mat 456- final project presentation (athanasios siadimas, peter drogos)

8/12/2019 MAT 456- Final Project Presentation (Athanasios Siadimas, Peter Drogos)

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A study of

determinants of

plasma retinol and

beta-carotene

Final Project

MAT 456

Autumn 2013

Peter Drogos

Athanasios Siadimas


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INTRODUCTION

BACKGROUND PURPOSE AND OBJECTIVES

ANALYSIS RESULTS

DESCRIPTIVE

MULTIPLE REGRESSION MODELCONCLUSIONS

DISCUSSION

INDEX


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Beta-carotene and retinol are among the most

widely studied compounds in various populations, for

both human plasma concentrations and dietary intake.This situation dues to their inverse relationship with the

development of several diseases like cancer,

cardiovascular disease and cataracts. This study

examines:

When beta-carotene is exposed to certain variables,

which levels and factors are affected?

When beta-carotene is exposed to certain variables,how are these levels are affected?

INTRODUCTION


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A few studies have suggested that drinking andsmoking habits, dietary intake, gender, and age

influence plasma concentration of carotenoids (likebeta-carotene), and to a lesser extent, theconcentration of retinol.

Previous studies have indicated that if a

persons dietary history show greater consumptionof green or yellow leafy vegetables, which havemore amount of carotene, the intake level of beta-carotene is high.

Two basic previous studies are: the research of Russell-Briefel R, and

the research of Stryker WS

BACKGROUND


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In this study, we use data from an observationalexperiment with 315 patients. In this experiment,

there are totally 14 independent variables,11numerical and 3 categorical, and we tried to findout:

Do any of these personal characteristics have an

effect on a persons plasma beta-carotenelevels?

PURPOSE


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The Multiple Regression model:

Research Hypothesis: The factors that affect beta-

carotene.

Method: We follow a multiple regression model tofind out which variables affect beta-carotene.

ANALYSIS


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The descriptive statistics for our study are:

ANALYSIS

Variable Label Mean Median Std Dev Std Error Skewnessage Age in years 50.15 48 14.58 0.82 0.3

betadiet beta-carotene 2185.6 1802 1473.89 83.04 1.61quetelet (weight/(height^2)) 26.16 24.74 6.01 0.34 1.38

calories calories consumption 1796.65 1666.8 680.35 38.33 1.75fat fat consumption 77.03 72.9 33.83 1.91 1.1

fiber fiber consumption 12.79 12.1 5.33 0.3 1.15alcohol drinks per week 3.28 0.3 12.32 0.69 13.82cholest Cholesterol 242.46 206.3 131.99 7.44 1.48retdiet retinol consumption 832.71 707 589.29 33.2 4.47

betaplasma Plasma beta-carotene 189.89 140 183 10.31 3.56

retplasma Plasma Retinol 602.79 566 208.9 11.77 1.31


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The categorical variables :

ANALYSIS

Gendersex Frequency Percent

Cumulative

FrequencyCumulative

PercentMale 42 13.33 42 13.33Female 273 86.67 315 100.00

Smoking statussmokstat Frequency Percent

Cumulative

FrequencyCumulative

PercentNever 157 49.84 157 49.84Former 115 36.51 272 86.35Current 43 13.65 315 100.00

Vitamin Use

vituse Frequency PercentCumulative

FrequencyCumulative

PercentYes, fairly often 122 38.73 122 38.73Yes ,not often 82 26.03 204 64.76No 111 35.24 315 100.00


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The variable betaplasma:

ANALYSIS


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The multiple regression model: Transformations

ANALYSIS


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Box Cox Transformation

ANALYSIS


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The multiple regression model: Correlation matrix

ANALYSIS

Pearson Correlation Coefficients, N = 315

Prob > |r| under H0: Rho=0

age quetelet calories fat fiber alcohol cholest betadiet retdiet betaplasma retplasmaage 1.00000 -0.01746

0.7575-0.17677

0.0016-0.16948

0.00250.04485

0.42760.05158

0.3615-0.11361

0.04390.07187

0.2033-0.00961

0.86510.10113

0.07310.21167

0.0002quetelet -0.01746

0.75751.00000 0.00353

0.95030.04875

0.3885-0.08762

0.1207-0.07270

0.19820.11026

0.0506-0.00660

0.90710.03206

0.5708-0.22939


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ANALYSIS


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The multiple regression model:

Create dummy variables as

The female (sex=2) was used as reference level. The

current smokers (smokstat=3) were used asreference level for smoking status. The people whodidnt take vitamin use (vituse=3), were used asreference level for vitamin use.

ANALYSIS

SEX MALE FEMALE1 1 02 0 0

SMOKSTAT SMOK1 SMOK21 1 02 0 13 0 0VITUSE VITUSE1 VITUSE21 1 02 0 13 0 0


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The multiple regression model: Results

ANALYSIS

Parameter EstimatesVariable Label DF

Parameter

EstimateStandard

Errort Valu

e Pr > |t|Intercept Intercept 1 5.07472 0.24179 20.99


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The multiple regression model: Results

FINAL MODEL

The selection method chosen was the STEPWISEselection method.

ANALYSIS


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Checking for Multicollinearity:Variance Inflation Factor

Parameter Estimates

Variable Label DF Parameter

Estimate

Standard

Error

t Value Pr > |t| Variance

Inflation

Intercept Intercept 1 5.07472 0.24179 20.99


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For the variable betaplasma For the log(betaplasma)

DIAGNOSTICS


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The coefficient of 0.031 for the vit1 will show howmuch to add and subtract from the predictedvalue if the subject take vitamin fairly often. The

vit2 will show how much to add and subtract fromthe predicted value if the subject take vitamin notoften. The coefficient of 0.17 for the smokeneverwill show how much to add and subtract from thepredicted value if the subject never smoked.

Notice that the dependent variable is NOT

betaplasma but its logarithmic valueLog(betaplasma). For example, a jump from 2.72to 7.39 of Log(betaplasma) means an 1-unitincrease of betaplasma because the naturallogarithm of 2.72 equals 1 and the naturallogarithm 7.39 equals to 2.

The R square value of 0.2281 indicating that aboutthe 23% of the variation of the dependent valuecan be explained by the variation of theindependent values.

CONCLUSIONS

CO C S O S


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This present study examined the main researchhypothesis:

by what and how beta- carotene is affected. Inorder to do so, we used various statistical tests uponthe data of this study to determine thesehypotheses.

The most important findings of this study given thisspecific data set were that:

Beta-carotene is in fact affected by some factorssuch as age, sex, smoking habit, quetelet, vitamin

use, consumed calories, fiber, and dietary beta-carotene and these variables are statisticallysignificant on plasma beta-carotene levels.

CONCLUSIONS

mat 456- final project presentation (athanasios siadimas, peter drogos)

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