Sampling Strategy

We could determine if a intervention is meeting the target through a census, sampling plan ,or LQAS. 

 LQAS 

 

 LQAS is a primary classification tool 

 

 Lots are classified as performing "acceptable, unacceptable, low or high" 

 

 

 The goal is to shift resources with unacceptable coverage and study best practice with acceptable coverage 

 Classification is based on the sample from the population 

 We just need 19 households to sample, a much smaller sample than most population estimates 

 To classify as high or low we need to define a target, or decision threshold

 

 We to choose a threshold that makes the chance of a mistake very small 

 We want at least 1 - alpha chance to be in the upper category based on probability for being in the upper (chance of is type 1 error is alpha) 

 

 

 

 

 As always increasing sample size decreases error. Also we can decrease the size of alpha. 

 LQAS classifies whether something is likely to meet the threshold or not. It does not measure prevalence or probability. 

 Time Series 

 A times series is a set of data point collected over time,. These might be measurements of a daily process and are quite common in surveillance. Statistical models for time series is indexed by time (X or y) and may or may not be independent or identically distributed. 

 Collection tools and methods 

 

 Regression methods for modeling trends 

 Fourier/wavelet analysis for modeling seasonality 

 ARIMA modeling for correlation 

 

 When modeling a time series we can choose to use a retrospective or prospective approach to analysis. This allows use to use times series for event detection, interpretation of past results, forecasting or decision making. 

 General Approach 

 

 Plot the series and examine the main features

 

 Trend 

 Seasonal component 

 Any apparent changes in behavior 

 Any outlying observations 

 

 

 Model trend and seasonal components to get stationary residuals

 

 Regression methods are useful for this 

 

 

 Choose a model to fit the residuals 

 In some settings, forecasting can be performed by forecasting the residuals and then adding on predicted trend and seasonal component 

 

 Once we have a time series model we can apply it to a retrospective or prospective application.