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Study A CHD risk factors

Study A › CHD risk factors

framingham.csv4,240 rows · 16 columns

age	sysBP	BMI	TenYearCHD
39	106	26.97	0
46	121	28.73	0
48	127.5	25.34	0
61	150	28.58	1
46	130	23.1	0
43	180	30.3	0

Which risk factors relate most to ten-year CHD?

Thinking

Shell.ExecRscript framingham_stats.RDone

Age and systolic BP are the strongest, then hypertension and diastolic BP.

with TenYearCHD	r	95% CI	p
age	0.23	0.20 to 0.25	< .001
sysBP	0.22	0.19 to 0.24	< .001
prevalentHyp	0.18	0.15 to 0.21	< .001
diaBP	0.15	0.12 to 0.17	< .001

Supervised

Study A CHD risk factors

Study A › CHD risk factors

R 4.4RunningRan in 0.4 s

1# Framingham -- linear regression output (OLS)2df <- read.csv("framingham.csv", check.names = FALSE)3 4# 2. multiple: sysBP5m2 <- lm(sysBP ~ age + male + BMI + heartRate + totChol,6         data = df)7print(summary(m2))

Console

Call:lm(formula = sysBP ~ age + male + BMI + heartRate + totChol,     data = df) Residuals:    Min      1Q  Median      3Q     Max -64.300 -12.481  -2.465   9.921 125.865  Coefficients:             Estimate Std. Error t value Pr(>|t|)    (Intercept) 22.531293   3.091221   7.289 3.72e-13 ***age          0.864168   0.035665  24.230  < 2e-16 ***male        -1.128182   0.596856  -1.890   0.0588 .  BMI          1.425642   0.072987  19.533  < 2e-16 ***heartRate    0.290839   0.024571  11.837  < 2e-16 ***totChol      0.036349   0.006852   5.305 1.19e-07 ***---Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 18.84 on 4165 degrees of freedom  (69 observations deleted due to missingness)Multiple R-squared:  0.2642,	Adjusted R-squared:  0.2633 F-statistic: 299.1 on 5 and 4165 DF,  p-value: < 2.2e-16

	male	age	education	currentSmoker	cigsPerDay	BPMeds	prevalentHyp	totChol	sysBP	diaBP	BMI	heartRate	glucose	TenYearCHD
1	1	39	4	0	0	0	0	195	106	70	26.97	80	77	0
2	0	46	2	0	0	0	0	250	121	81	28.73	95	76	0
3	1	48	1	1	20	0	0	245	127.5	80	25.34	75	70	0
4	0	61	3	1	30	0	1	225	150	95	28.58	65	103	1
5	0	46	3	1	23	0	0	285	130	84	23.1	85	85	0
6	0	43	2	0	0	0	1	228	180	110	30.3	77	99	0
7	0	63	1	0	0	0	0	205	138	71	33.11	60	85	1
8	0	45	2	1	20	0	0	313	100	71	21.68	79	78	0
9	1	52	1	0	0	0	1	260	141.5	89	26.36	76	79	0
10	1	43	1	1	30	0	1	225	162	107	23.61	93	88	0
11	0	50	1	0	0	0	0	254	133	76	22.91	75	76	0
12	0	43	2	0	0	0	0	247	131	88	27.64	72	61	0
13	1	46	1	1	15	0	1	294	142	94	26.31	98	64	0
14	0	41	3	0	0	1	1	332	124	88	31.31	65	84	0
15	0	39	2	1	9	0	0	226	114	64	22.35	85	NA	0
16	0	38	2	1	20	0	1	221	140	90	21.35	95	70	1
17	1	48	3	1	10	0	1	232	138	90	22.37	64	72	0
18	0	46	2	1	20	0	0	291	112	78	23.38	80	89	1
19	0	38	2	1	5	0	0	195	122	84.5	23.24	75	78	0
20	1	41	2	0	0	0	0	195	139	88	26.88	85	65	0

==============================================================================
0. SAMPLE
==============================================================================
Rows: 4240   Columns: 16

==============================================================================
1. SIMPLE LINEAR REGRESSION: systolic BP ~ age
==============================================================================

Call:
lm(formula = sysBP ~ age, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-57.382 -13.627  -2.627  10.182 148.042 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) 82.14199    1.82570   44.99   <2e-16 ***
age          1.01276    0.03628   27.91   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 20.25 on 4238 degrees of freedom
Multiple R-squared:  0.1553,	Adjusted R-squared:  0.1551 
F-statistic:   779 on 1 and 4238 DF,  p-value: < 2.2e-16


Equation: sysBP = 82.142 + 1.0128 * age
n = 4240, R2 = 0.1553, adj R2 = 0.1551, residual SE = 20.253 mmHg
95% CI for slope: [0.9416, 1.0839]

==============================================================================
2. MULTIPLE LINEAR REGRESSION: sysBP ~ age + male + BMI + heartRate + totChol
==============================================================================

Call:
lm(formula = sysBP ~ age + male + BMI + heartRate + totChol, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-64.300 -12.481  -2.465   9.921 125.865 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) 22.531293   3.091221   7.289 3.72e-13 ***
age          0.864168   0.035665  24.230  < 2e-16 ***
male        -1.128182   0.596856  -1.890   0.0588 .  
BMI          1.425642   0.072987  19.533  < 2e-16 ***
heartRate    0.290839   0.024571  11.837  < 2e-16 ***
totChol      0.036349   0.006852   5.305 1.19e-07 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 18.84 on 4165 degrees of freedom
  (69 observations deleted due to missingness)
Multiple R-squared:  0.2642,	Adjusted R-squared:  0.2633 
F-statistic: 299.1 on 5 and 4165 DF,  p-value: < 2.2e-16


n = 4171, R2 = 0.2642, adj R2 = 0.2633, F = 299.07, residual SE = 18.840

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Study A

Study A 4 files

framingham.csvCSV · 192 KB2m
framingham_analysis.RR · 6 KB2m
R_fig3_correlation.pngPNG · 258 KB2m
framingham_analysis.pngPNG · 360 KB1h

Riset itu berat. Statistiknya tidak perlu

Mengumpulkan data butuh berbulan-bulan. Analisisnya tidak perlu selama itu.

Di mana 588 studi klinis tersendat:

44,3%
Rekrutmen pasien
26,7%
Pengumpulan data
21,5%
Analisis dan interpretasi data
20,4%
Desain studi
20,2%
Publikasi

Tiap kotak mewakili 1% dari 588 studi. Cullati et al., BMC Medical Research Methodology, 2016

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