MIIPW - IPW and Mean Score Methods for Time-Course Missing Data

Contains functions for data analysis of Repeated measurement using GEE. Data may contain missing value in response and covariates. For parameter estimation through Fisher Scoring algorithm, Mean Score and Inverse Probability Weighted method combining with Multiple Imputation are used when there is missing value in covariates/response. Reference for mean score method, inverse probability weighted method is Wang et al(2007)<doi:10.1093/biostatistics/kxl024>.

Last updated 2 years ago

2.00 score 2 scripts 219 downloads

SurviMChd - High Dimensional Survival Data Analysis with Markov Chain Monte Carlo

High dimensional survival data analysis with Markov Chain Monte Carlo(MCMC). Currently supports frailty data analysis. Allows for Weibull and Exponential distribution. Includes function for interval censored data.

Last updated 8 months ago

1.00 score 183 downloads

jmBIG - Joint Longitudinal and Survival Model for Big Data

Provides analysis tools for big data where the sample size is very large. It offers a suite of functions for fitting and predicting joint models, which allow for the simultaneous analysis of longitudinal and time-to-event data. This statistical methodology is particularly useful in medical research where there is often interest in understanding the relationship between a longitudinal biomarker and a clinical outcome, such as survival or disease progression. This can be particularly useful in a clinical setting where it is important to be able to predict how a patient's health status may change over time. Overall, this package provides a comprehensive set of tools for joint modeling of BIG data obtained as survival and longitudinal outcomes with both Bayesian and non-Bayesian approaches. Its versatility and flexibility make it a valuable resource for researchers in many different fields, particularly in the medical and health sciences.

Last updated 8 months ago

1.00 score 202 downloads

highMLR - Feature Selection for High Dimensional Survival Data

Perform high dimensional Feature Selection in the presence of survival outcome. Based on Feature Selection method and different survival analysis, it will obtain the best markers with optimal threshold levels according to their effect on disease progression and produce the most consistent level according to those threshold values. The functions' methodology is based on by Sonabend et al (2021) <doi:10.1093/bioinformatics/btab039> and Bhattacharjee et al (2021) <arXiv:2012.02102>.

Last updated 2 years ago

1.00 score 168 downloads

QoLMiss - Scales Score Calculation from Quality of Life Data

There are three functions: qol, miss_qol and miss_patient takes input of the data set containing the answers of QOL questionnaire. It will compute the three types of domain based scale scores: Global, Functional, and Symptoms. In case of missing data, the miss_qol and miss_patient functions will make the required changes and then calculate the domain-wise scale scores. Finally, provide an output replacing the question columns with the domain-based scale scores in the original data set.

Last updated 3 years ago

1.00 score 150 downloads

designsize - Sample Size Calculation of Various Study Designs

Different sample size calculations with different study designs. These techniques are explained by Chow (2007) <doi:10.1201/9781584889830>.

Last updated 3 years ago

1.00 score 220 downloads

afthd - Accelerated Failure Time for High Dimensional Data with MCMC

Functions for Posterior estimates of Accelerated Failure Time(AFT) model with MCMC and Maximum likelihood estimates of AFT model without MCMC for univariate and multivariate analysis in high dimensional gene expression data are available in this 'afthd' package. AFT model with Bayesian framework for multivariate in high dimensional data has been proposed by Prabhash et al.(2016) <doi:10.21307/stattrans-2016-046>.

Last updated 3 years ago

1.00 score 7 scripts 265 downloads

SurvHiDim - High Dimensional Survival Data Analysis

High dimensional time to events data analysis with variable selection technique. Currently support LASSO, clustering and Bonferroni's correction.

Last updated 3 years ago

1.00 score 1 scripts 143 downloads

longit - High Dimensional Longitudinal Data Analysis Using MCMC

High dimensional longitudinal data analysis with Markov Chain Monte Carlo(MCMC). Currently support mixed effect regression with or without missing observations by considering covariance structures. It provides estimates by missing at random and missing not at random assumptions. In this R package, we present Bayesian approaches that statisticians and clinical researchers can easily use. The functions' methodology is based on the book "Bayesian Approaches in Oncology Using R and OpenBUGS" by Bhattacharjee A (2020) <doi:10.1201/9780429329449-14>.

Last updated 4 years ago

1.00 score 124 downloads