Effects of influential points and sample size on the selection and replicability of multivariable fractional polynomial models.

BACKGROUND: The multivariable fractional polynomial (MFP) approach combines variable selection using backward elimination with a function selection procedure (FSP) for fractional polynomial (FP) functions. It is a relatively simple approach which can be easily understood without advanced training in statistical modeling. For continuous variables, a closed test procedure is used to decide between no effect, linear, FP1, or FP2 functions. Influential points (IPs) and small sample sizes can both have a strong impact on a selected function and MFP model. METHODS: We used simulated data with six continuous and four categorical predictors to illustrate approaches which can help to identify IPs with an influence on function selection and the MFP model. Approaches use leave-one or two-out and two related techniques for a multivariable assessment. In eight subsamples, we also investigated the effects of sample size and model replicability, the latter by using three non-overlapping subsamples with the same sample size. For better illustration, a structured profile was used to provide an overview of all analyses conducted. RESULTS: The results showed that one or more IPs can drive the functions and models selected. In addition, with a small sample size, MFP was not able to detect some non-linear functions and the selected model differed substantially from the true underlying model. However, when the sample size was relatively large and regression diagnostics were carefully conducted, MFP selected functions or models that were similar to the underlying true model. CONCLUSIONS: For smaller sample size, IPs and low power are important reasons that the MFP approach may not be able to identify underlying functional relationships for continuous variables and selected models might differ substantially from the true model. However, for larger sample sizes, a carefully conducted MFP analysis is often a suitable way to select a multivariable regression model which includes continuous variables. In such a case, MFP can be the preferred approach to derive a multivariable descriptive model.

Comparison of variable selection procedures and investigation of the role of shrinkage in linear regression-protocol of a simulation study in low-dimensional data.

In low-dimensional data and within the framework of a classical linear regression model, we intend to compare variable selection methods and investigate the role of shrinkage of regression estimates in a simulation study. Our primary aim is to build descriptive models that capture the data structure parsimoniously, while our secondary aim is to derive a prediction model. Simulation studies are an important tool in statistical methodology research if they are well designed, executed, and reported. However, bias in favor of an "own" preferred method is prevalent in most simulation studies in which a new method is proposed and compared with existing methods. To overcome such bias, neutral comparison studies, which disregard the superiority or inferiority of a particular method, have been proposed. In this paper, we designed a simulation study with key principles of neutral comparison studies in mind, though certain unintentional biases cannot be ruled out. To improve the design and reporting of a simulation study, we followed the recently proposed ADEMP structure, which entails defining the aims (A), data-generating mechanisms (D), estimand/target of analysis (E), methods (M), and performance measures (P). To ensure the reproducibility of results, we published the protocol before conducting the study. In addition, we presented earlier versions of the design to several experts whose feedback influenced certain aspects of the design. We will compare popular penalized regression methods (lasso, adaptive lasso, relaxed lasso, and nonnegative garrote) that combine variable selection and shrinkage with classical variable selection methods (best subset selection and backward elimination) with and without post-estimation shrinkage of parameter estimates.

Effect of climatic variability on malaria trends in Baringo County, Kenya.

BACKGROUND: Malaria transmission in arid and semi-arid regions of Kenya such as Baringo County, is seasonal and often influenced by climatic factors. Unravelling the relationship between climate variables and malaria transmission dynamics is therefore instrumental in developing effective malaria control strategies. The main aim of this study was to describe the effects of variability of rainfall, maximum temperature and vegetation indices on seasonal trends of malaria in selected health facilities within Baringo County, Kenya. METHODS: Climate variables sourced from the International Research Institute (IRI)/Lamont-Doherty Earth Observatory (LDEO) climate database and malaria cases reported in 10 health facilities spread across four ecological zones (riverine, lowland, mid-altitude and highland) between 2004 and 2014 were subjected to a time series analysis. A negative binomial regression model with lagged climate variables was used to model long-term monthly malaria cases. The seasonal Mann-Kendall trend test was then used to detect overall monotonic trends in malaria cases. RESULTS: Malaria cases increased significantly in the highland and midland zones over the study period. Changes in malaria prevalence corresponded to variations in rainfall and maximum temperature. Rainfall at a time lag of 2 months resulted in an increase in malaria transmission across the four zones while an increase in temperature at time lags of 0 and 1 month resulted in an increase in malaria cases in the riverine and highland zones, respectively. CONCLUSION: Given the existence of a time lag between climatic variables more so rainfall and peak malaria transmission, appropriate control measures can be initiated at the onset of short and after long rains seasons.

Ecological niche modelling of Rift Valley fever virus vectors in Baringo, Kenya.

BACKGROUND: Rift Valley fever (RVF) is a vector-borne zoonotic disease that has an impact on human health and animal productivity. Here, we explore the use of vector presence modelling to predict the distribution of RVF vector species under climate change scenario to demonstrate the potential for geographic spread of Rift Valley fever virus (RVFV). OBJECTIVES: To evaluate the effect of climate change on RVF vector distribution in Baringo County, Kenya, with an aim of developing a risk map for spatial prediction of RVF outbreaks. METHODOLOGY: The study used data on vector presence and ecological niche modelling (MaxEnt) algorithm to predict the effect of climatic change on habitat suitability and the spatial distribution of RVF vectors in Baringo County. Data on species occurrence were obtained from longitudinal sampling of adult mosquitoes and larvae in the study area. We used present (2000) and future (2050) Bioclim climate databases to model the vector distribution. RESULTS: Model results predicted potential suitable areas with high success rates for Culex quinquefasciatus, Culex univitattus, Mansonia africana, and Mansonia uniformis. Under the present climatic conditions, the lowlands were found to be highly suitable for all the species. Future climatic conditions indicate an increase in the spatial distribution of Cx. quinquefasciatus and M. africana. Model performance was statistically significant. CONCLUSION: Soil types, precipitation in the driest quarter, precipitation seasonality, and isothermality showed the highest predictive potential for the four species.