Meta-Analysis of Hazard Ratios
|
|
|
- Alexia Esther Foster
- 10 years ago
- Views:
Transcription
1 NCSS Statstcal Softare Chapter 458 Meta-Analyss of Hazard Ratos Introducton Ths module performs a meta-analyss on a set of to-group, tme to event (survval), studes n hch some data may be censored. These studes have a treatment group and a control group. Each study s result may be summarzed by the log hazard rato and ts standard error. The program provdes a complete set of numerc reports and plots to allo the nvestgaton and presentaton of the studes. The plots nclude the forest plot and radal plot. Both fxed-, and random-, effects models are avalable for analyss. Meta-Analyss refers to methods for the systematc reve of a set of ndvdual studes th the am to combne ther results. Meta-analyss has become popular for a number of reasons:. The adopton of evdence-based medcne hch requres that all relable nformaton s consdered.. The desre to avod narratve reves hch are often msleadng. 3. The desre to nterpret the large number of studes that may have been conducted about a specfc treatment. 4. The desre to ncrease the statstcal poer of the results be combnng many small-sze studes. The goals of meta-analyss may be summarzed as follos. A meta-analyss sees to systematcally reve all pertnent evdence, provde quanttatve summares, ntegrate results across studes, and provde an overall nterpretaton of these studes. We have found many boos and artcles on meta-analyss. In ths chapter, e brefly summarze the nformaton n Sutton et al. (000) and Thompson (998). Refer to those sources for more detals about ho to conduct a meta-analyss. As for the partcular topc of combnng hazard rato studes n a meta-analyss, the boo by Parmar and Machn (995) and the paper by Parmar et al.(998) are essental readng. The paper provdes nstructons on ho to obtan estmates of the hazard rato and ts standard error from trals that do not report these tems explctly (a stuaton that s common) NCSS, LLC. All Rghts Reserved.
2 NCSS Statstcal Softare Meta-Analyss of Hazard Ratos Treatment Effect Hazard Rato The most recommended sngle summary statstc for quantfyng the treatment effect n studes usng survval data s the (log) hazard rate. Ths statstc s chosen because t can be calculated from tme-to-event data th censorng and because t measures the sze of the dfference beteen to Kaplan-Meer curves. The Cox-Mantel estmate of the hazard rato s formed by dvdng the hazard rate under treatment by the hazard rate under control. Thus, t measures the change n rs of treatment versus control over the follo-up perod. Snce the dstrbuton of the log hazard rato s nearly normal, the log transformaton s appled. The formula for the hazard rate s HR CM here O s the observed number of events (deaths) n group, E s the expected number of events (deaths) n group, and H s the overall hazard rate for the th group. The calculaton of the E s explaned n Parmar and Machn (995). H H O O A confdence nterval for HR s found by frst transformng to the log scale hch s better approxmated by the normal dstrbuton, calculatng the lmts, and then transformng bac to the orgnal scale. The calculaton s made usng here ln T C T C / E / E ( HR ) ± z ( SE ) SE CM α / ln HR + CM ET T C ln HR CM An alternatve estmate of HR that s sometmes used s the Mantel-Haenszel estmator hch s calculated usng O HRMH exp here V s the hypergeometrc varance. For further detals, see Parmar and Machn (995). A confdence nterval for HR s found by frst transformng to the log scale hch s better approxmated by the normal dstrbuton, calculatng the lmts, and then transformng bac to the orgnal scale. The calculaton s made usng here ln T E E V C T ( HR ) ± z ( SE ) MH SE α / ln HR MH V ln HR MH If the log hazard rato and ts standard error are not reported n a partcular study t ll have to be estmated from the logran test statstc, p-value, or from the Kaplan-Meer curves. Detals of ho to do ths are presented n Parmar et al. (998). Suppose you have obtaned the results for studes, labeled,,. Each study conssts of a treatment group (T) and a control group (C). The results of each study are summarzed by to statstcs: ln ( HR ) the log hazard rato. SE ln ( HR ) the standard error of the log hazard rato NCSS, LLC. All Rghts Reserved.
3 NCSS Statstcal Softare Meta-Analyss of Hazard Ratos It ll be useful n the sequel to mae the follong defnton of the eghts. v ( ) SE ln HR / v Hypothess Tests In the dscusson belo, e let θ represent ln HR. Several hypothess tests have be developed to test the varous hypotheses that may be of nterest. These ll be defned next. Overall Null Hypothess To statstcal tests have been devsed to test the overall null hypothess that all treatment effects are zero. The null hypothess s rtten H : θ θ,, 0 Nondrectonal Test The nondrectonal alternatve hypothess that at least one θ 0 may be tested by comparng the quantty th a χ dstrbuton. X ND θ Drectonal Test A test of the more nterestng drectonal alternatve hypothess that θ θ 0 for all may be tested by comparng the quantty X D θˆ th a χ dstrbuton. Note that ths tests the hypothess that all effects are equal to the same nonzero quantty. Effect-Equalty (Heterogenety) Test When the overall null hypothess s rejected, the next step s to test hether all effects are equal, that s, hether the effects are homogeneous. Specfcally, the hypothess s H : θ θ,, 0 versus the alternatve that at least one effect s dfferent, that s, that the effects are heterogeneous. Ths may also be nterpreted as a test of the study-by-treatment nteracton. Ths hypothess s tested usng Cochran s Q test hch s gven by NCSS, LLC. All Rghts Reserved.
