North Carolina Program Integrity Sampling/Extrapolation Practicum Bradford Woodard, M.S. Senior Health Data Analyst.

Slides:



Advertisements
Similar presentations
HS 67 - Intro Health Statistics Describing Distributions with Numbers
Advertisements

Sampling: Final and Initial Sample Size Determination
Confidence Intervals This chapter presents the beginning of inferential statistics. We introduce methods for estimating values of these important population.
LECTURE 5 Assertions and Tests of Detail
McGraw-Hill Ryerson Copyright © 2011 McGraw-Hill Ryerson Limited. Adapted by Peter Au, George Brown College.
QBM117 Business Statistics Statistical Inference Sampling 1.
BA 555 Practical Business Analysis
Point and Confidence Interval Estimation of a Population Proportion, p
Sampling Methods and Sampling Distributions Chapter.
Statistical Inference and Sampling Introduction to Business Statistics, 5e Kvanli/Guynes/Pavur (c)2000 South-Western College Publishing.
8-4 Testing a Claim About a Mean
Confidence Intervals Confidence Interval for a Mean
Unit 4: Monitoring Data Quality For HIV Case Surveillance Systems #6-0-1.
Statistical Sampling Overview and Principles Alvin Binns
1 Psych 5500/6500 Statistics and Parameters Fall, 2008.
Slide 9-1 © The McGraw-Hill Companies, Inc., 2006 Audit Sampling.
MATH1342 S08 – 7:00A-8:15A T/R BB218 SPRING 2014 Daryl Rupp.
Thursday, January 30, 2014MAT 312. Thursday, January 30, 2014MAT 312.
Many times in statistical analysis, we do not know the TRUE mean of a population of interest. This is why we use sampling to be able to generalize the.
Confidence Intervals Confidence Interval for a Mean
© 2014 by Pearson Higher Education, Inc Upper Saddle River, New Jersey All Rights Reserved HLTH 300 Biostatistics for Public Health Practice, Raul.
Unit 1 Accuracy & Precision.  Data (Singular: datum or “a data point”): The information collected in an experiment. Can be numbers (quantitative) or.
Many times in statistical analysis, we do not know the TRUE mean of a population of interest. This is why we use sampling to be able to generalize the.
Chapter 3 Descriptive Measures
Descriptive Statistics Chapter 2. § 2.4 Measures of Variation.
1 Homologues Group Meeting Slovenia, October 2009 Republika SlovenijaEuropean Union Ljubljana, October 2009 Risk of Error on Closure Ljubljana,
Analysis of Distribution If the sample is truly random and there is no bias in the sampling then the expected distribution would be a smooth bell-shaped.
Lecture 6 Forestry 3218 Forest Mensuration II Lecture 6 Double Sampling Cluster Sampling Sampling for Discrete Variables Avery and Burkhart, Chapter 3.
Audit Sampling: An Overview and Application to Tests of Controls
VII-1 Stratification Case study to illustrate alternative methods to stratify a sampling frame Dr. Will Yancey, CPA This material is the property of the.
OPENING QUESTIONS 1.What key concepts and symbols are pertinent to sampling? 2.How are the sampling distribution, statistical inference, and standard.
Medical Statistics as a science
Describing Quantitative Data with Numbers Section 1.3.
Business Statistics: A Decision-Making Approach, 6e © 2005 Prentice-Hall, Inc. Chap 1-1 A Population is the set of all items or individuals of interest.
Notes 1.3 (Part 1) An Overview of Statistics. What you will learn 1. How to design a statistical study 2. How to collect data by taking a census, using.
Section 6-3 Estimating a Population Mean: σ Known.
The Single-Sample t Test Chapter 9. t distributions >Sometimes, we do not have the population standard deviation. (that’s actually really common). >So.
Statistics 1: Introduction to Probability and Statistics Section 3-2.
IPDET Module 9: Choosing the Sampling Strategy. IPDET © Introduction Introduction to Sampling Types of Samples: Random and Nonrandom Determining.
Lecture Slides Elementary Statistics Twelfth Edition
INFERENTIAL STATISTICS DOING STATS WITH CONFIDENCE.
Hypothesis Testing for the Mean:
Many times in statistical analysis, we do not know the TRUE mean of a population on interest. This is why we use sampling to be able to generalize the.
Exploratory data analysis, descriptive measures and sampling or, “How to explore numbers in tables and charts”
Sect. 1-3 Experimental Design Objective: SWBAT learn how to design a statistical Study, How to collect data by taking a census using a sampling, using.
1.3 Experimental Design. What is the goal of every statistical Study?  Collect data  Use data to make a decision If the process to collect data is flawed,
Describe Quantitative Data with Numbers. Mean The most common measure of center is the ordinary arithmetic average, or mean.
Critical Appraisal Course for Emergency Medicine Trainees Module 2 Statistics.
MATH Section 7.2.
Test of a Population Median. The Population Median (  ) The population median ( , P 50 ) is defined for population T as the value for which the following.
Statistics: Experimental Design
Warm Up – Take out a ½ sheet of paper…
Data Analysis.
Inference about Two Means - Independent Samples
CHAPTER 4 Designing Studies
Developing the Sampling Plan
[ March 9, 2017] [ Bill Bowles, Audit Supervisor]
Summary Statistics 9/23/2018 Summary Statistics
Substantive Test Sampling
جمعیت –نمونه –روشهای نمونه گیری دکتر محسن عسکرشاهی دکترای آمار زيستی
Chapter 3 Section 4 Measures of Position.
Random Sampling + RAT-Stats.
Mean, Median, Mode The Mean is the simple average of the data values. Most appropriate for symmetric data. The Median is the middle value. It’s best.
Statistics 1: Introduction to Probability and Statistics
6A Types of Data, 6E Measuring the Centre of Data
Problem DC 10-1, Page 547 (Original Problem)
Data Collection and Sampling Techniques
STAT 515 Statistical Methods I Sections
Presentation transcript:

