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Samenvatting

Samenvatting Wetenschappelijke Vorming 2 - Statistiek

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Deze samenvatting bevat al het materiaal uit de les in een duidelijk overzicht mbv afbeeldingen, tekeningen, oefeningen, berekeningen, ... Punt behaald in eerste zit: 18/20

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​Statistiek 2​

,​Herhaling:​​beschrijvende​​en​​inferentiële​​statistiek​​..................................................................​​5​
​Introductie:​​quiz​​.......................................................................................................................​​5​
​Basic​​statistical​​concepts​​.........................................................................................................​​7​
​Sample​​vs​​population​​.........................................................................................................​​7​
​Statisch​​significant​​vs​​klinisch​​relevant​​..............................................................................​​7​
​Methods​​of​​research​​..........................................................................................................​​7​
​Types​​of​​data​​.....................................................................................................................​​8​
​Summarizing​​data​​..............................................................................................................​​8​
​1.​​Measures​​of​​location​​................................................................................................​​8​
​Quartiles​​.......................................................................................................................​​8​
​2.​​Measures​​of​​variation​​...............................................................................................​​9​
​Grafic​​Representation:​​boxplot​​....................................................................................​​9​
​Hypothesis​​testing​​for​​a​​population​​parameter​​........................................................................​​9​
​Algemene​​procedure​​.............................................................................................................​​10​
​1.​​Toetsingsprobleem​​.......................................................................................................​​10​
​2.​​Toetstingsgrootheid:​​een​​gepaste​​test​​statistic​​kiezen​​.................................................​​10​
​3a.​​Beslisregel​​-​​kritisch​​punt​​...........................................................................................​​11​
​3a.​​Beslisregel​​-​​p-waarde​​...............................................................................................​​11​
​Overzicht​​van​​statistische​​testen​​om​​2​​of​​meer​​means/proporties​​te​​vergelijken​​..................​​12​
​Introductie​​........................................................................................................................​​12​
​One-sample​​t-test​​............................................................................................................​​13​
​One-sample​​t-test​​in​​R​...............................................................................................​​14​
​One-sample​​t-test:​​assumpties​​en​​opmerkingen​​.......................................................​​14​
​Paired​​t-test​​......................................................................................................................​​14​
​Paired​​t-test​​in​​R​........................................................................................................​​15​
​Unpaired​​t-test​​.................................................................................................................​​15​
​Unpaired​​t-test​​in​​R​....................................................................................................​​15​
​Unpaired​​t-test​​assumpties​​en​​opmerkingen​​.............................................................​​16​
​One-sample​​z-test​​............................................................................................................​​16​
​One-sample​​z-test​​in​​R​..............................................................................................​​17​
​Two-sample​​z-test​​............................................................................................................​​17​
​Two-sample​​z-test​​in​​R​..............................................................................................​​18​
​Two-sample​​z-test​​......................................................................................................​​18​
​Lineaire​​regressie​​......................................................................................................................​​19​
​Van​​statistische​​testen​​naar​​regressiemodellen​​....................................................................​​19​
​Regressiemodellen​​................................................................................................................​​19​
​Enkelvoudige​​lineaire​​regressie​​.............................................................................................​​20​
​Voorbeeld:​​oestriol​​niveau​​..........................................................................................​​20​
​De​​modelonderstellingen​​.................................................................................................​​21​
​Schatten​​van​​intercept​​en​​richtingscoëfficiënt​​..................................................................​​22​
​In​​R​..................................................................................................................................​​23​