4 NCSS Statstcal Softare here Meta-Analyss of Hazard Ratos Q ˆ θ ( ˆ θ θˆ ) ˆ θ The test s conducted by comparng Q to a χ dstrbuton. Fxed versus Random Effects Combned Confdence Interval If the effects are assumed to be equal (homogeneous), ether through testng or from other consderatons, a fxed effects model may be used to construct a combned confdence nterval. Hoever, f the effects are heterogeneous, a random effects model should be used to construct the combned confdence nterval. Fxed Effects Model The fxed effects model assumes homogenety of study results. That s, t assumes that θ θ for all. Ths assumpton may not be realstc hen combnng studes th dfferent patent pools, protocols, follo-up strateges, doses, duratons, etc. If the fxed effects model s adopted, the nverse varance-eghted method as descrbed by Sutton (000) page 58 s used to calculate the confdence nterval for θ. The formulas used are ˆ θ ± ˆ z α / V here z α / s the approprate percentage pont from the standardzed normal dstrbuton and ˆ θ ( ˆ) Vˆ θ ˆ θ ( ˆ θ ) Random Effects Model The random effects model assumes that the ndvdual θ come from a random dstrbuton th fxed mean θ and varance σ. Sutton (000) page 74 presents the formulas necessary to conduct a random effects analyss usng the eghted method. The formulas used are ˆ θ ± z ˆ ˆ α / V θ here z α / s the approprate percentage pont from the standardzed normal dstrbuton and NCSS, LLC. All Rghts Reserved.
5 NCSS Statstcal Softare Meta-Analyss of Hazard Ratos ˆ θ Vˆ ˆ θ ˆ θ +τˆ Q + f > ˆ τ U Q 0 otherse Q ( ˆ θ θˆ ) U ( ) s s Graphcal Dsplays A number of plots have been devsed to dsplay the nformaton n a meta-analyss. These nclude the forest plot, the radal plot, and the L Abbe plot. More ll be sad about each of these plots n the Output secton. Data Structure The data are entered nto a dataset usng one ro per study. To varables are requred to hold the log hazard rato and ts standard error. In addton to these, an addtonal varable s usually used to hold a short (3 or 4 character) label. Another varable may be used to hold a groupng varable. As an example, e ll use a dataset gvng the results for survval studes. The results of these studes are recorded n the MetaHR dataset. You should load ths database to see ho the data are arranged NCSS, LLC. All Rghts Reserved.
6 NCSS Statstcal Softare Meta-Analyss of Hazard Ratos Procedure Optons Ths secton descrbes the optons avalable n ths procedure. Varables Tab The optons on ths screen control the varables that are used n the analyss. Varables Log(Hazard Rato) Varable Specfy the varable contanng the log hazard rato of each study. Each ro of data represents a separate study. Note that the base of the logarthm (e or 0) s arbtrary. Hoever, t must be consstent throughout the dataset. S.E. Log(Hazard Rato) Varable Specfy the varable contanng the standard error of the log hazard rato of each study. Each ro of data represents a separate study. Note that the base of the logarthm (e or 0) s arbtrary. Hoever, t must be consstent throughout the dataset. Varables Optonal Varables Label Varable Specfy an optonal varable contanng a label for each study (ro) n the database. Ths label should be short (< 8 letters) so that t can ft on the plots. Group Varable Specfy an optonal varable contanng a group dentfcaton value. Each unque value of ths varable ll receve ts on plottng symbol on the forest plots. Some reports are sorted by these group values. Combne Studes Method Combne Studes Usng Specfy the method used to combne treatment effects. Use the Fxed Effects method hen you do not ant to account for the varaton beteen studes. Use the Random Effects method hen you ant to account for the varaton beteen studes as ell as the varaton thn the studes NCSS, LLC. All Rghts Reserved.
7 NCSS Statstcal Softare Meta-Analyss of Hazard Ratos Reports Tab The optons on ths screen control the appearance of the reports. Select Reports Summary Report - Outcome Detal Reports Indcate hether to dsplay the correspondng report. Alpha Level Ths settng controls the confdence coeffcent used n the confdence lmts. Note that 00 x ( - alpha)% confdence lmts ll be calculated. Ths must be a value beteen 0 and 0.5. The most common choce s 0.05, hch results n 95% confdence ntervals. Report Optons Sho Notes Indcate hether to sho the notes at the end of reports. Although these notes are helpful at frst, they may tend to clutter the output. Ths opton lets you omt them. Precson Specfy the precson of numbers n the report. A sngle-precson number ll sho seven-place accuracy, hle a double-precson number ll sho thrteen-place accuracy. Note that the reports are formatted for sngle precson. If you select double precson, some numbers may run nto others. Also note that all calculatons are performed n double precson regardless of hch opton you select here. Sngle precson s for reportng purposes only. Varable Names Ths opton lets you select hether to dsplay only varable names, varable labels, or both. Report Optons Decmal Places Probablty Values Z Values Ths settng controls the number of dgts to the rght of the decmal place that are dsplayed hen shong ths tem. Plots Tab The optons on ths panel control the ncluson and the appearance of the forest plot and the radal plot. Select Plots Forest Plot Radal Plot Indcate hether to dsplay the correspondng plot. Clc the plot format button to change the plot settngs. Storage Tab These optons let you specfy f, and here on the dataset, varous statstcs are stored. Warnng: Any data already n these columns are replaced by the ne data. Be careful not to specfy columns that contan mportant data NCSS, LLC. All Rghts Reserved.
