- Respondent driven sampling (RDS) is an approach to sampling design utilizing the networks of social relationships that connect members of the target population to facilitate sampling by chain referral methods. Although this leads to biased sampling (such as over-sampling participants with many acquaintances), most RDS studies typically measure each participant's degree, and under the fundamental RDS assumption (that the probability to sample an individual is proportional to his degree) use inverse-probability weighting in an attempt to correct for this bias. However, this assumption is tenuous at best, and should be avoided. Here we suggest a completely novel approach for inference in RDS which compensates for such problems by using a rich source of information that is usually ignored - the precise timing of recruitment. Our new approach, adapting methods developed for inference in epidemic processes, also allows us to develop new estimators for properties such as the prevalence of a disease and the total population size, as well as to test the assumption of recruitment proportional to degree. We find these estimators asymptotically consistent and normally distributed. This new approach thus has the potential to greatly improve the utility of data collected using RDS.