---
title: "\\textbf{DOVE -- \\underline{D}urability \\underline{O}f \\underline{V}accine \\underline{E}fficacy}"
date: August 31, 2021
output: 
  rmarkdown::pdf_document:
    extra_dependencies: "subfig"
vignette: >
  %\VignetteIndexEntry{DOVE-vignette}
  %\VignetteEngine{knitr::rmarkdown}
  \usepackage[utf8]{inputenc}
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
opt <- options()
options(continue="  ", width=70, prompt=" ")
on.exit(options(opt))
library(DOVE, quietly=TRUE)
```


\section{Introduction}

DOVE is an R package for evaluating the durability of vaccine efficacy (VE) in a 
randomized, placebo-controlled clinical trial with staggered enrollment of 
participants and potential crossover of placebo recipients (Lin et al., 2021a; 2021b). 
It inputs a rectangular dataset with the following information:

\begin{itemize}
\item \textbf{Entry time}: Calendar time when the participant enters the trial.

\item \textbf{Event time}: Calendar time when the participant experiences the 
clinical event of interest (e.g., symptomatic COVID-19) or their follow-up ends, 
whichever occurs first.
  
\item \textbf{Event status}: Binary indicator taking the value 1 if the clinical 
event of interest occurs before the end of follow-up and 0 otherwise.
  
\item \textbf{Vaccination status}: Binary indicator taking the value 1 if 
vaccination occurs before the end of follow-up and 0 otherwise.
  
\item \textbf{Vaccination time}: Calendar time when vaccination takes place, 
with an arbitrary non-negative value if the participant is not vaccinated.
  
\item \textbf{Covariates}: Baseline covariates (e.g., priority group, age, sex, 
ethnicity). 
\end{itemize}

\noindent Of note, an arbitrary number of baseline covariates can be included, and
all time variables are measured from the start of the trial and are 
specified in units of days. 


\vspace{.15in}

The two primary analysis tools of the package are \textit{dove()} and \textit{dove2()}, the
formal argument structures of which are similar and were chosen to resemble that of the 
\textit{coxph()} function of the \textbf{survival} package. 
The underlying methodologies for \textit{dove()} and \textit{dove2()} are 
detailed in Lin et al. (2021a) and Lin et al. (2021b), respectively.
Function \textit{dove()} 
allows the vaccine effect to be an arbitrary function of time,
whereas function \textit{dove2()} assumes that the 
log hazard ratio for the vaccine effect is a piecewise linear function of time.
Both functions return the estimated hazard ratio for each baseline covariate, the
estimated VE in reducing the attack rate (cumulative incidence),
the estimated VE in reducing the hazard rate (instantaneous risk), and the
estimated VE in reducing the attack rate over successive time periods.

We recommend \textit{dove()} for exploratory analyses and \textit{dove2()} for formal analyses.
The latter yields more stable estimates, together with proper confidence intervals, for VE in 
reducing the hazard rate.
Both functions handle potentially right-censored events (e.g., symptomatic COVID-19,
severe COVID-19, death).
For interval-censored infection endpoints, iDOVE should be used instead.

\vspace{.15in}

The DOVE package includes convenience functions \textit{print()}, \textit{plot()}, and
\textit{vaccine()}. Function \textit{vaccine()} is used to simplify the specification of 
input variables required in the model statements of \textit{dove()} and \textit{dove2()}, similar in 
spirit to the 
\textit{cluster()} function of  the \textbf{survival} package. A simulated
dataset is provided to illustrate the use of the software.

\section{Functions}

\subsection{\textit{vaccine()}}

This convenience function is used as a component of the right-hand side
of a formula object for the sole purpose of simplifying the specification
of required input variables: entry time, vaccination status,
and vaccination time. This function is not intended to be used as
a stand-alone feature; although for completeness, the function ensures
that the input data obey basic constraints and returns 
the data in a predictable format for use in internal functions.

\vspace{.15in}


The usage is
```{r eval=FALSE}
vaccine(entry_time, vaccination_status, vaccination_time)
```
where \texttt{entry\_time} is the time when the participant enters the trial,
\texttt{vaccination\_status} is the binary indicator of vaccination, and
\texttt{vaccination\_time} is the time when vaccination takes place.

\subsection{\textit{dove()}}

This function estimates VE as a
nonparametric function of time. The value object returned 
contains the estimated hazard ratio for each baseline covariate,
estimated VE in reducing the attack rate, $VE_a(t)$, and
in reducing the hazard rate, $VE_h(t)$, where $t$ is the time elapsed since vaccination, 
as well as the estimated VE in reducing the attack rate over $m$ successive 
time periods, $VE_a(0,t_1), VE_a(t_1,t_2), \ldots, VE_a(t_{m-1},t_m)$. 
By definition, $VE_a(0,t)=VE_a(t)$.

\vspace{.15in}


The function call takes the following form:

```{r eval=FALSE}
dove(formula, data, plots = TRUE, timePts = NULL, bandwidth = NULL)
```
where 
\begin{itemize}
\item \texttt{formula} is a model statement. See below for further details.
\item \texttt{data} is the data.frame object containing all required data as 
previously described.
\item \texttt{plots} is a logical object indicating whether graphical forms of 
the $VE_a(t)$ and $VE_h(t)$ results are to be generated. 
\item \texttt{timePts} is an optional vector object specifying the time points 
$(t_1, t_2, \ldots, t_m)$ for partitioning the study period. 
\item \texttt{bandwidth} is an optional numeric object specifying the tuning 
parameter for the bandwidth used in the kernel estimation of $VE_h(t)$. 
\end{itemize}
To obtain reliable estimates of $VE_a$ $(t_{j-1}, t_j)$ $(j=1,\ldots, m)$, 
we suggest using broad time periods, such as every month,
every two months, or every quarter.
If \texttt{timePts} is not provided, the default time periods are every 60 days. 
(If $\tau < 60$ days, timePts must be provided.) 
We suggest choosing \texttt{bandwidth} between 0.1 and 1.0: a smaller bandwidth 
yields a less biased
estimate of $VE_h(t)$, whereas a larger bandwidth yields a smoother estimate of 
the $VE_h$ curve.
The default value of \texttt{bandwidth} is 0.3. This input is ignored if 
\texttt{plots} is FALSE.

\vspace{.15in}

The model statement is a formula object. The left-hand side is a survival analysis
object as returned by the \textit{Surv()} function of the \textbf{survival} 
package and specifies
the event time and event status. The right-hand side is a combination of
baseline covariates and the previously described \textit{vaccine()} function. 
Categorical baseline covariates can be specified, and if provided, 
all other categories are compared to the first category.
The \texttt{formula} input takes the following general structure

```{r eval = FALSE}
Surv(event_time, event_status) ~ covariates + 
        vaccine(entry_time, vaccination_status, vaccination_time)
```

where 'event\_time', 'event\_status', 'covariates', 'entry\_time'
'vaccination\_status' and 'vaccination\_time' are place holders
indicating the data that are to be provided; they will be replaced by the variable names 
in the header of the input data. 

\vspace{.15in}

The two VE measures, $VE_a(t)$ and $VE_h(t)$, are
estimated up to the last observed event time.
However, the estimates near the end of crossover where there are very few 
placebo participants under follow-up may not be reliable.
We also constrain $VE_h$ to be 0 at day 0 and non-decreasing at the right tail.
For estimating $VE_a$ over successive time periods,
the last time period should not extend beyond the point that there are still 
a few placebo participants under follow-up.

### \textit{dove2()}

This function estimates VE under the assumption that the log hazard ratio for 
the vaccine effect is a piecewise linear function of time. The value object 
returned is similar to that returned by \textit{dove()} and contains the 
estimated hazard ratio for each baseline covariate, 
estimated VE in reducing the attack rate, $VE_a(t)$, and in reducing the 
hazard rate, $VE_h(t)$, where $t$ is the
time elapsed since vaccination, as well as the estimated VE in reducing the 
attack rates over $m$ successive 
time periods, $VE_a(0,t_1), VE_a(t_1,t_2), \ldots, VE_a(t_{m-1},t_m)$. 
The 95\% confidence intervals for all three measures of VE are provided.

\vspace{.15in}

The function call takes the following form:

