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by: Giovani Ullrich PhD


Giovani Ullrich PhD
GPA 3.5

Kenneth Koehler

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About this Document

Kenneth Koehler
Study Guide
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This 7 page Study Guide was uploaded by Giovani Ullrich PhD on Saturday September 26, 2015. The Study Guide belongs to STAT 565 at Iowa State University taught by Kenneth Koehler in Fall. Since its upload, it has received 41 views. For similar materials see /class/214398/stat-565-iowa-state-university in Statistics at Iowa State University.




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Date Created: 09/26/15
Stat 565 Formula Sheet Case control studies Cohort studies Randomized clinical trials Observational studies Experiments Relative risk Prdisease exposure P1 2 1 I51 1 150 RR 7 S A A A Prd1sease no exposure P0 109mm 111131 710130 P 1 P1 1 Odds ratlo OR 1 32 A 7 Pom Po 10401 2 1 Yu Bayes theorem PA B PrB A PTAPTB Chisquare test of independence for a two way contingency table Y2quot A2 2 K Y 39 X2 XXV ml Where mi 239 j mij Y McNemar s test Sign test exact binomial test 12 n n 1 n X2 7112 7121 l I lt7 0 H12 H21 1 J 2 Marginal odds ratio A i P1P2 2 i 1 1 Y11Y22 Y12Y21 P2P1 0W Y1Y2 Y1Y2 Y1Y2Y1Y2 Logistic regression 7TZ39 10 1 T 50 51X1i quot395ka239 XiT eXp1zT8 yz N BinnZ7TZ 139 1 7m 7n PTsuccess Xi H XTB exp 2 Conditional logistic regression Mil271 i i lj1 log 7 OZ Where Z i 0 2 A H21 1 7 32 i i 0 m B Randomization test Delta Method 9amp3 A A A T W waif aglta7 gt varlt 1 Covm 89mm 89mm 90x 95 001 ol Var z 90x 95 Survivor function 30 PrT Z t Density ft 2 Hazard function lim St 805 At ft 9109St h t At 0 7 lt gt At 805 30 9t Cummulative hazard Ht f8 hudu logSt Proportional hazards ht h0t 6XTB Accelerated failure time model 30 30teX 5 Maximum Likelihood Estimation la Weibull distribution 30 exp 6ta 151 E109 Lognormal distribution 30 l I 151 606p 013 109 u Kaplan Meier estimation A K n d A A A K 30 H VarltStgt m 3052 J mm mmm j v1 lt10g 10g tlgt e 10glt3tl72 Life table estimator Hg J J NelsonAalen estimator Z djnj t0ltt Generalized logrank test Group 1 2 Totals die dlj dgj dj survive m dlj ngj dgj nj d Totals m 112 A md A 61139 Vlj Humidjo dj WW 1 J WZWW WMWJ j1 j1 Power calculations and sample size determination Cox proportional hazards model W how eXTB 80 SotleXPltXTBgt Partial likelihood expXTB M5 JT 1 1 EkeRtjeXpXk B 5739 TL 6 J i 1 if tj is a failure time 0 if tj is a censoring time Ties 7 1 7 2 Breslow lt gt lt gt 71T273T475 71T273T475 7 1 7 2 7 17 2 773 774 775 lt57 17 2 773 774 775 Efron lt Average Partial Likelihood BreslowNelson Aalen Estimator A A d Ht logSot Z A 71 27613470 WPWZTB Time dependent covariates Strati cation Diagnostics Schoenfeld residuals Deviance residuals Martingale residuals Score df beta residuals Frailty models Limited failure cure models Crosssectional studies Longitudinal studies Let YZ Yii 39 39 39 7 YimiT denote a set of 111 repeated measurements on the i th subject or unit 139 17m Linear models Elt1ejgtXZ jawmi z391mz ViVarYZ ilm Generalized least square estimation 0 Z XVi1ltYZ M il B XiTVilXigt 1 XiTxZilYZ N N B XiTxilxigt1gt REML estimation r MU PXY Where PX XXTX 1XT and rank n rankX Linear Models with Random Effects YZ UZ N NXZ ZiU Ri with UZ N N0 V Yi N NXi87 ZiVZZT Bi Generalized linear models YZ Y 7 YmiT is the vector of 111 repeated observations on the i th subject or unit EYz39j Mij 510955 Of MW X55 5 Where h is the link function Also specify a distribution for Yi Logistic regression 7T2quot 10glt gt 0 1X1ij KXKij X55 1 7TZ3939 EK39Wi39 1 112 21 m J 7 7 7 7 V2 VarYZ MiG Tn MiG Tn Ri 7Tinllt1 Trim 7Ti7n11 7mm Poisson regression loglinear models 10guij 50 781X1ijquot kXKZj Yij N P0155011 W7 E097 W7 VWOz39j man 71 V Mimi V Uimi Generalized Estimating Equations GEE 0M1 0M1 m 050 05K 0 Z DiTiflYZ m Where DZ I i1 OMi li OMi li 050 05K m 71 BGEE Nlt57 219541197 gt 6 A om N n wu1xwvAefm n I Gog 9217 alquA 1312 gng M7 301 uogssaJBaH uossgod A om N n IKE IZJL I f JDA nf z g ZXMXG I n32 gf x xe z z 39Z39M I fl NH fab 919 n32 g X lt gt301 ALL uogssel ewd 011815301 390333 mopueH uogqnqgnsgq egumugg aqueBaN Z 1 1 2 EZMZHQANFRWRMM 4 QMZ 4 w w 4 w 39UOIQ39GUIIQSO GOU39GLI39GAOO QSHqO d


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