North Carolina Program Integrity Sampling/Extrapolation Practicum Bradford Woodard, M.S. Senior Health Data Analyst

Big Picture

Small Picture

Overall Training Plan Remain focused on the big picture Specific focus on small picture details Overall consistency with sampling policy Potential problems encountered in the process

RAT-STATS Software Examples of using the software to determine sample sizes and extrapolate findings

N.C. Sampling Policy Guidelines Minimum of 100 claim details per sample Maximum of 4 strata, with 30 claim details per strata Use lower bound 90% confidence level difference amount for recoupment Both paid and claims error rates must fall below 5% for extrapolation to not occur

N.C. Sampling Policy Guidelines Simple random sampling is one strategy Cluster sampling can be used (pulling all claims for a date of service, ambulance example) Must review a minimum of 30 clusters Previous slide cutoffs still apply Extrapolate at the cluster level

Steps in Process Describe population with summary statistics Stratify based on summary Determine sample size per strata Generate random numbers Pull sample Perform audit Extrapolate

Law of Large Numbers

Population Summary Determine count of total claims Determine mean, median, standard deviation Determine range from low to high Determine strata cutoffs

Stratification Summary Determine count of claims per strata Determine mean, median, standard deviation per strata Enter information in RAT-STATS sample size determiner to obtain sample size Follow PI sample guidelines

Generate Random Numbers & Pull Sample Use RAT-STATS random number generator to produce random numbers Number records in population from 1 to total for each strata Match random numbers to population numbers to pull sample

Audit Sample & Extrapolate Determine amount that should be paid for each record in sample Enter information in RAT-STATS variable appraisal module to determine extrapolated amount Compare extrapolated figures to original for precision Use lower bound 90% difference figure

Problems Encountered After the Audit Dates outside of allowed range – be sure to check them first!! If still a problem use RAT-STATS Post- stratification module Service types that cannot be extrapolated – Post-stratification

Remember the Big Picture!! 1.Goal is to go from sample to population 2.All steps aimed at measuring the population total

Big Picture Again