8 NCSS Statstcal Softare Data Storage Optons Meta-Analyss of Hazard Ratos Storage Opton Ths opton controls hether the values ndcated belo are stored on the dataset hen the procedure s run. Do not store data No data are stored even f they are checed. Store n empty columns only The values are stored n empty columns only. Columns contanng data are not used for data storage, so no data can be lost. Store n desgnated columns Begnnng at the Frst Storage Varable, the values are stored n ths column and those to the rght. If a column contans data, the data are replaced by the storage values. Care must be used th ths opton because t cannot be undone. Store Frst Item In The frst tem s stored n ths column. Each addtonal tem that s checed s stored n the columns mmedately to the rght of ths varable. Leave ths value blan f you ant the data storage to begn n the frst blan column on the rght-hand sde of the data. Warnng: any exstng data n these columns s automatcally replaced, so be careful. Data Storage Optons Select Items to Store th the Dataset Log(HR) - Weghts Indcate hether to store these ro-by-ro values, begnnng at the column ndcated by the Store Frst Item In opton. Example Meta-Analyss of Hazard Ratos Ths secton presents an example of ho to analyze the data contaned n the MetaHR dataset. Ths dataset contans data for sxteen randomzed clncal trals th survval endponts. You may follo along here by mang the approprate entres or load the completed template Example by clcng on Open Example Template from the Fle menu of the Meta-Analyss of Hazard Ratos ndo. Open the MetaHR dataset. From the Fle menu of the NCSS Data ndo, select Open Example Data. Clc on the fle MetaHR.NCSS. Clc Open. Open the Meta-Analyss of Hazard Ratos ndo. On the menus, select Analyss, then Meta-Analyss, then Meta-Analyss of Hazard Ratos. The Meta- Analyss of Hazard Ratos procedure ndo ll be dsplayed. On the menus, select Fle, then Ne Template. Ths ll fll the procedure th the default template NCSS, LLC. All Rghts Reserved.
9 NCSS Statstcal Softare Meta-Analyss of Hazard Ratos 3 Select the varables. Select the Varables tab. Set the Log(Hazard Rato) Varable to LogHR. Set the S.E. Log(Hazard Rato) Varable to SELogHR. Set the Label Varable to Study. 4 Specfy the reports. Select the Reports tab. Chec the Summary Report opton box. Chec the Heterogenety Tests opton box. Chec the Outcome Detal Reports opton box. On the Plots tab, chec the Forest Plot opton box. Chec Radal Plot opton box. 5 Run the procedure. From the Run menu, select Run Procedure. Alternatvely, just clc the green Run button. Run Summary Secton Parameter Value Parameter Value Log HR Varable LogHR SE(Log HR) Varable SELogHR Group Varable None Number Groups Ro Label Varable Study Ros Processed 6 Ths report records the varables that ere used and the number of ros that ere processed. Numerc Summary Secton Study Log HR SE(Log HR) S S S S S S S S S S S S S S S S [Combned] Average Ths report shos the nput data. You should scan t for any mstaes. Note that the Average lne provdes the estmated group average NCSS, LLC. All Rghts Reserved.
10 NCSS Statstcal Softare Meta-Analyss of Hazard Ratos Nondrectonal Zero-Effect Test Outcome Prob Ros Measure Ch-Square DF Level Combned Log(Hazard Rato) Ths reports the results of the nondrectonal zero-effect ch-square test desgned to test the null hypothess that all treatment effects are zero. The null hypothess s rtten H : θ θ,, 0 The alternatve hypothess s that at least one θ 0, that s, at least one study had a statstcally sgnfcant result. Ch-Square Ths s the computed ch-square value for ths test. The formula as presented earler. DF Ths s the degrees of freedom. For ths test, the degrees of freedom s equal to the number of studes. Prob Level Ths s the sgnfcance level of the test. If ths value s less than the nomnal value of alpha (usually 0.05), the test s statstcally sgnfcant and the alternatve s concluded. If the value s larger than the specfed value of alpha, no concluson can be dran other than that you do not have enough evdence to reject the null hypothess. Drectonal Zero-Effect Test Outcome Prob Ros Measure Ch-Square DF Level Combned Log(Hazard Rato) Ths reports the results of the drectonal zero-effect ch-square test desgned to test the overall null hypothess that all treatment effects are zero. The null hypothess s rtten H : θ θ,, 0 The alternatve hypothess s that θ θ 0 for all, that s, that all effects are equal to the same, non-zero value. Ch-Square Ths s the computed ch-square value for ths test. The formula as presented earler. DF Ths s the degrees of freedom. For ths test, the degrees of freedom s equal one. Prob Level Ths s the sgnfcance level of the test. If ths value s less than the specfed value of alpha (usually 0.05), the test s statstcally sgnfcant and the alternatve s concluded. If the value s larger than the specfed value of alpha, no concluson can be dran other than that you do not have enough evdence to reject the null hypothess. Effect-Equalty (Heterogenety) Test Outcome Cochran s Prob Treatment Measure Q DF Level Combned Log(Hazard Rato) NCSS, LLC. All Rghts Reserved.
11 NCSS Statstcal Softare Meta-Analyss of Hazard Ratos Ths reports the results of the effect-equalty (homogenety) test. Ths ch-square test as desgned to test the null hypothess that all treatment effects are equal. The null hypothess s rtten H : θ θ,, 0 The alternatve s that at least one effect s dfferent, that s, that the effects are heterogeneous. Ths may also be nterpreted as a test of the study-by-treatment nteracton. Ths test may help you determne hether to use a Fxed Effects model (used for homogeneous effects) or a Random Effects model (heterogeneous effects). Cochran s Q Ths s the computed ch-square value for Cochran s Q statstc. The formula as presented earler. DF Ths s the degrees of freedom. For ths test, the degrees of freedom s equal to the number of studes mnus one.. Prob Level Ths s the sgnfcance level of the test. If ths value s less than the specfed value of alpha (usually 0.05), the test s statstcally sgnfcant and the alternatve s concluded. If the value s larger than the specfed value of alpha, no concluson can be dran other than that you do not have enough evdence to reject the null hypothess. Log(Hazard Rato) Detal Secton 95.0% 95.0% Percent Loer Upper Random Log Hazard Standard Confdence Confdence Effects Study Rato Error Lmt Lmt Weght S S S S S S S S S S S S S S S S [Combned] Average Ths report dsplays results for the log hazard rato. Confdence Lmts These are the loer and upper confdence lmts (the formulas ere gven earler n ths chapter). Weghts The last column gves the relatve (percent) eght used n creatng the eghted average. Usng these values, you can decde ho much nfluence each study has on the eghted average NCSS, LLC. All Rghts Reserved.
