Dataset Information

A pseudo-random patient sampling method evaluated.

ABSTRACT: OBJECTIVES:To compare two human immunodeficiency virus (HIV) cohorts to determine whether a pseudo-random sample can represent the entire study population. STUDY DESIGN AND SETTING:HIV-positive patients receiving care at eight sites in seven Asian countries. The TREAT Asia HIV Observational database (TAHOD) pseudo-randomly selected a patient sample, while TREAT Asia HIV Observational database-Low Intensity Transfer (TAHOD-LITE) included all patients. We compared patient demographics, CD4 count, and HIV viral load testing for each cohort. Risk factors associated with CD4 count response, HIV viral load suppression (<400 copies/mL), and survival were determined for each cohort. RESULTS:There were 2,318 TAHOD patients and 14,714 TAHOD-LITE patients. Patient demographics, CD4 count, and HIV viral load testing rates were broadly similar between the cohorts. CD4 count response and all-cause mortality were consistent among the cohorts with similar risk factors. HIV viral load response appeared to be superior in TAHOD and many risk factors differed, possibly due to viral load being tested on a subset of patients. CONCLUSION:Our study gives the first empirical evidence that analysis of risk factors for completely ascertained end points from our pseudo-randomly selected patient sample may be generalized to our larger, complete population of HIV-positive patients. However, results can significantly vary when analyzing smaller or pseudo-random samples, particularly if some patient data are not completely missing at random, such as viral load results.

SUBMITTER: De La Mata NL

PROVIDER: S-EPMC5318236 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A pseudo-random patient sampling method evaluated.

De La Mata Nicole L NL Ahn Mi-Young MY Kumarasamy Nagalingeswaran N Ly Penh Sun PS Ng Oon Tek OT Nguyen Kinh Van KV Merati Tuti Parwati TP Pham Thuy Thanh TT Lee Man Po MP Durier Nicolas N Law Matthew G MG

Journal of clinical epidemiology 20161019

<h4>Objectives</h4>To compare two human immunodeficiency virus (HIV) cohorts to determine whether a pseudo-random sample can represent the entire study population.<h4>Study design and setting</h4>HIV-positive patients receiving care at eight sites in seven Asian countries. The TREAT Asia HIV Observational database (TAHOD) pseudo-randomly selected a patient sample, while TREAT Asia HIV Observational database-Low Intensity Transfer (TAHOD-LITE) included all patients. We compared patient demographi ...[more]

PMID: 27771357

Similar Datasets

Project description:Pseudo-random number generators (PRNGs) are software algorithms generating a sequence of numbers approximating the properties of random numbers. They are critical components in many information systems that require unpredictable and nonarbitrary behaviors, such as parameter configuration in machine learning, gaming, cryptography, and simulation. A PRNG is commonly validated through a statistical test suite, such as NIST SP 800-22rev1a (NIST test suite), to evaluate its robustness and the randomness of the numbers. In this paper, we propose a Wasserstein distance-based generative adversarial network (WGAN) approach to generating PRNGs that fully satisfy the NIST test suite. In this approach, the existing Mersenne Twister (MT) PRNG is learned without implementing any mathematical programming code. We remove the dropout layers from the conventional WGAN network to learn random numbers distributed in the entire feature space because the nearly infinite amount of data can suppress the overfitting problems that occur without dropout layers. We conduct experimental studies to evaluate our learned pseudo-random number generator (LPRNG) by adopting cosine-function-based numbers with poor random number properties according to the NIST test suite as seed numbers. The experimental results show that our LPRNG successfully converted the sequence of seed numbers to random numbers that fully satisfy the NIST test suite. This study opens the way for the "democratization" of PRNGs through the end-to-end learning of conventional PRNGs, which means that PRNGs can be generated without deep mathematical know-how. Such tailor-made PRNGs will effectively enhance the unpredictability and nonarbitrariness of a wide range of information systems, even if the seed numbers can be revealed by reverse engineering. The experimental results also show that overfitting was observed after about 450,000 trials of learning, suggesting that there is an upper limit to the number of learning counts for a fixed-size neural network, even when learning with unlimited data.

Dataset Information

A pseudo-random patient sampling method evaluated.

Publications

A pseudo-random patient sampling method evaluated.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets