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    Membrane Filtration Products multivariable fractional polynomial interaction approach
    Multivariable Fractional Polynomial Interaction Approach, supplied by Membrane Filtration Products, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    Article Title: Deeply digging the interaction effect in multiple linear regressions using a fractional-power interaction term
    Article Snippet: In multivariable fractional polynomials interaction (MFPI), the interaction term is quantified as βX 1 M X 2 N , but the potential values for M and N are too limited, only having eight numbers , which has no advantage over ordinary polynomial regressions.

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    Article Title: Deeply digging the interaction effect in multiple linear regressions using a fractional-power interaction term
    Article Snippet: .. Name and reference of original method , Polynomial regressions can calculate high order interaction effects such as X 1 M X 2 N , yet M and N are limited within a few integers. Compared with polynomial regressions, fractional polynomial regressions (FPRs) were proposed to model the effects of explanatory variables beyond integer exponents , . Royston and Sauerbrei further invented multivariable fractional polynomials interaction (MFPI), which can handle interactions of continuous predictors in the form of fractional polynomials. The algorithm of MFPI is available in Stata , which however gives limited options (i.e. −2, − 1, − 0.5, 0, 0.5, 1, 2, 3) for the powers of a predictor. The package for R, mfp , was designed to run MFPI, yet the function for treating interaction terms is still absent . References [1] P. Royston, D.G. Altman, Regression using fractional polynomials of continuous covariates: Parsimonious parametric modelling, Applied Statistics 43(3) (1994) 429. [2] P. Royston, D.G. Altman, Approximating statistical functions by using fractional polynomial regression, Statistician 46(3) (1997) 411-422. [3] P. Royston, W. Sauerbrei, A new measure of prognostic separation in survival data, Stat. Med. 23(5) (2004) 723-48. [4] P. Royston, MFPIGEN: Stata module for modelling and displaying interactions between continuous predictors, Statistical Software Components S457439, Boston College Department of Economics, revised 31 Oct 2012, 2012. [5] R Core Team, R: A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria, 2019. [6] Original by Gareth Ambler and modified by Axel Benner, mfp: Multivariable Fractional Polynomials, R package version 1.5.2. https://CRAN.R-project.org/package=mfp2015 . . ..

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    Article Title: Deeply digging the interaction effect in multiple linear regressions using a fractional-power interaction term
    Article Snippet: .. Name and reference of original method , Polynomial regressions can calculate high order interaction effects such as X 1 M X 2 N , yet M and N are limited within a few integers. Compared with polynomial regressions, fractional polynomial regressions (FPRs) were proposed to model the effects of explanatory variables beyond integer exponents , . Royston and Sauerbrei further invented multivariable fractional polynomials interaction (MFPI), which can handle interactions of continuous predictors in the form of fractional polynomials. The algorithm of MFPI is available in Stata , which however gives limited options (i.e. −2, − 1, − 0.5, 0, 0.5, 1, 2, 3) for the powers of a predictor. The package for R, mfp , was designed to run MFPI, yet the function for treating interaction terms is still absent . References [1] P. Royston, D.G. Altman, Regression using fractional polynomials of continuous covariates: Parsimonious parametric modelling, Applied Statistics 43(3) (1994) 429. [2] P. Royston, D.G. Altman, Approximating statistical functions by using fractional polynomial regression, Statistician 46(3) (1997) 411-422. [3] P. Royston, W. Sauerbrei, A new measure of prognostic separation in survival data, Stat. Med. 23(5) (2004) 723-48. [4] P. Royston, MFPIGEN: Stata module for modelling and displaying interactions between continuous predictors, Statistical Software Components S457439, Boston College Department of Economics, revised 31 Oct 2012, 2012. [5] R Core Team, R: A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria, 2019. [6] Original by Gareth Ambler and modified by Axel Benner, mfp: Multivariable Fractional Polynomials, R package version 1.5.2. https://CRAN.R-project.org/package=mfp2015 . . ..



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    Treatment effect estimates (chemoimmunotherapy versus chemotherapy) with continuous BMI by the method of Multivariate Fractional Polynomial Interaction with Cox proportional-hazard regression models stratified by trial for overall survival ( a ) and progression-free survival ( b ). The shaded area is the 95% CIs of the treatment effect estimated as hazard ratios (HRs) (black solid line). Vertical dotted lines mark the 5 th and 95 th percentile of BMI distribution of the pooled analysis cohort, at 19.8 kg/m 2 and 34.7 kg/m 2 respectively

    Journal: BMC Cancer

    Article Title: A lack of association between BMI and chemoimmunotherapy efficacy in advanced non-small cell lung cancer: Secondary analysis of the IMpower150 and IMpower130 clinical trials

    doi: 10.1186/s12885-024-12132-w

    Figure Lengend Snippet: Treatment effect estimates (chemoimmunotherapy versus chemotherapy) with continuous BMI by the method of Multivariate Fractional Polynomial Interaction with Cox proportional-hazard regression models stratified by trial for overall survival ( a ) and progression-free survival ( b ). The shaded area is the 95% CIs of the treatment effect estimated as hazard ratios (HRs) (black solid line). Vertical dotted lines mark the 5 th and 95 th percentile of BMI distribution of the pooled analysis cohort, at 19.8 kg/m 2 and 34.7 kg/m 2 respectively

    Article Snippet: Continuous BMI was modelled in stratified (by RCT) Cox proportional hazards regression by the method of (Multivariable) Fractional Polynomial Interaction (MFPI) [ ].

    Techniques: