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Transitions-Table1.R
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Transitions-Table1.R
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#CST Transition Modeling
#Lacto, Other, L_iners
cstDF <- merge(vagitypeDF,
myClinData[myClinData$BodySite=="Vagina" &
myClinData$subjectType=="Mom",
c("true_ga", "PID", "VisitNum",
"trimester", "SampleID")], by="SampleID")
cstDF <- cstDF %>% filter(true_ga > 0)
cstDF <- cstDF %>%
mutate(msmType = ifelse(cstDF$vagitypes %in% c(
"Atopobium_vaginae",
"Gardnerella_vaginalis", "Lactobacillus_crispatus_cluster", "Lactobacillus_gasseri_cluster", "Lactobacillus_iners", "Lactobacillus_jensenii", "Lachnospiraceae_BVAB1",
"No Type", "Prevotella_cluster2", "Sneathia_amnii", "TM7_OTU-H1"
),
as.character(vagitypes), "Other"))
cstDF$msmType <- factor(cstDF$msmType)
# levels are in alphabetical order
levels(cstDF$msmType) <- c(
"Other",
"Other",
"Other",
"Lacto",
"Lacto",
"Liners",
"Lacto",
"Other",
"Other",
"Other",
"Other", "Other"
)
cstDF$msmType <- factor(cstDF$msmType, levels=c("Lacto", "Liners", "Other"))
cbind(1:length(levels(cstDF$msmType)), levels(cstDF$msmType))
## [,1] [,2]
## [1,] "1" "Lacto"
## [2,] "2" "Liners"
## [3,] "3" "Other"
#Fit Markov chain model. Transition probabilities for a 90-day horizon.
library(msm)
myqmatrix <- matrix(rep(1,length(levels(cstDF$msmType))^2),
ncol=length(levels(cstDF$msmType)))
## Encode states as integers. cstDF$msmTypeInt <- as.integer(cstDF$msmType) cstDF$msmTrue_ga <- as.integer(cstDF$true_ga)
cstDF <- cstDF %>%
arrange(ParticipantID, true_ga) %>% mutate(msmSubject=as.integer(as.factor(as.character(ParticipantID))))
##msmType <- cstDF$msmTypeInt
msmCST <- msm(msmTypeInt ~ msmTrue_ga, subject=msmSubject, data=cstDF,
qmatrix=myqmatrix, gen.inits=TRUE) cstpmatrix <- pmatrix.msm(msmCST, t=90, ci="normal") rownames(cstpmatrix$estimates) <- levels(cstDF$msmType) colnames(cstpmatrix$estimates) <- levels(cstDF$msmType) cstpmatrix
## Lacto Liners
## Lacto 0.75355 (0.62822,0.8312) 0.09197 (0.05509,0.1860)
## Liners 0.06631 (0.03323,0.1964) 0.70920 (0.55153,0.8068)
## Other 0.13035 (0.07669,0.2371) 0.34115 (0.23055,0.4401)
## Other
## Lacto 0.15448 (0.09499,0.2452)
## Liners 0.22449 (0.14166,0.3307)
## Other 0.52850 (0.41762,0.6378)
#Lacto, Other, L_iners. Include race as a covariate. The differences are not significant.
AAsubjects <- as.character(
unique(mySurveyData$SubjectID[
!is.na(mySurveyData$african_american) & mySurveyData$african_american == "Yes" &
mySurveyData$caucasian == "No" & mySurveyData$american_indian_or_alaska_native == "No" & mySurveyData$asian == "No" &
mySurveyData$native_hawaiian == "No"]))
Csubjects <- as.character(
unique(mySurveyData$SubjectID[
!is.na(mySurveyData$caucasian) &
mySurveyData$african_american == "No" &
mySurveyData$caucasian == "Yes" & mySurveyData$american_indian_or_alaska_native == "No" & mySurveyData$asian == "No" &
mySurveyData$native_hawaiian == "No"]))
Csubjects <- Csubjects[!is.na(Csubjects)]
cstRaceDF <- cstDF[cstDF$ParticipantID %in% c(AAsubjects, Csubjects),]
cstRaceDF$ethnicity <- NA
cstRaceDF$ethnicity[cstRaceDF$ParticipantID %in% AAsubjects] <- "AA" cstRaceDF$ethnicity[cstRaceDF$ParticipantID %in% Csubjects] <- "C" cstRaceDF$ethnicity <- factor(cstRaceDF$ethnicity)
msmCST <- msm(msmTypeInt ~ msmTrue_ga,
subject=msmSubject, data=cstRaceDF, qmatrix=myqmatrix, gen.inits=TRUE, covariates =~ ethnicity)
AApmatrix <- pmatrix.msm(msmCST,covariates=list(ethnicity="AA"),t=90, ci="normal")
Cpmatrix <- pmatrix.msm(msmCST,covariates=list(ethnicity="C"),t=90, ci="normal") rownames(AApmatrix$estimates) <- levels(cstDF$msmType)
colnames(AApmatrix$estimates) <- levels(cstDF$msmType)
AApmatrix
