2-munge-and-explore.Rmd

---
title: "Processing PPMI"
author: "Jesse H. Krijthe"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE)
library(tidyverse)
library(broom)
library(knitr)
source("R/tools.R")

load("data/data.RData")
```


# Fixed Variables

```{r constant-variables}
df_constant <- subject_characteristics %>% 
  filter(!is.na(Group)) %>% 
  select(PATNO,Education,Sex,Age,`Disease duration`,Group,Center,ENROLL_DATE,CV_HISTORY) %>% 
  mutate(ENROLL_DATE=convert_monthyear_num(ENROLL_DATE)) %>% 
  filter(!(PATNO %in% c(41635,73467)))


# Checks
# Every patient in the dataset once
df_constant %>% count(PATNO,sort = TRUE) %>% {.$n ==1} %>% all
```

```{r constant-summary}
fac_to_str <- function(x) {
  x %>% 
    forcats::fct_infreq() %>% 
    table %>% 
    prop.table() %>% 
    {.*100} %>%  
    round(digits = 1) %>% 
    {paste(names(.), ., sep = ": ", collapse=", ")}
}

cont_to_str <- function(x) {
  paste0(x %>% 
    mean %>%
    round(digits = 1) %>% 
    as.character," (",
    x %>% 
    sd %>%
    round(digits = 1) %>% 
    as.character,")"
  )
    
}


df_constant %>% 
  group_by(Group) %>% 
  summarize(n=n(),
            Sex=fac_to_str(Sex), 
            Age=cont_to_str(Age),
            Duration=cont_to_str(`Disease duration`),
            Education=cont_to_str(Education),
            n_centers=length(unique(Center))) %>% 
  gather("Var","Val",-Group) %>% 
  mutate(Var=fct_inorder(Var)) %>% 
  spread(Group,Val) %>% 
  kable()
```

# Repeated Measures
We remove the U01 measurements and premature withdrawal measurements. 

```{r}
df_repeated <- sig %>% 
  select(PATNO,EVENT_ID,Date=INFODT) %>% 
  filter(!(EVENT_ID %in% c("U01","PW"))) %>% 
  left_join(df_constant %>% select(PATNO,ENROLL_DATE),by="PATNO") %>% 
  mutate(Time=`Date`-ENROLL_DATE) %>% 
  select(-ENROLL_DATE) %>% 
  mutate(Year = case_when(
    EVENT_ID=="BL" ~ 0,
    EVENT_ID=="V01" ~ 0.25,
    EVENT_ID=="V02" ~ 0.5,
    EVENT_ID=="V03" ~ 0.75,
    EVENT_ID=="V04" ~ 1,
    EVENT_ID=="V05" ~ 1.5,
    EVENT_ID=="V06" ~ 2,
    EVENT_ID=="V07" ~ 2.5,
    EVENT_ID=="V08" ~ 3,
    EVENT_ID=="V09" ~ 3.5,
    EVENT_ID=="V10" ~ 4,
    EVENT_ID=="V11" ~ 4.5,
    EVENT_ID=="V12" ~ 5,
    EVENT_ID=="V13" ~ 6,
    TRUE ~ NA_real_
  ))
```


## MDS-UDPRS

### Part I and II
```{r}
mds_1 %>% filter(PATNO!=54854) %>% count(PATNO,EVENT_ID,sort = TRUE) %>% {.$n ==1} %>% all
mds_2 %>% filter(PATNO!=54854) %>% count(PATNO,EVENT_ID,sort = TRUE) %>% {.$n ==1} %>% all
mds_4 %>% filter(PATNO!=54854) %>% count(PATNO,EVENT_ID,sort = TRUE) %>% {.$n ==1} %>% all

df_repeated <- df_repeated %>% 
  left_join(mds_1 %>% select(PATNO,EVENT_ID,MDS_UPDRS_I),by=c("PATNO","EVENT_ID")) %>% 
  left_join(mds_2 %>% select(PATNO,EVENT_ID,MDS_UPDRS_II),by=c("PATNO","EVENT_ID")) %>% 
  left_join(mds_4 %>% select(PATNO,EVENT_ID,MDS_UPDRS_IV,NP4WDYSK,NP4OFF),by=c("PATNO","EVENT_ID")) %>% 
  mutate(NP4WDYSK=as.integer(NP4WDYSK>0),NP4OFF=as.integer(NP4OFF>0))
```

### Part III: On-Off state measurements

At semi-annual visits, we do not know the time since last dose, so we can strictly only consider patients with no medication as having off visits at those times.

When we have two on or off measurements due to the time since last medication, we pick the measurement that corresponds to the purpose of the used form.

