Dental ISQ
Brief summary
The study identified influencing factors for prediction of implant stability quotient (ISQ). ISQ values were collected from 3 different groups containing 2 brands, SICare and Osstem.
Implants were performed by 2 surgeons:
   Group 1 was Surgeon 1 and SICare
   Group 2 was Surgeon 2 and SICare
   Group 3 was Surgeon 2 and Osstem
   Group 2 was Surgeon 2 and SICare
   Group 3 was Surgeon 2 and Osstem
ISQ Measurements were collected at the following time intervals:
   T1 measurements are immediately after implementation
   T2 measurements are right before dental restoration
   Time interval averaged ~4 months
   T2 measurements are right before dental restoration
   Time interval averaged ~4 months
t.test(Database_for_groups_1_2_3$MonthDiff, na.rm = T)##
## One Sample t-test
##
## data: Database_for_groups_1_2_3$MonthDiff
## t = 25.388, df = 299, p-value < 2.2e-16
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## 3.659197 4.274136
## sample estimates:
## mean of x
## 3.966667Results
At T1, need for bone grafting as a predictor significantly influenced ISQ values in all three groups.
At T2, implant diameter significantly influenced ISQ values in all three groups.
Attempt at recreating models for group 1
T1
lm(ISQ1~sex+
`immediate/delay`+
`bone graft`+
diameter+
`I stage labeled yes`+
torque,
data = Database_for_groups_1_2_3) %>%
summary()##
## Call:
## lm(formula = ISQ1 ~ sex + `immediate/delay` + `bone graft` +
## diameter + `I stage labeled yes` + torque, data = Database_for_groups_1_2_3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -23.7176 -2.8304 0.4614 3.0190 10.2039
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 59.20948 4.18053 14.163 < 2e-16 ***
## sex 1.37279 0.61032 2.249 0.02525 *
## `immediate/delay` 2.11729 0.64401 3.288 0.00114 **
## `bone graft` -6.62387 1.10174 -6.012 5.53e-09 ***
## diameter 1.99303 0.74964 2.659 0.00828 **
## `I stage labeled yes` 2.60435 0.62886 4.141 4.54e-05 ***
## torque 0.14741 0.02367 6.227 1.68e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.013 on 288 degrees of freedom
## (34 observations deleted due to missingness)
## Multiple R-squared: 0.3316, Adjusted R-squared: 0.3177
## F-statistic: 23.81 on 6 and 288 DF, p-value: < 2.2e-16
T2
# lm(ISQ2~diameter+
# DaysDiff+
# torque,
# data = Database_for_groups_1_2_3) %>% summary() #Days
#
# lm(ISQ2~diameter+
# WeekDiff+
# torque,
# data = Database_for_groups_1_2_3) %>% summary() #Weeks
lm(ISQ2~diameter+
torque+
MonthDiff,
data = Database_for_groups_1_2_3) %>% summary() #Months##
## Call:
## lm(formula = ISQ2 ~ diameter + torque + MonthDiff, data = Database_for_groups_1_2_3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -13.0313 -2.5088 0.4325 2.8484 8.3309
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 62.49950 2.73855 22.822 <2e-16 ***
## diameter 3.09071 0.63477 4.869 2e-06 ***
## torque 0.03993 0.01997 2.000 0.0466 *
## MonthDiff 0.06347 0.10435 0.608 0.5436
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.004 on 247 degrees of freedom
## (78 observations deleted due to missingness)
## Multiple R-squared: 0.1076, Adjusted R-squared: 0.09673
## F-statistic: 9.924 on 3 and 247 DF, p-value: 3.353e-06
Unfortunately, not very accurate!
Variable names for reference: (X1) male = 1, female = 2; (X2) age (X3) maxillary = 1, mandible = 2; (X4) immediate = 1, delayed = 2; (X5) bone grafting: no = 1, yes = 2, and (X6) diameter (X7) length (X8) I-stage = 2, II-stage = 1. Dummy variables were used for bone types (X10): type 1 = 100, type 2 = 010, type 3 = 001, and type 4 = 000. (X9) torque (X10) bone type (X11) T1-T2 time interval
95% confidence intervals
T1
( G1_ISQ1 <- read_excel("95% intervals.xlsx", sheet = "G1-ISQ1") )## # A tibble: 8 x 5
## `G1-ISQ1` Est SE LB UB
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Constant) 57.3 4.23 49.0 65.5
## 2 X9 0.131 0.025 0.082 0.18
## 3 X5 -4.99 1.14 -7.21 -2.77
## 4 X8 2.96 0.657 1.67 4.25
## 5 X3 1.47 0.652 0.193 2.75
## 6 X4 1.84 0.664 0.535 3.14
## 7 X6 1.67 0.754 0.191 3.15
## 8 X1 1.32 0.622 0.0979 2.54( G2_ISQ1 <- read_excel("95% intervals.xlsx", sheet = "G2-ISQ1") )## # A tibble: 8 x 5
## `G2-ISQ1` Est SE LB UB
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Constant) 57.4 4.47 48.7 66.2
## 2 X2 0.143 0.051 0.0430 0.243
## 3 X3 2.5 1.43 -0.305 5.30
## 4 X9 0.114 0.063 -0.00948 0.237
## 5 X5 -4.01 1.64 -7.22 -0.796
## 6 bone1 7.90 3.09 1.85 13.9
## 7 bone2 7.55 2.94 1.79 13.3
## 8 bone3 7.32 3.33 0.795 13.9( G3_ISQ1 <- read_excel("95% intervals.xlsx", sheet = "G3-ISQ1") )## # A tibble: 4 x 5
## `G3-ISQ1` Est SE LB UB
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Constant) 62.7 3.56 55.8 69.7
## 2 X9 0.277 0.069 0.142 0.412
## 3 X8 4.95 1.23 2.53 7.37
## 4 X5 -4.12 1.25 -6.58 -1.66T2
( G1_ISQ2 <- read_excel("95% intervals.xlsx", sheet = "G1-ISQ2") )## # A tibble: 4 x 5
## `G1-ISQ2` Est SE LB UB
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Constant) 57.0 3.04 51.0 63.0
## 2 X6 4.08 0.698 2.71 5.45
## 3 X11 0.014 0.005 0.0042 0.0238
## 4 X9 0.048 0.023 0.00292 0.0931( G2_ISQ2 <- read_excel("95% intervals.xlsx", sheet = "G2-ISQ2") )## # A tibble: 4 x 5
## `G2-ISQ2` Est SE LB UB
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Constant) 73.2 7.28 58.9 87.5
## 2 X7 -0.606 0.337 -1.27 0.0545
## 3 X6 3.45 1.22 1.06 5.85
## 4 X5 -2.66 1.11 -4.84 -0.487( G3_ISQ2 <- read_excel("95% intervals.xlsx", sheet = "G3-ISQ2") )## # A tibble: 4 x 5
## `G3-ISQ2` Est SE LB UB
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Constant) 50.6 4.76 41.3 59.9
## 2 X4 4.63 1.00 2.66 6.59
## 3 X3 2.65 0.752 1.17 4.12
## 4 X6 4.20 1.19 1.86 6.54