​1​

, ​ erklarende​​statistiek​​voor​​α​​en​​ß​...................................................................................​​25​
V
​F-test​​voor​​enkelvoudige​​lineaire​​regressie​​...........................................................................​​26​
​Voorbeeld​​F​​verdeling​​................................................................................................​​27​
​In​​R:​​opdrachten​​........................................................................................................​​28​
​T-test​​voor​​enkelvoudige​​lineaire​​regressie​​...........................................................................​​29​
​Relatie​​tussen​​T​​test​​en​​globale​​F​​test​​............................................................................​​30​
​Betrouwbaarheidsintervallen​​.................................................................................................​​30​
​BI​​voor​​de​​regressieparameters​​α​​en​​ß​...........................................................................​​30​
​Betrouwbaarheidsband​​....................................................................................................​​32​
​Predictie-interval​​voor​​y​​horende​​bij​​een​​gegeven​​x​​waarde​​.................................................​​32​
​Predictieinterval​​voor​​John:​​........................................................................................​​35​
​Welk​​punt​​zou​​het​​smalste​​BI​​hebben?​​.....................................................................​​35​
​Correlatiecoëfficiënt​​...............................................................................................................​​36​
​In​​R​..................................................................................................................................​​37​
​Verschil​​correlatieanalyse​​en​​enkelvoudige​​lineaire​​regressie​​...................................​​37​
​Verband​​tussen​​b​​en​​r​......................................................................................................​​37​
​Samenvatting​​lineaire​​regressie​​............................................................................................​​38​
​Meervoudige​​regressie​​........................................................................................................​​40​
​De​​regressievergelijking​​...................................................................................................​​40​
​Schatting​​van​​de​​parameters​​...........................................................................................​​41​
​Interpretatie​​van​​regressiecoëfficiënten​​...........................................................................​​41​
​Gestandaardiseerde​​regressiecoëfficiënt​​........................................................................​​42​
​In​​R:​​uitleg​​verschillende​​tekens!​​...............................................................................​​43​
​Voorbeeld​​hypertensie​​...............................................................................................​​44​
​Meervoudig​​lineair​​regressiemodel​​..................................................................................​​44​
​Toetsen​​voor​​de​​hele​​groep​​van​​regressoren​​..................................................................​​45​
​ANOVA​​(analysis​​of​​variance)​​tabel:​​..........................................................................​​46​
​In​​R:​​voorbeeld​​hypertensie​​.......................................................................................​​46​
​Kleine​​ANOVA​​tabel​​voor​​lineair​​regressie​​model​​......................................................​​46​
​Grote​​ANOVA​​tabel​​....................................................................................................​​47​
​Toetsen​​voor​​één​​regressor​​.............................................................................................​​47​
​In​​R:​​...........................................................................................................................​​48​
​De​​partiële​​F-test​​.............................................................................................................​​49​
​In​​R:​​kleine​​ANOVA​​....................................................................................................​​50​
​DUS​​ELR​​vs​​MLR​​Globale​​F​​en​​T​​test​​.............................................................................​​50​
​In​​R:​​grote​​ANOVA​​.....................................................................................................​​51​
​Berekening​​van​​REG​​SS,​​RES​​SS,​​…​.......................................................................​​51​
​EXAMEN:​​.........................................................................................................................​​51​
​Interactie-effecten​​............................................................................................................​​52​
​Categorische​​variabelen​​..................................................................................................​​53​
​Visualisatie​​van​​een​​interactie-effect​​..........................................................................​​54​


​2​

, ​ pdracht​​lineaire​​regressie​​-​​Framingham​​............................................................................​​55​
O
​Examenvoorbeeld​​lineaire​​regressie​​.....................................................................................​​55​
​Veralgemeende​​lineaire​​regressie​​............................................................................................​​56​
​Inleiding​​............................................................................................................................​​56​
​Bernoulli​​verdeling​​(herhaling)​​...............................................................................................​​56​
​Voorbeeld:​​varicella​​....................................................................................................​​57​
​Logistische​​regressie​​.............................................................................................................​​57​
​Veralgemeend​​lineaire​​modellen​​(GLM)​​................................................................................​​59​
​Schatten​​van​​de​​regressieparameters​​in​​GLM​​................................................................​​59​
​Maximum​​likelihood​​methode​​..........................................................................................​​60​
​Maximum​​likelihood​​methode:​​oefening​​.....................................................................​​60​
​Voordeel​​van​​ML​​........................................................................................................​​61​
​Voorbeeld:​​varicella​​....................................................................................................​​61​
​In​​R:​​interpretatie​​.......................................................................................................​​61​
​Interpretatie​​R​............................................................................................................​​62​
​In​​R​............................................................................................................................​​63​
​Wald​​test​​....................................................................................................................​​64​
​Betrouwbaarheidsinterval​​..........................................................................................​​65​
​Predictie​​...........................................................................................................................​​65​
​Voorbeeld​​...................................................................................................................​​66​
​EXAMENVOORBEELD​​OEFENING​​LINEAIRE​​REGRESSIE​​..............................................​​66​
​Samenvatting​​.........................................................................................................................​​67​
​Categorische​​variabelen​​..................................................................................................​​68​
​In​​R:​​...........................................................................................................................​​68​
​Meervoudige​​logistische​​regressie​​........................................................................................​​70​
​R-voorbeeld:​​varicella​​................................................................................................​​70​
​Aikake’s​​Information​​Criterion​​(AIC)​​......................................................................................​​73​
​Samenvatting:​​belangrijk​​om​​te​​weten​​...................................................................................​​73​
​Possoin​​regressie​​..................................................................................................................​​73​
​Voorbeeld:​​aantal​​insecten​​op​​bonenplanten​​.............................................................​​74​
​Poisson​​verdeling​​.............................................................................................................​​74​
​Voorbeeld:​​SENIC​​data​​..............................................................................................​​75​
​Transformatie​​...................................................................................................................​​76​
​Veralgemeende​​lineaire​​modellen​​(GLM)​​........................................................................​​76​
​Voorbeeld:​​SENIC​​data​​..............................................................................................​​77​
​In​​R​............................................................................................................................​​77​
​Interpretatie​​......................................................................................................................​​78​
​Toetsen​​van​​hypothese:​​Wald-test​​...................................................................................​​78​
​Interpretatie​​......................................................................................................................​​79​
​Categorische​​variabelen​​..................................................................................................​​79​
​R-voorbeeld​​SENIC:​​regio​​(eigenlijk​​al​​een​​meervoudig​​model)​​................................​​79​