```{r}
mds_3 %>% count(PAG_NAME,EVENT_ID) %>% spread(PAG_NAME,n)
mds_total %>% left_join(df_constant) %>% filter(Group=="PD") %>%  mutate(on_off = case_when(
    PD_MED_USE == "None" | (PD_MED_USE != "None" & ON_OFF_DOSE == ">=6hr") ~ "Off",
    PD_MED_USE != "None" & ON_OFF_DOSE == "<6hr" ~ "On",
    TRUE ~ "Other"
  )) %>% 
  count(on_off,EVENT_ID) %>%  spread(on_off,n)
```

```{r}
# Here we take use off measurements, or those for which we do not know the timing of medication
mds_other <- mds_total %>% 
  mutate(on_off = case_when(
    PD_MED_USE == "None" | (PD_MED_USE != "None" & ON_OFF_DOSE == ">=6hr") ~ "Other",
    PD_MED_USE != "None" & ON_OFF_DOSE == "<6hr" ~ "On",
    TRUE ~ "Other"
  )) %>% 
  mutate(matching_action = case_when(
    (on_off=="Other" & PAG_NAME=="NUPDRS3") | (on_off=="On" & PAG_NAME=="NUPDRS3A") ~ 1,
    TRUE ~ 0
  )) %>% 
  #{left_join(mds_off_left %>% count(PATNO,EVENT_ID,on_off) %>% filter(n>1),mds_off_left)}
  group_by(PATNO,EVENT_ID,on_off) %>% 
  arrange(desc(matching_action)) %>% 
  slice(1) %>% 
  ungroup %>% 
  select(-MDS_TOTAL) %>% 
  spread(on_off,MDS_UPDRS_III) %>% 
  rename(MDS_UPDRS_III_other=Other) %>% 
  filter(!is.na(MDS_UPDRS_III_other))

# Here we take into account the intent of the measurement signalled by the form used
mds_other <- mds_3 %>% 
  filter(PAG_NAME=="NUPDRS3") %>% 
  filter(PATNO!=54854) %>% 
  select(PATNO,EVENT_ID,MDS_UPDRS_III_other=MDS_UPDRS_III)

df_repeated <- left_join(df_repeated, mds_other %>% select(PATNO,EVENT_ID,MDS_UPDRS_III_other), by = c("PATNO", "EVENT_ID"))
```

```{r}
mds_off <- 
  mds_total %>% 
  mutate(on_off = case_when(
    PD_MED_USE == "None" | (PD_MED_USE != "None" & ON_OFF_DOSE == ">=6hr") ~ "Off",
    PD_MED_USE != "None" & ON_OFF_DOSE == "<6hr" ~ "On",
    TRUE ~ "Other"
  )) %>% 
  mutate(matching_action = case_when(
    (on_off=="Off" & PAG_NAME=="NUPDRS3") | (on_off=="On" & PAG_NAME=="NUPDRS3A") ~ 1,
    TRUE ~ 0
  )) %>% 
  group_by(PATNO,EVENT_ID,on_off) %>% 
  arrange(desc(matching_action)) %>% 
  slice(1) %>% 
  ungroup %>% 
  select(PATNO,EVENT_ID,on_off,MDS_UPDRS_III) %>% 
  spread(on_off,MDS_UPDRS_III) %>% 
  rename(MDS_UPDRS_III_off=Off,MDS_UPDRS_III_on=On) %>% 
  select(-Other)

df_repeated <- left_join(df_repeated, mds_off %>% select(PATNO,EVENT_ID,MDS_UPDRS_III_off,MDS_UPDRS_III_on), by = c("PATNO", "EVENT_ID"))
```

### Principal Components

Calculate PCA scores using SC,BL,ST and the regular visits only for the PD patients.

