Department of Mathematics and Statistics, Aliah University, IIA/27, New Town, Kolkata, 700160, India.
Department of Mathematics, Institute of Chemical Technology, Mumbai, 400019, India.
J Biol Phys. 2022 Sep;48(3):295-319. doi: 10.1007/s10867-022-09609-9. Epub 2022 Jul 2.
Modeling and analysis of biological growth curves are an age-old study area in which much effort has been dedicated to developing new growth equations. Recent efforts focus on identifying the correct model from a large number of equations. The relative growth rate (RGR), developed by Fisher (1921), has largely been used in the statistical inference of biological growth curve models. It is convenient to express growth equations using RGR, where RGR can be expressed as functions of size or time. Even though RGR is model invariant, it has limitations when it comes to identifying actual growth patterns. By proposing interval-specific rate parameters (ISRPs), Pal et al. (2018) appeared to solve this problem. The ISRP is based on the mathematical structure of the growth equations. Therefore, it is not model invariant. The current effort is to develop a measure of growth that is model invariant like RGR and shares the advantages of ISRP. We propose a new measure of growth, which we call instantaneous maturity rate (IMR). IMR is model invariant, which allows it to distinguish growth patterns more clearly than RGR. IMR is also scale-invariant and can take several forms including increasing, decreasing, constant, sigmoidal, bell-shaped, and bathtub. A wide range of possible IMR shapes makes it possible to identify different growth curves. The estimation procedure of IMR under a stochastic setup has been developed. Statistical properties of empirical IMR estimators have also been investigated in detail. In addition to extensive simulation studies, real data sets have been analyzed to prove the utility of IMR.
生物生长曲线的建模与分析是一个由来已久的研究领域,人们投入了大量精力来开发新的生长方程。最近的研究重点是从大量方程中确定正确的模型。相对生长率(RGR)由 Fisher(1921)提出,在生物生长曲线模型的统计推断中得到了广泛应用。使用 RGR 来表达生长方程很方便,其中 RGR 可以表示为大小或时间的函数。尽管 RGR 是模型不变的,但在识别实际生长模式时存在局限性。Pal 等人(2018)通过提出特定区间的速率参数(ISRP)似乎解决了这个问题。ISRP 基于生长方程的数学结构,因此不是模型不变的。目前的工作是开发一种与 RGR 一样具有模型不变性并具有 ISRP 优势的生长度量。我们提出了一种新的生长度量,称为瞬时成熟率(IMR)。IMR 是模型不变的,这使得它比 RGR 更能清晰地区分生长模式。IMR 也是尺度不变的,可以采用多种形式,包括递增、递减、常数、S 形、钟形和浴盆形。IMR 可能具有多种形状,这使得识别不同的生长曲线成为可能。我们已经开发了在随机设置下估计 IMR 的程序,并详细研究了经验 IMR 估计量的统计性质。除了广泛的模拟研究外,还分析了真实数据集以证明 IMR 的实用性。