How to assess effect of intervention in one state versus another using annual death rate data? I am a beginner in statistics. I have these unpublished data (cases and deaths) of a disease for 7 years (2004-2010) from 2 neighbouring states. The study was started in 2004. State 1 and State 2 received different treatments. The death rate is high in one State. I want to prove that treatment given in State 1 is superior compared to that given in State 2. Time series was not accepted because of many possible confounding variables.
I have SPSS and Comprehensive Meta-analysis software. Please guide me.


*

*What is the best design? Prospective cohort study or Case-control study?

*What is the best effect measure (Odds ratio, risk ratio, any other)?

*What is the best statistical test (?Chi square etc)

*Can I use meta-analysis in this case?

*Any other information you think that would be of use.
State 1         State 2 
Cases   Deaths  Cases   Deaths
2004    1125    5   2024    254
2005    1213    5   1978    209
2006    1003    4   2294    217
2007    1425    6   2312    249
2008    1172    4   1528    197
2009    1092    3   1683    204
2010    1316    4   2024    218

 A: I might have wrongly understood the data (see my comment above), but what would be so wrong in simply t-testing the difference in means of the deaths-to-cases ratios between the two series, assuming that the states are clearly independent from each other?
A t-test shows that State 2 performs far better than State 1 in preventing deaths in its cases of the disease:
year    state   ratio
2004    2   7.968504
2004    1   225
2005    2   9.464115
2005    1   242.6
2006    1   250.75
2006    2   10.57143
2007    1   237.5
2007    2   9.285141
2008    2   7.756345
2008    1   293
2009    1   364
2009    2   8.25
2010    2   9.284404
2010    1   329

Given the huge difference between the two series, and given that you cannot design a proper experiment, it seems to me that no superior level of "proof" is really needed to proclaim one state's approach superior to the other.
A: I'm not sure why time series is not being accepted as a solution to your problem since this is equally spaced chronological data .  Confounding variables although unknown in nature can be proxied by both ARIMA  structure and/or Detectable Interventions.  I'm not sure who is not accepting that time series analysis is appropriate so we disagree with that advice. 
Time series methods are not just pure autoregressive in form but easily extend to Polynomial Distributed Lags or ADL in user-specified supporting series such as the number of reported cases.
In my opinion this is an example of a pooled cross-sectional time series problem. Gregory Chow developed a test for the constancy of parameters across groups in 1960 ;   
http://en.wikipedia.org/wiki/Chow_test
http://en.wikipedia.org/wiki/Test_for_structural_change
In this case, the test needs to be front-ended with Outlier Detection to ensure a Gaussian set of errors  i.e. no proven anomalous data or in other words the error process can't be proven to be Non-Gaussian within each state.  
X1 is the number of cases and Y is the number of deaths.  X2 is the empirically identified point of anomaly ;(2009 .. period 6  for State1 and  2004 .. period 1 for State2  . Outlier Detection  led to identifying  one anomalous data point for each state reflecting some unknown background variable thus yielding a more robust estimate of the mortality rates.
Analysis of State1
State1    Y(T) = -.65649
                     +[X1(T)][.0046)]      CASES
                     +[X2(T)][-1.3608)]    PULSE6 I~P00006STATE1
                     +       [A(T)]
Suggesting an unusually low mortality rate for 2009
Analysis of State2
State2   Y(T) =  123.55
                     +[X1(T)][(+  .0468)]  CASES
                     +[X2(T)][(+ 35.7590)] PULSE1 I~P00001STATE2
                     +       [A(T)]
Suggesting an unusually high mortality rate for 2004
This leads to estimating two cleansed data points
STATE  YEAR            Y OBSERVED   Y ADJUSTED
STATE1 2009             3             4.36  
STATE2 2004             254         218.24
Replacing these two observed possibly errant values possibly due to some unspecified concomitant factor (“lurking Variable”) one computes a rate of.0046 for STATE1 and .0468 for STATE2.
The Chow Test for constancy of parameters across groups easily yields a rejection of the null hypothesis of equal coefficients, thus the states can be said to have significantly different mortality rates. Note that even though the “experiment was not a controlled one” it is possible to identify deterministic effects reflecting some uncontrolled input which untreated can distort the analysis.  
P.S. I am fully aware that we can’t have 4.36 or 218.24 mortalities in real life !
A: The final anlalysis was published in http://childscience.org/html/jpn/v10n4_3.html . This is an example of the power of StackExchange to bring together a keen researcher and a subject matter expert. Title of paper "Reducing case fatality rate of acute encephalitis syndrome in developing countries" . Journal of Pediatric Neurology