```{r}
mds_m <- mds_measurements %>% 
  left_join(df_constant,by="PATNO") %>% filter(Group=="PD") %>% 
  filter(EVENT_ID %in% c("SC","BL","ST",paste0("V0",1:9),paste0("V",10:13))) %>% 
  mutate(on_off = case_when(
    PD_MED_USE == "None" | (PD_MED_USE != "None" & ON_OFF_DOSE == ">=6hr") ~ "Off",
    PD_MED_USE != "None" & ON_OFF_DOSE == "<6hr" ~ "On",
    TRUE ~ "Other"
  )) %>% 
  mutate(matching_action = case_when(
    (on_off=="Off" & PAG_NAME=="NUPDRS3") | (on_off=="On" & PAG_NAME=="NUPDRS3A") ~ 1,
    TRUE ~ 0
  )) %>% 
  #{left_join(mds_off_left %>% count(PATNO,EVENT_ID,on_off) %>% filter(n>1),mds_off_left)}
  group_by(PATNO,EVENT_ID,on_off) %>% 
  arrange(desc(matching_action)) %>% 
  slice(1) %>% 
  ungroup %>% 
  group_by(PATNO,EVENT_ID) %>% 
  slice(1) %>% 
  ungroup 

mds_1 %>%
  left_join(df_constant,by="PATNO") %>% filter(Group=="PD") %>% 
  filter(EVENT_ID %in% c("SC","BL","ST",paste0("V0",1:9),paste0("V",10:13))) %>%
  select(PATNO,EVENT_ID,starts_with("NP1")) %>% 
  na.omit %>%
  {bind_cols(
      select(.,PATNO,EVENT_ID),
    select(.,-PATNO,-EVENT_ID) %>% princomp() %>% .$scores %>% as.data.frame() %>% rename_all(function(x){paste0("PC1_",x)})
  )} -> df_pc1
  
mds_2 %>%
  left_join(df_constant,by="PATNO") %>% filter(Group=="PD") %>% 
  filter(EVENT_ID %in% c("SC","BL","ST",paste0("V0",1:9),paste0("V",10:13))) %>%
  select(PATNO,EVENT_ID,starts_with("NP2")) %>% 
  na.omit %>%
  {bind_cols(
      select(.,PATNO,EVENT_ID),
    select(.,-PATNO,-EVENT_ID) %>% princomp() %>% .$scores %>% as.data.frame() %>% rename_all(function(x){paste0("PC2_",x)})
  )} -> df_pc2

mds_3 %>% 
  filter(PATNO!=54854) %>% 
  #filter(on_off=="Off") %>% 
  filter(PAG_NAME=="NUPDRS3") %>% 
  select(PATNO,EVENT_ID,starts_with("NP3")) %>% 
  na.omit %>%
  {bind_cols(
      select(.,PATNO,EVENT_ID),
    select(.,-PATNO,-EVENT_ID) %>% princomp() %>% .$scores %>% as.data.frame() %>% rename_all(function(x){paste0("PC3_",x)})
  )} -> df_pc3

# df_repeated <- df_repeated %>% 
#   left_join(df_pc1,by = c("PATNO", "EVENT_ID")) %>% 
#   left_join(df_pc2,by = c("PATNO", "EVENT_ID")) %>% 
#   left_join(df_pc3,by = c("PATNO", "EVENT_ID"))
```

## MoCA, SE, MCI, QUIP
No MOCA administered at baseline, so take the measurement at screening (SC, approximately 1 month earlier than baseline). If the adjusted MoCA score is missing, use the score calculated by the examiner.

```{r}
df_repeated <- df_repeated %>% 
  left_join(
    moca %>% mutate(EVENT_ID=replace(EVENT_ID,EVENT_ID=="SC","BL")) %>% 
      mutate(MOCA_adjusted=if_else(is.na(MOCA_adjusted) & !is.na(MCATOT), MCATOT, MOCA_adjusted)) %>% 
      select(PATNO,EVENT_ID,MoCA=MOCA_adjusted),
    by=c("PATNO","EVENT_ID")
  )

df_repeated <- df_repeated %>% 
  left_join(modified_schwab,by=c("PATNO","EVENT_ID"))

df_repeated <- df_repeated %>% 
  left_join(non_motor %>% select(PATNO,EVENT_ID,QUIP,QUIP8,QUIPany), by=c("PATNO","EVENT_ID"))

df_repeated <- df_repeated %>% 
  left_join(scopa %>% select(PATNO,EVENT_ID,`SCOPA-AUT`), by=c("PATNO","EVENT_ID"))
```

## Medication Use
How does medication in the medication log correspond to reported parkinson medication in the questionaire?