```{r eval=FALSE}
dove2(formula, data, plots = TRUE, changePts = NULL, 
      constantVE = FALSE, timePts = NULL)
```
where \texttt{formula}, \texttt{data}, \texttt{plots}, and \texttt{timePts} are
as described above for \textit{dove()}. Input \texttt{changePts} is an 
optional numerical vector 
to specify the change points, in units of days, of the piece-wise log-linear 
hazard ratio for the vaccine effect. If no change points are provided,
one change point will automatically be selected among Weeks 4, 5, 6, 7, 8 by 
the Akaike information criterion (AIC).
Input \texttt{constantVE} is a logical 
object indicating the VE trend after the last change point. 
If specified as TRUE, VE is assumed to be constant in the 
period after the last change point; otherwise it is allowed to
vary after the last change point. 
If \texttt{timePts} is not specified, the default sequence of the 
multiples of the first change point
will be used. 

\vspace{.15in}

The model statement is as previously described for \textit{dove()}. Specifically, the
\texttt{formula} input takes the following general structure

```{r eval=FALSE}
Surv(event_time, event_status) ~ covariates + 
        vaccine(entry_time, vaccination_statue, vaccination_time)
```

\vspace{.15in}

To ensure stability, we suggest placing change points at the time points 
where the events are relatively frequent
and not placing change points near the right tail. We estimate $VE_a(t)$ and $VE_h(t)$
up to the maximum of all observed event times.

\vspace{.15in}


### \textit{plot()}

When provided a value object of class DOVE (the object returned by \textit{dove()} and
\textit{dove2()}), this convenience function creates/recreates plots of the estimated VE 
in reducing the attack rate, $VE_a(t)$, and in reducing the hazard rate, $VE_h(t)$.

### \textit{print()}

When provided a value object of class DOVE (the object returned by \textit{dove()} and
\textit{dove2()}), the tabular results are displayed.

\section{Examples}

We use the dataset provided with the package, doveData, to illustrate
the analyses. 
This dataset was simulated under a priority-tier dependent 
crossover design with a ramping vaccine effect between dose 1 and dose 2 and 
contains the following observations for each of the 40,000 
participants:

\begin{itemize}
\item \textbf{entry.time}: The entry time in days.
\item \textbf{event.time}: The event time in days.
\item \textbf{event.status}: The event indicator (1=observed; 0=censored).
\item \textbf{vaccine.time}: The time of vaccination in days; NA if not vaccinated.
\item \textbf{vaccine.status}: The indicator of vaccination (1=vaccinated; 0 = not vaccinated).
\item \textbf{priority}: A composite baseline risk score taking values 1-5.
\item \textbf{sex}: A binary indicator of sex (male/female).
\end{itemize}

\vspace{.15in}
  
    
The data can be loaded in the usual way
```{r}
data(doveData)
```

```{r}
head(doveData)
```

\vspace{.15in}

The summary statistics are shown below
```{r}
summary(doveData)
```
We see that participants were enrolled in the 
study over a 4-month period (0 $\le$ entry.time $\le 120$ days), the 
follow-up ended on day 320 (event.time $\le$ 320 days) with a $\sim 6.7\%$ event rate, 
and $\sim 92\%$ of the participants were vaccinated over the course of 
the study period. In addition, 
the priority (risk) score is evenly distributed across participants, who are 
equally distributed between the two sex groups.

\subsection{ \textit{dove()} }

First, we illustrate a \textit{dove()} analysis. 
Here, we will include in our model statement baseline 
covariates, priority and sex. In addition, we will use the default partitioning
of the study period and the default tuning parameter for the bandwidth.
The function call takes the following form

