scholarly journals Optimising treatment decision rules through generated effect modifiers: a precision medicine tutorial

BJPsych Open ◽  
2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Eva Petkova ◽  
Hyung Park ◽  
Adam Ciarleglio ◽  
R. Todd Ogden ◽  
Thaddeus Tarpey
Keyword(s):  
Precision Medicine ◽  
Regression Models ◽  
Decision Rules ◽  
Treatment Decision ◽  
Differential Treatment ◽  
Learning Approaches ◽  
Linear Regression Models ◽  
Recent Developments ◽  
Composite Variables ◽  
Personalised Healthcare

Summary This tutorial introduces recent developments in precision medicine for estimating treatment decision rules. The objective of these developments is to advance personalised healthcare by identifying an optimal treatment option for each individual patient based on each patient's characteristics. The methods detailed in this tutorial define composite variables from the patient measures that can be viewed as ‘biosignatures’ for differential treatment response, which we have termed ‘generated effect modifiers’. In contrast to most machine learning approaches to precision medicine, these biosignatures are derived from linear and non-linear regression models and thus have the advantage of easy visualisation and ready interpretation. The methods are illustrated using examples from randomised clinical trials.

Using the CAMELS-GB large-sample dataset to investigate controls on baseflow (BFI)

2020 ◽  
Author(s):  
John Bloomfield ◽  
Nans Addor ◽  
Gemma Coxon ◽  
Mengyi Gong ◽  
Ben Marchant
Keyword(s):  
Regression Models ◽  
Sewage Treatment ◽  
Base Flow ◽  
Flow Index ◽  
Anthropogenic Influences ◽  
Learning Approaches ◽  
Linear Regression Models ◽  
Remotely Sensed Data ◽  
Large Sample ◽  
Catchment Characteristics

<p>Over the last decade or so many studies of the hydrologic characteristics of basins have been driven by the desire to develop models that enable prediction of particular signatures, such as baseflow and Base Flow Index (BFI), in ungauged basins (PUB). These studies typically focus on understanding how readily available mapped or remotely sensed data can be used to infer hydrologic signals. However, in the specific case of baseflow, there is a recognition that we still have a poor understanding of the relative influences of underlying hydrological processes at appropriate scales, particularly in anthropogenically impacted catchments. New opportunities are being offered to better understand relationships between BFI and various controls on baseflow through the production of large sample catchment datasets. Here we present the results of an analysis of one such large-sample dataset, CAMELS-GB, investigating the relative importance of different hydrogeological controls on baseflow, including factors such as: climatology; hydrogeology; geophysical catchment characteristics, e.g. soil characteristics and land cover; and, anthropogenic influences, e.g. discharge from reservoirs and from sewage treatment works (STWs), abstraction, and mains leakage.</p><p>CAMELS-GB consists of daily hydrometerorological time series for the period 1970-2015 and landscape, catchment and hydrogeological attributes for 671 catchments in Great Britain. Machine learning approaches, including random forest algorithms, are used to investigate the influence of catchment characteristics on BFI and to inform the selection of hydrologically reasonable parameters to quantify relationships using linear regression models. We describe how the regression models can be used to investigate and characterise the sensitivity of estimates of BFI to: i.) the underlying hydrogeological mapping; ii.) the spatial support scale of the analysis; and iii.) anthropogenic influences.   </p>

FAKTOR DETERMINAN PENYALURAN KREDIT BANK PERSERO

2018 ◽  
Vol 23 (1) ◽  
pp. 60-71
Author(s):  
Wigiyanti Masodah
Keyword(s):  
Interest Rate ◽  
Linear Regression ◽  
Interest Rates ◽  
Regression Models ◽  
Linear Regression Models ◽  
Negative Impacts ◽  
Main Activity ◽  
The Impact ◽  
The Given ◽  
Multiple Linear Regression Models

Offering credit is the main activity of a Bank. There are some considerations when a bank offers credit, that includes Interest Rates, Inflation, and NPL. This study aims to find out the impact of Variable Interest Rates, Inflation variables and NPL variables on credit disbursed. The object in this study is state-owned banks. The method of analysis in this study uses multiple linear regression models. The results of the study have shown that Interest Rates and NPL gave some negative impacts on the given credit. Meanwhile, Inflation variable does not have a significant effect on credit given. Keywords: Interest Rate, Inflation, NPL, offered Credit.

