Incorporating Statistical Methodology for a Research Proposal Shing Chang, IMSE ICE Conference Room, College of Engineering, K-State 11:30 AM – 1:00 PM, December 14, 2017

About my research areas Statistical process control Neural network control charts Profile monitoring Response Surface Methodology Design of experiments with multiple responses Big Data Analytics with applications in healthcare and manufacturing Fall prediction using Microsoft Kinect System-wise monitoring in cybermanufacturing

Funding agencies National Science Foundation NSF (Smart Connect Health) Unites States Department of Agriculture USDA (Life Cycle Costs of Camelina) Kansas Department of Transportation KDOT (Highway modeling via GPS coordinates)

NSF SCH: EXP: Smart Elderly Fall Prevention Protocol Using Personal Health Records This proposed research fills a critical gap in providing an objective, patient- centered, evidence-based analysis for fall prevention and quantitative evaluation of fall-related therapy of persons more than 65 years old.

Statistical Methods Involved Experimental Design Locations: where Allocations: how many

Research Tasks To achieve the proposed patient-centered, evidence-based health care delivery system, the following research tasks are proposed: (1) identifying decision-making mechanisms for the proposed multivariate multiscale sample entropy (MMSE) to detect changes in a patient’s medical records toward elevated fall risk; (2) identifying patient therapy improvement standards/protocols from physical/occupational therapists’ point of view; and (3) validating the proposed work based on de- identified elderly patients data obtained from Via Christi HOPE.

Proposed Method

Study 1. Testing the Hypothesis that Elderly People who have experienced falls possess different entropy scores from those who do not experience fall A sample size of 40 elderly people will be chosen from Via Christi HOPE in Wichita. The elderly people will be given wearable activity monitoring devices (Fitbit) and will be tested weekly by a simple 10 ft. walking test (TUG). Data collection will last for 12 months and will be done by the Via Christi HOPE personnel. De-identified data will be given to the projects PIs. Then those patients who suffer falls will be grouped together. A study will be conducted at the end of this period to identify changes in their health records with respect to the input parameters of the proposed MMSE algorithm. The MMSE scores from the fall group will then be compared to those from the non-fall group.

Study 1: Statistical Methods Involved How many variables? One: Entropy (E)– a measure of change over time Location issue: all of the data Allocations: 40 subjects H0: Ef=Enf where Ei is the entropy scores i=n or nf (f: those who have fallen; nf: those who have not fallen) Note that the sample sizes may be different from both groups

Study 2: Validating the Proposed Change-Based Protocol We propose to study the medical records of two groups of elderly patients from Via Christi HOPE, Wichita Kansas. Both groups should not be chosen from those who have fallen within a previous year. Therefore, the participants who suffered a fall in the first study will be replaced by new participants who fit this criterion. We may have to screen participants in terms of gender, age, and some other physiology criteria to ensure each group containing similar participants. We propose a sample size of 20 for each group to ensure adequate statistical detection power in terms of type II error. The first group will be composed of those who are subjected to PT/OT intervention as opposed to the second group that will not be given an intervention. Note that data from both groups will be taken daily and weekly, but the members in the experimental group will go through PT/OT interventions when a warning is given from the proposed MMSE algorithm. The main performance statistic is the percentage of falls experienced by both groups for a period of 12 months.

Statistical Methods Involved First study: 40 subjects Study 2: 20 in the intervention group and 20 in the control group H0: Nf=Nnf or H0: Pf=Pnf where Ni is the number of people who have fallen; i=n or nf (f: those who have fallen; nf: those who have not fallen) and Pi= Ni/20 Note that the sample sizes are the same for both groups How do you decide how many in each group? We propose a sample size of 20 for each group to ensure adequate statistical detection power in terms of type II error.

Power of the test Operating Characteristic Curves: two proportions Power=1- where  is the type II error, i.e. H0 is not true but we fail to identify it. It is balanced by type I error  which is the false alarm rate. The larger the sample size, the higher the power Operating Characteristic Curves: two proportions To solve: choose a power level, e.g. 80% and two proportions (e.g. p1=0.5 p2=0.1) to be distinguished Or if n=20 for each population, what is the power to detect a change from p=0.1 to 0.5?

Minitab: power and size