Simulate adverse event reporting in clinical trials with the goal of detecting under-reporting sites.
Monitoring of Adverse Event (AE) reporting in clinical trials is important for patient safety. We use bootstrap-based simulation to assign an AE under-reporting probability to each site in a clinical trial. The method is inspired by the ‘infer’ R package and Allen Downey’s blog article: “There is only one test!”.
An adverse event (AE) is any untoward medical occurrence in a patient or clinical investigation subject administered a pharmaceutical product and which does not necessarily have a causal relationship with this treatment. It is important for patient safety that AEs are reported back to the sponsor. An important part of quality monitoring is to detect clinical trial sites that are not propagating all of the AEs reported to them by their patients to the sponsor. In a clinical trial patients follow a strict visiting schedule at which times treatments are given and exams are being performed. Typically AEs get reported by a patient when they are at an on-site visit at the clinic. So the total number of AEs reported by a site depends on the number of patients enrolled at the site and the total number of visits.
In a nutshell we will perform the following steps.
Patient level AE data is characterized by the number of consecutive visits and the number of AEs that have been reported each time. For the maximum consecutive visit we sample from a normal distribution and for the AEs reported at each visit we sample from a poisson distribution.
Here we simulate the AEs generated by 3 patients
set.seed(1) replicate( 3, sim_test_data_patient( .f_sample_max_visit = function() rnorm(1, mean = 20, sd = 4), .f_sample_ae_per_visit = function(max_visit) rpois(max_visit, 0.5) ) )
## [] ##  0 1 1 2 4 5 6 6 6 6 7 7 8 8 9 12 12 ## ## [] ##  0 1 1 1 1 1 1 2 2 2 2 2 2 3 4 5 5 6 6 7 8 9 9 ## ## [] ##  0 0 1 2 2 3 3 3 3 3 3 3 4 4 6 6 6 6 7 7
In order to simulate patient data for an entire study we assume make the simplification that all sites have the same number of patients. Further we specify a fraction of sites that is under-reporting AEs.
df_visit <- sim_test_data_study( n_pat = 120, n_sites = 6, frac_site_with_ur = 0.4, ur_rate = 0.6, max_visit_mean = 20, max_visit_sd = 4, ae_per_visit_mean = 0.5 ) df_visit %>% head(10) %>% kable()
df_visit %>% select(site_number, is_ur) %>% distinct() %>% kable()
In our sample data 2 sites (S0001 and S0002) are under-reporting AEs
In an ongoing trial all patients will have a different number of consecutive visits. To find a cut-off visit to normalize the data we specify a single evaluation point for a given site based on the number of visits of the patient population.
For determining the visit number at which we are going to evaluate AE reporting we take the maximum visit of each patient at a site and take the median. Then we multiply with 0.75 which will give us a cut-off point determining which patient will be evaluated. Of those patients we will evaluate we take the minimum of all maximum visits hence ensuring that we take the highest visit number possible without excluding more patients from the analysis. In order to ensure that the sampling pool for that visit is large enough we ensure that at least 20% of all patients at the study are available for sampling. We limit visit_med75 to the 80% quantile of all patient maximum visits of the entire study.
For this we use
site_aggr to aggregate the simulated visit level data to site level. Adjusting
min_pat_pool will change the minimum ratio of patients available for sampling that determines the maximum values for visit_med75.
plot_visit_med75(df_visit, df_site, study_id_str = "A", n_site = 6)
## purple line: mean site ae of patients with visit_med75 ## grey line: patient included ## black dashed line: patient excluded ## dotted vertical line: visit_med75, 0.75 x median of maximum patient visits of site ## solid vertical line: visit_med75 adjusted, increased to minimum maximum patient visit of included patients ## dashed vertical line: maximum value for visit_med75 adjusted, 80% quantile of maximum patient visits of study
Using the adjusted visit_med75 will drop a few patients (dashed lines) but will by definition at least include 50% of all patients favoring patients that have higher number of visits. By looking at the mean ae development (purple line) we can already easily spot the two under-reporting sites. The same goes for looking at the
mean_ae_site_med75 column in
df_site. Next we will use bootstrap simulations to determine the probability for obtaining each mean AE value or lower at visit_med75 by chance.
We could use a classical parametric test and calculate the probability with which we can reject the NULL hypothesis for the AE counts that we observe at the visit_med75.
As we sample the AEs from a poisson distribution the R implementation of the poisson.test would be appropriate.
But there are four major problems that we often encounter when we try to describe real life count data with the poisson distribution which is only described by a single parameter:
The true distribution of the AE counts will vary from study to study with the degree of the influence of these 4 problems being unknown unless thoroughly investigated.
With the non-parametric approach that we propose we do not need to worry about these statistical assumptions. Since the distribution of the AE count from the simulated patient pool we draw from will be very close to the unknown true distribution of the underlying AE generating process.
How likely is it to get a mean AE value that is equal or lower to what we observe at site C with the same number of patients?
For illustration purposes we start to simulate 10 hypothetical patient groups for site C by drawing (with replacement) from all patients in the study marking those for which we have obtained an equal or lower mean AE value than initially observed (middle).
Instead of 10 times we simulate 1000 times and count how many times we have observed an equal or lower mean AE value than initially observed and convert it to a percentage (right).
To illustrate the effect of under-reporting we repeat the same process after having removed 2 AEs per patient from site C. We see how the probability for obtaining an equal or lower mean AE value than initially observed decreases (bottom).
plot_sim_examples(size_dot = 4, size_raster_label = 10)
We can run the above described simulation and as a benchmark also perform a poisson test.
We find that the probability of getting a mean ae at visit_med75 for sites S0001 and S0002 is 0 or near zero.
Our simulated test data set consists of just 6 sites. However, it is not uncommon for 100 sites or more to participate in the same clinical trial. This would mean that we need to perform 100 statistical tests, and applying a 5% significance threshold would lead to on average of 5 False Positives (FP). We therefore need to adjust the calculated p-values and bootstrapped probabilities using
stats::p.adjust(p, method = "BH"), which applies the Benjamin Hochberg Procedure.
eval_sites() uses the the inverted adjusted values to calculates the final bootstrapped AE under-reporting probability (prob_low_prob_ur) and includes the poisson test derived under-reporting probability as a reference (pval_prob_ur).
plot_study will plot mean ae development of all sites and visit level data for each flagged site with an AE under-reporting threshold of 95%.
plot_study(df_visit, df_site, df_eval, study = "A")