```{r}
events <- c("SC","BL","ST",paste0("V0",1:9),paste0("V",10:13))

# LEDD from medication log
df_usage1 <- ledds %>% 
  #filter(EVENT_ID %in% events) %>% 
  mutate(LEDDnonzero=LEDD>0.0) %>% 
  select(PATNO,EVENT_ID,LEDDnonzero)

# PD Medication from form
df_usage2 <- pd_med_use %>% 
  #filter(EVENT_ID %in% events) %>% 
  select(PATNO,EVENT_ID,PDMEDYN) %>% 
  mutate(PDMEDYN=as.logical(PDMEDYN))

# Levo from form
df_usage3 <- pd_med_use %>% 
  filter(EVENT_ID %in% events) %>% 
  select(PATNO,EVENT_ID,ONLDOPA) %>% 
  mutate(ONLDOPA=if_else(is.na(ONLDOPA),FALSE,TRUE))

# Levo from UPDRS form
df_usage4 <- mds_3 %>% 
  filter(EVENT_ID %in% events) %>% 
  select(PATNO,EVENT_ID,PD_MED_USE) %>% 
  distinct(PATNO,EVENT_ID,.keep_all = TRUE) %>% 
  mutate(LevoMed=PD_MED_USE %in% c(1,4,5,7))

df_repeated <- df_repeated %>% 
  left_join(df_usage1, by = c("PATNO", "EVENT_ID")) %>% 
  left_join(df_usage2, by = c("PATNO", "EVENT_ID")) %>%
  left_join(df_usage3, by = c("PATNO", "EVENT_ID")) %>% 
  left_join(df_usage4, by = c("PATNO", "EVENT_ID")) %>% 
  mutate(ONLDOPA=replace(ONLDOPA,EVENT_ID %in% c("SC","BL"),FALSE)) %>% 
  mutate(PDMEDYN=replace(PDMEDYN,EVENT_ID=="BL",FALSE)) %>% 
  mutate(ONLDOPA=if_else(is.na(ONLDOPA) & !is.na(LevoMed),LevoMed,ONLDOPA)) %>% 
  left_join(ledds, by=c("PATNO","EVENT_ID"))
  

# df_usage2 %>% filter(PATNO==3375)
# df_repeats %>% filter(PATNO==3375) %>% select(EVENT_ID,PDMEDYN,LEDDnonzero,ONLDOPA) %>% filter(EVENT_ID %in% events) %>% arrange(EVENT_ID)
```

```{r}
df_repeated %>% 
  filter(EVENT_ID %in% events) %>% 
  count(LEDDnonzero,PDMEDYN)

df_repeated %>% 
  filter(EVENT_ID %in% events) %>% 
  count(LEDDnonzero,ONLDOPA)

df_repeated %>% 
  filter(EVENT_ID %in% events) %>% 
  count(PDMEDYN,ONLDOPA)

df_repeated %>% 
  count(ONLDOPA,LevoMed)
```

```{r}
df_repeated %>% 
  left_join(df_constant, by="PATNO") %>% 
  filter(EVENT_ID %in% events) %>% 
  filter(Group=="PD") %>% 
  mutate(EVENT_ID=fct_relevel(EVENT_ID, c("SC","BL",paste0("V0",1:9),paste0("V",10:13),"ST"))) %>% 
  select(EVENT_ID,LEDDnonzero,ONLDOPA,PDMEDYN,LevoMed) %>% 
  gather("var","val",-EVENT_ID) %>%
  mutate(val=fct_relevel(fct_explicit_na(factor(val)),c("(Missing)","FALSE","TRUE"))) %>% 
  ggplot(aes(x=EVENT_ID,fill=val)) +
  geom_bar() +
  theme_krijthe_gray(base_size = 6) +
  ylab("Number of patients") +
  xlab("Visit") +
  ggtitle("Number of patients on therapy at different visits") +
  facet_wrap(~var)
```