12 NCSS Statstcal Softare Forest Plot Meta-Analyss of Hazard Ratos Ths plot presents the results for each study on one plot. The sze of the plot symbol s proportonal to the sample sze of the study. The ponts on the plot are sorted by the mean dfference. The lnes represent the confdence ntervals about the log hazard ratos. Note that the narroer the confdence lmts, the better. By studyng ths plot, you can determne the man conclusons that can be dran from the set of studes. For example, you can determne ho many studes ere sgnfcant (the confdence lmts do not ntersect the vertcal lne at 0.0) NCSS, LLC. All Rghts Reserved.
13 NCSS Statstcal Softare Radal Plot Meta-Analyss of Hazard Ratos The radal (or Galbrath) plot shos the z-statstc (outcome dvded by standard error) on the vertcal axs and a measure of eght on the horzontal axs. Studes that have the largest eght are closest to the Y axs. Studes thn the lmts are nterpreted as homogeneous. Studes outsde the lmts may be outlers NCSS, LLC. All Rghts Reserved.
Statistical algorithms in Review Manager 5
Statstcal algorthms n Reve Manager 5 Jonathan J Deeks and Julan PT Hggns on behalf of the Statstcal Methods Group of The Cochrane Collaboraton August 00 Data structure Consder a meta-analyss of k studes
CHAPTER 14 MORE ABOUT REGRESSION
CHAPTER 14 MORE ABOUT REGRESSION We learned n Chapter 5 that often a straght lne descrbes the pattern of a relatonshp between two quanttatve varables. For nstance, n Example 5.1 we explored the relatonshp
CHOLESTEROL REFERENCE METHOD LABORATORY NETWORK. Sample Stability Protocol
CHOLESTEROL REFERENCE METHOD LABORATORY NETWORK Sample Stablty Protocol Background The Cholesterol Reference Method Laboratory Network (CRMLN) developed certfcaton protocols for total cholesterol, HDL
SIMPLE LINEAR CORRELATION
SIMPLE LINEAR CORRELATION Smple lnear correlaton s a measure of the degree to whch two varables vary together, or a measure of the ntensty of the assocaton between two varables. Correlaton often s abused.
An Alternative Way to Measure Private Equity Performance
An Alternatve Way to Measure Prvate Equty Performance Peter Todd Parlux Investment Technology LLC Summary Internal Rate of Return (IRR) s probably the most common way to measure the performance of prvate
PSYCHOLOGICAL RESEARCH (PYC 304-C) Lecture 12
14 The Ch-squared dstrbuton PSYCHOLOGICAL RESEARCH (PYC 304-C) Lecture 1 If a normal varable X, havng mean µ and varance σ, s standardsed, the new varable Z has a mean 0 and varance 1. When ths standardsed
Causal, Explanatory Forecasting. Analysis. Regression Analysis. Simple Linear Regression. Which is Independent? Forecasting
Causal, Explanatory Forecastng Assumes cause-and-effect relatonshp between system nputs and ts output Forecastng wth Regresson Analyss Rchard S. Barr Inputs System Cause + Effect Relatonshp The job of
NPAR TESTS. One-Sample Chi-Square Test. Cell Specification. Observed Frequencies 1O i 6. Expected Frequencies 1EXP i 6
PAR TESTS If a WEIGHT varable s specfed, t s used to replcate a case as many tmes as ndcated by the weght value rounded to the nearest nteger. If the workspace requrements are exceeded and samplng has
Can Auto Liability Insurance Purchases Signal Risk Attitude?
Internatonal Journal of Busness and Economcs, 2011, Vol. 10, No. 2, 159-164 Can Auto Lablty Insurance Purchases Sgnal Rsk Atttude? Chu-Shu L Department of Internatonal Busness, Asa Unversty, Tawan Sheng-Chang
benefit is 2, paid if the policyholder dies within the year, and probability of death within the year is ).
REVIEW OF RISK MANAGEMENT CONCEPTS LOSS DISTRIBUTIONS AND INSURANCE Loss and nsurance: When someone s subject to the rsk of ncurrng a fnancal loss, the loss s generally modeled usng a random varable or
Calculation of Sampling Weights
Perre Foy Statstcs Canada 4 Calculaton of Samplng Weghts 4.1 OVERVIEW The basc sample desgn used n TIMSS Populatons 1 and 2 was a two-stage stratfed cluster desgn. 1 The frst stage conssted of a sample
DEFINING %COMPLETE IN MICROSOFT PROJECT
CelersSystems DEFINING %COMPLETE IN MICROSOFT PROJECT PREPARED BY James E Aksel, PMP, PMI-SP, MVP For Addtonal Informaton about Earned Value Management Systems and reportng, please contact: CelersSystems,
STATISTICAL DATA ANALYSIS IN EXCEL
Mcroarray Center STATISTICAL DATA ANALYSIS IN EXCEL Lecture 6 Some Advanced Topcs Dr. Petr Nazarov 14-01-013 [email protected] Statstcal data analyss n Ecel. 6. Some advanced topcs Correcton for
Lecture 2: Single Layer Perceptrons Kevin Swingler
Lecture 2: Sngle Layer Perceptrons Kevn Sngler [email protected] Recap: McCulloch-Ptts Neuron Ths vastly smplfed model of real neurons s also knon as a Threshold Logc Unt: W 2 A Y 3 n W n. A set of synapses
1. Measuring association using correlation and regression
How to measure assocaton I: Correlaton. 1. Measurng assocaton usng correlaton and regresson We often would lke to know how one varable, such as a mother's weght, s related to another varable, such as a
Latent Class Regression. Statistics for Psychosocial Research II: Structural Models December 4 and 6, 2006
Latent Class Regresson Statstcs for Psychosocal Research II: Structural Models December 4 and 6, 2006 Latent Class Regresson (LCR) What s t and when do we use t? Recall the standard latent class model
Traffic-light a stress test for life insurance provisions
MEMORANDUM Date 006-09-7 Authors Bengt von Bahr, Göran Ronge Traffc-lght a stress test for lfe nsurance provsons Fnansnspetonen P.O. Box 6750 SE-113 85 Stocholm [Sveavägen 167] Tel +46 8 787 80 00 Fax
HOUSEHOLDS DEBT BURDEN: AN ANALYSIS BASED ON MICROECONOMIC DATA*
HOUSEHOLDS DEBT BURDEN: AN ANALYSIS BASED ON MICROECONOMIC DATA* Luísa Farnha** 1. INTRODUCTION The rapd growth n Portuguese households ndebtedness n the past few years ncreased the concerns that debt
Vasicek s Model of Distribution of Losses in a Large, Homogeneous Portfolio
Vascek s Model of Dstrbuton of Losses n a Large, Homogeneous Portfolo Stephen M Schaefer London Busness School Credt Rsk Electve Summer 2012 Vascek s Model Important method for calculatng dstrbuton of
Brigid Mullany, Ph.D University of North Carolina, Charlotte
Evaluaton And Comparson Of The Dfferent Standards Used To Defne The Postonal Accuracy And Repeatablty Of Numercally Controlled Machnng Center Axes Brgd Mullany, Ph.D Unversty of North Carolna, Charlotte
CHAPTER 5 RELATIONSHIPS BETWEEN QUANTITATIVE VARIABLES
CHAPTER 5 RELATIONSHIPS BETWEEN QUANTITATIVE VARIABLES In ths chapter, we wll learn how to descrbe the relatonshp between two quanttatve varables. Remember (from Chapter 2) that the terms quanttatve varable
Stress test for measuring insurance risks in non-life insurance
PROMEMORIA Datum June 01 Fnansnspektonen Författare Bengt von Bahr, Younes Elonq and Erk Elvers Stress test for measurng nsurance rsks n non-lfe nsurance Summary Ths memo descrbes stress testng of nsurance
Time Value of Money Module
Tme Value of Money Module O BJECTIVES After readng ths Module, you wll be able to: Understand smple nterest and compound nterest. 2 Compute and use the future value of a sngle sum. 3 Compute and use the
1 Example 1: Axis-aligned rectangles
COS 511: Theoretcal Machne Learnng Lecturer: Rob Schapre Lecture # 6 Scrbe: Aaron Schld February 21, 2013 Last class, we dscussed an analogue for Occam s Razor for nfnte hypothess spaces that, n conjuncton
THE DISTRIBUTION OF LOAN PORTFOLIO VALUE * Oldrich Alfons Vasicek
HE DISRIBUION OF LOAN PORFOLIO VALUE * Oldrch Alfons Vascek he amount of captal necessary to support a portfolo of debt securtes depends on the probablty dstrbuton of the portfolo loss. Consder a portfolo
Instructions for Analyzing Data from CAHPS Surveys:
Instructons for Analyzng Data from CAHPS Surveys: Usng the CAHPS Analyss Program Verson 4.1 Purpose of ths Document...1 The CAHPS Analyss Program...1 Computng Requrements...1 Pre-Analyss Decsons...2 What
Characterization of Assembly. Variation Analysis Methods. A Thesis. Presented to the. Department of Mechanical Engineering. Brigham Young University
Characterzaton of Assembly Varaton Analyss Methods A Thess Presented to the Department of Mechancal Engneerng Brgham Young Unversty In Partal Fulfllment of the Requrements for the Degree Master of Scence
Module 2 LOSSLESS IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur
Module LOSSLESS IMAGE COMPRESSION SYSTEMS Lesson 3 Lossless Compresson: Huffman Codng Instructonal Objectves At the end of ths lesson, the students should be able to:. Defne and measure source entropy..
7 ANALYSIS OF VARIANCE (ANOVA)
7 ANALYSIS OF VARIANCE (ANOVA) Chapter 7 Analyss of Varance (Anova) Objectves After studyng ths chapter you should apprecate the need for analysng data from more than two samples; understand the underlyng
Analysis of Premium Liabilities for Australian Lines of Business
Summary of Analyss of Premum Labltes for Australan Lnes of Busness Emly Tao Honours Research Paper, The Unversty of Melbourne Emly Tao Acknowledgements I am grateful to the Australan Prudental Regulaton
The OC Curve of Attribute Acceptance Plans
The OC Curve of Attrbute Acceptance Plans The Operatng Characterstc (OC) curve descrbes the probablty of acceptng a lot as a functon of the lot s qualty. Fgure 1 shows a typcal OC Curve. 10 8 6 4 1 3 4
The Development of Web Log Mining Based on Improve-K-Means Clustering Analysis
The Development of Web Log Mnng Based on Improve-K-Means Clusterng Analyss TngZhong Wang * College of Informaton Technology, Luoyang Normal Unversty, Luoyang, 471022, Chna [email protected] Abstract.
7.5. Present Value of an Annuity. Investigate
7.5 Present Value of an Annuty Owen and Anna are approachng retrement and are puttng ther fnances n order. They have worked hard and nvested ther earnngs so that they now have a large amount of money on
How To Calculate The Accountng Perod Of Nequalty
Inequalty and The Accountng Perod Quentn Wodon and Shlomo Ytzha World Ban and Hebrew Unversty September Abstract Income nequalty typcally declnes wth the length of tme taen nto account for measurement.