Linear Regression Models for Interval-Valued Data using Log-transformation

2020 ◽  
Author(s):  
Nykolas Mayko Maia Barbosa ◽  
João Paulo Pordeus Gomes ◽  
César Lincoln Cavalcante Mattos ◽  
Diêgo Farias Oliveira
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Linear Regression Models ◽  
Log Transformation ◽  
Interval Valued

THE PREDICTIVE CONTENT OF DISAGGREGATED NORMAL INCOME: An Empirical Study in the JSX

2003 ◽  
Vol 5 (3) ◽  
pp. 363 ◽  
Author(s):  
Slamet Sugiri
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Stock Exchange ◽  
Manufacturing Firms ◽  
Single Step ◽  
Model Parameters ◽  
Linear Regression Models ◽  
Operating Income ◽  
Predictive Content ◽  
Multiple Step

The main objective of this study is to examine a hypothesis that the predictive content of normal income disaggregated into operating income and nonoperating income outperforms that of aggregated normal income in predicting future cash flow. To test the hypothesis, linear regression models are developed. The model parameters are estimated based on fifty-five manufacturing firms listed in the Jakarta Stock Exchange (JSX) up to the end of 1997.This study finds that empirical evidence supports the hypothesis. This evidence supports arguments that, in reporting income from continuing operations, multiple-step approach is preferred to single-step one.

scholarly journals Relationships between test positivity rate, total laboratory confirmed cases of malaria, and malaria incidence in high burden settings of Uganda: an ecological analysis

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Jaffer Okiring ◽  
Adrienne Epstein ◽  
Jane F. Namuganga ◽  
Victor Kamya ◽  
Asadu Sserwanga ◽  
...  
Keyword(s):  
Regression Models ◽  
Malaria Incidence ◽  
Temporal Changes ◽  
Linear Regression Models ◽  
High Burden ◽  
Exponential Regression ◽  
Malaria Morbidity ◽  
Laboratory Test Results ◽  
Combining Data ◽  
Catchment Areas

Abstract Background Malaria surveillance is critical for monitoring changes in malaria morbidity over time. National Malaria Control Programmes often rely on surrogate measures of malaria incidence, including the test positivity rate (TPR) and total laboratory confirmed cases of malaria (TCM), to monitor trends in malaria morbidity. However, there are limited data on the accuracy of TPR and TCM for predicting temporal changes in malaria incidence, especially in high burden settings. Methods This study leveraged data from 5 malaria reference centres (MRCs) located in high burden settings over a 15-month period from November 2018 through January 2020 as part of an enhanced health facility-based surveillance system established in Uganda. Individual level data were collected from all outpatients including demographics, laboratory test results, and village of residence. Estimates of malaria incidence were derived from catchment areas around the MRCs. Temporal relationships between monthly aggregate measures of TPR and TCM relative to estimates of malaria incidence were examined using linear and exponential regression models. Results A total of 149,739 outpatient visits to the 5 MRCs were recorded. Overall, malaria was suspected in 73.4% of visits, 99.1% of patients with suspected malaria received a diagnostic test, and 69.7% of those tested for malaria were positive. Temporal correlations between monthly measures of TPR and malaria incidence using linear and exponential regression models were relatively poor, with small changes in TPR frequently associated with large changes in malaria incidence. Linear regression models of temporal changes in TCM provided the most parsimonious and accurate predictor of changes in malaria incidence, with adjusted R2 values ranging from 0.81 to 0.98 across the 5 MRCs. However, the slope of the regression lines indicating the change in malaria incidence per unit change in TCM varied from 0.57 to 2.13 across the 5 MRCs, and when combining data across all 5 sites, the R2 value reduced to 0.38. Conclusions In high malaria burden areas of Uganda, site-specific temporal changes in TCM had a strong linear relationship with malaria incidence and were a more useful metric than TPR. However, caution should be taken when comparing changes in TCM across sites.

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