcitabine" [14,] "zidovudine and didanosine" "didanosine" "zidovudine and zalcitabine" [15,] "zidovudine and zalcitabine" "didanosine" "zidovudine and didanosine" [,4] [1,] "zidovudine" [2,] "zidovudine" [3,] "zidovudine" [4,] "zidovudine" [5,] "zidovudine" [6,] "zidovudine" [7,] "zidovudine" [8,] "zidovudine" [9,] "zidovudine" [10,] "zidovudine" [11,] "zidovudine" [12,] "zidovudine" [13,] "zidovudine" [14,] "zidovudine" [15,] "zidovudine" [ reached getOption("max.print") -- omitted 1054 rows ] To quantify the difference in performance behind the treatment rankings, we can use predict_treatment_outcome to extract the estimated quality of each treatment for each point: iai::predict_treatment_outcome(grid, train_X)   didanosine zidovudine zidovudine and didanosine zidovudine and zalcitabine 1 1041.331 932.0212 1087.372 1007.868 2 1041.331 932.0212 1087.372 1007.868 3 1041.331 932.0212 1087.372 1007.868 4 1071.780 996.8385 1052.419 1105.696 5 1041.331 932.0212 1087.372 1007.868 6 1071.780 996.8385 1052.419 1105.696 7 1071.780 996.8385 1052.419 1105.696 8 1041.331 932.0212 1087.372 1007.868 9 1041.331 932.0212 1087.372 1007.868 10 1071.780 996.8385 1052.419 1105.696 11 1041.331 932.0212 1087.372 1007.868 12 1071.780 996.8385 1052.419 1105.696 13 1041.331 932.0212 1087.372 1007.868 14 1041.331 932.0212 1087.372 1007.868 15 1071.780 996.8385 1052.419 1105.696 [ reached 'max' / getOption("max.print") -- omitted 1054 rows ] ## Evaluating Optimal Policy Trees It is critical for a fair evaluation that we do not evaluate the quality of the policy using rewards from our existing reward estimator trained on the training set. This is to avoid any information from the training set leaking through to the out-of-sample evaluation. Instead, what we need to do is to estimate a new set of rewards using only the test set, and evaluate the policy against these rewards: test_rewards <- iai::fit_predict( reward_lnr, test_X, test_treatments, test_died, test_times, propensity_score_criterion = "misclassification", outcome_score_criterion = "harrell_c_statistic") test_rewards$predictions$reward   didanosine zidovudine zidovudine and didanosine zidovudine and zalcitabine 1 1194.0187 1135.6776 1188.1092 1412.7220 2 1334.0375 1107.7136 1094.2021 1135.7435 3 1113.5301 1565.1480 1177.0666 1158.4334 4 1045.8976 1415.2554 1178.1970 1187.0529 5 -678.6386 951.8971 872.0108 947.5150 6 1178.7468 1075.7631 1166.3991 940.8757 7 771.3231 625.2559 3263.3268 751.2474 8 750.5452 162.1744 750.0566 782.4900 9 1152.3730 1803.8306 1126.5204 1206.2716 10 1104.0607 1080.2692 1182.4904 1767.5082 11 1743.5536 1114.7534 1164.9641 1154.5840 12 608.2099 1160.5157 1179.8565 1186.7581 13 -790.8765 709.1615 838.7242 979.8873 14 1070.0974 947.2137 1148.4886 1433.5791 15 -2130.8484 1090.4045 1179.1558 1015.1111 [ reached 'max' / getOption("max.print") -- omitted 1055 rows ] test_rewards$score$propensity  [1] 0.262685 test_rewards$score$outcome  $zidovudine and zalcitabine
[1] 0.7223475

$zidovudine [1] 0.718315$didanosine
[1] 0.7463456

$zidovudine and didanosine [1] 0.738453 test_rewards$score$censoring  $zidovudine and zalcitabine
[1] 0.6025603