SPEE Recommended Evaluation Practice #6 Definition of Decline Curve Parameters Background:
SPEE Recommended Evaluaton Practce #6 efnton of eclne Curve Parameters Background: The producton hstores of ol and gas wells can be analyzed to estmate reserves and future ol and gas producton rates and
Section 5.4 Annuities, Present Value, and Amortization
Secton 5.4 Annutes, Present Value, and Amortzaton Present Value In Secton 5.2, we saw that the present value of A dollars at nterest rate per perod for n perods s the amount that must be deposted today
THE METHOD OF LEAST SQUARES THE METHOD OF LEAST SQUARES
The goal: to measure (determne) an unknown quantty x (the value of a RV X) Realsaton: n results: y 1, y 2,..., y j,..., y n, (the measured values of Y 1, Y 2,..., Y j,..., Y n ) every result s encumbered
Time Value of Money. Types of Interest. Compounding and Discounting Single Sums. Page 1. Ch. 6 - The Time Value of Money. The Time Value of Money
Ch. 6 - The Tme Value of Money Tme Value of Money The Interest Rate Smple Interest Compound Interest Amortzng a Loan FIN21- Ahmed Y, Dasht TIME VALUE OF MONEY OR DISCOUNTED CASH FLOW ANALYSIS Very Important
BERNSTEIN POLYNOMIALS
On-Lne Geometrc Modelng Notes BERNSTEIN POLYNOMIALS Kenneth I. Joy Vsualzaton and Graphcs Research Group Department of Computer Scence Unversty of Calforna, Davs Overvew Polynomals are ncredbly useful
Forecasting the Direction and Strength of Stock Market Movement
Forecastng the Drecton and Strength of Stock Market Movement Jngwe Chen Mng Chen Nan Ye [email protected] [email protected] [email protected] Abstract - Stock market s one of the most complcated systems
Staff Paper. Farm Savings Accounts: Examining Income Variability, Eligibility, and Benefits. Brent Gloy, Eddy LaDue, and Charles Cuykendall
SP 2005-02 August 2005 Staff Paper Department of Appled Economcs and Management Cornell Unversty, Ithaca, New York 14853-7801 USA Farm Savngs Accounts: Examnng Income Varablty, Elgblty, and Benefts Brent
Demographic and Health Surveys Methodology
samplng and household lstng manual Demographc and Health Surveys Methodology Ths document s part of the Demographc and Health Survey s DHS Toolkt of methodology for the MEASURE DHS Phase III project, mplemented
Statistical Methods to Develop Rating Models
Statstcal Methods to Develop Ratng Models [Evelyn Hayden and Danel Porath, Österrechsche Natonalbank and Unversty of Appled Scences at Manz] Source: The Basel II Rsk Parameters Estmaton, Valdaton, and
1 De nitions and Censoring
De ntons and Censorng. Survval Analyss We begn by consderng smple analyses but we wll lead up to and take a look at regresson on explanatory factors., as n lnear regresson part A. The mportant d erence
the Manual on the global data processing and forecasting system (GDPFS) (WMO-No.485; available at http://www.wmo.int/pages/prog/www/manuals.
Gudelne on the exchange and use of EPS verfcaton results Update date: 30 November 202. Introducton World Meteorologcal Organzaton (WMO) CBS-XIII (2005) recommended that the general responsbltes for a Lead
CS 2750 Machine Learning. Lecture 3. Density estimation. CS 2750 Machine Learning. Announcements
Lecture 3 Densty estmaton Mlos Hauskrecht [email protected] 5329 Sennott Square Next lecture: Matlab tutoral Announcements Rules for attendng the class: Regstered for credt Regstered for audt (only f there
Exhaustive Regression. An Exploration of Regression-Based Data Mining Techniques Using Super Computation
Exhaustve Regresson An Exploraton of Regresson-Based Data Mnng Technques Usng Super Computaton Antony Daves, Ph.D. Assocate Professor of Economcs Duquesne Unversty Pttsburgh, PA 58 Research Fellow The
Binomial Link Functions. Lori Murray, Phil Munz
Bnomal Lnk Functons Lor Murray, Phl Munz Bnomal Lnk Functons Logt Lnk functon: ( p) p ln 1 p Probt Lnk functon: ( p) 1 ( p) Complentary Log Log functon: ( p) ln( ln(1 p)) Motvatng Example A researcher
Joe Pimbley, unpublished, 2005. Yield Curve Calculations
Joe Pmbley, unpublshed, 005. Yeld Curve Calculatons Background: Everythng s dscount factors Yeld curve calculatons nclude valuaton of forward rate agreements (FRAs), swaps, nterest rate optons, and forward
Computer-assisted Auditing for High- Volume Medical Coding
Computer-asssted Audtng for Hgh-Volume Medcal Codng Computer-asssted Audtng for Hgh- Volume Medcal Codng by Danel T. Henze, PhD; Peter Feller, MS; Jerry McCorkle, BA; and Mark Morsch, MS Abstract The volume
Luby s Alg. for Maximal Independent Sets using Pairwise Independence
Lecture Notes for Randomzed Algorthms Luby s Alg. for Maxmal Independent Sets usng Parwse Independence Last Updated by Erc Vgoda on February, 006 8. Maxmal Independent Sets For a graph G = (V, E), an ndependent
Institute of Informatics, Faculty of Business and Management, Brno University of Technology,Czech Republic
Lagrange Multplers as Quanttatve Indcators n Economcs Ivan Mezník Insttute of Informatcs, Faculty of Busness and Management, Brno Unversty of TechnologCzech Republc Abstract The quanttatve role of Lagrange
Regression Models for a Binary Response Using EXCEL and JMP
SEMATECH 997 Statstcal Methods Symposum Austn Regresson Models for a Bnary Response Usng EXCEL and JMP Davd C. Trndade, Ph.D. STAT-TECH Consultng and Tranng n Appled Statstcs San Jose, CA Topcs Practcal
Updating the E5810B firmware
Updatng the E5810B frmware NOTE Do not update your E5810B frmware unless you have a specfc need to do so, such as defect repar or nstrument enhancements. If the frmware update fals, the E5810B wll revert
Section 5.3 Annuities, Future Value, and Sinking Funds
Secton 5.3 Annutes, Future Value, and Snkng Funds Ordnary Annutes A sequence of equal payments made at equal perods of tme s called an annuty. The tme between payments s the payment perod, and the tme
Texas Instruments 30X IIS Calculator
Texas Instruments 30X IIS Calculator Keystrokes for the TI-30X IIS are shown for a few topcs n whch keystrokes are unque. Start by readng the Quk Start secton. Then, before begnnng a specfc unt of the
Recurrence. 1 Definitions and main statements
Recurrence 1 Defntons and man statements Let X n, n = 0, 1, 2,... be a MC wth the state space S = (1, 2,...), transton probabltes p j = P {X n+1 = j X n = }, and the transton matrx P = (p j ),j S def.