```{r}
df_repeated %>% 
  left_join(df_constant, by="PATNO") %>% 
  filter(EVENT_ID %in% events) %>% 
  filter(Group=="PD") %>% 
  ggplot(aes(x=EVENT_ID,y=LEDD,group=PATNO)) +
  geom_point(alpha=0.05) +
  geom_line(alpha=0.05) +
  theme_krijthe_gray(base_size = 6) +
  xlab("Visit") +
  ggtitle("LEDD over time per patient")
```


```{r, warning=FALSE}
conmed %>% 
  filter(DISMED==1) %>% 
  group_by(PATNO) %>% arrange(STARTDT) %>% summarize(STARTDT=STARTDT[1]) %>% ungroup %>% select(PATNO,STARTDT) %>% mutate(Type="Med") %>% 
  left_join(sig %>% filter(EVENT_ID=="ST") %>%  
  mutate(STARTDT=convert_monthyear_num(ORIG_ENTRY)) %>% select(PATNO,STARTDT),by="PATNO") %>% 
  mutate(diff=STARTDT.y-STARTDT.x) %>% 
  left_join(subject_characteristics,by="PATNO") %>% 
  filter(ENROLL_CAT=="PD") %>% 
  ggplot(aes(x=diff)) +
  geom_histogram(bins=100) + ggtitle("Difference time ST measurement and first DISMED==1 medication") +
  theme_krijthe_gray(base_size = 6)

# bind_rows(sig %>% filter(EVENT_ID=="ST") %>%  
#   mutate(STARTDT=convert_monthyear_num(ORIG_ENTRY)) %>% select(PATNO,STARTDT) %>% mutate(Type="ST"),
# 
# conmed %>% filter(LEDD!=0.0) %>% arrange(STARTDT) %>% select(PATNO,STARTDT) %>% mutate(Type="Med")
# ) %>% 
#   left_join(subject_characteristics,by="PATNO") %>% 
#   filter(ENROLL_CAT=="PD") %>% 
#   ggplot(aes(x=STARTDT,y=factor(PATNO),color=Type)) +
#   geom_point(alpha=1,size=0.5) +
#   theme(axis.text.y = element_blank(),panel.grid.major.y = element_line(size=0.02,color="black"))
```

## Medication LEDD
```{r}
# df_usage <- ledds %>% 
#   mutate(Levo=LEDD>0.0) %>% 
#   filter(EVENT_ID %in% c("BL","V02","V04","V05","V06")) %>% 
#   select(PATNO,EVENT_ID,Levo) %>% 
#   spread(EVENT_ID,Levo)

df_usage_cont <- ledds %>% 
  mutate(Levo=LEDD) %>% 
  mutate(Levo=round(Levo/100)*100) %>% 
  mutate(Levo=ifelse(Levo>500,500,Levo)) %>% 
  filter(EVENT_ID %in% c("BL","V02","V04","V05","V06")) %>% 
  select(PATNO,EVENT_ID,Levo) %>% 
  spread(EVENT_ID,Levo)
#df_usage_cont  

#df_usage_long

df_usage_cont_long <-
  ledds %>% 
  mutate(Levo=LEDD) %>% 
  filter(EVENT_ID %in% c("BL","V02","V04","V05","V06")) %>% 
  select(PATNO,EVENT_ID,Levo) %>% 
  arrange(PATNO,EVENT_ID) %>% 
  complete(PATNO,EVENT_ID) %>% 
  group_by(PATNO) %>% 
  mutate(Levo_prev=lag(Levo)) %>% 
  mutate(Levo_next=lead(Levo)) %>% 
  ungroup

df_usage_cont %>% 
  gather("EVENT","Value",-PATNO) %>% 
  ggplot(aes(x=Value)) + 
  geom_histogram(bins=50)

```