$zidovudine [1] 0.5611245$didanosine
[1] 0.6080065

$zidovudine and didanosine [1] 0.5448434 We see the scores are similar to those on the training set, giving us confidence that the estimated rewards are a fair reflection of reality, and will serve as a good basis for evaluation. We can now evaluate the quality using these new estimated rewards. First, we will calculate the average survival time under the treatments prescribed by the tree for the test set. To do this, we use predict_outcomes which uses the model to make prescriptions and looks up the predicted outcomes under these prescriptions: policy_outcomes <- iai::predict_outcomes(grid, test_X, test_rewards$predictions$reward)   [1] 1188.10917 1094.20212 1177.06658 1178.19702 872.01081 940.87565 [7] 751.24744 750.05663 1206.27161 1182.49039 1164.96405 1179.85646 [13] 838.72417 1148.48859 1015.11106 1107.43456 1719.67019 1341.00882 [19] -33.96301 252.85546 1348.36051 1140.90320 -1392.41932 910.74883 [25] 1116.11398 1392.66871 1168.83016 832.27184 1108.43842 1063.42210 [31] 1327.02281 1449.81945 1158.35389 1307.46239 1122.13401 1338.69251 [37] 1170.61667 1105.72337 1177.45346 1174.34497 1164.05590 1144.58269 [43] 903.44999 1113.02317 1137.75844 1138.55335 1112.06960 1005.16941 [49] 1472.20035 1199.33044 1075.19811 1163.71621 1178.63241 1179.94737 [55] 1204.13715 1154.38423 1119.77749 1251.45116 1202.65741 1186.48096 [ reached getOption("max.print") -- omitted 1010 entries ] We can then get the average estimated survival times under our treatment policy: mean(policy_outcomes)  [1] 1060.484 We can compare this number to the average estimated survival time under the treatment assignments that were actually observed: mean(test_rewards$predictions$reward[cbind(1:length(test_treatments), test_treatments)])  [1] 931.9281 We see that our policy leads to a sizeable improvement in survival times compared to the randomized assignments. ## Survival Probability as Outcome In the example above, we addressed the problem with the goal of assigning treatments to maximize the expected survival time of each patient. As an alternative way of looking at the problem, we can also try to assign treatments that maximize each patient's probability of survival at a given point in time. In this case, we will try to assign treatments to maximize the probability of surviving at least 2.5 years. The only change to the previous workflow is that we need to use the evaluation_time parameter on the reward estimator to specify the time of interest: reward_lnr <- iai::categorical_survival_reward_estimator( propensity_estimator = iai::random_forest_classifier(), outcome_estimator = iai::random_forest_survival_learner(), reward_estimator = "doubly_robust", random_seed = 1, evaluation_time = (365 * 2.5), )  We then proceed to estimate rewards on the training set and train an Optimal Policy Tree as before: train_rewards <- iai::fit_predict( reward_lnr, train_X, train_treatments, train_died, train_times, propensity_score_criterion = "misclassification", outcome_score_criterion = "harrell_c_statistic") grid <- iai::grid_search( iai::optimal_tree_policy_maximizer( random_seed = 1, minbucket = 10, ), max_depth = 1:5, ) iai::fit(grid, subset(train_X, select = -c(cd420, cd820)), train_rewards$predictions$reward) iai::get_learner(grid)  Optimal Trees Visualization We see that the resulting tree finds leaves that have very different responses to the treatments. In particular, Node 7 prescribes zidovudine and didanosine with an estimated 97% 2.5-year survival rate, whereas in the adjacent Node 6, the estimated survival rate under this treatment is only 51%. We can also proceed to estimate the rewards on the test set in the same way as before: test_rewards <- iai::fit_predict( reward_lnr, test_X, test_treatments, test_died, test_times, propensity_score_criterion = "misclassification", outcome_score_criterion = "harrell_c_statistic")  We can then get the average estimated 2.5-year survival probability under our treatment policy: policy_outcomes <- iai::predict_outcomes(grid, test_X, test_rewards$predictions$reward) mean(policy_outcomes)  [1] 0.7423757 We can compare this number to the average estimated 2.5-year survival probability under the treatment assignments that were actually observed: mean(test_rewards$predictions\$reward[cbind(1:length(test_treatments),
test_treatments)])

[1] 0.5642159

We see that our policy leads to a sizeable improvement in survival probability compared to the randomized assignments.