To manage leave, meeting institutional requirements and treating individual staff members fairly and consistently.
Corporate Polces & Procedures Human Resources - Document CPP216 Leave Management Frst Produced: Current Verson: Past Revsons: Revew Cycle: Apples From: 09/09/09 26/10/12 09/09/09 3 years Immedately Authorsaton:
How To Understand The Results Of The German Meris Cloud And Water Vapour Product
Ttel: Project: Doc. No.: MERIS level 3 cloud and water vapour products MAPP MAPP-ATBD-ClWVL3 Issue: 1 Revson: 0 Date: 9.12.1998 Functon Name Organsaton Sgnature Date Author: Bennartz FUB Preusker FUB Schüller
Meta-analysis in Psychological Research.
Internatonal Journal of Psychologcal Research, 010. Vol. 3. No. 1. ISSN mpresa (prnted 011-084 ISSN electrónca (electronc 011-079 Sánchez-Meca, J., Marín-Martínez, F., (010. Meta-analyss n Psychologcal
Answer: A). There is a flatter IS curve in the high MPC economy. Original LM LM after increase in M. IS curve for low MPC economy
4.02 Quz Solutons Fall 2004 Multple-Choce Questons (30/00 ponts) Please, crcle the correct answer for each of the followng 0 multple-choce questons. For each queston, only one of the answers s correct.
Proceedings of the Annual Meeting of the American Statistical Association, August 5-9, 2001
Proceedngs of the Annual Meetng of the Amercan Statstcal Assocaton, August 5-9, 2001 LIST-ASSISTED SAMPLING: THE EFFECT OF TELEPHONE SYSTEM CHANGES ON DESIGN 1 Clyde Tucker, Bureau of Labor Statstcs James
Nordea G10 Alpha Carry Index
Nordea G10 Alpha Carry Index Index Rules v1.1 Verson as of 10/10/2013 1 (6) Page 1 Index Descrpton The G10 Alpha Carry Index, the Index, follows the development of a rule based strategy whch nvests and
FREQUENCY OF OCCURRENCE OF CERTAIN CHEMICAL CLASSES OF GSR FROM VARIOUS AMMUNITION TYPES
FREQUENCY OF OCCURRENCE OF CERTAIN CHEMICAL CLASSES OF GSR FROM VARIOUS AMMUNITION TYPES Zuzanna BRO EK-MUCHA, Grzegorz ZADORA, 2 Insttute of Forensc Research, Cracow, Poland 2 Faculty of Chemstry, Jagellonan
What is Candidate Sampling
What s Canddate Samplng Say we have a multclass or mult label problem where each tranng example ( x, T ) conssts of a context x a small (mult)set of target classes T out of a large unverse L of possble
1. Fundamentals of probability theory 2. Emergence of communication traffic 3. Stochastic & Markovian Processes (SP & MP)
6.3 / -- Communcaton Networks II (Görg) SS20 -- www.comnets.un-bremen.de Communcaton Networks II Contents. Fundamentals of probablty theory 2. Emergence of communcaton traffc 3. Stochastc & Markovan Processes
Reporting Forms ARF 113.0A, ARF 113.0B, ARF 113.0C and ARF 113.0D FIRB Corporate (including SME Corporate), Sovereign and Bank Instruction Guide
Reportng Forms ARF 113.0A, ARF 113.0B, ARF 113.0C and ARF 113.0D FIRB Corporate (ncludng SME Corporate), Soveregn and Bank Instructon Gude Ths nstructon gude s desgned to assst n the completon of the FIRB
PRACTICE 1: MUTUAL FUNDS EVALUATION USING MATLAB.
PRACTICE 1: MUTUAL FUNDS EVALUATION USING MATLAB. INDEX 1. Load data usng the Edtor wndow and m-fle 2. Learnng to save results from the Edtor wndow. 3. Computng the Sharpe Rato 4. Obtanng the Treynor Rato
Course outline. Financial Time Series Analysis. Overview. Data analysis. Predictive signal. Trading strategy
Fnancal Tme Seres Analyss Patrck McSharry [email protected] www.mcsharry.net Trnty Term 2014 Mathematcal Insttute Unversty of Oxford Course outlne 1. Data analyss, probablty, correlatons, vsualsaton
total A A reag total A A r eag
hapter 5 Standardzng nalytcal Methods hapter Overvew 5 nalytcal Standards 5B albratng the Sgnal (S total ) 5 Determnng the Senstvty (k ) 5D Lnear Regresson and albraton urves 5E ompensatng for the Reagent
Survey Weighting and the Calculation of Sampling Variance
Survey Weghtng and the Calculaton of Samplng Varance Survey weghtng... 132 Calculatng samplng varance... 138 PISA 2012 TECHNICAL REPORT OECD 2014 131 Survey weghts are requred to facltate analyss of PISA
10.2 Future Value and Present Value of an Ordinary Simple Annuity
348 Chapter 10 Annutes 10.2 Future Value and Present Value of an Ordnary Smple Annuty In compound nterest, 'n' s the number of compoundng perods durng the term. In an ordnary smple annuty, payments are
WORKING PAPER. C.D. Howe Institute. The Effects of Tax Rate Changes on Tax Bases and the Marginal Cost of Public Funds for Provincial Governments
MARCH 211 C.D. Howe Insttute WORKING PAPER FISCAL AND TAX COMPETITIVENESS The Effects of Tax Rate Changes on Tax Bases and the Margnal Cost of Publc Funds for Provncal Governments Bev Dahlby Ergete Ferede
Quantization Effects in Digital Filters
Quantzaton Effects n Dgtal Flters Dstrbuton of Truncaton Errors In two's complement representaton an exact number would have nfntely many bts (n general). When we lmt the number of bts to some fnte value
DO LOSS FIRMS MANAGE EARNINGS AROUND SEASONED EQUITY OFFERINGS?