## Lacto Liners
## Lacto 0.760357 ( 5.495e-01,1.1603) 0.120908 (2.229e-109,0.2695)
## Liners 0.008111 ( 2.298e-03,1.1603) 0.757802 (2.229e-109,0.8648)
## Other 0.044507 ( 1.397e-02,0.9583) 0.336228 (5.928e-109,0.4615)
## Other
## Lacto 0.118735 ( 4.742e-02,0.4367)
## Liners 0.234086 ( 5.565e-02,0.4567)
## Other 0.619266 ( 4.507e-01,1.1603)
rownames(Cpmatrix$estimates) <- levels(cstDF$msmType)
colnames(Cpmatrix$estimates) <- levels(cstDF$msmType)
Cpmatrix
## Lacto Liners
## Lacto 0.82561 (0.19957,0.9103) 0.02509 (0.00739,0.6716)
## Liners 0.09302 (0.01850,0.4837) 0.83070 (0.34341,0.9463)
## Other 0.27175 (0.08964,0.5366) 0.13773 (0.04039,0.4015)
## Other
## Lacto 0.14930 (0.06452,0.2754)
## Liners 0.07628 (0.01582,0.3768)
## Other 0.59052 (0.28805,0.7788)
#Lcrisp/Ljens/Lgass, Liners, Gvag, BVAB1/Sneathia/Pcluster2/TM7, Other
cstDF <- merge(vagitypeDF,
myClinData[myClinData$BodySite=="Vagina" &
myClinData$subjectType=="Mom",
c("true_ga", "PID", "VisitNum",
"trimester", "SampleID")], by="SampleID")
cstDF <- cstDF %>% filter(true_ga > 0)
cstDF <- cstDF %>%
mutate(msmType = ifelse(cstDF$vagitypes %in% c(
"Atopobium_vaginae",
"Gardnerella_vaginalis", "Lachnospiraceae_BVAB1", "Lactobacillus_crispatus_cluster", "Lactobacillus_gasseri_cluster", "Lactobacillus_iners", "Lactobacillus_jensenii",
"No Type", "Prevotella_cluster2", "Sneathia_amnii", "TM7_OTU-H1"
),
as.character(vagitypes), "Other"))
cstDF$msmType <- factor(cstDF$msmType)
# levels are in alphabetical order
levels(cstDF$msmType) <- c(
"BVAB1",
"Gvag",
"BVAB1", "Lcr_je_ga", "Lcr_je_ga", "Liners", "Lcr_je_ga", "Other",
"Other",
"BVAB1",
"BVAB1", "BVAB1"
)
cstDF$msmType <- factor(cstDF$msmType, levels=c("Lcr_je_ga", "Liners", "BVAB1", "Gvag", "Other"))
cbind(1:length(levels(cstDF$msmType)), levels(cstDF$msmType))
## [,1] [,2]
## [1,] "1" "Lcr_je_ga"
## [2,] "2" "Liners"
## [3,] "3" "BVAB1"
## [4,] "4" "Gvag"
## [5,] "5" "Other"
#Fit Markov chain model. Transition probabilities for a 90-day horizon.
library(msm)
myqmatrix <- matrix(rep(1,length(levels(cstDF$msmType))^2),
ncol=length(levels(cstDF$msmType)))
## Encode states as integers. cstDF$msmTypeInt <- as.integer(cstDF$msmType) cstDF$msmTrue_ga <- as.integer(cstDF$true_ga)
cstDF <- cstDF %>%
arrange(ParticipantID, true_ga) %>% mutate(msmSubject=as.integer(as.factor(as.character(ParticipantID))))
msmCST <- msm(msmTypeInt ~ msmTrue_ga, subject=msmSubject, data=cstDF,
qmatrix=myqmatrix, gen.inits=TRUE) cstpmatrix <- pmatrix.msm(msmCST, t=90, ci="normal") rownames(cstpmatrix$estimates) <- levels(cstDF$msmType) colnames(cstpmatrix$estimates) <- levels(cstDF$msmType)
cstpmatrix
## Lcr_je_ga Liners
## Lcr_je_ga 0.75883 (9.175e-96,Inf) 0.09295 (4.144e-02,Inf)
## Liners 0.07279 (6.920e-95,Inf) 0.70997 (4.556e-01,Inf)
## BVAB1 0.06928 (9.175e-96,Inf) 0.34373 (5.704e-02,Inf)
## Gvag 0.03632 (3.744e-94,Inf) 0.34710 (1.732e-01,Inf)
## Other 0.26039 (3.014e-94,Inf) 0.32296 (1.918e-01,Inf)
## BVAB1 Gvag
## Lcr_je_ga 0.03546 (3.024e-62,Inf) 0.02573 (5.577e-08,Inf)
## Liners 0.10847 (3.429e-58,Inf) 0.02585 (1.304e-06,Inf)
## BVAB1 0.44245 (3.024e-62,Inf) 0.03101 (6.406e-08,Inf)
## Gvag 0.33228 (7.871e-57,Inf) 0.20930 (2.950e-06,Inf)
## Other 0.18313 (1.975e-57,Inf) 0.08672 (2.950e-06,Inf)
## Other
## Lcr_je_ga 0.08703 (2.091e-02,Inf)
## Liners 0.08292 (4.888e-02,Inf)
## BVAB1 0.11353 (1.232e-02,Inf)
## Gvag 0.07500 (2.255e-02,Inf)
## Other 0.14681 (7.592e-02,Inf)
#Without confidence intervals:
cstpmatrix <- pmatrix.msm(msmCST, t=90) rownames(cstpmatrix) <- levels(cstDF$msmType) colnames(cstpmatrix) <- levels(cstDF$msmType) cstpmatrix
## Lcr_je_ga Liners BVAB1 Gvag Other
## Lcr_je_ga 0.75883120 0.09295199 0.0354602 0.02572613 0.08703047
## Liners 0.07278871 0.70997215 0.1084702 0.02584629 0.08292270
## BVAB1 0.06927681 0.34372788 0.4424534 0.03101058 0.11353136
## Gvag 0.03632308 0.34710271 0.3322757 0.20929913 0.07499934
## Other 0.26038982 0.32295834 0.1831266 0.08671559 0.14680964