"There are three kinds of lies: lies, damned lies and statistics. - Mark Twain

"There are three kinds of lies: lies, damned lies and statistics. - Mark Twain

Statistik metoder til indsamling, analyse, fortolkning og præsentation af data I (sundhedsvidenskabelig) praksis objektiv metode til analyse af data Grundlaget for evidens Myter om statistik Statistik er en eksakt videnskab/metode Der findes én, og kun én, rigtig metode at analysere data på

NB: Der blev bygget et offentligt toilet lige overfor museet Anden Verdenskrig Antallet af amerikanske soldater dræbt i tjeneste: 408.000 Antallet af civile dræbt i USA i samme periode: 375.000 UDSAGN: Det var lige så farligt at være civil, som at være soldat i kamp under 2. verdenskrig Museumsbesøg Et amerikansk museum havde følgende besøgstal indtil 2006 2002 2003 2004 2005 2006 305.000 314.000 309.000 325.000 211.000 UDSAGN: Amerikanerne blev pludselig markant mindre interesseret i kunst.

Effekten af indførelse af hjelm for amerikanske soldater: Antallet af hovedskader steg kraftigt efter indførelsen af hjelm til amerikanske soldater under 1. verdenskrig. Antal døde var ikke medregnet i kategorien af hovedskader

Typisk afsnit i en statistik bog In general, the population mean of a finite population of size N is given by And the population variance is given by In many practical situations, the true variance of a population is not known a priori and must be computed somehow. When dealing with extremely large populations, it is not possible to count every object in the population. A common task is to estimate the variance of a population from a sample. We take a sample of n values y 1,..., y n from the population, where n < N, and estimate the mean and variance on the basis of this sample.

For one-sample t-test For paired two sample t-test For independent two sample t-test - equal variance For independent two sample t-test - un-equal variance

Fordelinger af populationer

2 typer af statistik Deskriptiv statistik: beskriver indhentet data fra en given population Inferential Statistik: drager konklusioner om en generel population ud fra en stikprøve. Population: Hele gruppen af elementer Parametre: beskriver en population (fx middeltal og varians) Observation: Én værdi (eller sæt af værdier) udtrukket fra et element (individ) i populationen Stikprøve: Et antal (tilfældigt valgte) observationer fra populationen

Normalfordelingen

Statistiske metoder i sundhedsvidenskabelig forskning

Hvad skal man overveje? Hvilken datatype: kvalitativ eller kvantitativ, norminal eller ordinal, diskret eller kontinuerlig? Hvilket design: parret eller u-parret Hvor mange grupper: 2 eller flere Hvilket Signifikans-niveau forkaster hypotesen (type 1 fejl) Hvilket antal observationer skal til for at sikre tilstrækkelig power (type 2 fejl) ER DATA NORMALFORDELT/PARAMETRISK?

Normalfordelingen

Hypotese-test Hvad er det, man undersøger? Populært: er der forskel imellem grupperne? Reelt: hvad er sandsynligheden for, at den forskel vi ser imellem grupperne skyldes tilfældigheder? - DVS STIKPRØVERNE FAKTISK STAMMER FRA SAMME FORDELING!

Hypotese-test Hvordan skal output fra en hypotese-test fortolkes? 2 centrale output: p-værdi (probability): sandsynligheden for at middeltallet for stikprøven stammer fra populationens fordeling Konfidensinterval (KI): det interval, det reelle middeltal for populationen (med 95% sandsynlighed), befinder sig indenfor

Effekt-size Udføres ofte komplementært til hypotese-test særligt når man vil undersøge effekten af en intervention Populært: Hvor stor effekten har interventionen? Reelt: Hvor stor del af differencen imellem stikprøvernes middeltal kan forklares med interventionen? MANGE måder at udregne effect-size/estimat Udregnes som: Differensen divideret med variansen MILLION-DOLLAR?: Er 0 (eller 1) indeholdt i KI?

Korrelation og regression Populært: er der sammenhæng imellem to variable? Reelt: hvor stor en del af variationen af den ene variabel kan forklares ved variationen af den anden variabel? 4 datasæt med samme korrelationskoeficient, r=0.816 (Anscombe's quartet)

Statistical Analyses and Sample Size Calculation To achieve 80% power at an α level of.05 (two tailed), 25 participants per group would be required to detect a mean difference in change for whole body lean mass of 1 kg (standard deviation of 1.25 kg) at the end of the 12-week intervention. This was based on a number of reports showing marked reductions in muscle loss in patients with prostate cancer undergoing AST. 6,32,33 Based on our previous experience with exercise trials, we anticipated an attrition rate of up to 10%. As a result, to adequately ensure that we had sufficient participant numbers at the end of the intervention, 57 participants were recruited and randomly assigned to EX (n = 29) and CO (n = 28). Data were analyzed using the SPSS version 15 (SPSS Inc, Chicago, IL) statistical software package. Normality of the distribution for outcome measures was tested using the Kolmogorov-Smirnof test. Analyses included standard descriptive statistics, independent t-tests, χ 2, and analysis of covariance adjusted for baseline values, AST time, use of antiandrogen, number of medications, and education. To determine if general health changes were mediated by changes in lean mass and functioning, correlations were explored between self-reported general health and objective measures of lean mass and muscle strength. An intention-to-treat approach was used for all analyses including missing data in all analyses by imputing change across time to be zero. However, one participant dropped-out after baseline testing and did not return the SF-36 questionnaire and there was no QLQ-30 data for seven participants as the instrument was included in the assessment battery after these participants entered the study. All tests were two tailed and an α level of.050 was required for significance. Sample size beregning Test for normalitet Primære effekt analyser Sekundære analyser (correlations-test) Statistisk metode og redegørelse for drop-out

Quality of Life and Adverse Events Quality of life assessed by the SF-36 (Table 6) showed better change scores for general health (P =.022), vitality (P =.019), and the physical health composite scores (P =.020) for the EX group. Change in general health was associated with change in whole body lean mass (r =.385; P =.039) and approached significance for change in average muscle strength (r =.249; P =.064).

Statistical analysis. Nonparametric statistics were used for the analyses, since not all data were normally distributed. To evaluate the effect of intervention over time, a Friedman test was used with post hoc Wilcoxon's test. Any between-group differences were analyzed with Kruskal-Wallis tests and subsequent Mann-Whitney U- test. Spearman's Rho was used for the correlation analysis on a limited number of data. Analysis were performed on collapsed data for all three groups, except for the relation between the delta change in type II muscle fiber area and stair walking power after 12 wk of RT. Data are presented as mean values SE. A P value of less than 0.05 were considered significant.

Mammografi Screening Virker det? Olsen et al. 2005. BMJ vs Jørgensen et al. 2010, BMJ

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