Tuesday, August 7, 2012

Benign Fasciculation Syndrome-BFS (Part II)


Study Background Information:

Please note: this survey meets human research criteria as outlined by the “Committee on Human Experimentation” and the “Helsinki Declaration of 1975” for the following reasons: 1. The survey was anonymous; 2. The participation in the survey was voluntary; 3. The privacy and confidentiality of the participants is maintained and protected; and 4. Survey participants were notified in advance that results would be shared publicly.

The video example of chronic BFS twitching in the primary author’s lower leg can be found on his website: http://patrickbohan.elementfx.com/BFS.htm. All tabulated data in this paper is original. The survey and subsequent data was not a clinical trial of any kind. However, the survey consisted of control questions to eliminate people from the data analysis who have not been officially diagnosed with BFS. Finally, the authors of this paper have no conflicts of interest and therefore, no information to disclose. In fact, the authors are independent and have no affiliation to any university, group, organization, or company what so ever and therefore, received no funding for this project.

User Groups:

The subjects for this study included 152 individuals who have been formally diagnosed with BFS. If participants in the survey answered “no” to the question “Have they been officially diagnosed with BFS?”, they were omitted from the data analysis.

People were contacted via social network forums listed below to participate in the survey:

Facebook: https://www.facebook.com/#!/groups/88467288815/

Internet: http://www.nextination.com/aboutbfs/

Generally speaking, people who seek join social networking sites dealing with medical conditions have a chronic condition.

Survey and Data:

Information regarding all data gathering and the survey / tools used, is listed below:

A survey was created in Google Docs and can be found at the following link: https://spreadsheets.google.com/spreadsheet/viewform?hl=en_US&authkey=CJvBgaQM&formkey=dElCQkFBRWlvY1ZSTThKTmNsbEg4d0E6MQ#gid=0

The survey will be open indefinitely with the hope to grow the sample size and therefore, better understand the disorder.

The Survey can also be reached from the Author’s BFS webpage: http://patrickbohan.elementfx.com/BFS.htm Click on the link “BFS Survey”.

The data results for the 152 participants (total participation was 161; however, 9 were considered outliers due to not having a BFS diagnosis) can also be found on my web page: http://patrickbohan.elementfx.com/BFS.htm. Click on the “Survey Data” link. This will open an excel file. The “BFS” and “BFS No Zero” tabs contain the raw survey data. The “Data Summary” tab contains a complete statistical analysis of each variable. The “Statistical Significance Result” tab contains the statistical significant data between variables (t-statistic data). [8] The “Correlation Results” tab in the excel file contains the correlation data between variables. Only those variables that show high statistical significance are analyzed for correlation. Since the survey data is based on a rank-order system (ordinal data), the Spearman method of correlation is be used. [9] This excel file is included as supplementary data with this writing.

Sample Size

What is the correct sample size for this survey study? First, we need to determine (estimate) how many people suffer from severe and chronic BFS symptoms (Population Size). Symptoms must be bad enough for a patient to see a neurologist to be officially diagnosed with BFS after possibly having an EMG and or brain MRI performed to rule out ALS and MS. According to the Center for Disease Control about 1 in 10,000 people in the U.S. have ALS and about 1 in 600 people suffer from Parkinson’s disease. At these rates, it means as many as 700,000 people around the globe can have ALS and 12 million people can have Parkinson’s disease. If the rate of chronic BFS is comparable to the rate of ALS and even Parkinson’s disease, the sample size of the survey would need to be 384 people to tolerate a 5% error and a 95% confidence level. There are dozens of online calculators available to compute and verify these calculations. Our present level of survey participation has approximately a 75% confidence level.

Data Analysis

The data was first analyzed to determine if outliers exist for the data of each variable. Those data points outside of plus or minus 3 standard deviations were considered outliers and omitted from the calculation by placing brackets [] around the result in the raw data on the excel file.

Most of the models generated from the BFS survey have very low adjusted R² values (the results are not linear) and are therefore, not very good models to predict future outcomes. [8] However, t-statistic measurements are a good measure of statistical significance. T-statistic results with an absolute value greater than 2 designates strong statistical significance between variables (~95% probability), and t-statistic results with an absolute value between 1.8 and 2 (~85 to 95% probability) is considered moderate statistical significance between parameters. Once this survey reaches its goal of 384 participants, a Spearman correlation study will be conducted on those parameters that show high statistical significance. Spearman results can be broken down as follows: +/- 0.5 to 1 for strong correlation, +/- 0.3 to 0.5 for moderate correlation, +/- 0.1 to 0.3 for weak correlation, and 0 to +/-0.1 as no correlation. [9]

Each question in the survey, e.g., age, sex, experiencing pins and needles, how well yoga works, etc., is a variable or parameter (terms used interchangeably in this paper). When modeling variables using a linear regression model, there are two sets of variables - x and y. In the data result array (on the “Statistical Significance Result” tab in the excel file) the horizontal axis is for y variables and the vertical axis is for x variables (this is reversed from conventional algebra, but it facilitated getting the data into the table using this reversed format, in this case). Only one variable is allowed for y in a linear regression analysis, but multiple variables can be used for x (as long as there are more equations than unknowns). For this study, the x variables were grouped into seven classifications – General (G), Causes / Triggers (CA), Stressors (ST – those variables that can make BFS symptoms worse), Symptoms (S), Body Parts Affected (B), Remedies (RE), and Various (V). For instance, the General (G) classification of variables consists of 7 parameters: age, sex, region, number of years with symptoms, years diagnosed, EMG, and MRI. Various (V) includes variables such as are symptoms getting worse over time or what part of the day is worse for symptoms.

The “Statistical Significant Result” tab is a matrix of t-statistic results that is 57 long by 57 wide. T-statistic data was not obtained for x variables within the same classification. For instance, Age as a y variable was not modeled against other General (G) parameters such as sex, region, years with symptoms etc. These results are designated as “na” within the statistical significance (t-statistic) matrix. Also, data in the matrix signified with ND (No Data) indicates the data was not linear dependent so no results were computed.

The data on the “BFS” tab was used to model all results except for Remedies (RE). When Remedy parameters were the y variable the excel file tab “BFS No Zero” data was used to model the results. It isn’t necessary to find statistical significance for remedies that people have not tried (a “0” response means people did not try the remedy). Hence, the data within the “BFS No Zero” tab is the same as the data on the “BFS” tab except “0” responses to Remedy questions were omitted from the data. The model results of RE parameters using the “BFS No Zero” tab will result in fewer data points (smaller sample size, n) in the model. For this reason, the results from these models, including t-statistic results, may prove to be less conclusive because the data size is in some cases significantly smaller. Hence, when evaluating the data models for RE correlation, sample size should be noted. When Remedies (RE) are grouped together as the x variables, the data on the “BFS” tab was used to run the models. Only a few people have tried all potential remedies, hence the sample size would only be a single digit number if the “BFS No Zero” tab data was used to model RE results as the x variable.

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