graphPAF - Estimating and Displaying Population Attributable Fractions

Estimation and display of various types of population attributable fraction and impact fractions. As well as the usual calculations of attributable fractions and impact fractions, functions are provided for attributable fraction nomograms and fan plots, continuous exposures, for pathway specific population attributable fractions, and for joint, average and sequential population attributable fractions.

Last updated 7 months ago

3.78 score 3 stars 6 scripts 299 downloads

corlink - Record Linkage, Incorporating Imputation for Missing Agreement Patterns, and Modeling Correlation Patterns Between Fields

A matrix of agreement patterns and counts for record pairs is the input for the procedure. An EM algorithm is used to impute plausible values for missing record pairs. A second EM algorithm, incorporating possible correlations between per-field agreement, is used to estimate posterior probabilities that each pair is a true match - i.e. constitutes the same individual.

Last updated 2 years ago

2.70 score 2 scripts 150 downloads

averisk - Calculation of Average Population Attributable Fractions and Confidence Intervals

Average population attributable fractions are calculated for a set of risk factors (either binary or ordinal valued) for both prospective and case- control designs. Confidence intervals are found by Monte Carlo simulation. The method can be applied to either prospective or case control designs, provided an estimate of disease prevalence is provided. In addition to an exact calculation of AF, an approximate calculation, based on randomly sampling permutations has been implemented to ensure the calculation is computationally tractable when the number of risk factors is large.

Last updated 8 years ago

1.30 score 1 scripts 321 downloads

EBrank - Empirical Bayes Ranking

Empirical Bayes ranking applicable to parallel-estimation settings where the estimated parameters are asymptotically unbiased and normal, with known standard errors. A mixture normal prior for each parameter is estimated using Empirical Bayes methods, subsequentially ranks for each parameter are simulated from the resulting joint posterior over all parameters (The marginal posterior densities for each parameter are assumed independent). Finally, experiments are ordered by expected posterior rank, although computations minimizing other plausible rank-loss functions are also given.

Last updated 8 years ago

1.00 score 1 stars 1 scripts 141 downloads