Issues in Analysis of Time Location Sampling (TLS)

1
Issues in Analysis of Time Location Sampling
(TLS)

• Keith Sabin, PhD
• Strategic Information and Research
• WHO/HIV Department

2
Citation

• John M. Karon. The Analysis of Time-location
  Sampling Study Data. ASA Section on Survey
  Research Methods.
• (Available for free on internet)
• Young Mens Survey, Phase II YMS
• Survey of MSM in 6 US cities, 1998-2000
• Four cities data used in presented analysis

3
Statistical Foundation of TLS

• Mimics multistage cluster sampling
• Venues are Primary Units/Clusters
• Individuals are Elementary Units
• If enumerations conducted, probability
  proportional to size possible
• Self-weighting for venue attendance
• Needs to be reconfirmed at sampling event

4
Challenge

• Developing a valid mechanism to adjust for
  unequal selection probabilities of individuals

5
Underlying challenge of TLS Analysis

• Produces a probability sample of visits to
• venues included
• Therefore visit is correct unit of analysis
• Participants will revisit venues
• Multiple chances of selection
• However Visitor is main interest
• Ergo, unequal selection probabilities

6
To Weight or Not to Weight

• Weights derived from participants self-reported
  frequency of attendance at venues (not
  necessarily those sampled)

7
Analysis weight and of participants, by
reported frequency of attendance at bars and
night clubs
8
Alternative analyses of HIV prevalence, and
standard errors of estimates
DE applies to clustered, weighted analysis only
9
Alternative clustered analyses of the prevalence
of Hepatitis B and unprotected anal
intercourse,and standard errors for these
estimates
DE applies to clustered, weighted analysis only
10
Design effects and affecting factors
HBV Hepatitis B virus CVw2 square of the
coefficient of variation of the analysis weights.
NC algorithm did not converge. The p-values are
from a logistic regression mixed model.
11
99 confidence intervals for HIV prevalence by
venue, city C, conditional on number of men
sampled at a venue
Observed prevalence
Overall prevalence
12
Unprotected anal intercourse (UAI) last 6 months
as risk factor for HIV Hepatitis B in City C,
alternative logistic models
13
of men and odds ratios for association between
Hepatitis B prevalence UAI last 6 months, by
frequency of attendance at bars clubs
14
Conclusions

• Unweighted analysis convenience
• Should not be used for size estimates
• Sampling fractions for weights survey of visits
• Clustering effects of venues should always be
  examined

15
Recommendations

• Collect information on frequency persons in the
  population of interest attend venues in the
  sampling frame
• Proxy how frequently a person attends each type
  of venue in the sampling frame
• Applies to ALL venues in the frame
• Account for design effects in sample size
  calculations
• Get a good statistician!