​3​

, ​ ergelijking​​met​​logistische​​regressie​​........................................................................​​80​
V
​Meervoudige​​Poisson​​Regressie​​.....................................................................................​​80​
​R-voorbeeld:​​SENIC​​..................................................................................................​​81​
​Samenvatting​​types​​...................................................................................................................​​83​
​Overlevingsanalyse​​en​​Cox​​regressie​​.....................................................................................​​84​
​Overlevingsanalyse​​...............................................................................................................​​84​
​Definitie​​en​​notatie​​.....................................................................................................​​85​
​Belangrijke​​concepten​​I​...................................................................................................​​85​
​Belangrijke​​concepten​​II​​..................................................................................................​​86​
​Voorbeeld​​...................................................................................................................​​86​
​Overlevingsfunctie​​of​​-curve​​............................................................................................​​87​
​Overlevingsanalyse​​met​​censurering​​...............................................................................​​89​
​Types​​van​​censurering​​...............................................................................................​​89​
​Rechtse​​censurering​​........................................................................................................​​91​
​Voorbeeld​​...................................................................................................................​​91​
​Voorbeeld:​​Duur​​van​​remissie​​in​​een​​klinische​​..........................................................​​91​
​Niet-parametrische​​schatting​​van​​S(t*)​​......................................................................​​92​
​Overlevingsfunctie​​in​​geval​​van​​censurering:​​Kaplan-Meier​​............................................​​92​
​Voorbeeld​​...................................................................................................................​​94​
​In​​R​............................................................................................................................​​95​
​In​​R:​​Kaplan-meier​​.....................................................................................................​​96​
​Vergelijken​​van​​overlevingsfuncties​​.................................................................................​​96​
​Regressie​​voor​​overlevingsanalyse​​.......................................................................................​​98​
​Cox​​proportional​​hazards​​model​​......................................................................................​​98​
​Schatten​​van​​de​​regressieparameters​​.............................................................................​​98​
​Interpretatie​​......................................................................................................................​​99​
​Proportional​​hazards​​assumptie​​......................................................................................​​99​
​Coxmodel​​in​​R​.........................................................................................................​​100​
​Predictie​​.........................................................................................................................​​102​
​Overzicht​​..................................................................................................................................​​103​




​4​

,​Herhaling: beschrijvende en inferentiële statistiek​

​Introductie: quiz​
​1.​ ​Which type of data is given in the following examples:​
​○​ ​Male/female ⇒ kwalitatief nominaal​
​○​ ​Number of heart beats per minute ⇒ kwantitatief discreet​
​○​ ​Blood pressure ⇒ kwantitatief continu​
​2.​ ​What is the median value of the observations xi : 80,90,110,125,130,135?​
​⇒ (110+125)/2 = 117,5​
​3.​ ​How does the previous result change when adding an observation 140 to the​
​aforementioned series? ⇒ mediaan = 125​
​4.​ ​How do you calculate the sample mean and variance of the series?​
​⇒ variantie = waarde van spreiding​
​○​ ​Waarden groter dan gem ⇒ pos bijdrage​
​○​ ​Waarden kleiner dan gem ⇒ neg bijdrage​
​⇒ niet handig dus kijken naar​​gekwadrateerde vorm​​v variantie​​en dan delen door n-1​
​⇒ s² volgt een verdeling ⇒ geheel is stochastisch​