```{r fig.show='hide', echo=TRUE, eval=FALSE}
result1 <- dove(formula = Surv(event.time, event.status) ~ priority + sex + 
                          vaccine(entry.time, vaccine.status, vaccine.time), 
                data = doveData)
```
```{r echo = FALSE}
result1 <- readRDS(file = "doveResult.rds")
```
\begin{verbatim}
## tau = 320

## timePts not given; default values will be used

## method converged after 13 iterations
\end{verbatim}
\vspace{.15in}


The function returns an object of class DOVE containing a list with the following elements. For 
brevity, we show only a snapshot of the large tabular results.

\noindent \textbf{call}: The unevaluated call.

\vspace{.1in}

\noindent \textbf{Covariate Effects}: The estimated hazard ratio for each 
covariate, together with the (estimated) standard error, the $95\%$ confidence 
interval, and the two-sided p-value for testing no covariate effect.
      
```{r}
result1$covariates
```

\vspace{.15in}
 

      
\noindent \textbf{Vaccine Efficacy}: Element \textbf{\$efficacy} contains the 
estimated VE in reducing 
the attack rate at each observed event time, together 
with its standard error and the $95\%$ confidence interval. In addition, the 
raw estimate of the hazard ratio at each observed event time is provided.
      
```{r}
head(result1$vaccine$efficacy)
tail(result1$vaccine$efficacy)
```

Element \textbf{\$period\_efficacy} contains the estimated VE in 
reducing the attack rate over each time period, 
its standard error, and the $95\%$ confidence interval.
    
```{r}
result1$vaccine$period_efficacy
```

\vspace{.15in}


The graphical depictions of estimates returned in \textbf{vaccine\$efficacy} are 
generated by default by \textit{dove()} and are shown in Figure \ref{fig:doveFigs}.
This figure can be regenerated using \textit{plot()} as follows:

```{r eval=FALSE}
plot(x = result1)
```

```{r, echo=FALSE, out.width='49%', fig.cap='\\label{fig:doveFigs}Plots auto-generated by \\textit{dove()}. On the left, the estimated VE curve in reducing the attack rate, $VE_a(t)$ (black) and its $95\\%$ confidence intervals (green) are shown as a function of the time since vaccination. On the right, the estimated VE curve in reducing the hazard ratio, $VE_h(t)$, is shown as a function of the time since vaccination.', fig.show='hold', fig.align='center'}
knitr::include_graphics(
  path=c("dove1a.pdf","dove1b.pdf"),
  auto_pdf = getOption("knitr.graphics.auto_pdf", FALSE),
  dpi = NULL,
  error = getOption("knitr.graphics.error", TRUE)
)
```

\subsection{\textit{dove2()}}


In the first analysis illustrating \textit{dove2()}, 
we set Week 4 as the change point and assume a potentially 
waning VE after 4 weeks. We estimate $VE_a$ over 0-4, 4-16, 16-28, 28-40 weeks. 
Note that all times must be provided in the unit of days. The function call takes the following form

```{r fig.show='hide', echo=TRUE}
result2 <- dove2(formula = Surv(event.time, event.status) ~ priority + sex + 
                           vaccine(entry.time, vaccine.status, vaccine.time), 
                 data = doveData,
                 changePts = 4*7,
                 timePts = c(4, 16, 28, 40)*7)
```


\vspace{.15in}

The function returns an S3 object of class DOVE, which contains a list 
object with the following information.

\vspace{.1in}

\noindent \textbf{call}: The unevaluated call.

```{r}
result2$call
```
\vspace{.15in}

\noindent \textbf{changePts}: The changePts of the analysis.

```{r}
result2$changePts
```

\vspace{.15in}

\noindent \textbf{Covariate Effects}: The estimated (log) hazard ratio of each covariate, 
together with the estimated standard error, the $95\%$ confidence 
interval, and the two-sided p-value for testing no covariate effect. 

