scholarly journals Characterization of fertility related antisperm antibodies— a step towards causal treatment of immunological infertility and immuno-contraception

2010 ◽  
Vol 12 (6) ◽  
pp. 793-794 ◽  
Author(s):  
Gerhard Haidl
Keyword(s):  
Antisperm Antibodies ◽  
Immunological Infertility ◽  
Causal Treatment

Characterization of human sperm antigens reacting with antisperm antibodies from autologous sera and seminal plasma in a fertile population

1995 ◽  
Vol 28 (1) ◽  
pp. 61-73 ◽  
Author(s):  
Roberto Paradisi ◽  
Annalisa Pession ◽  
Emanuela Bellavia ◽  
Maurizio Focacci ◽  
Carlo Flamigni
Keyword(s):  
Seminal Plasma ◽  
Human Sperm ◽  
Antisperm Antibodies ◽  
Sperm Antigens

scholarly journals Veridical Causal Inference for Comparative Effectiveness Research Using Medical Claims

2020 ◽  
Author(s):  
Ryan Ross ◽  
Xu Shi ◽  
Megan Caram ◽  
Pheobe Tsao ◽  
Paul Lin ◽  
...  
Keyword(s):  
Repeated Measures ◽  
Sample Selection ◽  
Population Based ◽  
Online Version ◽  
Time To Event ◽  
Number Of Patients ◽  
Causal Treatment ◽  
Eden Prairie ◽  
Binary Count

Medical insurance claims are becoming increasingly common data sources to answer a variety of questions in biomedical research. Although comprehensive in terms of longitudinal characterization of disease development and progression for a potentially large number of patients, population-based studies using these datasets require thoughtful modification to sample selection and analytic strategies, relative to other types of studies. Along with complex selection bias and missing data issues, claims-based studies are purely observational, which limits effective understanding and characterization of the treatment differences between groups being compared. All these issues contribute to a crisis in reproducibility and replication of comparative findings. This paper offers some practical guidance to the full analytical process, demonstrates methods for estimating causal treatment effects on several types of outcomes common to such studies, such as binary, count, time to event and longitudinally varying repeated measures outcomes, and aims to increase transparency and reproducibility. We provide an online version of the paper with readily implementable code for the entire analysis pipeline to serve as a guided tutorial for practitioners. The online version can be accessed at https://rydaro.github.io/. The analytic pipeline is illustrated using a sub-cohort of patients with advanced prostate cancer from the large Clinformatics TM Data Mart Database (OptumInsight, Eden Prairie, Minnesota), consisting of 73 million distinct private payer insurees from 2001-2016.

Sign in / Sign up

Export Citation Format

Share Document