​5.​ ​Explain the difference between Xi and xi ?​
​○​ ​Grote x: verdeling van alle mogelijke steekproef varianties = verdeling f​
​○​ ​Kleine x: één waarde uit die verdeling (getal)​
​6.​




​5​

,​7.​
​ een ongepaarde T-test of twee steekproeven T-test: er vanuit gaan dat normaal​

​verdeeld EN de variantie gelijk verdeeld is​




​8.​
​ binaire uitkomst: 0 of 1 (ja of nee) DUS guy kwadraat test: wat is de proportie van​

​mensen die influenza krijgen in verschillende samenlevingen​




​6​

,​Basic statistical concepts​

​Sample vs population​
​Population​
​●​ ​A population refers to a​​well-defined group of subjects​​in which the researcher is​
​interested from a scientific point of view​
​●​ ​Often, a population is​​too large​​(or even infinite)​​to examine all subjects (too expensive,​
​too time-consuming, ...)​
​Sample​
​●​ ​A sample is a​​finite collection of study subjects​​for which observed characteristics and​
​response values are recorded​
​●​ ​Sample needs to be​​representative​​in order to provide​​valid inference at the population​
​level​


​Statisch significant vs klinisch relevant​
​Statistical significant:​
​●​ ​Statistical significance is based on measurements, observations, numbers, ...​
​●​ ​Statistical expertise is required​
​Clinical relevant:​
​●​ ​Which research questions are relevant to answer?​​Is​​het verschil betekenisvol?​
​●​ ​Clinical relevance is determined using​​domain-specific​​expertise​
​●​ ​Medical doctor, clinical investigator, lab-researcher, ...​
​⇒Statistical significant ≠ clinical relevant​


​Methods of research​
​Two large groups of research/studies:​
​1.​ ​Experimental studies​
​○​ ​Studying the​​effect of a treatment​
​○​ ​Example: Clinical trials​
​■​ ​Randomisation​
​■​ ​Blinding​
​■​ ​Placebo​
​○​ ​Aim: Does a causal relationship exist?​
​2.​ ​Observational studies​
​○​ ​No active intervention​
​○​ ​Example: Has smoking an effect on the development of lung cancer?​
​○​ ​In general no conclusions about causal relationships, only​​evidence of potential​
​associations​




​7​

, ​Types of data​
​Qualitative data:​
​●​ ​Nominal data​​: categorical data used to classify an​​object of characteristic, e.g., gender,​
​group membership, diagnosis​
​●​ ​Ordinal data​​: categorical data with specific ordering,​​e.g., opinion polls asking whether​
​we strongly disagree, disagree, agree or strongly agree​
​Quantitative (numeric) data:​
​●​ ​Discrete data​​: measurement or count (ordered) data​​for which values cannot lie​
​arbitrarily close to each other, e.g., the number of pregnancies of a woman​
​●​ ​Continuous data​​: measurement data which could take​​all values within a range, e.g.,​
​individual’s weight, or length​


​Summarizing data​
​1.​ ​Measures of location:​
​○​ ​(arithmetic) mean​
​○​ ​median​
​○​ ​(quartiles)​
​2.​ ​Measures of variation:​
​○​ ​variance and standard deviation​
​○​ ​range​
​○​ ​interquartile range (IQR)​

​1. Measures of location​
​ ean​
M
​The (arithmetic) mean x of numeric observations x1,...,xn is given by ⇒​

​ edian​
M
​The median of a series of n numeric observations is, after ordering of the values in this series,​
​●​ ​the middle value, if n is an odd number,​
​●​ ​the arithmetic mean of the two middle numbers, if n is even​

I​n case of observations in the following frequency table (n observations; p​
​different values of x) then the arithmetic mean is given by:​



​Quartiles​
​ ​ ​the​​first quartile Q1​​is the number with rank (n+1)/4​

​●​ ​the​​second quartile Q2​​is the number with rank (n+1)/2​
​●​ ​the​​third quartile Q3​​is the number with rank 3(n+1)/4​
​Non-rationale ranks:​




​8​

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