NSCLC tutorial updated

Author: ivborissov

docs/src/index.md

@@ -21,11 +21,10 @@ The researcher defines ranges and distributions for model parameters and perform
 
 2. **Selection of a VPop from the plausible population**
 Although the plausible population satisfies biological constraints, it does not necessarily reproduce clinical trial outcomes. The goal of this step is therefore to select a subset of patients that matches reported clinical endpoints.
-This subset selection problem is formulated as a mixed-integer programming (MIP) problem. Binary variables 𝑥𝑖 ∈ {0,1} indicate whether a plausible patient is included in the VPop, subject to a constraint on the desired VPop size. The objective function minimizes the mismatch between simulated and experimental data across multiple clinical endpoints.
+This subset selection problem is formulated as a mixed-integer programming (MIP) problem. Binary variables xᵢ ∈ {0,1} indicate whether a plausible patient is included in the VPop, subject to a constraint on the desired VPop size. The objective function minimizes the mismatch between simulated and experimental data across multiple clinical endpoints.
 Since individual patient data are often unavailable, the method focuses on endpoints reported as cohort-level statistics, such as means, std, quantiles, and survival data.
 
-Details on the mathematical formulation of the objective function terms, corresponding to different types of clinical data, can be found in DigiPopData package documentation:
-https://hetalang.github.io/DigiPopData.jl/dev/
+Details on the mathematical formulation of the objective function terms, corresponding to different types of clinical data, can be found in [DigiPopData package documentation](https://hetalang.github.io/DigiPopData.jl/dev/)
 
 ## Citation
 

docs/src/tutorial/Braniff2024_NSCLC.md

@@ -10,18 +10,18 @@ In the original study, individual patient data were used for VPop selection, inc
 First, we load the simulated plausible population and use the `load_vpop` function to select the columns in the plausible patient table that correspond to clinically reported endpoints.
 
 ```julia
-using VPopMIP, CSV, DataFrames, Plots, StatsBase, StatsPlots
+using VPopMIP, CSV, DataFrames, Plots, StatsBase
 
-ppopdf = CSV.read("../../../models/Braniff2024/ppopdf1000.csv", DataFrame)
-ppop = load_vpop(ppopdf1000; endpoints=["best_dSLD", "time_to_best", "time_to_pfs", "SLD_baseline"])
+ppopdf = CSV.read("./models/Braniff2024/ppopdf1000.csv", DataFrame)
+ppop = load_vpop(ppopdf; endpoints=["best_dSLD", "time_to_best", "time_to_pfs", "SLD_baseline"])
 ```
 
 Next, we load clinical data (individual patient data for the *drug* and *placebo* regimens) and convert it into summary statistics using predefined metrics available in [DigiPopData](https://hetalang.github.io/DigiPopData.jl/dev/), to represent a more realistic scenario.
 
 ```julia
 # Experimental cohort
-exp_placebo = CSV.read("../../../models/Braniff2024/synthetic_cohort_placebo.csv", DataFrame)
-exp_drug = CSV.read("../../../models/Braniff2024/synthetic_cohort_drug.csv", DataFrame)
+exp_placebo = CSV.read("./models/Braniff2024/synthetic_cohort_placebo.csv", DataFrame)
+exp_drug = CSV.read("./models/Braniff2024/synthetic_cohort_drug.csv", DataFrame)
 expdf = vcat(exp_placebo, exp_drug)
 
 # size of cohort (assuming the same for drug and placebo)
@@ -77,12 +77,15 @@ vpnum = 112
 vpop = select_cohort(ppop, data, vpnum; scip_limits_gap = 0.01)
 ```
 
-We can also visualize the results and compare the plausible population with the selected cohort.
+We can also visualize the results and compare patients selected at random with those selected by the MIP algorithm.
 
 ```julia
 vpopdf = filter(:scenario => x -> x == "drug",DataFrame(vpop))
 ppopdf = filter(:scenario => x -> x == "drug",DataFrame(ppop))
 
+rand_vpopdf = ppopdf[sample(1:nrow(ppopdf), vpnum; replace=false), :]
+rand_vpop = load_vpop(rand_vpopdf)
+
 function SLD_base_sim_exp(
     df;
     exp_mean = mean(exp_drug.SLD_baseline),
@@ -198,11 +201,11 @@ function dSLD_sim_exp(
     return p
 end
 
-p11 = SLD_base_sim_exp(ppopdf)
+p11 = SLD_base_sim_exp(rand_vpopdf)
 p12 = SLD_base_sim_exp(vpopdf)
-p21 = dSLD_sim_exp(ppopdf)
+p21 = dSLD_sim_exp(rand_vpopdf)
 p22 = dSLD_sim_exp(vpopdf)
-p31 = plot(ppop, pfs_drug_bind; dpi=400, xguide="Time (days)", yguide="PFS (%)")
+p31 = plot(rand_vpop, pfs_drug_bind; dpi=400, xguide="Time (days)", yguide="PFS (%)")
 p32 = plot(vpop, pfs_drug_bind; dpi=400, xguide="Time (days)", yguide="PFS (%)")
 
 p = plot(
@@ -212,7 +215,7 @@ p = plot(
     layout = (3, 2),
     size = (1200, 1200),
     margins=5Plots.mm, 
-    plot_title = "Plausible Population vs Selected VPop (drug regimen)"
+    plot_title = "Random Selection vs MIP-based Selected VPop (drug regimen)"
 )
 ```
 ![braniff2024plot](./Braniff2024plot.png)