Clinical Trial data - request help on variable number of comparisons per trial

Questions v5
11

Ok we are getting somewhere. For anyone interested code is below. I only had to make some slight changes which I think made the models the same:

dm=pm.Deterministic("dm",d[trt]-d[trt[:,0:1]])

This effectively vectorizes the loop that they were doing, Other parts vectorize automatically by broadcasting so no change was necessary. The posteriors that I now get are well defined for every study since they get all used

posterior
posterior1586×2318 361 KB

And how do you want to generalize this?


import arviz as az
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import pymc as pm
import pytensor.tensor as pt
import pytensor


data=pd.read_csv("data.csv")

trials=data[["n1","n2"]]
events=data[["r1","r2"]]
trt=data[["t1","t2"]].to_numpy()-1
N=len(trials)
studies=data["study"]
treatments=["SK","t-PA","at-PA","SK+t-PA","r-PA","TNK","PTCA"]
coords = {"studies": studies, "treatments":treatments}
reference=np.array([0,1,1,1,1,1,1])

with pm.Model(coords=coords) as femodel:
    r=pm.MutableData('r',events)
    n=pm.MutableData('n',trials)

    d_=pm.Normal("d_",mu=0,sigma=100,dims="treatments")
    d=pm.Deterministic("d",d_*reference,dims="treatments")

    mu=pm.Normal("mu",mu=0,sigma=100,dims="studies",shape=(N,1))
    dm=pm.Deterministic("dm",d[trt]-d[trt[:,0:1]])

    linpred=pm.Deterministic("linpred",mu+dm)
    p=pm.Deterministic("p",pm.invlogit(linpred))
    y=pm.Binomial("y",n=n,p=p,observed=r)

    fedata=pm.sample(1000,tune=500)

az.plot_posterior(fedata)

data.csv (1.6 KB)