```{r, fig.cap="The month the measurement should have taken place vs. the actual time the measurement was done."}
df_repeated %>% 
  left_join(df_constant, by="PATNO") %>% 
  filter(EVENT_ID %in% events) %>% 
  filter(Group=="PD") %>% 
  ggplot(aes(x=Time,y=Year)) +geom_point() +theme_krijthe_gray() +
  scale_x_continuous(breaks=seq(0,6,by=1))
```

# Number of samples
```{r}
df_repeated %>% 
  left_join(df_constant, by="PATNO") %>% 
  filter(EVENT_ID %in% events) %>% 
  filter(Group=="PD") %>% 
  select(-Time) %>% 
  #select(-starts_with("PC"),"PC1_Comp.1","PC1_Comp.2","PC1_Comp.3") %>% 
  filter(!is.na(Year)) %>% 
  gather(Measurement,Value,-PATNO,-EVENT_ID,-Year,-Date) %>% 
  group_by(Year,Measurement) %>% 
  summarize(Observations=sum(!is.na(Value))) %>% 
  ggplot(aes(x=Year,y=Observations,fill=Measurement)) +
  geom_bar(stat = "identity") +
  facet_wrap(~Measurement,ncol=3) +
  theme(legend.position="none") +
  scale_x_continuous(breaks=0:6)
```

# Trajectories and average trajectories
```{r average-trajectories, fig.height=4, fig.width=2}
df_repeated %>% 
  left_join(df_constant, by="PATNO") %>% 
  filter(EVENT_ID %in% events) %>% 
  filter(Group=="PD") %>% 
  select(PATNO,Time,MDS_UPDRS_I,MDS_UPDRS_II, MDS_UPDRS_III_other,MDS_UPDRS_III_off,MoCA) %>% 
  gather("Measurement","Value",-PATNO,-Time) %>% 
  filter(!is.na(Value)) %>% 
  ggplot(aes(x=Time,y=Value)) +
  geom_line(aes(group=PATNO),alpha=0.1) +
  geom_smooth(color="red") +
  facet_wrap(~Measurement,ncol=2,scales="free_y") +
  theme(legend.position="none")
```

```{r}
modelused <- function(.x){lm(Value~Time,data=.x)}

df_repeated %>% 
  gather("Outcome","Value",-PATNO,-EVENT_ID,-Time,-Date,-Year) %>% 
  left_join(df_constant,by="PATNO") %>% 
  filter(Group=="PD") %>% 
  group_by(Outcome) %>% 
  nest() %>% 
  mutate(model=map(data,modelused)) %>% 
  mutate(prop=map(model,glance)) %>% 
  unnest(prop) %>% 
  select(-data,-model) %>% 
  kable(caption="Regression of outcomes on the Month variable")
```

```{r example-trajectories}
df_repeated %>% 
  #left_join(ledds, by=c("PATNO","EVENT_ID")) %>%  
  select(PATNO,Time,starts_with("MDS"),MoCA,LEDD) %>% 
  gather(Outcome,Value,-PATNO,-Time,-LEDD) %>% 
  na.omit %>% 
  {filter(., PATNO %in% sample(unique(.$PATNO),5))} %>% 
  ggplot(aes(x=Time,y=Value,group=PATNO,color=LEDD>0)) +
  geom_line(alpha=0.7) +
  geom_point() +
  facet_wrap(~Outcome,ncol=3)
```

Patterns around the start of therapy
```{r}
ledds_start <- df_repeated %>% 
  filter(PDMEDYN) %>% arrange(Time) %>% 
  group_by(PATNO) %>% 
  slice(1) %>% 
  ungroup %>% 
  select(PATNO,Time_start=Time)

df_repeated %>% 
  left_join(ledds_start, by=c("PATNO")) %>%  
  mutate(Time=Time-Time_start) %>% 
  select(PATNO,Time, starts_with("MDS"),MoCA,LEDD,PDMEDYN) %>% 
  gather(Outcome,Value,-PATNO,-Time,-LEDD,-PDMEDYN) %>% 
  na.omit %>% 
  #{filter(., PATNO %in% sample(unique(.$PATNO),5))} %>% 
  #filter(LEDD==0) %>% 
  ggplot(aes(x=Time,y=Value,group=PATNO,color=PDMEDYN)) +
  geom_line(alpha=0.1) +
  geom_point(alpha=0.1) +
  facet_wrap(~Outcome,ncol=3) +
  coord_cartesian(xlim=c(-0.5,0.5))
  
```

```{r}
# Might be useful at some point
df_repeated_wide <- df_repeated %>% 
  filter(EVENT_ID %in% c("BL","V06")) %>% 
  gather("Measurement","Value",-PATNO,-EVENT_ID) %>% 
  unite("Var",EVENT_ID,Measurement) %>% 
  distinct(PATNO,Var,.keep_all = TRUE) %>%
  spread(Var,Value)
```

```{r}
save(df_repeated, df_constant, file="data/causal-ppmi.RData")
```