Publications by Mark Sanderson-cimino
ALLFTD Renewal August
The following analyses are separated into two overall samples. The first includes all ALLFTD participants and investigates the performance of the UDS measures. The results from this analysis serve were used to evaluate the effectiveness of the TabCAT measures. Those analyses include: UDS Forest Plots, UDS Power Analyses, UDS The second analysi...
6643 sym Python (104892 sym/129 pcs) 149 img 35 tbl
Tabcat ALLFTD Renewal.v4
Follow-up analsyes ##Residualized Global Score Missigness Missigness by FTLD CDR Global ###There is clearly an increase in missingness as you increase in CDR. It does look like Match was given to folks even with higher CDR and its missingness is similar to that of the fluency variables. ###Missigness by syndrome ####Similar to CDR, match hold...
3949 sym 104 img 15 tbl
Tabcat.v3 (controlling for ftld cdr sob)
Missigness Missigness by FTLD CDR Global ###There is clearly an increase in missingness as you increase in CDR. It does look like Match was given to folks even with higher CDR and its missingness is similar to that of the fluency variables. ##Missigness by syndrome ###Similar to CDR, match holds up pretty well. Other TabCAT tasks have higher ...
2126 sym 53 img 7 tbl
Tabcat R markdown .v2
Missigness Missigness by FTLD CDR Global ###There is clearly an increase in missingness as you increase in CDR. It does look like Match was given to folks even with higher CDR and its missingness is similar to that of the fluency variables. ##Missigness by syndrome ###Similar to CDR, match holds up pretty well. Other TabCAT tasks have higher ...
2270 sym 53 img 7 tbl
Tabcat Grant
#Fam. baseline scores predicting change in FTLD CDR ##Project 1: Familial FTLD Baseline association between the TabCAT-EXAMINER and sum of boxes is pretty good. If you lke this let me know and I’ll make a better version. We don’t have much variance here for longitudinal change in CDR. Almost everyone stays the ...
1482 sym 42 img
ALLFTD Renewal Markdown,v2
Tableone Full Sample Description Overall n 2559 age_at_visit (mean (SD)) 58.95 (14.20) education (mean (SD)) 15.87 (2.63) ftldcdr_sb (mean (SD)) 4.93 (5.37) sex = Male (%) 1270 ( 49.6) ftldcdr_glob (%) 0 783 ( 30.6) 0.5 450 ( 17.6) 1 648 ( 25.3) 2 546 ( 21.3) 3 132 ( 5.2) genetic_factor (%) C9 282 (...
549 sym Python (25087 sym/8 pcs) 35 img 12 tbl
temp
Tableone Full Sample Description Overall n 2275 age_at_visit (mean (SD)) 58.87 (14.23) education (mean (SD)) 15.87 (2.63) ftldcdr_sb (mean (SD)) 5.02 (5.45) sex = Male (%) 1115 (49.0) ftldcdr_glob (%) 0 699 (30.7) 0.5 390 (17.1) 1 561 (24.7) 2 503 (22.1) 3 122 ( 5.4) genetic_factor (%) C9 252 (13.8)...
366 sym Python (23102 sym/8 pcs) 31 img 10 tbl
7 17 24 ALLFTD UDS
## [1] "Full sample description" ## ## Overall ## n 2275 ## age_at_visit (mean (SD)) 58.87 (14.23) ## education (mean (SD)) 15.87 (2.63) ## ftldcd...
72 sym Python (57064 sym/44 pcs) 25 img
Results replicates check
df.rep.import<-read.csv("~/Current Projects/Replicate_smartphone/analysis.mark_04.18.24_edit.csv") for (i.metric in unique(df.rep.import$Metric)) { df.metric<-as.data.frame(df.rep.import) df.metric<-df.metric %>% filter(Metric == i.metric) m.full <- df.metric |> ggplot(aes(x=Coefficient, y = Independent.variabl...
5 sym Python (834 sym/1 pcs) 4 img
RedLAT preanalysis update
Stratified by demo_place_clnaf Argentina Brazil Chile Colombia Mexico Other Peru US p test n 120 152 192 929 392 22 414 1 rundots_clnaf (mean (SD)...
704 sym 364 img