PLATCOV-SAP

Statistical analysis plan for the PLATCOV trial. The PLATCOV trial is registered at clinicaltrials.gov number NCT05041907

This work is licensed under a Creative Commons Attribution 4.0 International License.

Publications and outputs

This git repo will change over time. For reproducibility of the individual results, each trial output has its own git repo.

Ivermectin data and results: github repo PLATCOV-Ivermectin and preprint.

Casirivimab/imdevimab and remdesivir (the parenteral treatment arms): github repo and preprint not yet online

Overview

This github repo provides the study protocol (master protocol) and the statistical analysis plan (both in folder Analysis_Plan and PLATCOV_SAP_*.pdf), and the generic code used for the statistical analysis of each study arm (Generic_intervention_analysis.qmd)

Each interim analysis is done by running the full workflow given in Generic_intervention_analysis.qmd applied to the data from each arm separately. Each analysis is encoded as a separate csv file with the intervention and the concurrent controls (either negative controls for the futility/success analyses, or both negative and positive controls for the non-inferiority analyses).

The generic statistical analysis for each arm does the following:

• Loads data: the dataset is specified via the variable intervention. The reference arm also needs to be specified (eg no study drug);
• Checks patients in dataset against ITT data and produces a mITT variable (does the patient have at least 3 days where samples are per protocol?);
• Makes some summary data plots and tables;
• Sets up the model runs for stan along with run parameters (number of chains etc..);
• Model fitting is done separately via the code in run_models_local.R (to do on local machine) or run_models.R (to do on cluster). The stan models are provided in the folder Stan_models;
• Checks for convergence and compares model fits
• Displays results
• Some sensitivity analyses

The underlying data are not made publicly available until publication of results.

Model structure

The stan models all treat the PCR data as left-censored. All viral load data are on the log base 10 viral copies per mL scale. We fit regression models with varying degrees of complexity:

• Base model M0: individual/site random effects for slope and intercept
• M1: add human RNaseP correction (more human cells taken up by swab should in theory indicate more virus)
• M2: covariate effects (age, number of vaccine doses, serology, time since symtom onset)
• M3: non-linear version of M1, whereby the virus can still be in a growth phase at the start of the follow-up and then decreases.

Software needed

The R packages needed are:

• rstan (interfaces with stan)
• loo (for model comparison)
• censReg (censored regression - sensitivity analysis)
• RColorBrewer and tictoc (plotting and timing)

Any questions or comments drop me a message at jwatowatson at gmail dot com