DO LOSS FIRMS MANAGE EARNINGS AROUND SEASONED EQUITY OFFERINGS? Fernando Comran, Unversty of San Francsco, School of Management, 2130 Fulton Street, CA 94117, Unted States, [email protected] Tatana Fedyk,
Vembu StoreGrid Windows Client Installation Guide
Ser v cepr ov dered t on Cl enti nst al l at ongu de W ndows Vembu StoreGrd Wndows Clent Installaton Gude Download the Wndows nstaller, VembuStoreGrd_4_2_0_SP_Clent_Only.exe To nstall StoreGrd clent on
ANALYZING THE RELATIONSHIPS BETWEEN QUALITY, TIME, AND COST IN PROJECT MANAGEMENT DECISION MAKING
ANALYZING THE RELATIONSHIPS BETWEEN QUALITY, TIME, AND COST IN PROJECT MANAGEMENT DECISION MAKING Matthew J. Lberatore, Department of Management and Operatons, Vllanova Unversty, Vllanova, PA 19085, 610-519-4390,
Traffic-light extended with stress test for insurance and expense risks in life insurance
PROMEMORIA Datum 0 July 007 FI Dnr 07-1171-30 Fnansnspetonen Författare Bengt von Bahr, Göran Ronge Traffc-lght extended wth stress test for nsurance and expense rss n lfe nsurance Summary Ths memorandum
RESEARCH ON DUAL-SHAKER SINE VIBRATION CONTROL. Yaoqi FENG 1, Hanping QIU 1. China Academy of Space Technology (CAST) yaoqi.feng@yahoo.
ICSV4 Carns Australa 9- July, 007 RESEARCH ON DUAL-SHAKER SINE VIBRATION CONTROL Yaoq FENG, Hanpng QIU Dynamc Test Laboratory, BISEE Chna Academy of Space Technology (CAST) [email protected] Abstract
The Effects of Geodetic Configuration of the Network in Deformation Analysis
The Effects of Geodetc Confguraton of the Network n Deformaton Analyss M. Onur KAPLAN, Tevfk AYAN and Serdar EROL Turkey Key words: Network confguraton, deformaton analyss, optmzaton, confdence ellpses
Survival analysis methods in Insurance Applications in car insurance contracts
Survval analyss methods n Insurance Applcatons n car nsurance contracts Abder OULIDI 1 Jean-Mare MARION 2 Hervé GANACHAUD 3 Abstract In ths wor, we are nterested n survval models and ther applcatons on
Portfolio Loss Distribution
Portfolo Loss Dstrbuton Rsky assets n loan ortfolo hghly llqud assets hold-to-maturty n the bank s balance sheet Outstandngs The orton of the bank asset that has already been extended to borrowers. Commtment
Outsourcing inventory management decisions in healthcare: Models and application
European Journal of Operatonal Research 154 (24) 271 29 O.R. Applcatons Outsourcng nventory management decsons n healthcare: Models and applcaton www.elsever.com/locate/dsw Lawrence Ncholson a, Asoo J.
EE31 Series. Manual. Logger & Visualisation Software. BA_EE31_VisuLoggerSW_01_eng // Technical data are subject to change V1.0
EE31 Seres Manual Logger & Vsualsaton Software BA_EE31_VsuLoggerSW_01_eng // Techncal data are subject to change V1.0 Logger & Vsualsaton Software - EE31 Seres GENERAL Ths software has been developed by
Bayesian Network Based Causal Relationship Identification and Funding Success Prediction in P2P Lending
Proceedngs of 2012 4th Internatonal Conference on Machne Learnng and Computng IPCSIT vol. 25 (2012) (2012) IACSIT Press, Sngapore Bayesan Network Based Causal Relatonshp Identfcaton and Fundng Success
VRT012 User s guide V0.1. Address: Žirmūnų g. 27, Vilnius LT-09105, Phone: (370-5) 2127472, Fax: (370-5) 276 1380, Email: info@teltonika.
VRT012 User s gude V0.1 Thank you for purchasng our product. We hope ths user-frendly devce wll be helpful n realsng your deas and brngng comfort to your lfe. Please take few mnutes to read ths manual
Risk-based Fatigue Estimate of Deep Water Risers -- Course Project for EM388F: Fracture Mechanics, Spring 2008
Rsk-based Fatgue Estmate of Deep Water Rsers -- Course Project for EM388F: Fracture Mechancs, Sprng 2008 Chen Sh Department of Cvl, Archtectural, and Envronmental Engneerng The Unversty of Texas at Austn
World currency options market efficiency
Arful Hoque (Australa) World optons market effcency Abstract The World Currency Optons (WCO) maket began tradng n July 2007 on the Phladelpha Stock Exchange (PHLX) wth the new features. These optons are
Credit Limit Optimization (CLO) for Credit Cards
Credt Lmt Optmzaton (CLO) for Credt Cards Vay S. Desa CSCC IX, Ednburgh September 8, 2005 Copyrght 2003, SAS Insttute Inc. All rghts reserved. SAS Propretary Agenda Background Tradtonal approaches to credt
