template_design_report_pdf.Rmd

---
title: "*pfhrp2/3* Planner: Design report"
subtitle: "*Downloaded on: `r Sys.Date()` | DRpower interactive app v1.0.0*"
output: 
  pdf_document:
    fig_caption: yes
    number_sections: no
    latex_engine: xelatex
    df_print: kable
    fig_width: 6
    fig_height: 4
header-includes:
  - \usepackage{booktabs}
mainfont: Arial
documentclass: article
links-as-notes: true
geometry: 
  - margin=0.75in
  - top=0.5in
papersize: letter
always_allow_html: true
params:
  design_ss_icc: NA
  design_ss_prev: NA
  design_final_sizes: NA
  design_nclusters: NA
  design_paramprev: NA
  design_paramicc: NA
  design_paramsims: NA
  design_poweroutput: NA
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)

library(tidyverse)
library(kableExtra)

set.seed(10)
```

```{r create objects from params, echo=F}
df_final_sizes <- params$design_final_sizes
n_clusters <- params$design_nclusters
param_prev <- params$design_paramprev
param_icc <- params$design_paramicc
param_sims <- params$design_paramsims
power_output <- params$design_poweroutput
```

## Background
<!-- \begin{center}\rule{7in}{0.4pt}\end{center} -->
This report presents the results generated by using the [*pfhrp2/3* planner web application](https://github.com/shaziaruybal/DRpower_app). This application was used to help guide the appropriate number of clusters and a target sample size per cluster for the design of a *pfhrp2/3* deletion prevalence study. For more information on the statistical method used, see the [DRpower R package website](https://mrc-ide.github.io/DRpower).

## Final cluster sizes
<!-- \begin{center}\rule{7in}{0.4pt}\end{center} -->

The adjusted sample sizes based on the expected drop-out proportion in each of the `r params$design_nclusters` clusters are shown below and should be considered for study design. 

```{r echo=F}
kable(df_final_sizes, 
      format = "latex",
      booktabs = T,
      digits = 2) %>%
  kable_styling(
    bootstrap_options = "striped",
    position = "left",
    full_width = F)
```

## Estimated power
<!-- \begin{center}\rule{7in}{0.4pt}\end{center} -->

To estimate the power of the study assuming the target sample sizes as per above, we used the [DRpower R package](https://mrc-ide.github.io/DRpower) with the following parameters: 

<!-- - The sample sizes for the `r params$design_nclusters` clusters (shown above) -->
- Prevalence of `r params$design_paramprev`%
- Intra-cluster correlation of `r params$design_paramicc` 
- `r params$design_paramsims` simulations

The estimated power is `r power_output$power`% (95%CI: `r ceiling(power_output$lower)` - `r ceiling(power_output$upper)`%).

```{r fig.height=3, fig.width=3, echo=F}
power_output %>% 
  ggplot() +
  geom_segment(aes(x = " ", xend = " ",y = lower, yend = upper), color = "black", linewidth = 1) +
  geom_point(aes(x = " ", y = power),
             size = 2,
             shape = 21,
             fill = "mediumpurple") +
  geom_hline(yintercept = 80, color = "darkgrey", linetype = "dashed") +
  geom_text(aes(x= " ", y = 83, label = "80% threshold"), color = "darkgrey", size = 3) +
  scale_y_continuous(labels = scales::percent_format(1, scale = 1), limits = c(0,100)) +
  labs(x = "",
       y = "Estimated power") +
  theme_light() +
  theme(text = element_text(size = 12))
```