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Systat 13 from Systat Software Inc

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How to deal with a regression where I have an unbalanced design with an unequal number of replicates between dependent and independent variables.

OK, first off, I wasn't involved in the research methods/data analysis planning process. I was brought in after the data was collected, when they found out they didn't have anyone who could actually perform the analysis (i.e. >5 yr ago).
So, I kinda threw out their plan for the analysis they wrote into the research plan, because I didn't think it was the best way to go about answering the question they wanted answered. (that and because they typically use softwares, such as excel or systat, which are my non-preferred tools)
Description of the Data:
  • The goal was to determine the effects of a drug in vivo based on an in vitro assay.
  • Both the in vivo and in vitro data is discretely measured with a scale [0,+infinity), but the cutoff for clinical efficacy is values <6
  • The data was collected in healthy volunteers and in patients. So there are three major groups of subjects:
    1. patients whose in vitro says they are 'sensitive' and the in vivo matches (because they are on a correct dose)
    2. patients whose in vitro says they are 'sensitive' and the in vivo doesn't match (because they are on too low of a dose)
    3. patients whose in vitro says they are 'resistant' and the in vivo "should" never match (mechanism of drug resistance means that no increase in dose will help)
  • They pulled the in vivo data in quadruplicate, but the in vitro data in triplicate (I don't know why)
  • The healthy volunteers have in vitro data taken from when on and off the drug; whereas the patients only have in vitro data taken when on the drug
My Current Plan:
  • Was going to do a regression in R (they originally only planned for an ANOVA/ANCOVA, but seeing how the goal was to make predictions, I figured a full multivariate regression model was more appropriate)
  • Was going to build the model that basically looked like:
     lme(InVivo ~ InVitro * Dose, random = 1|ID) 
  • considering also doing a logistic model for resistant/sensitive based on the clinical cutoff we use, but figured that a regression on the actual values would be more appropriate, then leave the clinical decision for whoever is doing the prediction
Major Questions:
  1. How do I account for the fact that, because the the number of InVivo measures doesn't match the InVitro measures for a given covariate (mainly dose)? As of now I've wrangled the data into a long format with both In vivo and In vitro measures all in the same column, but marked by a 1/0 flag indicator in a "Vitro_1" column of the data frame.
  2. How can I modify the random effect portion of the lme model to account for intra-assay variability?
  3. Are there any other considerations that might be appropriate for this analysis? (I'm currently considering post-hoc tests for temporal effects, but they should be nominal given a washouts between dosing level changes)
P.S. I'm so sorry about how long this is, I just want to make sure that whatever superhero decides this post is worth responding to has all the info they might need. I really appreciate any and all help; I work with data analysis that is similar to this, but I kinda picked up this project as a charity case. That and it was supposed to be a quick way to get a publication for myself haha.
submitted by More_Momus to statistics

Global Statistical Software Market 2020 – Huge Market Growth Till 2025: IBM, JMP Statistical Software, The MathWorks, etc.

Global Statistical Software Market 2020 – Huge Market Growth Till 2025: IBM, JMP Statistical Software, The MathWorks, etc.

Statistical Software Market Report 2020

Empirical Data Insights furnishes the latest report on the ’Statistical Software market’ Analysis and Forecast 2020-2025, outlining key insights and presenting a competitive advantage to clients through a comprehensive report. This report analyses the Statistical Software’s industry coverage, current market competitive status, and market outlook. Global Statistical Software players, to describe, define and analyze the value, market competition landscape, market share, SWOT analysis, and development plans in the future comprise some of the key features, in the report.
Global “Statistical Software Market” Research Report compiles the latest industry data, key players analysis, market share, growth rate, opportunities and trends, investment strategy for your reference in analyzing the global market. Many companies are operating in the market and overseeing their businesses through joint ventures, which is likely to benefit the overall market.
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This report includes the following manufacturers: IBM, JMP Statistical Software, The MathWorks, Qlik, Analytical Software, Minitab, SAS Institute, StataCorp, Systat Software
Global Statistical Software Market by Type Segment Cloud Based, on Premise
Global Statistical Software Market Applications: Small & Medium Business, Large Business, Other Organizations
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Report Highlights: 1) Detail pricing analysis on the basis of product, application, and regional segments 2) The detailed assessment of the vendor landscape and leading companies to help understand the level of competition in the global Statistical Software Market 3) Deep insights about regulatory and investment scenarios of the global Statistical Software Market 4) Analysis of market effect factors and their impact on the forecast and outlook of the global Statistical Software Market 5) A roadmap of growth opportunities available in the global Statistical Software Market with the identification of key factors 6) The exhaustive analysis of various trends of the global Statistical Software Market to help identify market expansions
Statistical Software Market by Region Segmentation: North America Country (United States, Canada) Asia Country (China, Japan, India, Korea) Europe Country (Germany, UK, France, Italy) Other Country (Middle East, Africa, GCC)
Benefits of Purchasing Statistical Software Market Report: Analyst Support: Get your query resolved from our expert analysts before and after purchasing the report. Customer’s Satisfaction: Our expert team will assist with all your research needs and customize the report. Inimitable Expertise: Analysts will provide deep insights into the reports. Assured Quality: We focus on the quality and accuracy of the report.
The global Statistical Software market size is expected to gain huge market traction in the forecast period of 2020 to 2025. The Statistical Software market report provides a detailed analysis of global market size, regional and country-level market size, segmentation market growth, market share, competitive Landscape, sales analysis, the impact of domestic and global market players. Regional analysis is another highly comprehensive part of the research and analysis segment of the global Statistical Software market presented in the report. Global Info Research analysts understand competitive strengths and provide competitive analysis for each competitor separately.
To Know More About The Assumptions in This Market Report: https://www.empiricaldatainsights.com/industry-report/statistical-software-market/2086/
Request customized copy of Statistical Software report If you wish to find more details of the report or want customization, contact us. You can get a detailed overview of the entire research here. If you have any special requirements, please let us know and we will customize the report as you want.
The report segments the market into various sub-segments, thereby encompassing the overall market. The approximations of the revenue numbers for the entire market and its sub-segments are also additionally incorporated in this report. Moreover, the report highlights some of the major growth prospects, including new product launches, M&A, R&D, collaborations, joint ventures, agreements, partnerships, and growth of the key players operating in the Statistical Software market. It determines the factors that are directly influencing the market which comprises production strategies and methodologies, development platforms, and the product model.
submitted by swan_murphy to TechInsightreports

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