```{r}
result2$covariates
```

When no baseline covariates are provided, this element will be NA.

\vspace{.15in}


\noindent \textbf{Vaccine Efficacy}: Element \textbf{\$VE\_a} contains the 
daily VE estimate in reducing the attack rate, together 
with its standard error and the $95\%$ confidence interval. 
Element \textbf{\$VE\_h} contains the daily VE estimate in reducing 
the hazard rate, together 
with its standard error and the $95\%$ confidence interval. 

```{r}
head(result2$vaccine$VE_a)
tail(result2$vaccine$VE_a)
head(result2$vaccine$VE_h)
tail(result2$vaccine$VE_h)
```

Element \textbf{\$VE\_period} contains the estimated VE in 
reducing the attack rate over each time period, 
its standard error, and the $95\%$ confidence interval.

```{r}
result2$vaccine$VE_period
```

\vspace{.15in}



The graphical depictions of $VE_a$ and $VE_h$ estimates are 
generated by default by \textit{dove2()} and are shown in 
Figure \ref{fig:dove2Figs}. This figure can be regenerated using 
\textit{plot()} as follows:
  
```{r fig.show='hide', eval=FALSE}
plot(x = result2)
```

```{r, fig.cap='\\label{fig:dove2Figs}Plots auto-generated by \\textit{dove2()}. On the left, the estimated VE curve in reducing the attack rate, $VE_a(t)$ (black) and its $95\\%$ confidence intervals (green) are shown as a function of the time since vaccination. On the right, the estimated VE curve in reducing the hazard ratio, $VE_h(t)$ (black) and its $95\\%$ confidence intervals (green) are shown as a function of the time since vaccination.', fig.show="hold", out.width="50%", echo=FALSE}
knitr::include_graphics(
  path=c("dove2a.pdf","dove2b.pdf"),
  auto_pdf = getOption("knitr.graphics.auto_pdf", FALSE),
  dpi = NULL,
  error = getOption("knitr.graphics.error", TRUE)
)
```
\vspace{.15in}

In the final analysis, we have the software use AIC to choose a change point 
among Weeks 4, 5, 6, 7, 8. 
We assume a constant VE after the change point, and thus only the constant VE 
is estimated. The function call takes the following form

```{r echo=TRUE}
result3 <- dove2(formula = Surv(event.time, event.status) ~ priority + sex + 
                           vaccine(entry.time, vaccine.status, vaccine.time), 
                 data = doveData,
                 constantVE = TRUE)
```



\vspace{.15in}


The function returns a list object containing the following items. 

\vspace{.1in}

\noindent \textbf{changePts}: The change point selected.

```{r}
result3$changePts
```

\vspace{.15in}


\noindent \textbf{Covariate Effects}: The estimated (log) hazard ratio of each covariate, 
together with the estimated standard error, the $95\%$ confidence 
interval, and the two-sided p-value for testing no covariate effect.

```{r}
result3$covariates
```

\vspace{.15in}



\noindent \textbf{Vaccine Efficacy}: Element \textbf{\$VE} contains the 
estimated constant VE, together 
with its standard error and the $95\%$ confidence interval. 


```{r}
result3$vaccine$VE
```

Plots cannot be generated when \texttt{constantVE} = TRUE.

```{r}
plot(x = result3)
```


\section{References}

Lin DY,  Zeng D, Gilbert PB (2021a). 
Evaluating the long-term efficacy of COVID-19 vaccines. 
Clinical Infectious Diseases, ciab226, 
https://doi.org/10.1093/cid/ciab226

Lin, D-Y, Gu, Y., Zeng, D., Janes, H. E., and Gilbert, P. B. (2021b). 
Evaluating Vaccine Efficacy Against SARS-CoV-2 Infection.
Clinical Infectious Diseases, ciab630, https://doi.org/10.1093/cid/ciab630

